{"type": "FeatureCollection", "features": [{"id": "10.5194/gmd-10-1903-2017", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:47Z", "type": "Journal Article", "created": "2017-05-17", "title": "GLEAM\u00a0v3: satellite-based land evaporation and root-zone soil moisture", "description": "<p>Abstract. The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised, aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented. Key changes relative to the previous version include (1)\uffc2\uffa0a revised formulation of the evaporative stress, (2)\uffc2\uffa0an optimized drainage algorithm, and (3)\uffc2\uffa0a new soil moisture data assimilation system. GLEAM\uffc2\uffa0v3 is used to produce three new data sets of terrestrial evaporation and root-zone soil moisture, including a 36-year data set spanning 1980\uffe2\uff80\uff932015, referred to as v3a (based on satellite-observed soil moisture, vegetation optical depth and snow-water equivalent, reanalysis air temperature and radiation, and a multi-source precipitation product), and two satellite-based data sets. The latter share most of their forcing, except for the vegetation optical depth and soil moisture, which are based on observations from different passive and active C- and L-band microwave sensors (European Space Agency Climate Change Initiative, ESA CCI) for the v3b data set (spanning 2003\uffe2\uff80\uff932015) and observations from the Soil Moisture and Ocean Salinity (SMOS) satellite in the v3c data set (spanning 2011\uffe2\uff80\uff932015). Here, these three data sets are described in detail, compared against analogous data sets generated using the previous version of GLEAM (v2), and validated against measurements from 91 eddy-covariance towers and 2325 soil moisture sensors across a broad range of ecosystems. Results indicate that the quality of the v3 soil moisture is consistently better than the one from v2: average correlations against in situ surface soil moisture measurements increase from 0.61 to 0.64 in the case of the v3a data set and the representation of soil moisture in the second layer improves as well, with correlations increasing from 0.47 to 0.53. Similar improvements are observed for the v3b and c data sets. Despite regional differences, the quality of the evaporation fluxes remains overall similar to the one obtained using the previous version of GLEAM, with average correlations against eddy-covariance measurements ranging between 0.78 and 0.81 for the different data sets. These global data sets of terrestrial evaporation and root-zone soil moisture are now openly available at www.GLEAM.eu and may be used for large-scale hydrological applications, climate studies, or research on land\uffe2\uff80\uff93atmosphere feedbacks.                     </p>", "keywords": ["TERRESTRIAL WATER FLUXES", "QE1-996.5", "PONDEROSA PINE", "CARBON-DIOXIDE EXCHANGE", "WACMOS-ET PROJECT", "TRIPLE COLLOCATION ANALYSIS", "DATA ASSIMILATION SYSTEM", "Geology", "15. Life on land", "01 natural sciences", "DECIDUOUS FOREST", "EDDY-COVARIANCE", "PARAMETER RETRIEVAL MODEL", "13. Climate action", "Earth and Environmental Sciences", "ENERGY-BALANCE", "14. Life underwater", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://gmd.copernicus.org/articles/10/1903/2017/gmd-10-1903-2017.pdf"}, {"href": "https://doi.org/10.5194/gmd-10-1903-2017"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/gmd-10-1903-2017", "name": "item", "description": "10.5194/gmd-10-1903-2017", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/gmd-10-1903-2017"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-08-05T00:00:00Z"}}, {"id": "10.1016/j.agrformet.2006.01.007", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:15:19Z", "type": "Journal Article", "created": "2006-02-25", "title": "A Multi-Site Analysis Of Random Error In Tower-Based Measurements Of Carbon And Energy Fluxes", "description": "Measured surface-atmosphere fluxes of energy (sensible heat, H, and latent heat, LE) and CO2 (FCO2) represent the \u2018\u2018true\u2019\u2019 flux plus or minus potential random and systematic measurement errors. Here, we use data from seven sites in the AmeriFlux network, including five forested sites (two of which include \u2018\u2018tall tower\u2019\u2019 instrumentation), one grassland site, and one agricultural site, to conduct a cross-site analysis of random flux error. Quantification of this uncertainty is a prerequisite to model-data synthesis (data assimilation) and for defining confidence intervals on annual sums of net ecosystem exchange or making statistically valid comparisons between measurements and model predictions. We differenced paired observations (separated by exactly 24 h, under similar environmental conditions) to infer the characteristics of the random error in measured fluxes. Random flux error more closely follows a double-exponential (Laplace), rather than a normal (Gaussian), distribution, and increase as a linear function of the magnitude of the flux for all three scalar fluxes. Across sites, variation in the random error follows consistent and robust patterns in relation to environmental variables. For example, seasonal differences in the random error for H are small, in contrast to both LE and FCO2, for which the random errors are roughly three-fold larger at the peak of the growing season compared to the dormant season. Random errors also generally scale with Rn (H and LE) and PPFD (FCO2). For FCO2 (but not H or LE), the random error decreases with increasing wind speed. Data from two sites suggest that FCO2 random error may be slightly smaller when a closed-path, rather than open-path, gas analyzer is used.", "keywords": ["Random error", "Flux", "550", "carbon", "Uncertainty", "0207 environmental engineering", "AmeriFlux", "Eddy covariance", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Carbon", "flux", "Measurement error", "13. Climate action", "Natural Resources and Conservation", "Data assimilation", "eddy covariance", "Ameriflux", "uncertainty", "random error", "data assimilation", "measurement error", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.agrformet.2006.01.007"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20and%20Forest%20Meteorology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agrformet.2006.01.007", "name": "item", "description": "10.1016/j.agrformet.2006.01.007", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agrformet.2006.01.007"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2006-01-01T00:00:00Z"}}, {"id": "10.1002/ldr.3656", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:13:59Z", "type": "Journal Article", "created": "2020-06-07", "title": "Herbivores stimulate respiration from labile and recalcitrant soil carbon pools in grasslands of Yellowstone National Park", "description": "Abstract<p>Quantifying the effects of grazing on soil organic carbon (SOC) decomposition is of crucial importance for understanding soil C dynamics. However, less attention has been paid to the pool\uffe2\uff80\uff90specific SOC decomposition and the underlying factors associated with each C pool, representing critical knowledge gaps on soil C dynamics. In this study, we applied a state\uffe2\uff80\uff90of\uffe2\uff80\uff90the\uffe2\uff80\uff90art Bayesian data assimilation technique to re\uffe2\uff80\uff90analyze previous soil incubation data to examine how herbivores influenced the fraction and cumulative respiration of labile and recalcitrant soil C pools from seven edaphically diverse sites in Yellowstone National Park, whereas those variables were not explored in the earlier study. Our results showed that grazing significantly increased cumulative respiration from both labile and recalcitrant C pools. Greater cumulative respiration from the labile C pool was related to grazers increasing labile C pool fractions, while higher cumulative respiration from the recalcitrant C pool was associated with grazers accelerating the decomposition rate of the recalcitrant C pool. Cumulative respiration from both labile and recalcitrant C pools was positively correlated with shoot biomass, soil gravimetric moisture, and soil C and nitrogen content. Our results underscore how knowledge of pool\uffe2\uff80\uff90specific SOC decomposition can provide a better mechanistic understanding of soil C dynamics along topo\uffe2\uff80\uff90edaphic gradients in grazed grassland.</p", "keywords": ["2. Zero hunger", "decomposition", "recalcitrant carbon pool", "0401 agriculture", " forestry", " and fisheries", "soil incubation | microorganisms", "04 agricultural and veterinary sciences", "herbivores grazing", "plant productivity", "15. Life on land", "data assimilation", "labile carbon pool"]}, "links": [{"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1002/ldr.3656"}, {"href": "https://doi.org/10.1002/ldr.3656"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land%20Degradation%20%26amp%3B%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/ldr.3656", "name": "item", "description": "10.1002/ldr.3656", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/ldr.3656"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-07T00:00:00Z"}}, {"id": "10.1007/978-3-031-12786-1_33", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:14:04Z", "type": "Report", "created": "2023-01-01", "title": "MONARCH Regional Reanalysis of\u00a0Desert Dust Aerosols: An Initial Assessment", "description": "Open AccessWe acknowledge the DustClim project which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union\u2019s Horizon 2020 research and innovation programme (Grant n. 690462). BSC co-authors also acknowledge support from the European Research Council under the European Union\u2019s Horizon 2020 research and innovation programme (grant n. 773051; FRAGMENT), the AXA Research Fund, the 60 Spanish Ministry of Science, Innovation and Universities (grant n. RYC-2015-18690 and CGL2017-88911-R), the European Union\u2019s Horizon 2020 research and innovation programme (grant n. 792103; SOLWARIS). This work has been partially funded by the contribution agreement between AEMET and BSC to carry out development and improvement activities of the products and services supplied by the WMO Sand and Dust Storm Regional Centres. Jer\u00f3nimo Escribano and Martina Klose have received funding from the European Union\u2019s Horizon 2020 research and innovation programme, respectively, under the Marie Sk\u0142odowska-Curie grant agreements H2020-MSCA-COFUND-2016- 65 754433 and H2020-MSCA-IF-2017-789630. Martina Klose further acknowledges support through the Helmholtz Association\u2019s Initiative and Networking Fund (grant agreement n. VH-NG-1533). We acknowledge PRACE (eDUST, eFRAGMENT1, and eFRAGMENT2) and RES (AECT-2019-3-0001, AECT-2020-1-0007, AECT-2020-3-0013) for awarding access to MareNostrum at the BSC and for providing technical support.", "keywords": ["\u00c0rees tem\u00e0tiques de la UPC::Enginyeria mec\u00e0nica::Mec\u00e0nica de fluids", "info:eu-repo/classification/ddc/550", "Aerosol speciation", "550", "ddc:550", "Aerosol data assimilation", "Dust", "Aerosols atmosf\u00e8rics", "Atmospheric aerosols", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Enginyeria ambiental", "Earth sciences", "Aerosol regional reanalysis", "Pols -- Control", "13. Climate action", "2023 OA procedure", "Modis deep blue", "Dust control"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1007/978-3-031-12786-1"}, {"href": "https://link.springer.com/content/pdf/10.1007/978-3-031-12786-1_33"}, {"href": "https://doi.org/10.1007/978-3-031-12786-1_33"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/978-3-031-12786-1_33", "name": "item", "description": "10.1007/978-3-031-12786-1_33", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/978-3-031-12786-1_33"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.1016/j.agwat.2021.107290", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:15:24Z", "type": "Journal Article", "created": "2021-11-22", "title": "Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions", "description": "Accurate estimation of evapotranspiration (ET) is of crucial importance in water science and hydrological process understanding especially in semi-arid/arid areas since ET represents more than 85% of the total water budget. FAO-56 is one of the widely used formulations to estimate the actual crop evapotranspiration (ET c act) due to its operational nature and since it represents a reasonable compromise between simplicity and accuracy. In this vein, the objective of this paper was to examine the possibility of improving ET c act estimates through remote sensing data assimilation. For this purpose, remotely sensed soil moisture (SM) and Land surface temperature (LST) data were simultaneously assimilated into FAO-dualK c. Surface SM observations were assimilated into the soil evaporation (E s) component through the soil evaporation coefficient, and LST data were assimilated into the actual crop transpiration (T c act) component through the crop stress coefficient. The LST data were used to estimate the water stress coefficient (K s) as a proxy of LST (LST proxy). The FAO-Ks was corrected by assimilating LST proxy derived from Landsat data based on the variances of predicted errors on K s estimates from FAO-56 model and thermal-derived K s. The proposed approach was tested over a semi-arid area in Morocco using first, in situ data collected during 2002-2003 and 2015-2016 wheat growth seasons over two different fields and then, remotely sensed data derived from disaggregated Soil Moisture Active Passive (SMAP) SM and Landsat-LST sensors were used. Assimilating SM data leads to an improvement of the ET c act model prediction: the root mean square error (RMSE) decreased from 0.98 to 0.65 mm/day compared to the classical FAO-dualK c using in situ SM. Moreover, assimilating both in situ SM and LST data provided more accurate results with a RMSE error of 0.55 mm/day. By using SMAP-based SM and Landsat-LST, results also improved in comparison with standard FAO and reached a RMSE of 0.73 mm/day against eddy-covariance ET c act measurements.", "keywords": ["2. Zero hunger", "0106 biological sciences", "Evapotranspiration", "550", "Evapotranspiration Data assimilation FAO-dualK c Soil moisture Land surface temperature", "0207 environmental engineering", "02 engineering and technology", "15. Life on land", "01 natural sciences", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "FAO-dualK(c)", "13. Climate action", "Data assimilation", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Soil moisture", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment", "Land surface temperature"]}, "links": [{"href": "https://doi.org/10.1016/j.agwat.2021.107290"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20Water%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agwat.2021.107290", "name": "item", "description": "10.1016/j.agwat.2021.107290", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agwat.2021.107290"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-01T00:00:00Z"}}, {"id": "10.1029/2024jg008231", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:17:33Z", "type": "Journal Article", "created": "2024-10-17", "title": "Assimilation of Sentinel\u20101 Backscatter to Update AquaCrop Estimates of Soil Moisture and Crop Biomass", "description": "Abstract<p>This study assesses the potential of regional microwave backscatter data assimilation (DA) in AquaCrop for the first time, using NASA's Land Information System. The objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates. SSM and crop biomass simulations from AquaCrop were updated using Sentinel\uffe2\uff80\uff901 synthetic aperture radar observations, over three regions in Europe in two separate DA experiments. The first experiment concerned updating SSM using VV\uffe2\uff80\uff90polarized backscatter and the corrections were propagated via the model to the biomass. In the second experiment, the DA setup was extended by also updating the biomass with VH\uffe2\uff80\uff90polarized backscatter. SSM was evaluated with local in situ data and with downscaled Soil Moisture Active Passive (SMAP) retrievals for all cropland grid cells, whereas crop biomass was compared to SMAP vegetation optical depth and the Copernicus dry matter productivity. The assimilation showed mixed results for root mean square error and Pearson's correlation, with slight overall improvements in the (anomaly) correlations of updated SSM relative to independent in situ and satellite data. By contrast, the biomass estimates obtained with backscatter DA did not agree better with reference data sets. Overall, the SSM evaluation showed that there is potential in using Sentinel\uffe2\uff80\uff901 backscatter for assimilation in AquaCrop, but the present setup was not able to improve crop biomass estimates. Our study reveals how the complex interaction between SSM, crop biomass and backscatter affect the impact and performance of DA, offering insight into ways to optimize DA for crop growth estimation.</p", "keywords": ["SURFACE", "SIMULATE YIELD RESPONSE", "LAND INFORMATION-SYSTEM", "FRAMEWORK", "AquaCrop", "MODEL", "Earth and Environmental Sciences", "IRRIGATION", "Sentinel-1 SAR", "NETWORK", "soil moisture", "data assimilation", "SATELLITE", "crop biomass"]}, "links": [{"href": "https://doi.org/10.1029/2024jg008231"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1029/2024jg008231", "name": "item", "description": "10.1029/2024jg008231", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1029/2024jg008231"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-01T00:00:00Z"}}, {"id": "10.1029/2019gb006393", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:17:31Z", "type": "Journal Article", "created": "2020-02-07", "title": "Sources of Uncertainty in Regional and Global Terrestrial CO 2 Exchange Estimates", "description": "<p>The Global Carbon Budget 2018 (GCB2018) estimated by the atmospheric CO  growth rate, fossil fuel emissions, and modeled (bottom\uffe2\uff80\uff90up) land and ocean fluxes cannot be fully closed, leading to a \uffe2\uff80\uff9cbudget imbalance,\uffe2\uff80\uff9d highlighting uncertainties in GCB components. However, no systematic analysis has been performed on which regions or processes contribute to this term. To obtain deeper insight on the sources of uncertainty in global and regional carbon budgets, we analyzed differences in Net Biome Productivity (NBP) for all possible combinations of bottom\uffe2\uff80\uff90up and top\uffe2\uff80\uff90down data sets in GCB2018: (i) 16 dynamic global vegetation models (DGVMs), and (ii) 5 atmospheric inversions that match the atmospheric CO  growth rate. We find that the global mismatch between the two ensembles matches well the GCB2018 budget imbalance, with Brazil, Southeast Asia, and Oceania as the largest contributors. Differences between DGVMs dominate global mismatches, while at regional scale differences between inversions contribute the most to uncertainty. At both global and regional scales, disagreement on NBP interannual variability between the two approaches explains a large fraction of differences. We attribute this mismatch to distinct responses to El\uffc2\uffa0Ni\uffc3\uffb1o\uffe2\uff80\uff93Southern Oscillation variability between DGVMs and inversions and to uncertainties in land use change emissions, especially in South America and Southeast Asia. We identify key needs to reduce uncertainty in carbon budgets: reducing uncertainty in atmospheric inversions (e.g., through more observations in the tropics) and in land use change fluxes, including more land use processes and evaluating land use transitions (e.g., using high\uffe2\uff80\uff90resolution remote\uffe2\uff80\uff90sensing), and, finally, improving tropical hydroecological processes and fire representation within DGVMs.</p>", "keywords": ["[SDE] Environmental Sciences", "FLUXES", "550", "BURNED AREA PRODUCT", "atmospheric inversions", "01 natural sciences", "Environnement et pollution", "DATA ASSIMILATION", "Ph\u00e9nom\u00e8nes atmosph\u00e9riques", "PLANT FUNCTIONAL TYPES", "global carbon budget", "carbon cycle", "ATMOSPHERIC CO2", "0105 earth and related environmental sciences", "LAND-COVER CHANGE", "FOSSIL-FUEL", "VEGETATION MODEL ORCHIDEE", "15. Life on land", "ddc:910", "CARBON-DIOXIDE EMISSIONS", "13. Climate action", "[SDE]Environmental Sciences", "dynamic global vegetation models", "contr\u00f4le de la pollution", "Technologie de l'environnement", "INCORPORATING SPITFIRE"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019GB006393"}, {"href": "https://doi.org/10.1029/2019gb006393"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Biogeochemical%20Cycles", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1029/2019gb006393", "name": "item", "description": "10.1029/2019gb006393", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1029/2019gb006393"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-02-01T00:00:00Z"}}, {"id": "10.1109/lgrs.2021.3073484", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:18:29Z", "type": "Journal Article", "created": "2021-06-10", "title": "Sentinel-1 Backscatter Assimilation Using Support Vector Regression or the Water Cloud Model at European Soil Moisture Sites", "description": "Sentinel-1 backscatter observations were assimilated into the Global Land Evaporation Amsterdam Model (GLEAM) using an ensemble Kalman filter. As a forward operator, which is required to simulate backscatter from soil moisture and leaf area index (LAI), we evaluated both the traditional water cloud model (WCM) and the support vector regression (SVR). With SVR, a closer fit between backscatter observations and simulations was achieved. The impact on the correlation between modeled and in situ soil moisture measurements was similar when assimilating the Sentinel data using WCM (\u0394 R = +0.037) or SVR (\u0394 R = +0.025).", "keywords": ["Vegetation mapping", "support vector regression (SVR)", "Technology and Engineering", "Data models", "0211 other engineering and technologies", "Computational modeling", "02 engineering and technology", "15. Life on land", "Geotechnical Engineering and Engineering Geology", "01 natural sciences", "Backscatter", "radar backscatter", "Soil", "Earth and Environmental Sciences", "LAND EVAPORATION", "Data assimilation", "Soil moisture", "Electrical and Electronic Engineering", "soil moisture", "Moisture", "SMOS", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/8859/9651998/09451176.pdf?arnumber=9451176"}, {"href": "https://doi.org/10.1109/lgrs.2021.3073484"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Geoscience%20and%20Remote%20Sensing%20Letters", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/lgrs.2021.3073484", "name": "item", "description": "10.1109/lgrs.2021.3073484", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/lgrs.2021.3073484"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.1111/gcb.14325", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:18:38Z", "type": "Journal Article", "created": "2018-05-26", "title": "Biotic responses buffer warming-induced soil organic carbon loss in Arctic tundra", "description": "Abstract<p>Climate warming can result in both abiotic (e.g., permafrost thaw) and biotic (e.g., microbial functional genes) changes in Arctic tundra. Recent research has incorporated dynamic permafrost thaw in Earth system models (ESMs) and indicates that Arctic tundra could be a significant future carbon (C) source due to the enhanced decomposition of thawed deep soil C. However, warming\uffe2\uff80\uff90induced biotic changes may influence biologically related parameters and the consequent projections inESMs. How model parameters associated with biotic responses will change under warming and to what extent these changes affect projected C budgets have not been carefully examined. In this study, we synthesized six data sets over 5\uffc2\uffa0years from a soil warming experiment at the Eight Mile Lake, Alaska, into the TerrestrialECOsystem (TECO) model with a probabilistic inversion approach. TheTECOmodel used multiple soil layers to track dynamics of thawed soil under different treatments. Our results show that warming increased light use efficiency of vegetation photosynthesis but decreased baseline (i.e., environment\uffe2\uff80\uff90corrected) turnover rates ofSOCin both the fast and slow pools in comparison with those under control. Moreover, the parameter changes generally amplified over time, suggesting processes of gradual physiological acclimation and functional gene shifts of both plants and microbes. TheTECOmodel predicted that field warming from 2009 to 2013 resulted in cumulative C losses of 224 or 87\uffc2\uffa0g/m2, respectively, without or with changes in those parameters. Thus, warming\uffe2\uff80\uff90induced parameter changes reduced predicted soil C loss by 61%. Our study suggests that it is critical to incorporate biotic changes inESMs to improve the model performance in predicting C dynamics in permafrost regions.</p>", "keywords": ["550", "Climate Change", "Permafrost", "acclimation", "carbon modeling", "01 natural sciences", "climate warming", "Soil", "Theoretical", "Models", "soil carbon", "Photosynthesis", "biotic responses", "data assimilation", "Tundra", "Soil Microbiology", "0105 earth and related environmental sciences", "Ecology", "500", "Biological Sciences", "Models", " Theoretical", "Plants", "15. Life on land", "Carbon", "Climate Action", "Environmental sciences", "Biological sciences", "Earth sciences", "13. Climate action", "Environmental Sciences", "Alaska", "permafrost"]}, "links": [{"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.14325"}, {"href": "https://doi.org/10.1111/gcb.14325"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/gcb.14325", "name": "item", "description": "10.1111/gcb.14325", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/gcb.14325"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-06-12T00:00:00Z"}}, {"id": "10.1175/BAMS-D-17-0138.1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:19:16Z", "type": "Journal Article", "created": "2018-09-11", "title": "MSWEP V2 global 3-hourly 0.1\u00b0 precipitation: methodology and quantitative assessment", "description": "Abstract<p>We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979\uffe2\uff80\uff932017. MSWEP V2 is unique in several aspects: i) full global coverage (all land and oceans); ii) high spatial (0.1\uffc2\uffb0) and temporal (3 hourly) resolution; iii) optimal merging of P estimates based on gauges [WorldClim, Global Historical Climatology Network-Daily (GHCN-D), Global Summary of the Day (GSOD), Global Precipitation Climatology Centre (GPCC), and others], satellites [Climate Prediction Center morphing technique (CMORPH), Gridded Satellite (GridSat), Global Satellite Mapping of Precipitation (GSMaP), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT)], and reanalyses [European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and Japanese 55-year Reanalysis (JRA-55)]; iv) distributional bias corrections, mainly to improve the P frequency; v) correction of systematic terrestrial P biases using river discharge Q observations from 13,762 stations across the globe; vi) incorporation of daily observations from 76,747 gauges worldwide; and vii) correction for regional differences in gauge reporting times. MSWEP V2 compares substantially better with Stage IV gauge\uffe2\uff80\uff93radar P data than other state-of-the-art P datasets for the United States, demonstrating the effectiveness of the MSWEP V2 methodology. Global comparisons suggest that MSWEP V2 exhibits more realistic spatial patterns in mean, magnitude, and frequency. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 955, 781, and 1,025 mm yr\uffe2\uff88\uff921, respectively. Other P datasets consistently underestimate P amounts in mountainous regions. Using MSWEP V2, P was estimated to occur 15.5%, 12.3%, and 16.9% of the time on average for the global, land, and ocean domains, respectively. MSWEP V2 provides unique opportunities to explore spatiotemporal variations in P, improve our understanding of hydrological processes and their parameterization, and enhance hydrological model performance.</p>", "keywords": ["LAND", "SATELLITE-OBSERVATIONS", "EXTREME-PRECIPITATION", "GAUGE OBSERVATIONS", "TROPICAL RAINFALL", "PASSIVE MICROWAVE", "15. Life on land", "01 natural sciences", "6. Clean water", "MODEL", "ERA-INTERIM REANALYSIS", "DATA ASSIMILATION", "13. Climate action", "Earth and Environmental Sciences", "NETWORK", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://journals.ametsoc.org/downloadpdf/journals/bams/100/3/bams-d-17-0138.1.xml"}, {"href": "https://doi.org/10.1175/BAMS-D-17-0138.1"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Bulletin%20of%20the%20American%20Meteorological%20Society", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1175/BAMS-D-17-0138.1", "name": "item", "description": "10.1175/BAMS-D-17-0138.1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1175/BAMS-D-17-0138.1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-03-01T00:00:00Z"}}, {"id": "10.5194/bg-14-1969-2017", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:37Z", "type": "Journal Article", "created": "2016-11-28", "title": "Modelling sun-induced fluorescence and photosynthesis with a land surface model at local and regional scales in northern Europe", "description": "<p>Abstract. Recent satellite observations of sun-induced chlorophyll fluorescence (SIF) are thought to provide a large-scale proxy for gross primary production (GPP), thus providing a new way to assess the performance of land surface models (LSMs). In this study, we assessed how well SIF is able to predict GPP in the Fenno-Scandinavian region and what potential limitations for its application exist. We implemented a SIF model into the JSBACH LSM and used active leaf level chlorophyll fluorescence measurements (ChlF) to evaluate the performance of the SIF module at a coniferous forest at Hyyti\uffc3\uffa4l\uffc3\uffa4, Finland. We also compared simulated GPP and SIF at four Finnish micrometeorological flux measurement sites to observed GPP as well as to satellite observed SIF. Finally, we conducted a regional model simulation for the Fenno-Scandinavian region with JSBACH and compared the results to SIF retrievals from the GOME-2 (Global Ozone Monitoring Experiment-2) space-borne spectrometer and to observation-based regional GPP estimates. Both observations and simulations revealed that SIF can be used to estimate GPP at both site and regional scales. The GOME-2 based SIF was a better proxy for GPP than the remotely sensed fAPAR (fraction of absorbed photosynthetic active radiation by vegetation), even though high SIF values occurred during early spring at the northern latitudes, although these are not likely to be associated with photosynthesis.                         </p>", "keywords": ["EDDY COVARIANCE", "DATA ASSIMILATION SYSTEM", "FLUX MEASUREMENTS", "SCOTS PINE FOREST", "01 natural sciences", "7. Clean energy", "Ecology", " Evolution", " Behavior and Systematics; Earth-Surface Processes", "CO2 EXCHANGE", "PHOTOSYSTEM-II", "Life", "QH501-531", "QH540-549.5", "SDG 15 - Life on Land", "0105 earth and related environmental sciences", "QE1-996.5", "Ecology", "BOREAL CONIFEROUS FOREST", "BIOCHEMICAL-MODEL", "Forestry", "Geology", "15. Life on land", "TERRESTRIAL CHLOROPHYLL FLUORESCENCE", "Physical sciences", "Environmental sciences", "13. Climate action", "ITC-ISI-JOURNAL-ARTICLE", "ENERGY-BALANCE", "ITC-GOLD"]}, "links": [{"href": "https://cris.unibo.it/bitstream/11585/585578/2/bg-14-1969-2017.pdf"}, {"href": "https://bg.copernicus.org/articles/14/1969/2017/bg-14-1969-2017.pdf"}, {"href": "https://doi.org/10.5194/bg-14-1969-2017"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-14-1969-2017", "name": "item", "description": "10.5194/bg-14-1969-2017", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-14-1969-2017"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-11-28T00:00:00Z"}}, {"id": "10.3390/rs13142667", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:02Z", "type": "Journal Article", "created": "2021-07-07", "title": "Irrigation amounts and timing retrieval through data assimilation of surface soil moisture into the FAO-56 approach in the South Mediterranean region", "description": "<p>Agricultural water use represents more than 70% of the world\uffe2\uff80\uff99s freshwater through irrigation water inputs that are poorly known at the field scale. Irrigation monitoring is thus an important issue for optimizing water use in particular with regards to the water scarcity that the semi-arid regions are already facing. In this context, the aim of this study is to develop and evaluate a new approach to predict seasonal to daily irrigation timing and amounts at the field scale. The method is based on surface soil moisture (SSM) data assimilated into a simple land surface (FAO-56) model through a particle filter technique based on an ensemble of irrigation scenarios. The approach is implemented in three steps. First, synthetic experiments are designed to assess the impact of the frequency of observation, the errors on SSM and the a priori constraints on the irrigation scenarios for different irrigation techniques (flooding and drip). In a second step, the method is evaluated using in situ SSM measurements with different revisit times (3, 6 and 12 days) to mimic the available SSM product derived from remote sensing observation. Finally, SSM estimates from Sentinel-1 are used. Data are collected on different wheat fields grown in Morocco, for both flood and drip irrigation techniques in addition to rainfed fields used for an indirect evaluation of the method performance. Using in situ data, accurate results are obtained. With an observation every 6 days to mimic the Sentinel-1 revisit time, the seasonal amounts are retrieved with R &gt; 0.98, RMSE &lt; 32 mm and bias &lt; 2.5 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation as more than 70% of the detected irrigation events have a time difference from actual irrigation events shorter than 4 days. Over the drip irrigated fields, the statistical metrics are R = 0.74, RMSE = 24.8 mm and bias = 2.3 mm for irrigation amounts cumulated over 15 days. When using SSM products derived from Sentinel-1 data, the statistical metrics on 15-day cumulated amounts slightly dropped to R = 0.64, RMSE = 28.7 mm and bias = 1.9 mm. The metrics on the seasonal amount retrievals are close to assimilating in situ observations with R = 0.99, RMSE = 33.5 mm and bias = \uffe2\uff88\uff9218.8 mm. Finally, among four rainfed seasons, only one false event was detected. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas.</p>", "keywords": ["550", "Science", "particle filters", "0207 environmental engineering", "02 engineering and technology", "01 natural sciences", "irrigation timing and amounts", "Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture irrigation timing and amounts", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "semi-arid Mediterranean region", "data assimilation", "0105 earth and related environmental sciences", "FAO-56", "2. Zero hunger", "Q", "15. Life on land", "surface soil moisture", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "winter wheat", "irrigation timing and amounts; surface soil moisture; data assimilation; particle filters; FAO-56; Sentinel-1; semi-arid Mediterranean region; winter wheat", "13. Climate action", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "ZONE MEDITERRANEENNE", "Sentinel-1", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/14/2667/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/14/2667/pdf"}, {"href": "https://doi.org/10.3390/rs13142667"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13142667", "name": "item", "description": "10.3390/rs13142667", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13142667"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-07T00:00:00Z"}}, {"id": "10.25678/000161", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:20:35Z", "type": "Software", "title": "Data for: Data Assimilation and Online Parameter Optimization in Groundwater Modeling using Nested Particle Filters", "keywords": ["modelling", "particle filter", "hydrogeology", "groundwater", "data assimilation"], "contacts": [{"organization": "Ramgraber, Max, Albert, Carlo, Schirmer, Mario,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.25678/000161"}, {"rel": "self", "type": "application/geo+json", "title": "10.25678/000161", "name": "item", "description": "10.25678/000161", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.25678/000161"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.3389/feart.2020.00359", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:20:39Z", "type": "Journal Article", "created": "2020-10-26", "title": "On the Definition of the Strategy to Obtain Absolute InSAR Zenith Total Delay Maps for Meteorological Applications", "description": "Atmospheric Phase Screens (APSs) derived from Interferometric Synthetic Aperture Radar (InSAR) observations contain the difference between the tropospheric water-vapor-induced delay of two acquisition epochs, i.e., the slave and the master (or reference) epochs. Using estimates of the atmospheric state coming from independent sources, for example numerical models and/or Global Navigation Satellite System (GNSS) observations, the APSs can be transformed into absolute maps of Tropospheric Delay (Zenith Total Delay or ZTD), related to the columnar atmospheric water vapor content. In this work, a systematic comparison between various APS and ZTD products aims to determine a convenient strategy to go from APSs to InSAR-derived absolute ZTD maps, highlighting the uncertainties and approximations introduced in the entire processing. The main problem to solve is the evaluation of a sufficiently accurate high-resolution master delay map. Different sources of data and two different approaches to derive the master are validated and compared to define the most suitable strategy for meteorological applications. Maps of ZTD obtained by an iterative interpolation of a global atmospheric circulation model values results in being more suited than those derived from the assimilation of GNSS observations into an NWP model. A time average approach to estimate the master map is more robust than the single epoch approach with respect to the choice of the master epoch. Still, the choice of a proper master epoch in the InSAR processing chain as well as that of the maps to be averaged crucially result in the estimate of the master.", "keywords": ["ZTD", "Science", "Q", "0211 other engineering and technologies", "numerical weather prediction", "02 engineering and technology", "01 natural sciences", "master estimate", "GNSS meteorology", "13. Climate action", "water vapor", "APS; data assimilation; GNSS meteorology; master estimate; numerical weather prediction; SAR meteorology; water vapor; ZTD;", "SAR meteorology", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://boa.unimib.it/bitstream/10281/527752/2/feart-08-00359.pdf"}, {"href": "https://re.public.polimi.it/bitstream/11311/1158610/1/feart-08-00359.pdf"}, {"href": "https://doi.org/10.3389/feart.2020.00359"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Earth%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/feart.2020.00359", "name": "item", "description": "10.3389/feart.2020.00359", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/feart.2020.00359"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-26T00:00:00Z"}}, {"id": "10.3389/frwa.2021.767910", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:20:45Z", "type": "Journal Article", "created": "2021-12-14", "title": "Combining Models of Root-Zone Hydrology and Geoelectrical Measurements: Recent Advances and Future Prospects", "description": "<p>Recent advances in measuring and modeling root water uptake along with refined electrical petrophysical models may help fill the existing gap in hydrological root model parametrization. In this paper, we discuss the choices to be made to combine root-zone hydrology and geoelectrical data with the aim of characterizing the active root zone. For each model and observation type we discuss sources of uncertainty and how they are commonly addressed in a stochastic inversion framework. We point out different degrees of integration in the existing hydrogeophysical approaches to parametrize models of root-zone hydrology. This paper aims at giving emphasis to stochastic approaches, in particular to Data Assimilation (DA) schemes, that are generally identified as the best way to combine geoelectrical data with Root Water Uptake (RWU) models. In addition, the study points out a more suitable objective function taken from the optimal transport theory that better captures complex geometry of root systems. Another pathway for improvement of geoelectrical data integration into RWU models using DA relies on the use of stem based methods as a leverage to introduce more extensive root knowledge into RWU macroscopic hydrological models.</p>", "keywords": ["hydrogeophysics", "0208 environmental biotechnology", "0207 environmental engineering", "02 engineering and technology", "data assimilation; geoelectrical imaging; hydrogeophysics; inversion; root water uptake; soil-plant modeling; Wasserstein distance", "Environmental technology. Sanitary engineering", "root water uptake", "Hydrogeophysics", "inversion", "geoelectrical imaging", "soil-plant modeling", "Wasserstein distance", "data assimilation", "TD1-1066"]}, "links": [{"href": "https://www.research.unipd.it/bitstream/11577/3410667/1/frwa-03-767910.pdf"}, {"href": "https://doi.org/10.3389/frwa.2021.767910"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/frwa.2021.767910", "name": "item", "description": "10.3389/frwa.2021.767910", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/frwa.2021.767910"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-14T00:00:00Z"}}, {"id": "10.3389/frwa.2022.981745", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:20:45Z", "type": "Journal Article", "created": "2022-09-16", "title": "Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication", "description": "<p>The beginning of the 21st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems.</p", "keywords": ["[SDE] Environmental Sciences", "Land surface modeling", "VEGETATION OPTICAL DEPTH", "IMPACT", "info:eu-repo/classification/ddc/333.7", "snow", "Environmental technology. Sanitary engineering", "01 natural sciences", "land surface modeling", "RETRIEVALS", "targeted observations", "vegetation", "Snow", "Targeted observations", "SNOW DEPTH", "SOIL-MOISTURE ASSIMILATION", "data assimilation", "TD1-1066", "0105 earth and related environmental sciences", "GRACE DATA ASSIMILATION", "EQUIVALENT", "microwave remote sensing", "Vegetation", "LDAS-MONDE", "BRIGHTNESS TEMPERATURE OBSERVATIONS", "15. Life on land", "Microwave remote sensing", "13. Climate action", "Earth and Environmental Sciences", "SIMULATION", "Data assimilation", "data assimilation", " soil moisture", " snow", " vegetation", " microwave remote sensing", " land surface modeling", " targeted observation", "Soil moisture", "soil moisture"]}, "links": [{"href": "https://cris.unibo.it/bitstream/11585/894502/2/frwa-04-981745%20%282%29.pdf"}, {"href": "https://doi.org/10.3389/frwa.2022.981745"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/frwa.2022.981745", "name": "item", "description": "10.3389/frwa.2022.981745", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/frwa.2022.981745"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-16T00:00:00Z"}}, {"id": "10.3390/agronomy9050255", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:20:49Z", "type": "Journal Article", "created": "2019-05-21", "title": "Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield using a re-calibration data assimilation approach. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields. LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The assimilation of LAI in EPIC provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed. The diverging results obtained in the two years indicated that the assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.</p></article>", "keywords": ["2. Zero hunger", "yield estimation", "S", "Leaf Area Index", "EPIC model", "Agriculture", "Crop growth model", "04 agricultural and veterinary sciences", "15. Life on land", "crop growth model", "Yield estimation", "13. Climate action", "Leaf area index", "Data assimilation", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "data assimilation"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://doi.org/10.3390/agronomy9050255"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agronomy9050255", "name": "item", "description": "10.3390/agronomy9050255", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agronomy9050255"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-21T00:00:00Z"}}, {"id": "10.3390/rs12244018", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:01Z", "type": "Journal Article", "created": "2020-12-08", "title": "Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensing data (the vegetation condition index (VCI), temperature condition index (TCI), vegetation health ind ex (VHI), soil moisture condition index (SMCI) and soil water index for different soil layers (SWI)) and (2) key land surface variables (Land Area Index (LAI), soil moisture (SM) at different depths, soil evaporation and plant transpiration) from a Land Data Assimilation System (LDAS) over 2000\u20132017. A lagged correlation analysis was conducted to assess the relationships between the drought indices and cereal yield at monthly time scales. The VCI and LAI around the heading stage (March-April) are highly linked to yield for all provinces (R = 0.94 for the Khemisset province), while a high link for TCI occurs during the development stage in January-February (R = 0.83 for the Beni Mellal province). Interestingly, indices related to soil moisture in the superficial soil layer are correlated with yield earlier in the season around the emergence stage (December). The results demonstrate the clear added value of using an LDAS compared with using a remote sensing product alone, particularly concerning the soil moisture in the root-zone, considered a key variable for yield production, that is not directly observable from space. The time scale of integration is also discussed. By integrating the indices on the main phenological stages of wheat using a dynamic threshold approach instead of the monthly time scale, the correlation between indices and yield increased by up to 14%. In addition, the contributions of VCI and TCI to VHI were optimized by using yield anomalies as proxies for drought. This study opens perspectives for the development of drought early warning systems in Morocco and over North Africa, as well as for seasonal crop yield forecasting.</p></article>", "keywords": ["[SDE] Environmental Sciences", "550", "Science", "0207 environmental engineering", "Agricultural drought", "02 engineering and technology", "01 natural sciences", "630", "Environmental science", "remote sensing", "Land data assimilation systems", "Pathology", "assimilation systems", "Biology", "land data assimilation systems", "0105 earth and related environmental sciences", "2. Zero hunger", "Global and Planetary Change", "Vegetation Monitoring", "Water content", "Ecology", "Drought", "Global Forest Drought Response and Climate Change", "Q", "Hydrology (agriculture)", "Geology", "cereal yield", "Remote Sensing in Vegetation Monitoring and Phenology", "FOS: Earth and related environmental sciences", "Remote sensing", "semiarid region", "15. Life on land", "agricultural drought", "Agronomy", "6. Clean water", "Cereal yield", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "[SDE]Environmental Sciences", "Global Drought Monitoring and Assessment", "Environmental Science", "Physical Sciences", "Leaf area index", "Medicine", "Semiarid region", "land data", "Vegetation (pathology)"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/24/4018/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/24/4018/pdf"}, {"href": "https://doi.org/10.3390/rs12244018"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs12244018", "name": "item", "description": "10.3390/rs12244018", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12244018"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-08T00:00:00Z"}}, {"id": "10.5194/gmd-2021-98", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:48Z", "type": "Journal Article", "created": "2021-11-30", "title": "Performance analysis of regional AquaCrop (v6.1) biomass  and surface soil moisture simulations using satellite  and in situ observations", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world's growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires efficient models and data to monitor land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1 to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30\u2009arcsec (\u223c1\u2009km). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database version 1.2 (HWSDv1.2), and generic crop information. The setup with a generic crop is chosen as a baseline for a future satellite-based data assimilation system. The relative temporal variability in daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active\u2013Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean temporal correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the short-term and inter-annual variability, with a mean anomaly correlation of 0.46 for daily biomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model adequately simulates crop production and soil moisture and provides a suitable setup for subsequent satellite-based data assimilation.</p></article>", "keywords": ["YIELD RESPONSE", "2. Zero hunger", "LAND", "QE1-996.5", "Science & Technology", "PRODUCTIVITY", "04 Earth Sciences", "0207 environmental engineering", "UNCERTAINTY", "Geology", "02 engineering and technology", "15. Life on land", "7. Clean energy", "01 natural sciences", "WHEAT YIELD", "37 Earth sciences", "DATA ASSIMILATION", "13. Climate action", "ASSESSMENTS", "Physical Sciences", "IMPLEMENTATION", "FAO CROP MODEL", "Geosciences", " Multidisciplinary", "HIGH-RESOLUTION", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://gmd.copernicus.org/articles/14/7309/2021/gmd-14-7309-2021.pdf"}, {"href": "https://doi.org/10.5194/gmd-2021-98"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/gmd-2021-98", "name": "item", "description": "10.5194/gmd-2021-98", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/gmd-2021-98"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-17T00:00:00Z"}}, {"id": "10.5194/egusphere-egu2020-21951", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:43Z", "type": "Journal Article", "created": "2019-05-21", "title": "Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield using a re-calibration data assimilation approach. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields. LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The assimilation of LAI in EPIC provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed. The diverging results obtained in the two years indicated that the assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.</p></article>", "keywords": ["2. Zero hunger", "yield estimation", "S", "Leaf Area Index", "EPIC model", "Agriculture", "Crop growth model", "04 agricultural and veterinary sciences", "15. Life on land", "crop growth model", "Yield estimation", "13. Climate action", "Data assimilation", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "data assimilation"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://doi.org/10.5194/egusphere-egu2020-21951"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/egusphere-egu2020-21951", "name": "item", "description": "10.5194/egusphere-egu2020-21951", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/egusphere-egu2020-21951"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-21T00:00:00Z"}}, {"id": "10.5194/essd-2021-358", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:46Z", "type": "Journal Article", "created": "2021-10-28", "title": "The MONARCH high-resolution reanalysis of desert dust aerosol over Northern Africa, the Middle East and Europe (2007\u20132016)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. One of the challenges in studying desert dust aerosol along with its numerous interactions and impacts is the paucity of direct in-situ measurements, particularly in the areas most affected by dust storms. Satellites typically provide columnintegrated aerosol measurements, but observationally-constrained continuous 3D dust fields are needed to assess dust variability, climate effects and impacts upon a variety of socio-economic sectors. Here, we present a high resolution regional reanalysis data set of desert dust aerosols that covers Northern Africa, the Middle East and Europe along with the Mediterranean sea and parts of Central Asia, and the Atlantic and Indian Oceans between 2007 and 2016. The horizontal resolution is 0.1\u00b0 latitude\u2009\u00d7\u20090.1\u00b0 longitude, and the temporal resolution is 3 hours. The reanalysis was produced using Local Ensemble Transform Kalman Filter (LETKF) data assimilation in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) developed at the Barcelona Supercomputing Center (BSC). The assimilated data are coarse-mode dust optical depth retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Level 2 products. The reanalysis data set consists of upper air (dust mass concentrations and extinction coefficient), surface (dust deposition and solar irradiance fields, among them) and total column (e.g., dust optical depth and load) variables. Some dust variables, such as concentrations and wet and dry deposition, are expressed for a binned size distribution that ranges from 0.2 to 20\u2009\u03bcm in particle diameter. Both analysis and first-guess (analysis-initialized simulation) fields are available for the variables that are diagnosed from the state vector. A set of ensemble statistics is archived for each output variable, namely the ensemble mean, standard deviation, maximum and median. The spatial and temporal distribution of the dust fields follows well-known dust cycle features controlled by seasonal changes in meteorology and vegetation cover. The analysis is statistically closer to the assimilated retrievals than the first-guess, which proves the consistency of the data assimilation method. Independent evaluation using AERONET dust-filtered optical depth retrievals indicates that the reanalysis data set is highly accurate (mean bias\u2009=\u2009\u22120.05, RMSE\u2009=\u20090.12, r\u2009=\u20090.81 when compared to retrievals from the spectral de-convolution algorithm on a 3-hourly basis). Verification statistics are broadly homogeneous in space and time with regional differences that can be partly attributed to model limitations (e.g., poor representation of small-scale emission processes), presence of aerosols other than dust in the observations used in the evaluation, and differences in the number of observations among seasons. Such a reliable high-resolution historical record of atmospheric desert dust will allow a better quantification of dust impacts upon key sectors of society and economy, including health, solar energy production and transportation. The reanalysis data set (Di Tomaso et al., 2021) is distributed via a Thematic Real-time Environmental Distributed Data Service (THREDDS) at BSC and freely available at http://hdl.handle.net/21.12146/c6d4a608-5de3-47f6-a004-67cb1d498d98.                         </p></article>", "keywords": ["Desert dust aerosol", "550", "Climate", "MINERAL-COMPOSITION", "Aerosols atmosf\u00e8rics", "01 natural sciences", "Dust emission", "[SDU] Sciences of the Universe [physics]", "LETKF", "Local ensemble transform Kalman filter", "DATA ASSIMILATION", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia", "Pols -- Control", "SDG 3 - Good Health and Well-being", "MONARCH", "SAHARAN DUST", "SDG 13 - Climate Action", "SIZE DISTRIBUTION", "GE1-350", "Desert", "CONVECTIVE ADJUSTMENT SCHEME", "Aerosol measurements", "Multiscale Online Nonhydrostatic AtmospheRe CHemistry model", "0105 earth and related environmental sciences", "QE1-996.5", "info:eu-repo/classification/ddc/550", ":Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia [\u00c0rees tem\u00e0tiques de la UPC]", "ddc:550", "Geology", "1 MODEL DESCRIPTION", "OPTICAL-PROPERTIES", "MONARCH modeling system", "Atmospheric aerosols", "Environmental sciences", "Earth sciences", "PM10 CONCENTRATIONS", "900", "Dust aerosol", "13. Climate action", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "SINGLE-SCATTERING ALBEDO", "MEDITERRANEAN BASIN", "Dust control"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/417480/1/prod_471097-doc_191235.pdf"}, {"href": "https://essd.copernicus.org/articles/14/2785/2022/essd-14-2785-2022.pdf"}, {"href": "https://doi.org/10.5194/essd-2021-358"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-2021-358", "name": "item", "description": "10.5194/essd-2021-358", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-2021-358"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-28T00:00:00Z"}}, {"id": "10.5194/hess-25-17-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:49Z", "type": "Journal Article", "created": "2021-01-04", "title": "Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors", "description": "<p>Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as \uffe2\uff80\uff9copen-loop\uffe2\uff80\uff9d models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byr\uffc3\uffa5ns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5\uffe2\uff80\uff89cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50\uffe2\uff80\uff89% of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of\uffc2\uffa00.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.                     </p>", "keywords": ["Technology", "CLIMATE-CHANGE", "550", "GLOBAL-SCALE EVALUATION", "NEAR-SURFACE", "RADIOFREQUENCY INTERFERENCE", "T", "AMSR-E", "4 DECADES", "15. Life on land", "Environmental technology. Sanitary engineering", "01 natural sciences", "6. Clean water", "HEIHE RIVER-BASIN", "AGRICULTURAL SITES", "G", "Environmental sciences", "DATA ASSIMILATION", "13. Climate action", "Earth and Environmental Sciences", "Geography. Anthropology. Recreation", "GE1-350", "TD1-1066", "SMOS", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://eprints.soton.ac.uk/471538/1/hess_25_17_2021.pdf"}, {"href": "https://doi.org/10.5194/hess-25-17-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/hess-25-17-2021", "name": "item", "description": "10.5194/hess-25-17-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-25-17-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-05-19T00:00:00Z"}}, {"id": "10.5194/hess-25-5749-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:49Z", "type": "Journal Article", "created": "2021-11-09", "title": "The International Soil Moisture Network: serving  Earth system science for over a decade", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. In\u00a02009, the International Soil Moisture Network\u00a0(ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et\u00a0al.,\u00a02011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28\u00a0October\u00a02021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000\u00a0active users and over 1000\u00a0scientific publications referencing the data sets provided by the network. As of July\u00a02021, the ISMN now contains the data of 71\u00a0networks and 2842\u00a0stations located all over the globe, with a time period spanning from\u00a01952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70\u2009% of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.                     </p></article>", "keywords": ["[SDE] Environmental Sciences", "Technology", "Atmospheric Science", "550", "Soil Moisture", "TA Engineering (General). Civil engineering (General)", "02 engineering and technology", "Soil Moisture; ISMN; IMA_CAN1; swc; STEMS", "Spatial variability", "Environmental technology. Sanitary engineering", "01 natural sciences", "Agency (philosophy)", "remote sensing", "Antecedent wetness conditions", "Engineering", "Geography. Anthropology. Recreation", "GE1-350", "TD1-1066", "Smos brightness temperature", "Heihe river-basin", "T", "Soil Water Retention", "Leaf-area index", "004", "FOS: Philosophy", " ethics and religion", "Programming language", "Earth and Planetary Sciences", "Physical Sciences", "name=Water Science and Technology", "/dk/atira/pure/subjectarea/asjc/1900/1901", "Medicine", "name=Earth and Planetary Sciences (miscellaneous)", "Mechanics and Transport in Unsaturated Soils", "Environmental Engineering", "Soil Moisture International Network", "0207 environmental engineering", "Epistemology", "Environmental science", "G", "Database", "Soil Moisture; network", "Arctic Permafrost Dynamics and Climate Change", "Scope (computer science)", "Land data assimilation", "Civil and Structural Engineering", "0105 earth and related environmental sciences", "info:eu-repo/classification/ddc/550", "Consecutive dry days", "in situ", "FOS: Environmental engineering", "AMSR-E", "15. Life on land", "Remote Sensing of Soil Moisture", "Globe", "Computer science", "Environmental sciences", "QE Geology", "Philosophy", "Ophthalmology", "In-situ measurements", "13. Climate action", "ITC-ISI-JOURNAL-ARTICLE", "global scale", "Environmental Science", "G70.212-70.215 Geographic information system", "soil moisture", "ITC-GOLD", "/dk/atira/pure/subjectarea/asjc/2300/2312", "Wireless sensor network"]}, "links": [{"href": "https://iris.polito.it/bitstream/11583/2998914/1/prod_447100-doc_161016.pdf"}, {"href": "https://iris.polito.it/bitstream/11583/2998914/2/prod_447100-doc_178365.pdf"}, {"href": "https://research.unipg.it/bitstream/11391/1498417/2/2021_The%20international%20soil_OA.pdf"}, {"href": "https://cris.unibo.it/bitstream/11585/910145/1/Dourigo_etal_2021.pdf"}, {"href": "https://doi.org/10.5194/hess-25-5749-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/hess-25-5749-2021", "name": "item", "description": "10.5194/hess-25-5749-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-25-5749-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-09T00:00:00Z"}}, {"id": "1854/LU-01JM1T576ZX50W7293M9RBH0RG", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:01Z", "type": "Journal Article", "created": "2024-10-17", "title": "Assimilation of Sentinel\u20101 Backscatter to Update AquaCrop Estimates of Soil Moisture and Crop Biomass", "description": "Abstract<p>This study assesses the potential of regional microwave backscatter data assimilation (DA) in AquaCrop for the first time, using NASA's Land Information System. The objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates. SSM and crop biomass simulations from AquaCrop were updated using Sentinel\uffe2\uff80\uff901 synthetic aperture radar observations, over three regions in Europe in two separate DA experiments. The first experiment concerned updating SSM using VV\uffe2\uff80\uff90polarized backscatter and the corrections were propagated via the model to the biomass. In the second experiment, the DA setup was extended by also updating the biomass with VH\uffe2\uff80\uff90polarized backscatter. SSM was evaluated with local in situ data and with downscaled Soil Moisture Active Passive (SMAP) retrievals for all cropland grid cells, whereas crop biomass was compared to SMAP vegetation optical depth and the Copernicus dry matter productivity. The assimilation showed mixed results for root mean square error and Pearson's correlation, with slight overall improvements in the (anomaly) correlations of updated SSM relative to independent in situ and satellite data. By contrast, the biomass estimates obtained with backscatter DA did not agree better with reference data sets. Overall, the SSM evaluation showed that there is potential in using Sentinel\uffe2\uff80\uff901 backscatter for assimilation in AquaCrop, but the present setup was not able to improve crop biomass estimates. Our study reveals how the complex interaction between SSM, crop biomass and backscatter affect the impact and performance of DA, offering insight into ways to optimize DA for crop growth estimation.</p", "keywords": ["Science & Technology", "SURFACE", "SIMULATE YIELD RESPONSE", "Environmental Sciences & Ecology", "Geology", "LAND INFORMATION-SYSTEM", "0404 Geophysics", "FRAMEWORK", "AquaCrop", "MODEL", "1158423N#56471461", "Earth and Environmental Sciences", "IRRIGATION", "Physical Sciences", "Sentinel-1 SAR", "NETWORK", "Geosciences", " Multidisciplinary", "soil moisture", "Life Sciences & Biomedicine", "data assimilation", "3706 Geophysics", "Environmental Sciences", "SATELLITE", "crop biomass"]}, "links": [{"href": "https://doi.org/1854/LU-01JM1T576ZX50W7293M9RBH0RG"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-01JM1T576ZX50W7293M9RBH0RG", "name": "item", "description": "1854/LU-01JM1T576ZX50W7293M9RBH0RG", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-01JM1T576ZX50W7293M9RBH0RG"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-01T00:00:00Z"}}, {"id": "10.5281/zenodo.14243689", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:22:32Z", "type": "Report", "title": "A digital twin for arable crops and for grass", "description": "There is an opportunity to use process-based cropping systems models (CSMs) to support tactical farm management decisions, by monitoring the status of the farm, by predicting what will happen in the next few weeks, and by exploring scenarios. In practice, the responses of a CSM will deviate more and more from reality as time progresses because the model is an abstraction of the real system and only approximates the responses of the real system. This limitation may be overcome by using the CSM as a digital twin. A digital twin (DT) is a model of a specific physical object, that is kept synchronized by using real-time observations on that object. In this paper we present the Digital Future Farm (DFF), a digital twin for arable and dairy farming. The DFF comprises access to data sources (e.g. weather, soils, farm management, remote sensing), a suite of models, and utilities for data assimilation and visualization of simulation results. The working of the DFF is demonstrated with examples from a multi-year experiment and from a commercial potato farm. In addition to a CSM, the DFF is also demonstrated to work with a summary model for potato growth. Initial experiences\u00a0indicate that the DFF produces information that is helpful to farmers but it is difficult to evaluate\u00a0the performance of the DFF in quantitative terms because of variability between years, fields,\u00a0and the lack of availability of on-farm data. The most immediate contribution of the DFF is to\u00a0provide farmers with a ranking of their fields according to how urgently they need an\u00a0intervention. Experiences with the DFF have helped to formulate further research questions.", "keywords": ["machine learning", "fertilization", "Life Science", "process-based model", "recursive neural network", "Kalman filter", "data assimilation", "nitrogen", "irrigation"], "contacts": [{"organization": "van Evert, F.K., Boersma, S., van Oort, P.A.J., Maestrini, B., Kopanja, M., Mimic, Gordan, Pronk, A.A.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14243689"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14243689", "name": "item", "description": "10.5281/zenodo.14243689", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14243689"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10261/277923", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:24:30Z", "type": "Journal Article", "created": "2022-07-18", "title": "Net irrigation requirement under different climate scenarios using AquaCrop over Europe", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Global soil water availability is challenged by the effects of climate change and a growing population. On average, 70\u2009% of freshwater extraction is attributed to agriculture, and the demand is increasing. In this study, the effects of climate change on the evolution of the irrigation water requirement to sustain current crop productivity are assessed by using the Food and Agriculture Organization (FAO) crop growth model AquaCrop version 6.1. The model is run at 0.5\u2218lat\u00d70.5\u2218long resolution over the European mainland, assuming a general C3-type of crop, and forced by climate input data from the Inter-Sectoral Impact Model Intercomparison Project phase three (ISIMIP3). First, the AquaCrop surface soil moisture (SSM) forced with two types of ISIMIP3 historical meteorological datasets is evaluated with satellite-based SSM estimates in two ways. When driven by ISIMIP3a reanalysis meteorology, daily simulated SSM values have an unbiased root mean square difference of 0.08 and 0.06\u2009m3\u2009m\u22123, with SSM retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, respectively, for the years 2015\u20132016 (2016 is the end year of the reanalysis data). When forced with ISIMIP3b meteorology from five global climate models (GCMs) for the years 2015\u20132020, the historical simulated SSM climatology closely agrees with the satellite-based SSM climatologies. Second, the evaluated AquaCrop model is run to quantify the future irrigation requirement, for an ensemble of five GCMs and three different emission scenarios. The simulated net irrigation requirement (Inet) of the three summer months for a near and far future climate period (2031\u20132060 and 2071\u20132100) is compared to the baseline period of 1985\u20132014 to assess changes in the mean and interannual variability of the irrigation demand. Averaged over the continent and the model ensemble, the far future Inet is expected to increase by 22\u2009mm per month (+30\u2009%) under a high-emission scenario Shared Socioeconomic Pathway (SSP) 3\u20137.0. Central and southern Europe are the most impacted, with larger Inet increases. The interannual variability in Inet is likely to increase in northern and central Europe, whereas the variability is expected to decrease in southern regions. Under a high mitigation scenario (SSP1\u20132.6), the increase in Inet will stabilize at around 13\u2009mm per month towards the end of the century, and interannual variability will still increase but to a smaller extent. The results emphasize a large uncertainty in the Inet projected by various GCMs.</p></article>", "keywords": ["IMPACTS", "LAND", "Technology", "Environmental Engineering", "AGRICULTURE", "DEFICIT IRRIGATION", "SIMULATE YIELD RESPONSE", "0207 environmental engineering", "UNCERTAINTY", "02 engineering and technology", "CROP WATER PRODUCTIVITY", "Environmental technology. Sanitary engineering", "01 natural sciences", "0905 Civil Engineering", "G", "DATA ASSIMILATION", "Geography. Anthropology. Recreation", "GE1-350", "Geosciences", " Multidisciplinary", "TD1-1066", "0105 earth and related environmental sciences", "2. Zero hunger", "Science & Technology", "3707 Hydrology", "T", "Geology", "15. Life on land", "TRENDS", "6. Clean water", "MODEL", "Environmental sciences", "0907 Environmental Engineering", "13. Climate action", "Physical Sciences", "Water Resources", "4013 Geomatic engineering", "0406 Physical Geography and Environmental Geoscience", "3709 Physical geography and environmental geoscience"]}, "links": [{"href": "https://biblio.vub.ac.be/vubirfiles/86261359/Busschaert_etal_2022_HESS.pdf"}, {"href": "https://hess.copernicus.org/articles/26/3731/2022/hess-26-3731-2022.pdf"}, {"href": "https://doi.org/10261/277923"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10261/277923", "name": "item", "description": "10261/277923", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/277923"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-12T00:00:00Z"}}, {"id": "10.60692/7hann-x9205", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:24:07Z", "type": "Journal Article", "created": "2020-12-08", "title": "Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensing data (the vegetation condition index (VCI), temperature condition index (TCI), vegetation health ind ex (VHI), soil moisture condition index (SMCI) and soil water index for different soil layers (SWI)) and (2) key land surface variables (Land Area Index (LAI), soil moisture (SM) at different depths, soil evaporation and plant transpiration) from a Land Data Assimilation System (LDAS) over 2000\u20132017. A lagged correlation analysis was conducted to assess the relationships between the drought indices and cereal yield at monthly time scales. The VCI and LAI around the heading stage (March-April) are highly linked to yield for all provinces (R = 0.94 for the Khemisset province), while a high link for TCI occurs during the development stage in January-February (R = 0.83 for the Beni Mellal province). Interestingly, indices related to soil moisture in the superficial soil layer are correlated with yield earlier in the season around the emergence stage (December). The results demonstrate the clear added value of using an LDAS compared with using a remote sensing product alone, particularly concerning the soil moisture in the root-zone, considered a key variable for yield production, that is not directly observable from space. The time scale of integration is also discussed. By integrating the indices on the main phenological stages of wheat using a dynamic threshold approach instead of the monthly time scale, the correlation between indices and yield increased by up to 14%. In addition, the contributions of VCI and TCI to VHI were optimized by using yield anomalies as proxies for drought. This study opens perspectives for the development of drought early warning systems in Morocco and over North Africa, as well as for seasonal crop yield forecasting.</p></article>", "keywords": ["[SDE] Environmental Sciences", "550", "Science", "0207 environmental engineering", "Agricultural drought", "02 engineering and technology", "01 natural sciences", "630", "Environmental science", "remote sensing", "Land data assimilation systems", "Pathology", "assimilation systems", "Biology", "land data assimilation systems", "0105 earth and related environmental sciences", "2. Zero hunger", "Global and Planetary Change", "Vegetation Monitoring", "Water content", "Ecology", "Drought", "Global Forest Drought Response and Climate Change", "Q", "Hydrology (agriculture)", "Geology", "cereal yield", "Remote Sensing in Vegetation Monitoring and Phenology", "FOS: Earth and related environmental sciences", "Remote sensing", "semiarid region", "15. Life on land", "agricultural drought", "Agronomy", "6. Clean water", "Cereal yield", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "[SDE]Environmental Sciences", "Global Drought Monitoring and Assessment", "Environmental Science", "Physical Sciences", "Leaf area index", "Medicine", "Semiarid region", "land data", "Vegetation (pathology)"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/24/4018/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/24/4018/pdf"}, {"href": "https://doi.org/10.60692/7hann-x9205"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.60692/7hann-x9205", "name": "item", "description": "10.60692/7hann-x9205", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.60692/7hann-x9205"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-08T00:00:00Z"}}, {"id": "20.500.14017/81a6df94-d40c-4db1-86dc-539a3cb8aaf8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:18Z", "type": "Journal Article", "created": "2022-07-18", "title": "Net irrigation requirement under different climate scenarios using AquaCrop over Europe", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Global soil water availability is challenged by the effects of climate change and a growing population. On average, 70\u2009% of freshwater extraction is attributed to agriculture, and the demand is increasing. In this study, the effects of climate change on the evolution of the irrigation water requirement to sustain current crop productivity are assessed by using the Food and Agriculture Organization (FAO) crop growth model AquaCrop version 6.1. The model is run at 0.5\u2218lat\u00d70.5\u2218long resolution over the European mainland, assuming a general C3-type of crop, and forced by climate input data from the Inter-Sectoral Impact Model Intercomparison Project phase three (ISIMIP3). First, the AquaCrop surface soil moisture (SSM) forced with two types of ISIMIP3 historical meteorological datasets is evaluated with satellite-based SSM estimates in two ways. When driven by ISIMIP3a reanalysis meteorology, daily simulated SSM values have an unbiased root mean square difference of 0.08 and 0.06\u2009m3\u2009m\u22123, with SSM retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, respectively, for the years 2015\u20132016 (2016 is the end year of the reanalysis data). When forced with ISIMIP3b meteorology from five global climate models (GCMs) for the years 2015\u20132020, the historical simulated SSM climatology closely agrees with the satellite-based SSM climatologies. Second, the evaluated AquaCrop model is run to quantify the future irrigation requirement, for an ensemble of five GCMs and three different emission scenarios. The simulated net irrigation requirement (Inet) of the three summer months for a near and far future climate period (2031\u20132060 and 2071\u20132100) is compared to the baseline period of 1985\u20132014 to assess changes in the mean and interannual variability of the irrigation demand. Averaged over the continent and the model ensemble, the far future Inet is expected to increase by 22\u2009mm per month (+30\u2009%) under a high-emission scenario Shared Socioeconomic Pathway (SSP) 3\u20137.0. Central and southern Europe are the most impacted, with larger Inet increases. The interannual variability in Inet is likely to increase in northern and central Europe, whereas the variability is expected to decrease in southern regions. Under a high mitigation scenario (SSP1\u20132.6), the increase in Inet will stabilize at around 13\u2009mm per month towards the end of the century, and interannual variability will still increase but to a smaller extent. The results emphasize a large uncertainty in the Inet projected by various GCMs.                     </p></article>", "keywords": ["IMPACTS", "LAND", "Technology", "Environmental Engineering", "AGRICULTURE", "DEFICIT IRRIGATION", "SIMULATE YIELD RESPONSE", "0207 environmental engineering", "UNCERTAINTY", "02 engineering and technology", "CROP WATER PRODUCTIVITY", "Environmental technology. Sanitary engineering", "01 natural sciences", "0905 Civil Engineering", "G", "DATA ASSIMILATION", "Geography. Anthropology. Recreation", "GE1-350", "Geosciences", " Multidisciplinary", "TD1-1066", "0105 earth and related environmental sciences", "2. Zero hunger", "Science & Technology", "3707 Hydrology", "T", "Geology", "15. Life on land", "TRENDS", "6. Clean water", "MODEL", "Environmental sciences", "0907 Environmental Engineering", "13. Climate action", "Physical Sciences", "Water Resources", "4013 Geomatic engineering", "0406 Physical Geography and Environmental Geoscience", "3709 Physical geography and environmental geoscience"]}, "links": [{"href": "https://hess.copernicus.org/articles/26/3731/2022/hess-26-3731-2022.pdf"}, {"href": "https://doi.org/20.500.14017/81a6df94-d40c-4db1-86dc-539a3cb8aaf8"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.14017/81a6df94-d40c-4db1-86dc-539a3cb8aaf8", "name": "item", "description": "20.500.14017/81a6df94-d40c-4db1-86dc-539a3cb8aaf8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.14017/81a6df94-d40c-4db1-86dc-539a3cb8aaf8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-12T00:00:00Z"}}, {"id": "20.500.14017/cddeded8-2ef5-4f98-a327-9b94e00ba846", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:18Z", "type": "Journal Article", "created": "2021-11-30", "title": "Performance analysis of regional AquaCrop (v6.1) biomass  and surface soil moisture simulations using satellite  and in situ observations", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world's growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires efficient models and data to monitor land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1 to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30\u2009arcsec (\u223c1\u2009km). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database version 1.2 (HWSDv1.2), and generic crop information. The setup with a generic crop is chosen as a baseline for a future satellite-based data assimilation system. The relative temporal variability in daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active\u2013Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean temporal correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the short-term and inter-annual variability, with a mean anomaly correlation of 0.46 for daily biomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model adequately simulates crop production and soil moisture and provides a suitable setup for subsequent satellite-based data assimilation.                     </p></article>", "keywords": ["YIELD RESPONSE", "2. Zero hunger", "LAND", "QE1-996.5", "Science & Technology", "PRODUCTIVITY", "04 Earth Sciences", "0207 environmental engineering", "UNCERTAINTY", "Geology", "02 engineering and technology", "15. Life on land", "7. Clean energy", "01 natural sciences", "WHEAT YIELD", "37 Earth sciences", "DATA ASSIMILATION", "13. Climate action", "ASSESSMENTS", "Physical Sciences", "IMPLEMENTATION", "FAO CROP MODEL", "Geosciences", " Multidisciplinary", "HIGH-RESOLUTION", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://gmd.copernicus.org/articles/14/7309/2021/gmd-14-7309-2021.pdf"}, {"href": "https://doi.org/20.500.14017/cddeded8-2ef5-4f98-a327-9b94e00ba846"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.14017/cddeded8-2ef5-4f98-a327-9b94e00ba846", "name": "item", "description": "20.500.14017/cddeded8-2ef5-4f98-a327-9b94e00ba846", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.14017/cddeded8-2ef5-4f98-a327-9b94e00ba846"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-17T00:00:00Z"}}, {"id": "323285e6daa0eef692bb66c188779b9f", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:26:07Z", "type": "Other", "title": "Perspective on Satellite-Based Land Data Assimilation to Estimate Water Cycle Components in an Era of Advanced Data Availability and Model Sophistication", "description": "Provisionally accepted: The final, formatted version of the article will be published soon. Project Co-ordinators: Dr. Jose Alfonso G\u00f3mez Calero (Instituto de Agricultura Sostenible (IAS-CISC), Dr. Weifeng Xu (Fujian Agriculture and Forest University, FAFU). -- Trabajo desarrollado bajo la financiaci\u00f3n del proyecto \u201cSoil Hydrology research platform underpinning innovation to manage water scarcity in European and Chinese cropping Systems\u201d (773903), coordinado por Jos\u00e9 Alfonso G\u00f3mez Calero, investigador del Instituto de Agricultura Sostenible (IAS). The beginning of the 21st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems. This research is supported by Belspo EODAHR (SR/00/376), the European Commission, Horizon 2020 SHui (773903), FWO CONSOLIDATION (G0A7320N), ESA 4D-MED (4000136272/21/I-EF) and KU Leuven C1 (C14/21/057). Peer reviewed", "keywords": ["2. Zero hunger", "Microwave remote sensing", "Land surface modeling", "Vegetation", "13. Climate action", "Snow", "Targeted observations", "Data assimilation", "Soil moisture", "15. Life on land"], "contacts": [{"organization": "De Lannoy, Gabrielle, Bechtold, Michel, Albergel, Cl\u00e9ment, Brocca, Luca, Calvet, Jean-Christophe, Carrassi, Alberto, Crow, Wade T., De Rosnay, Patricia, Durand, Michael, Forman, Bart, Geppert, Gernot, Girotto, Manuela, Franssen, Harrie-Jan Hendricks, Jonas, Tobias, Kumar, Sujay V., Lievens, Hans, Lu, Yang, Massari, Christian, Pauwels, Valentjn, Reichle, Rolf, Steele-Dunne, Susan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/323285e6daa0eef692bb66c188779b9f"}, {"rel": "self", "type": "application/geo+json", "title": "323285e6daa0eef692bb66c188779b9f", "name": "item", "description": "323285e6daa0eef692bb66c188779b9f", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/323285e6daa0eef692bb66c188779b9f"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "11585/910145", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:24:53Z", "type": "Journal Article", "created": "2021-11-09", "title": "The International Soil Moisture Network: serving  Earth system science for over a decade", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. In\u00a02009, the International Soil Moisture Network\u00a0(ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et\u00a0al.,\u00a02011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28\u00a0October\u00a02021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000\u00a0active users and over 1000\u00a0scientific publications referencing the data sets provided by the network. As of July\u00a02021, the ISMN now contains the data of 71\u00a0networks and 2842\u00a0stations located all over the globe, with a time period spanning from\u00a01952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70\u2009% of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.</p></article>", "keywords": ["[SDE] Environmental Sciences", "Technology", "Atmospheric Science", "550", "Soil Moisture", "TA Engineering (General). Civil engineering (General)", "02 engineering and technology", "Soil Moisture; ISMN; IMA_CAN1; swc; STEMS", "SMOS BRIGHTNESS TEMPERATURE", "Spatial variability", "Environmental technology. Sanitary engineering", "01 natural sciences", "Agency (philosophy)", "remote sensing", "Antecedent wetness conditions", "Engineering", "Geography. Anthropology. Recreation", "GE1-350", "Geosciences", " Multidisciplinary", "TD1-1066", "Smos brightness temperature", "Heihe river-basin", "T", "Soil Water Retention", "Geology", "Leaf-area index", "004", "FOS: Philosophy", " ethics and religion", "Programming language", "HEIHE RIVER-BASIN", "Earth and Planetary Sciences", "Physical Sciences", "Water Resources", "name=Water Science and Technology", "/dk/atira/pure/subjectarea/asjc/1900/1901", "Medicine", "0406 Physical Geography and Environmental Geoscience", "name=Earth and Planetary Sciences (miscellaneous)", "3709 Physical geography and environmental geoscience", "Mechanics and Transport in Unsaturated Soils", "Environmental Engineering", "SPATIAL VARIABILITY", "IN-SITU MEASUREMENTS", "0207 environmental engineering", "Epistemology", "0905 Civil Engineering", "Environmental science", "G", "Database", "LAND DATA ASSIMILATION", "Soil Moisture; network", "WIRELESS SENSOR NETWORK", "Arctic Permafrost Dynamics and Climate Change", "Scope (computer science)", "Land data assimilation", "Civil and Structural Engineering", "0105 earth and related environmental sciences", "info:eu-repo/classification/ddc/550", "Science & Technology", "3707 Hydrology", "Consecutive dry days", "LEAF-AREA INDEX", "in situ", "FOS: Environmental engineering", "AMSR-E", "15. Life on land", "Remote Sensing of Soil Moisture", "ANTECEDENT WETNESS CONDITIONS", "Globe", "Computer science", "Environmental sciences", "QE Geology", "0907 Environmental Engineering", "Philosophy", "Ophthalmology", "In-situ measurements", "13. Climate action", "ITC-ISI-JOURNAL-ARTICLE", "global scale", "Environmental Science", "G70.212-70.215 Geographic information system", "4013 Geomatic engineering", "soil moisture", "CONSECUTIVE DRY DAYS", "ITC-GOLD", "/dk/atira/pure/subjectarea/asjc/2300/2312", "Wireless sensor network"]}, "links": [{"href": "https://iris.polito.it/bitstream/11583/2998914/1/prod_447100-doc_161016.pdf"}, {"href": "https://iris.polito.it/bitstream/11583/2998914/2/prod_447100-doc_178365.pdf"}, {"href": "https://cris.unibo.it/bitstream/11585/910145/1/Dourigo_etal_2021.pdf"}, {"href": "https://doi.org/11585/910145"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11585/910145", "name": "item", "description": "11585/910145", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11585/910145"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-09T00:00:00Z"}}, {"id": "1854/LU-01JKX2FJKXN38WB8P6CAQC7AEH", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:01Z", "type": "Journal Article", "created": "2022-09-16", "title": "Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication", "description": "<p>The beginning of the 21st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems.</p", "keywords": ["[SDE] Environmental Sciences", "Land surface modeling", "VEGETATION OPTICAL DEPTH", "info:eu-repo/classification/ddc/333.7", "IMPACT", "snow", "Environmental technology. Sanitary engineering", "01 natural sciences", "land surface modeling", "RETRIEVALS", "targeted observations", "vegetation", "Snow", "Targeted observations", "SNOW DEPTH", "SOIL-MOISTURE ASSIMILATION", "data assimilation", "TD1-1066", "0105 earth and related environmental sciences", "Science & Technology", "GRACE DATA ASSIMILATION", "EQUIVALENT", "3707 Hydrology", "microwave remote sensing", "Vegetation", "LDAS-MONDE", "BRIGHTNESS TEMPERATURE OBSERVATIONS", "15. Life on land", "Microwave remote sensing", "13. Climate action", "Earth and Environmental Sciences", "Physical Sciences", "SIMULATION", "Data assimilation", "data assimilation", " soil moisture", " snow", " vegetation", " microwave remote sensing", " land surface modeling", " targeted observation", "Water Resources", "Soil moisture", "soil moisture"]}, "links": [{"href": "https://cris.unibo.it/bitstream/11585/894502/2/frwa-04-981745%20%282%29.pdf"}, {"href": "https://doi.org/1854/LU-01JKX2FJKXN38WB8P6CAQC7AEH"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-01JKX2FJKXN38WB8P6CAQC7AEH", "name": "item", "description": "1854/LU-01JKX2FJKXN38WB8P6CAQC7AEH", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-01JKX2FJKXN38WB8P6CAQC7AEH"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-16T00:00:00Z"}}, {"id": "20.500.14243/417480", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:18Z", "type": "Journal Article", "created": "2021-10-28", "title": "The MONARCH high-resolution reanalysis of desert dust aerosol over Northern Africa, the Middle East and Europe (2007\u20132016)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. One of the challenges in studying desert dust aerosol along with its numerous interactions and impacts is the paucity of direct in-situ measurements, particularly in the areas most affected by dust storms. Satellites typically provide columnintegrated aerosol measurements, but observationally-constrained continuous 3D dust fields are needed to assess dust variability, climate effects and impacts upon a variety of socio-economic sectors. Here, we present a high resolution regional reanalysis data set of desert dust aerosols that covers Northern Africa, the Middle East and Europe along with the Mediterranean sea and parts of Central Asia, and the Atlantic and Indian Oceans between 2007 and 2016. The horizontal resolution is 0.1\u00b0 latitude\u2009\u00d7\u20090.1\u00b0 longitude, and the temporal resolution is 3 hours. The reanalysis was produced using Local Ensemble Transform Kalman Filter (LETKF) data assimilation in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) developed at the Barcelona Supercomputing Center (BSC). The assimilated data are coarse-mode dust optical depth retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Level 2 products. The reanalysis data set consists of upper air (dust mass concentrations and extinction coefficient), surface (dust deposition and solar irradiance fields, among them) and total column (e.g., dust optical depth and load) variables. Some dust variables, such as concentrations and wet and dry deposition, are expressed for a binned size distribution that ranges from 0.2 to 20\u2009\u03bcm in particle diameter. Both analysis and first-guess (analysis-initialized simulation) fields are available for the variables that are diagnosed from the state vector. A set of ensemble statistics is archived for each output variable, namely the ensemble mean, standard deviation, maximum and median. The spatial and temporal distribution of the dust fields follows well-known dust cycle features controlled by seasonal changes in meteorology and vegetation cover. The analysis is statistically closer to the assimilated retrievals than the first-guess, which proves the consistency of the data assimilation method. Independent evaluation using AERONET dust-filtered optical depth retrievals indicates that the reanalysis data set is highly accurate (mean bias\u2009=\u2009\u22120.05, RMSE\u2009=\u20090.12, r\u2009=\u20090.81 when compared to retrievals from the spectral de-convolution algorithm on a 3-hourly basis). Verification statistics are broadly homogeneous in space and time with regional differences that can be partly attributed to model limitations (e.g., poor representation of small-scale emission processes), presence of aerosols other than dust in the observations used in the evaluation, and differences in the number of observations among seasons. Such a reliable high-resolution historical record of atmospheric desert dust will allow a better quantification of dust impacts upon key sectors of society and economy, including health, solar energy production and transportation. The reanalysis data set (Di Tomaso et al., 2021) is distributed via a Thematic Real-time Environmental Distributed Data Service (THREDDS) at BSC and freely available at http://hdl.handle.net/21.12146/c6d4a608-5de3-47f6-a004-67cb1d498d98.</p></article>", "keywords": ["Desert dust aerosol", "550", "Climate", "MINERAL-COMPOSITION", "Aerosols atmosf\u00e8rics", "01 natural sciences", "Dust emission", "[SDU] Sciences of the Universe [physics]", "LETKF", "Local ensemble transform Kalman filter", "DATA ASSIMILATION", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia", "Pols -- Control", "SDG 3 - Good Health and Well-being", "MONARCH", "SAHARAN DUST", "SIZE DISTRIBUTION", "GE1-350", "Desert", "CONVECTIVE ADJUSTMENT SCHEME", "Aerosol measurements", "Multiscale Online Nonhydrostatic AtmospheRe CHemistry model", "0105 earth and related environmental sciences", "QE1-996.5", "info:eu-repo/classification/ddc/550", ":Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia [\u00c0rees tem\u00e0tiques de la UPC]", "ddc:550", "Geology", "1 MODEL DESCRIPTION", "OPTICAL-PROPERTIES", "MONARCH modeling system", "Atmospheric aerosols", "Environmental sciences", "Earth sciences", "PM10 CONCENTRATIONS", "900", "Dust aerosol", "13. Climate action", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "SINGLE-SCATTERING ALBEDO", "MEDITERRANEAN BASIN", "Dust control"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/417480/1/prod_471097-doc_191235.pdf"}, {"href": "https://essd.copernicus.org/articles/14/2785/2022/essd-14-2785-2022.pdf"}, {"href": "https://doi.org/20.500.14243/417480"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.14243/417480", "name": "item", "description": "20.500.14243/417480", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.14243/417480"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-28T00:00:00Z"}}, {"id": "2117/383484", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:24Z", "type": "Report", "created": "2023-01-01", "title": "MONARCH Regional Reanalysis of\u00a0Desert Dust Aerosols: An Initial Assessment", "description": "Open AccessWe acknowledge the DustClim project which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union\u2019s Horizon 2020 research and innovation programme (Grant n. 690462). BSC co-authors also acknowledge support from the European Research Council under the European Union\u2019s Horizon 2020 research and innovation programme (grant n. 773051; FRAGMENT), the AXA Research Fund, the 60 Spanish Ministry of Science, Innovation and Universities (grant n. RYC-2015-18690 and CGL2017-88911-R), the European Union\u2019s Horizon 2020 research and innovation programme (grant n. 792103; SOLWARIS). This work has been partially funded by the contribution agreement between AEMET and BSC to carry out development and improvement activities of the products and services supplied by the WMO Sand and Dust Storm Regional Centres. Jer\u00f3nimo Escribano and Martina Klose have received funding from the European Union\u2019s Horizon 2020 research and innovation programme, respectively, under the Marie Sk\u0142odowska-Curie grant agreements H2020-MSCA-COFUND-2016- 65 754433 and H2020-MSCA-IF-2017-789630. Martina Klose further acknowledges support through the Helmholtz Association\u2019s Initiative and Networking Fund (grant agreement n. VH-NG-1533). We acknowledge PRACE (eDUST, eFRAGMENT1, and eFRAGMENT2) and RES (AECT-2019-3-0001, AECT-2020-1-0007, AECT-2020-3-0013) for awarding access to MareNostrum at the BSC and for providing technical support.", "keywords": ["\u00c0rees tem\u00e0tiques de la UPC::Enginyeria mec\u00e0nica::Mec\u00e0nica de fluids", "info:eu-repo/classification/ddc/550", "Aerosol speciation", "550", "ddc:550", "Aerosol data assimilation", "Dust", "Aerosols atmosf\u00e8rics", "Atmospheric aerosols", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Enginyeria ambiental", "Earth sciences", "Aerosol regional reanalysis", "Pols -- Control", "13. Climate action", "2023 OA procedure", "Modis deep blue", "Dust control"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1007/978-3-031-12786-1"}, {"href": "https://link.springer.com/content/pdf/10.1007/978-3-031-12786-1_33"}, {"href": "https://doi.org/2117/383484"}, {"rel": "self", "type": "application/geo+json", "title": "2117/383484", "name": "item", "description": "2117/383484", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/383484"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "2164/6134", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:27Z", "type": "Journal Article", "created": "2016-05-13", "title": "Modeling Soil Processes: Review, Key Challenges, and New Perspectives", "description": "Core Ideas                     <p>                                                                           <p>A community effort is needed to move soil modeling forward.</p>                                                                             <p>Establishing an international soil modeling consortium is key in this respect.</p>                                                                             <p>There is a need to better integrate existing knowledge in soil models.</p>                                                                             <p>Integration of data and models is a key challenge in soil modeling.</p>                                                                     </p>                     <p>The remarkable complexity of soil and its importance to a wide range of ecosystem services presents major challenges to the modeling of soil processes. Although major progress in soil models has occurred in the last decades, models of soil processes remain disjointed between disciplines or ecosystem services, with considerable uncertainty remaining in the quality of predictions and several challenges that remain yet to be addressed. First, there is a need to improve exchange of knowledge and experience among the different disciplines in soil science and to reach out to other Earth science communities. Second, the community needs to develop a new generation of soil models based on a systemic approach comprising relevant physical, chemical, and biological processes to address critical knowledge gaps in our understanding of soil processes and their interactions. Overcoming these challenges will facilitate exchanges between soil modeling and climate, plant, and social science modeling communities. It will allow us to contribute to preserve and improve our assessment of ecosystem services and advance our understanding of climate\uffe2\uff80\uff90change feedback mechanisms, among others, thereby facilitating and strengthening communication among scientific disciplines and society. We review the role of modeling soil processes in quantifying key soil processes that shape ecosystem services, with a focus on provisioning and regulating services. We then identify key challenges in modeling soil processes, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes. We discuss how the soil modeling community could best interface with modern modeling activities in other disciplines, such as climate, ecology, and plant research, and how to weave novel observation and measurement techniques into soil models. We propose the establishment of an international soil modeling consortium to coherently advance soil modeling activities and foster communication with other Earth science disciplines. Such a consortium should promote soil modeling platforms and data repository for model development, calibration and intercomparison essential for addressing contemporary challenges.</p>", "keywords": ["organic-matter dynamics", "550", "Sciences de l\u2019environnement & \u00e9cologie", "QH301 Biology", "Knowledge management", "0208 environmental biotechnology", "ECOSYSTEM SERVICES", "02 engineering and technology", "soil processes", "01 natural sciences", "Physical Geography and Environmental Geoscience", "Sciences de la Terre", "Biological process", "ANZSRC::3707 Hydrology", "DROUGHT SEVERITY INDEX", "SYNTHETIC-APERTURE RADAR", "ANZSRC::4106 Soil sciences", "SDG 13 - Climate Action", "Climate change", "0503 Soil Sciences", "GROUND-PENETRATING RADAR", "Integration of knowledge", "Life sciences", "ANZSRC::050399 Soil Sciences not elsewhere classified", "synthetic-aperture radar", "Physical Sciences", "Water Resources", "Knowledge and experience", "MULTIPLE ECOSYSTEM SERVICES", "knowledge integration", "570", "DIFFUSE-REFLECTANCE SPECTROSCOPY", "Environmental Engineering", "Physique", " chimie", " math\u00e9matiques & sciences de la terre", "Scientific discipline", "0703 Crop and Pasture Production", "0207 environmental engineering", "Soil Science", "soil science", "ORGANIC-MATTER DYNAMICS", "DATA ASSIMILATION", "Physical", " chemical", " mathematical & earth Sciences", "ANZSRC::0503 Soil Sciences", "Science disciplines", "PEDOTRANSFER FUNCTIONS", "Feedback mechanisms", "mod\u00e9lisation", "ground-penetrating radar", "Science & Technology", "ANZSRC::080110 Simulation and Modelling", "15. Life on land", "Sciences de la terre & g\u00e9ographie physique", "multiple ecosystem services", "root water-uptake", "Observation and measurement", "DIGITAL ELEVATION MODEL", "Quality of predictions", "SATURATED-UNSATURATED FLOW", "ARBUSCULAR MYCORRHIZAL FUNGI", "sciences du sol", "HYDRAULIC-PROPERTIES", "2. Zero hunger", "Agriculture", "diffuse-reflectance spectroscopy", "4106 Soil sciences", "ORGANIC-MATTER", "digital elevation model", "SDG 13 \u2013 Ma\u00dfnahmen zum Klimaschutz", "Sciences du vivant", "Uncertainty analysis", "0406 Physical Geography and Environmental Geoscience", "Life Sciences & Biomedicine", "Crop and Pasture Production", "101028 Mathematical modelling", "international soil modeling consortium", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Environmental Sciences & Ecology", "arbuscular mycorrhizal fungi", "Ecosystems", "Climate models", "QH301", "Environmental sciences & ecology", "Life Science", "SEDIMENT TRANSPORT MODELS", "data integration", "sediment transport models", "approche ecosyst\u00e9mique", "0105 earth and related environmental sciences", "info:eu-repo/classification/ddc/550", "3707 Hydrology", "soil modeling", "ROOT WATER-UPTAKE", "SOLUTE TRANSPORT", "13. Climate action", "Earth and Environmental Sciences", "Soil Sciences", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "Earth Sciences", "Earth sciences & physical geography", "Soils", "101028 Mathematische Modellierung", "saturated-unsaturated flow", "Environmental Sciences", "root water-uptake", " sediment transport models", " diffuse-reflectance spectroscopy", " arbuscular mycorrhizal fungi", " multiple ecosystem services", " saturated-unsaturated flow", " ground-penetrating radar", " synthetic-aperture radar", " digital elevation model", " organic-matter dynamics."]}, "links": [{"href": "https://orbi.uliege.be/bitstream/2268/263634/1/Vereecken%20VZJ%202016.pdf"}, {"href": "http://onlinelibrary.wiley.com/wol1/doi/10.2136/vzj2015.09.0131/fullpdf"}, {"href": "https://escholarship.org/content/qt6976n34c/qt6976n34c.pdf"}, {"href": "https://doi.org/2164/6134"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Vadose%20Zone%20Journal", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2164/6134", "name": "item", "description": "2164/6134", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2164/6134"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-05-01T00:00:00Z"}}, {"id": "25d43a6391aa3b144884152e00849bf4", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:34Z", "type": "Report", "title": "Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication", "description": "unspecified<p>The beginning of the 21<sup>st</sup> century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems.</p>", "keywords": ["microwave remote sensing", "targeted observations", "vegetation", "snow", "soil moisture", "data assimilation", "land surface modeling"], "contacts": [{"organization": "De Lannoy, Gabri\u00eblle J.M. (author), Bechtold, Michel (author), Albergel, Cl\u00e9ment (author), Brocca, Luca (author), Calvet, Jean Christophe (author), Carrassi, Alberto (author), Crow, Wade T. (author), de Rosnay, Patricia (author), Steele-Dunne, S.C. (author),", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/25d43a6391aa3b144884152e00849bf4"}, {"rel": "self", "type": "application/geo+json", "title": "25d43a6391aa3b144884152e00849bf4", "name": "item", "description": "25d43a6391aa3b144884152e00849bf4", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/25d43a6391aa3b144884152e00849bf4"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "2804595293", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-25T16:25:39Z", "type": "Journal Article", "created": "2018-05-26", "title": "Biotic responses buffer warming\u2010induced soil organic carbon loss in Arctic tundra", "description": "Abstract<p>Climate warming can result in both abiotic (e.g., permafrost thaw) and biotic (e.g., microbial functional genes) changes in Arctic tundra. Recent research has incorporated dynamic permafrost thaw in Earth system models (ESMs) and indicates that Arctic tundra could be a significant future carbon (C) source due to the enhanced decomposition of thawed deep soil C. However, warming\uffe2\uff80\uff90induced biotic changes may influence biologically related parameters and the consequent projections inESMs. How model parameters associated with biotic responses will change under warming and to what extent these changes affect projected C budgets have not been carefully examined. In this study, we synthesized six data sets over 5\uffc2\uffa0years from a soil warming experiment at the Eight Mile Lake, Alaska, into the TerrestrialECOsystem (TECO) model with a probabilistic inversion approach. TheTECOmodel used multiple soil layers to track dynamics of thawed soil under different treatments. Our results show that warming increased light use efficiency of vegetation photosynthesis but decreased baseline (i.e., environment\uffe2\uff80\uff90corrected) turnover rates ofSOCin both the fast and slow pools in comparison with those under control. Moreover, the parameter changes generally amplified over time, suggesting processes of gradual physiological acclimation and functional gene shifts of both plants and microbes. TheTECOmodel predicted that field warming from 2009 to 2013 resulted in cumulative C losses of 224 or 87\uffc2\uffa0g/m2, respectively, without or with changes in those parameters. Thus, warming\uffe2\uff80\uff90induced parameter changes reduced predicted soil C loss by 61%. Our study suggests that it is critical to incorporate biotic changes inESMs to improve the model performance in predicting C dynamics in permafrost regions.</p", "keywords": ["550", "Climate Change", "Permafrost", "acclimation", "carbon modeling", "01 natural sciences", "climate warming", "Soil", "Theoretical", "Models", "soil carbon", "Photosynthesis", "biotic responses", "data assimilation", "Tundra", "Soil Microbiology", "0105 earth and related environmental sciences", "Ecology", "500", "Biological Sciences", "Models", " Theoretical", "Plants", "15. Life on land", "Carbon", "Climate Action", "Environmental sciences", "Biological sciences", "Earth sciences", "13. Climate action", "Environmental Sciences", "Alaska", "permafrost"]}, "links": [{"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.14325"}, {"href": "https://doi.org/2804595293"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2804595293", "name": "item", "description": "2804595293", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2804595293"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-06-12T00:00:00Z"}}, {"id": "2945065301", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:43Z", "type": "Journal Article", "created": "2019-05-21", "title": "Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield using a re-calibration data assimilation approach. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields. LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The assimilation of LAI in EPIC provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed. The diverging results obtained in the two years indicated that the assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.</p></article>", "keywords": ["2. Zero hunger", "yield estimation", "S", "Leaf Area Index", "EPIC model", "Agriculture", "Crop growth model", "04 agricultural and veterinary sciences", "15. Life on land", "crop growth model", "Yield estimation", "13. Climate action", "Leaf area index", "Data assimilation", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "data assimilation"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://doi.org/2945065301"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2945065301", "name": "item", "description": "2945065301", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2945065301"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-21T00:00:00Z"}}, {"id": "29802797", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-25T16:25:45Z", "type": "Journal Article", "created": "2018-05-26", "title": "Biotic responses buffer warming\u2010induced soil organic carbon loss in Arctic tundra", "description": "Abstract<p>Climate warming can result in both abiotic (e.g., permafrost thaw) and biotic (e.g., microbial functional genes) changes in Arctic tundra. Recent research has incorporated dynamic permafrost thaw in Earth system models (ESMs) and indicates that Arctic tundra could be a significant future carbon (C) source due to the enhanced decomposition of thawed deep soil C. However, warming\uffe2\uff80\uff90induced biotic changes may influence biologically related parameters and the consequent projections inESMs. How model parameters associated with biotic responses will change under warming and to what extent these changes affect projected C budgets have not been carefully examined. In this study, we synthesized six data sets over 5\uffc2\uffa0years from a soil warming experiment at the Eight Mile Lake, Alaska, into the TerrestrialECOsystem (TECO) model with a probabilistic inversion approach. TheTECOmodel used multiple soil layers to track dynamics of thawed soil under different treatments. Our results show that warming increased light use efficiency of vegetation photosynthesis but decreased baseline (i.e., environment\uffe2\uff80\uff90corrected) turnover rates ofSOCin both the fast and slow pools in comparison with those under control. Moreover, the parameter changes generally amplified over time, suggesting processes of gradual physiological acclimation and functional gene shifts of both plants and microbes. TheTECOmodel predicted that field warming from 2009 to 2013 resulted in cumulative C losses of 224 or 87\uffc2\uffa0g/m2, respectively, without or with changes in those parameters. Thus, warming\uffe2\uff80\uff90induced parameter changes reduced predicted soil C loss by 61%. Our study suggests that it is critical to incorporate biotic changes inESMs to improve the model performance in predicting C dynamics in permafrost regions.</p", "keywords": ["550", "Climate Change", "Permafrost", "acclimation", "carbon modeling", "01 natural sciences", "climate warming", "Soil", "Theoretical", "Models", "soil carbon", "Photosynthesis", "biotic responses", "data assimilation", "Tundra", "Soil Microbiology", "0105 earth and related environmental sciences", "Ecology", "500", "Biological Sciences", "Models", " Theoretical", "Plants", "15. Life on land", "Carbon", "Climate Action", "Environmental sciences", "Biological sciences", "Earth sciences", "13. Climate action", "Environmental Sciences", "Alaska", "permafrost"]}, "links": [{"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.14325"}, {"href": "https://doi.org/29802797"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "29802797", "name": "item", "description": "29802797", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/29802797"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-06-12T00:00:00Z"}}, {"id": "3033508559", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:52Z", "type": "Journal Article", "created": "2020-06-07", "title": "Herbivores stimulate respiration from labile and recalcitrant soil carbon pools in grasslands of Yellowstone National Park", "description": "Abstract<p>Quantifying the effects of grazing on soil organic carbon (SOC) decomposition is of crucial importance for understanding soil C dynamics. However, less attention has been paid to the pool\uffe2\uff80\uff90specific SOC decomposition and the underlying factors associated with each C pool, representing critical knowledge gaps on soil C dynamics. In this study, we applied a state\uffe2\uff80\uff90of\uffe2\uff80\uff90the\uffe2\uff80\uff90art Bayesian data assimilation technique to re\uffe2\uff80\uff90analyze previous soil incubation data to examine how herbivores influenced the fraction and cumulative respiration of labile and recalcitrant soil C pools from seven edaphically diverse sites in Yellowstone National Park, whereas those variables were not explored in the earlier study. Our results showed that grazing significantly increased cumulative respiration from both labile and recalcitrant C pools. Greater cumulative respiration from the labile C pool was related to grazers increasing labile C pool fractions, while higher cumulative respiration from the recalcitrant C pool was associated with grazers accelerating the decomposition rate of the recalcitrant C pool. Cumulative respiration from both labile and recalcitrant C pools was positively correlated with shoot biomass, soil gravimetric moisture, and soil C and nitrogen content. Our results underscore how knowledge of pool\uffe2\uff80\uff90specific SOC decomposition can provide a better mechanistic understanding of soil C dynamics along topo\uffe2\uff80\uff90edaphic gradients in grazed grassland.</p", "keywords": ["2. Zero hunger", "decomposition", "recalcitrant carbon pool", "0401 agriculture", " forestry", " and fisheries", "soil incubation | microorganisms", "04 agricultural and veterinary sciences", "herbivores grazing", "plant productivity", "15. Life on land", "data assimilation", "labile carbon pool"]}, "links": [{"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1002/ldr.3656"}, {"href": "https://doi.org/3033508559"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land%20Degradation%20%26amp%3B%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3033508559", "name": "item", "description": "3033508559", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3033508559"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-07T00:00:00Z"}}, {"id": "3113036323", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:57Z", "type": "Journal Article", "created": "2020-12-08", "title": "Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensing data (the vegetation condition index (VCI), temperature condition index (TCI), vegetation health ind ex (VHI), soil moisture condition index (SMCI) and soil water index for different soil layers (SWI)) and (2) key land surface variables (Land Area Index (LAI), soil moisture (SM) at different depths, soil evaporation and plant transpiration) from a Land Data Assimilation System (LDAS) over 2000\u20132017. A lagged correlation analysis was conducted to assess the relationships between the drought indices and cereal yield at monthly time scales. The VCI and LAI around the heading stage (March-April) are highly linked to yield for all provinces (R = 0.94 for the Khemisset province), while a high link for TCI occurs during the development stage in January-February (R = 0.83 for the Beni Mellal province). Interestingly, indices related to soil moisture in the superficial soil layer are correlated with yield earlier in the season around the emergence stage (December). The results demonstrate the clear added value of using an LDAS compared with using a remote sensing product alone, particularly concerning the soil moisture in the root-zone, considered a key variable for yield production, that is not directly observable from space. The time scale of integration is also discussed. By integrating the indices on the main phenological stages of wheat using a dynamic threshold approach instead of the monthly time scale, the correlation between indices and yield increased by up to 14%. In addition, the contributions of VCI and TCI to VHI were optimized by using yield anomalies as proxies for drought. This study opens perspectives for the development of drought early warning systems in Morocco and over North Africa, as well as for seasonal crop yield forecasting.</p></article>", "keywords": ["[SDE] Environmental Sciences", "550", "Science", "0207 environmental engineering", "Agricultural drought", "02 engineering and technology", "01 natural sciences", "630", "Environmental science", "remote sensing", "Land data assimilation systems", "Pathology", "assimilation systems", "Biology", "land data assimilation systems", "0105 earth and related environmental sciences", "2. Zero hunger", "Global and Planetary Change", "Vegetation Monitoring", "Water content", "Ecology", "Drought", "Global Forest Drought Response and Climate Change", "Q", "Hydrology (agriculture)", "Geology", "cereal yield", "Remote Sensing in Vegetation Monitoring and Phenology", "FOS: Earth and related environmental sciences", "Remote sensing", "semiarid region", "15. Life on land", "agricultural drought", "Agronomy", "6. Clean water", "Cereal yield", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "[SDE]Environmental Sciences", "Global Drought Monitoring and Assessment", "Environmental Science", "Physical Sciences", "Leaf area index", "Medicine", "Semiarid region", "land data", "Vegetation (pathology)"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/24/4018/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/24/4018/pdf"}, {"href": "https://doi.org/3113036323"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3113036323", "name": "item", "description": "3113036323", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3113036323"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-08T00:00:00Z"}}, {"id": "3217588385", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:26:07Z", "type": "Journal Article", "created": "2021-11-22", "title": "Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions", "description": "Accurate estimation of evapotranspiration (ET) is of crucial importance in water science and hydrological process understanding especially in semi-arid/arid areas since ET represents more than 85% of the total water budget. FAO-56 is one of the widely used formulations to estimate the actual crop evapotranspiration (ET c act) due to its operational nature and since it represents a reasonable compromise between simplicity and accuracy. In this vein, the objective of this paper was to examine the possibility of improving ET c act estimates through remote sensing data assimilation. For this purpose, remotely sensed soil moisture (SM) and Land surface temperature (LST) data were simultaneously assimilated into FAO-dualK c. Surface SM observations were assimilated into the soil evaporation (E s) component through the soil evaporation coefficient, and LST data were assimilated into the actual crop transpiration (T c act) component through the crop stress coefficient. The LST data were used to estimate the water stress coefficient (K s) as a proxy of LST (LST proxy). The FAO-Ks was corrected by assimilating LST proxy derived from Landsat data based on the variances of predicted errors on K s estimates from FAO-56 model and thermal-derived K s. The proposed approach was tested over a semi-arid area in Morocco using first, in situ data collected during 2002-2003 and 2015-2016 wheat growth seasons over two different fields and then, remotely sensed data derived from disaggregated Soil Moisture Active Passive (SMAP) SM and Landsat-LST sensors were used. Assimilating SM data leads to an improvement of the ET c act model prediction: the root mean square error (RMSE) decreased from 0.98 to 0.65 mm/day compared to the classical FAO-dualK c using in situ SM. Moreover, assimilating both in situ SM and LST data provided more accurate results with a RMSE error of 0.55 mm/day. By using SMAP-based SM and Landsat-LST, results also improved in comparison with standard FAO and reached a RMSE of 0.73 mm/day against eddy-covariance ET c act measurements.", "keywords": ["0106 biological sciences", "2. Zero hunger", "Evapotranspiration", "550", "Evapotranspiration Data assimilation FAO-dualK c Soil moisture Land surface temperature", "0207 environmental engineering", "02 engineering and technology", "15. Life on land", "01 natural sciences", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "FAO-dualK(c)", "13. Climate action", "Data assimilation", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Soil moisture", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment", "Land surface temperature"]}, "links": [{"href": "https://doi.org/3217588385"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20Water%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3217588385", "name": "item", "description": "3217588385", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3217588385"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-01T00:00:00Z"}}, {"id": "oai:dnet:digitalcsic_::7935fd3fc8ea6e8c9c2214f78909b8f9", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:32:57Z", "type": "Other", "title": "Perspective on Satellite-Based Land Data Assimilation to Estimate Water Cycle Components in an Era of Advanced Data Availability and Model Sophistication", "description": "Provisionally accepted: The final, formatted version of the article will be published soon. Project Co-ordinators: Dr. Jose Alfonso G\u00f3mez Calero (Instituto de Agricultura Sostenible (IAS-CISC), Dr. Weifeng Xu (Fujian Agriculture and Forest University, FAFU). -- Trabajo desarrollado bajo la financiaci\u00f3n del proyecto \u201cSoil Hydrology research platform underpinning innovation to manage water scarcity in European and Chinese cropping Systems\u201d (773903), coordinado por Jos\u00e9 Alfonso G\u00f3mez Calero, investigador del Instituto de Agricultura Sostenible (IAS). The beginning of the 21st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems. This research is supported by Belspo EODAHR (SR/00/376), the European Commission, Horizon 2020 SHui (773903), FWO CONSOLIDATION (G0A7320N), ESA 4D-MED (4000136272/21/I-EF) and KU Leuven C1 (C14/21/057). Peer reviewed", "keywords": ["2. Zero hunger", "Microwave remote sensing", "Land surface modeling", "Vegetation", "13. Climate action", "Snow", "Targeted observations", "Data assimilation", "Soil moisture", "15. Life on land"], "contacts": [{"organization": "De Lannoy, Gabrielle, Bechtold, Michel, Albergel, Cl\u00e9ment, Brocca, Luca, Calvet, Jean-Christophe, Carrassi, Alberto, Crow, Wade T., De Rosnay, Patricia, Durand, Michael, Forman, Bart, Geppert, Gernot, Girotto, Manuela, Franssen, Harrie-Jan Hendricks, Jonas, Tobias, Kumar, Sujay V., Lievens, Hans, Lu, Yang, Massari, Christian, Pauwels, Valentjn, Reichle, Rolf, Steele-Dunne, Susan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/oai:dnet:digitalcsic_::7935fd3fc8ea6e8c9c2214f78909b8f9"}, {"rel": "self", "type": "application/geo+json", "title": "oai:dnet:digitalcsic_::7935fd3fc8ea6e8c9c2214f78909b8f9", "name": "item", "description": "oai:dnet:digitalcsic_::7935fd3fc8ea6e8c9c2214f78909b8f9", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/oai:dnet:digitalcsic_::7935fd3fc8ea6e8c9c2214f78909b8f9"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Data+assimilation&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Data+assimilation&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Data+assimilation&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Data+assimilation&offset=43", "hreflang": "en-US"}], "numberMatched": 43, "numberReturned": 43, "distributedFeatures": [], "timeStamp": "2026-05-26T04:14:42.994967Z"}