Abstract. Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These come in the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to further unravel the influence of climate on vegetation dynamics. However, as advocated in this article, commonly used statistical methods are often too simplistic to represent complex climate\uffe2\uff80\uff93vegetation relationships due to linearity assumptions. Therefore, as an extension of linear Granger-causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods, and predictive modelling by means of random forests. Experimental results on global data sets indicate that, with this framework, it is possible to detect non-linear patterns that are much less visible with traditional Granger-causality methods. In addition, we discuss extensive experimental results that highlight the importance of considering non-linear aspects of climate\uffe2\uff80\uff93vegetation dynamics.
", "keywords": ["QE1-996.5", "0207 environmental engineering", "TIME-SERIES", "Geology", "02 engineering and technology", "15. Life on land", "SOIL-MOISTURE", "SAMPLE TESTS", "SURFACE-TEMPERATURE", "01 natural sciences", "RANDOM FORESTS", "CARBON-DIOXIDE", "NDVI DATA", "13. Climate action", "Earth and Environmental Sciences", "PRECIPITATION", "GLOBAL TERRESTRIAL ECOSYSTEMS", "SDG 13 - Climate Action", "SATELLITE", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://gmd.copernicus.org/articles/10/1945/2017/gmd-10-1945-2017.pdf"}, {"href": "https://doi.org/10.5194/gmd-10-1945-2017"}, {"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-10-1945-2017", "name": "item", "description": "10.5194/gmd-10-1945-2017", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/gmd-10-1945-2017"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-05-17T00:00:00Z"}}, {"id": "/publisher", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:13:46Z", "type": "Dataset", "title": "Soil moisture and temperature datasets in LIDO orchard", "description": "Soil moisture and temperature timeserie datasets of the apple orchard located in the Laimburg Integrated Digital Orchard in Bolzano province. The timeseries collects data about soil moisture and temperature at 20 cm, 30 cm and 40 cm under the soil surface in 3 different position of the orchard. Acquisition frequency is 30 minutes for all the parameters.", "keywords": ["agricultural-and-aquaculture-facilities", "cct", "eu", "sensor", "soil", "soil-moisture", "temperature", "water"]}, "links": [{"href": "https://doi.org/10.48784/d41997c7-d9ea-4f10-bd2e-baf1d96bb8f8"}, {"href": "http://data.europa.eu/88u/dataset/d41997c7-d9ea-4f10-bd2e-baf1d96bb8f8"}, {"rel": "self", "type": "application/geo+json", "title": "/publisher", "name": "item", "description": "/publisher", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items//publisher"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "/", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:13:46Z", "type": "Dataset", "title": "DrSch\u00e4r mais field monitoring timeseries", "description": "Timeseries of the DrSch\u00e4r field monitoring in Este (PD) during summer 2022. Dataset of the soil temperature and soil humidity collected from different type of sensors distributed among the mais field. In the field were installed different types of sensors as many TDR sensors and one capacitive sensor; they collect temperature and humidity of the soil. The data from the sensor were collected by two networks: a Zigbee-based and one LoraWAN-based network.", "keywords": ["capacitive", "environmental-monitoring-facilities", "eu", "humidity", "irrigation", "lorawan", "mais", "sensor", "soil", "soil-moisture", "tdr", "temperature", "water", "zigbee"]}, "links": [{"href": "http://data.europa.eu/88u/dataset/17af841d-1329-4c5a-a8b8-c4326f0614f9"}, {"rel": "self", "type": "application/geo+json", "title": "/", "name": "item", "description": "/", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items//"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "10.1016/j.rse.2023.113621", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:16:30Z", "type": "Journal Article", "created": "2023-05-13", "title": "Optimisation of AquaCrop backscatter simulations using Sentinel-1 observations", "description": "In preparation for active microwave-based data assimilation into a crop modeling system, the mapping of daily 1-km AquaCrop model (v6.1) biomass and surface soil moisture to backscatter was optimised, using two forward operators, i.e. the Water Cloud Model (WCM) and the Support Vector Regression (SVR). Both forward operators were calibrated (2014\u20132018) with 1-km Sentinel-1 backscatter ( ) observations in VV and VH polarisation, for three different study domains in Europe. For the validation period (2019\u20132021), the simulations showed reasonable performances around Czech Republic and the Iberian Peninsula, to good performances over Belgium, but with strong variations within each domain. The domain-averaged root mean square difference between the model and Sentinel-1 remained below 2 dB for both forward operators and all three study domains, and the mean bias for VV remained close to 0 dB, and close 0.5 dB for the VH polarisation. The WCM and SVR performed better in VV than VH and overall the SVR performed slightly better in mapping the AquaCrop soil moisture and vegetation to backscatter than the WCM. Additionally, the assumed linear relationship in the WCM between soil moisture and soil holds better for VV than for VH. The remaining differences between WCM or SVR simulations and Sentinel-1 observations are mainly caused by AquaCrop model errors.", "keywords": ["Agriculture and Food Sciences", "Crop biomass", "YIELD RESPONSE", "ASSIMILATION", "Backscatter modeling", "LEAF-AREA INDEX", "RADAR BACKSCATTER", "BIOMASS", "SAR BACKSCATTER", "AquaCrop optimisation", "13. Climate action", "SURFACE SOIL-MOISTURE", "Earth and Environmental Sciences", "SUPPORT", "Sentinel-1", "WATER", "Soil moisture", "FAO CROP MODEL"]}, "links": [{"href": "https://doi.org/10.1016/j.rse.2023.113621"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing%20of%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.rse.2023.113621", "name": "item", "description": "10.1016/j.rse.2023.113621", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.rse.2023.113621"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-01T00:00:00Z"}}, {"id": "10.1029/2021ms002730", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:17:25Z", "type": "Journal Article", "created": "2022-02-17", "title": "Characterising the response of vegetation cover to water limitation in Africa using geostationary satellites", "description": "AbstractHydrological interactions between vegetation, soil, and topography are complex, and heterogeneous in semi\uffe2\uff80\uff90arid landscapes. This along with data scarcity poses challenges for large\uffe2\uff80\uff90scale modeling of vegetation\uffe2\uff80\uff90water interactions. Here, we exploit metrics derived from daily Meteosat data over Africa at ca. 5\uffc2\uffa0km spatial resolution for ecohydrological analysis. Their spatial patterns are based on Fractional Vegetation Cover (FVC) time series and emphasize limiting conditions of the seasonal wet to dry transition: the minimum and maximum FVC of temporal record, the FVC decay rate and the FVC integral over the decay period. We investigate the relevance of these metrics for large scale ecohydrological studies by assessing their co\uffe2\uff80\uff90variation with soil moisture, and with topographic, soil, and vegetation factors. Consistent with our initial hypothesis, FVC minimum and maximum increase with soil moisture, while the FVC integral and decay rate peak at intermediate soil moisture. We find evidence for the relevance of topographic moisture variations in arid regions, which, counter\uffe2\uff80\uff90intuitively, is detectable in the maximum but not in the minimum FVC. We find no clear evidence for wide\uffe2\uff80\uff90spread occurrence of the \uffe2\uff80\uff9cinverse texture effect\uffe2\uff80\uff9d on FVC. The FVC integral over the decay period correlates with independent data sets of plant water storage capacity or rooting depth while correlations increase with aridity. In arid regions, the FVC decay rate decreases with canopy height and tree cover fraction as expected for ecosystems with a more conservative water\uffe2\uff80\uff90use strategy. Thus, our observation\uffe2\uff80\uff90based products have large potential for better understanding complex vegetation\uffe2\uff80\uff90water interactions from regional to continental scales.
", "keywords": ["Physical geography", "GROUNDWATER-DEPENDENT ECOSYSTEMS", "water limitation", "GC1-1581", "geostationary", "SOIL-MOISTURE", "Oceanography", "01 natural sciences", "ecohydrology", "ROOTING DEPTH", "ACTIVE-ROLE", "WOODY COVER", "0105 earth and related environmental sciences", "fractional vegetation cover", "HYDROLOGIC PROCESSES", "15. Life on land", "6. Clean water", "GB3-5030", "MODEL", "CLIMATE", "13. Climate action", "Earth and Environmental Sciences", "PRECIPITATION", "Africa", "PATTERNS", "Research Article"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2021MS002730"}, {"href": "https://doi.org/10.1029/2021ms002730"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Advances%20in%20Modeling%20Earth%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1029/2021ms002730", "name": "item", "description": "10.1029/2021ms002730", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1029/2021ms002730"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-25T00:00:00Z"}}, {"id": "10.1088/1748-9326/aa7145", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:18:10Z", "type": "Journal Article", "created": "2017-05-05", "title": "Vegetation anomalies caused by antecedent precipitation in most of the world", "description": "Quantifying environmental controls on vegetation is critical to predict the net effect of climate change on global ecosystems and the subsequent feedback on climate. Following a non-linear Granger causality framework based on a random forest predictive model, we exploit the current wealth of multi-decadal satellite data records to uncover the main drivers of monthly vegetation variability at the global scale. Results indicate that water availability is the most dominant factor driving vegetation globally: about 61% of the vegetated surface was primarily water-limited during 1981\u20132010. This included semiarid climates but also transitional ecoregions. Intra-annually, temperature controls Northern Hemisphere deciduous forests during the growing season, while antecedent precipitation largely dominates vegetation dynamics during the senescence period. The uncovered dependency of global vegetation on water availability is substantially larger than previously reported. This is owed to the ability of the framework to (1) disentangle the co-linearities between radiation/temperature and precipitation, and (2) quantify non-linear impacts of climate on vegetation. Our results reveal a prolonged effect of precipitation anomalies in dry regions: due to the long memory of soil moisture and the cumulative, non-linear, response of vegetation, water-limited regions show sensitivity to the values of precipitation occurring three months earlier. Meanwhile, the impacts of temperature and radiation anomalies are more immediate and dissipate shortly, pointing to a higher resilience of vegetation to these anomalies. Despite being infrequent by definition, hydro-climatic extremes are responsible for up to 10% of the vegetation variability during the 1981\u20132010 period in certain areas, particularly in water-limited ecosystems. Our approach is a first step towards a quantitative comparison of the resistance and resilience signature of different ecosystems, and can be used to benchmark Earth system models in their representations of past vegetation sensitivity to changes in climate.", "keywords": ["Science", "QC1-999", "water", "TROPICAL FORESTS", "0207 environmental engineering", "02 engineering and technology", "SOIL-MOISTURE", "Environmental technology. Sanitary engineering", "01 natural sciences", "stress", "water stress", "global vegetation", "AMAZON", "FORESTS", "CLIMATE EXTREMES", "hydro-climatic extremes", "ecosystem resilience", "DRY-SEASON", "GE1-350", "TEMPERATURE", "SATELLITE", "TD1-1066", "0105 earth and related environmental sciences", "Physics", "Q", "Biology and Life Sciences", "15. Life on land", "6. Clean water", "Environmental sciences", "NDVI DATA", "13. Climate action", "Earth and Environmental Sciences", "GROWING-SEASON", "Granger causality", "CARBON-CYCLE"]}, "links": [{"href": "https://doi.org/10.1088/1748-9326/aa7145"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Research%20Letters", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1088/1748-9326/aa7145", "name": "item", "description": "10.1088/1748-9326/aa7145", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1088/1748-9326/aa7145"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-07-01T00:00:00Z"}}, {"id": "10.1029/2010jg001494", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:17:23Z", "type": "Journal Article", "created": "2011-02-03", "title": "Nitrous Oxide Emissions And Isotopic Composition In Urban And Agricultural Systems In Southern California", "description": "[1]\u00a0Nitrous oxide (N2O) is a powerful greenhouse gas increasing in atmospheric mixing ratio and linked to increasing amounts of reactive N in the environment, particularly fertilizer use in agriculture. The consequences of urbanization of agricultural land for global and regional N2O emissions are unclear, due to high spatial and temporal variability of fluxes from different ecosystems and relatively few studies of urban ecosystems. We measured fluxes and the stable isotope composition (\u03b415N and \u03b418O) of N2O over 1 year in urban (ornamental lawns and athletic fields) and agricultural (corn and vegetable fields) ecosystems near Los Angeles, California, United States. We found that urban landscapes (lawns and athletic fields) have annual N2O fluxes equal to or greater than agricultural fields. Fertilization rates of urban landscapes were equal to or greater than agricultural fields, with comparable N2O emissions factors. \u03b415N and \u03b418O of N2O varied widely in all ecosystems, and were not consistent with ecosystem type, season, soil moisture, or temperature. There was, however, a consistent response of \u03b415N-N2O to pulses of N2O emission following fertilization, with an initial depletion in \u03b415N relative to prefertilization values, then gradual enrichment to background values within about 1 week. Preliminary scaling calculations indicated that N2O emissions from urban landscapes are approximately equal to or greater than agricultural emissions in urbanized areas of southern California, which further implies that current estimates of regional N2O emissions (based on agricultural land area) may be too low.", "keywords": ["2. Zero hunger", "long-term", "denitrification", "variability", "methane", "cycle (with supplement)", "04 agricultural and veterinary sciences", "15. Life on land", "carbon-dioxide", "7. Clean energy", "01 natural sciences", "nitrification", "13. Climate action", "11. Sustainability", "global n2o budget", "soil-moisture", "0401 agriculture", " forestry", " and fisheries", "oxygen-exchange", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://escholarship.org/content/qt7q9586fd/qt7q9586fd.pdf"}, {"href": "https://doi.org/10.1029/2010jg001494"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1029/2010jg001494", "name": "item", "description": "10.1029/2010jg001494", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1029/2010jg001494"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2011-02-04T00:00:00Z"}}, {"id": "10.1029/2020jd034163", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:17:25Z", "type": "Journal Article", "created": "2021-07-23", "title": "Upgrading Land\u2010Cover and Vegetation Seasonality in the ECMWF Coupled System: Verification With FLUXNET Sites, METEOSAT Satellite Land Surface Temperatures, and ERA5 Atmospheric Reanalysis", "description": "AbstractIn this study, we show that limitations in the representation of land cover and vegetation seasonality in the European Centre for Medium\uffe2\uff80\uff90Range Weather Forecasting (ECMWF) model are partially responsible for large biases (up to \uffe2\uff88\uffbc10\uffc2\uffb0C, either positive or negative depending on the region) on the simulated daily maximum land surface temperature (LST) with respect to satellite Earth Observations (EOs) products from the Land Surface Analysis Satellite Application Facility. The error patterns were coherent in offline land\uffe2\uff80\uff90surface and coupled land\uffe2\uff80\uff90atmosphere simulations, and in ECMWF's latest generation reanalysis (ERA5). Subsequently, we updated the ECMWF model's land cover characterization leveraging on state\uffe2\uff80\uff90of\uffe2\uff80\uff90the\uffe2\uff80\uff90art EOs\uffe2\uff80\uff94the European Space Agency Climate Change Initiative land cover data set and the Copernicus Global Land Services leaf area index. Additionally, we tested a clumping parameterization, introducing seasonality to the effective low vegetation coverage. The updates reduced the overall daily maximum LST bias and unbiased root\uffe2\uff80\uff90mean\uffe2\uff80\uff90squared errors. In contrast, the implemented updates had a neutral impact on daily minimum LST. Our results also highlighted the complex regional heterogeneities in the atmospheric sensitivity to land cover and vegetation changes, particularly with issues emerging over eastern Brazil and northeastern Asia. These issues called for a re\uffe2\uff80\uff90calibration of model parameters (e.g., minimum stomatal resistance, roughness length, rooting depth), along with a revision of several model assumptions (e.g., snow shading by high vegetation).
", "keywords": ["Atmospheric Science", "CLIMATE-CHANGE", "IMPACT", "PREDICTION", "SNOW SCHEME", "ASSIMILATION", "MODELS", "15. Life on land", "SOIL-MOISTURE", "01 natural sciences", "PREDICTABILITY", "VARIABILITY", "Geophysics", "Space and Planetary Science", "13. Climate action", "Earth and Environmental Sciences", "Earth and Planetary Sciences (miscellaneous)", "SENSITIVITY", "Research Article", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1029/2020jd034163"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Atmospheres", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1029/2020jd034163", "name": "item", "description": "10.1029/2020jd034163", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1029/2020jd034163"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-02T00:00:00Z"}}, {"id": "10.1111/nyas.13912", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:18:57Z", "type": "Journal Article", "created": "2018-06-26", "title": "Land-atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges", "description": "AbstractDroughts and heatwaves cause agricultural loss, forest mortality, and drinking water scarcity, especially when they occur simultaneously as combined events. Their predicted increase in recurrence and intensity poses serious threats to future food security. Still today, the knowledge of how droughts and heatwaves start and evolve remains limited, and so does our understanding of how climate change may affect them. Droughts and heatwaves have been suggested to intensify and propagate via land\uffe2\uff80\uff93atmosphere feedbacks. However, a global capacity to observe these processes is still lacking, and climate and forecast models are immature when it comes to representing the influences of land on temperature and rainfall. Key open questions remain in our goal to uncover the real importance of these feedbacks: What is the impact of the extreme meteorological conditions on ecosystem evaporation? How do these anomalies regulate the atmospheric boundary layer state (event self\uffe2\uff80\uff90intensification) and contribute to the inflow of heat and moisture to other regions (event self\uffe2\uff80\uff90propagation)? Can this knowledge on the role of land feedbacks, when available, be exploited to develop geo\uffe2\uff80\uff90engineering mitigation strategies that prevent these events from aggravating during their early stages? The goal of our perspective is not to present a convincing answer to these questions, but to assess the scientific progress to date, while highlighting new and innovative avenues to keep advancing our understanding in the future.
", "keywords": ["Hot Temperature", "Climate Change", "drought", "SOIL-MOISTURE", "01 natural sciences", "CARBON-DIOXIDE", "heatwave", "SURFACE EVAPORATION", "CLIMATE EXTREMES", "Humans", "drought; heatwave; land feedback; land\u2013atmospheric interactions", "land feedback", "land\u2013atmospheric interactions", "SAHEL CLIMATE", "Ecosystem", "HEAT-WAVE", "0105 earth and related environmental sciences", "2. Zero hunger", "Agriculture", "Models", " Theoretical", "15. Life on land", "FOREST", "6. Clean water", "Droughts", "SUMMER", "WATER-VAPOR", "13. Climate action", "Earth and Environmental Sciences", "land-atmospheric interactions", "GRASSLAND ENERGY-EXCHANGE", "Perspectives"]}, "links": [{"href": "https://nyaspubs.onlinelibrary.wiley.com/doi/pdf/10.1111/nyas.13912"}, {"href": "https://doi.org/10.1111/nyas.13912"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Annals%20of%20the%20New%20York%20Academy%20of%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/nyas.13912", "name": "item", "description": "10.1111/nyas.13912", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/nyas.13912"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-06-25T00:00:00Z"}}, {"id": "10.3390/rs10101601", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:20:54Z", "type": "Journal Article", "created": "2018-10-09", "title": "Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models", "description": "Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates.
", "keywords": ["550", "Science", "TROPICAL RAIN-FOREST", "0208 environmental biotechnology", "evapotranspiration", "0207 environmental engineering", "02 engineering and technology", "interception", "SOIL-MOISTURE", "transpiration", "modelling", "partitioning", "soil evaporation", "uncertainty", "DROUGHT", "evapotranspiration; modelling; sensitivity; uncertainty; transpiration; soil evaporation; interception; partitioning", "CLIMATE-CHANGE", "Q", "Biology and Life Sciences", "PLANT TRANSPIRATION", "sensitivity", "6. Clean water", "CHIHUAHUAN DESERT", "GLOBAL TERRESTRIAL EVAPOTRANSPIRATION", "13. Climate action", "Earth and Environmental Sciences", "LAND EVAPORATION", "WATER-BALANCE", "FEEDBACKS", "[PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]", "[PHYS.ASTR] Physics [physics]/Astrophysics [astro-ph]"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/10/10/1601/pdf"}, {"href": "https://doi.org/10.3390/rs10101601"}, {"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/rs10101601", "name": "item", "description": "10.3390/rs10101601", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs10101601"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-10-09T00:00:00Z"}}, {"id": "10.3390/rs10111720", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:20:55Z", "type": "Journal Article", "created": "2018-10-31", "title": "Towards Estimating Land Evaporation at Field Scales Using GLEAM", "description": "The evaporation of water from land into the atmosphere is a key component of the hydrological cycle. Accurate estimates of this flux are essential for proper water management and irrigation scheduling. However, continuous and qualitative information on land evaporation is currently not available at the required spatio-temporal scales for agricultural applications and regional-scale water management. Here, we apply the Global Land Evaporation Amsterdam Model (GLEAM) at 100 m spatial resolution and daily time steps to provide estimates of land evaporation over The Netherlands, Flanders, and western Germany for the period 2013\uffe2\uff80\uff932017. By making extensive use of microwave-based geophysical observations, we are able to provide data under all weather conditions. The soil moisture estimates from GLEAM at high resolution compare well with in situ measurements of surface soil moisture, resulting in a median temporal correlation coefficient of 0.76 across 29 sites. Estimates of terrestrial evaporation are also evaluated using in situ eddy-covariance measurements from five sites, and compared to estimates from the coarse-scale GLEAM v3.2b, land evaporation from the Satellite Application Facility on Land Surface Analysis (LSA-SAF), and reference grass evaporation based on Makkink\uffe2\uff80\uff99s equation. All datasets compare similarly with in situ measurements and differences in the temporal statistics are small, with correlation coefficients against in situ data ranging from 0.65 to 0.95, depending on the site. Evaporation estimates from GLEAM-HR are typically bounded by the high values of the Makkink evaporation and the low values from LSA-SAF. While GLEAM-HR and LSA-SAF show the highest spatial detail, their geographical patterns diverge strongly due to differences in model assumptions, model parameterizations, and forcing data. The separate consideration of rainfall interception loss by tall vegetation in GLEAM-HR is a key cause of this divergence: while LSA-SAF reports maximum annual evaporation volumes in the Green Heart of The Netherlands, an area dominated by shrubs and grasses, GLEAM-HR shows its maximum in the national parks of the Veluwe and Heuvelrug, both densely-forested regions where rainfall interception loss is a dominant process. The pioneering dataset presented here is unique in that it provides observational-based estimates at high resolution under all weather conditions, and represents a viable alternative to traditional visible and infrared models to retrieve evaporation at field scales.
", "keywords": ["microwave remote sensing", "EVAPOTRANSPIRATION", "WACMOS-ET PROJECT", "Science", "FLUXNET", "Q", "LSA-SAF", "15. Life on land", "01 natural sciences", "6. Clean water", "MODEL", "CARBON", "VARIABILITY", "terrestrial evaporation", "root-zone soil moisture", "13. Climate action", "Earth and Environmental Sciences", "SURFACE EVAPORATION", "GLOBAL DATABASE", "WATER", "SOIL-MOISTURE RETRIEVALS", "terrestrial evaporation; root-zone soil moisture; microwave remote sensing; GLEAM; LSA-SAF", "GLEAM", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/10/11/1720/pdf"}, {"href": "https://doi.org/10.3390/rs10111720"}, {"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/rs10111720", "name": "item", "description": "10.3390/rs10111720", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs10111720"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-10-31T00:00:00Z"}}, {"id": "10.3389/frwa.2022.981745", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:20:38Z", "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": "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.
", "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": "10.5194/essd-13-4349-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:21:46Z", "type": "Journal Article", "created": "2021-09-07", "title": "ERA5-Land: a state-of-the-art global reanalysis dataset for land applications", "description": "Abstract. Framed within the Copernicus Climate Change Service (C3S) of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies. This is achieved through global high-resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5 most of the parameterizations that guarantees the use of the state-of-the-art land surface modelling applied to numerical weather prediction (NWP) models. A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9\uffe2\uff80\uff89km compared to 31\uffe2\uff80\uff89km (ERA5) or 80\uffe2\uff80\uff89km (ERA-Interim), whereas the temporal resolution is hourly as in ERA5. Evaluation against independent in situ observations and global model or satellite-based reference datasets shows the added value of ERA5-Land in the description of the hydrological cycle, in particular with enhanced soil moisture and lake description, and an overall better agreement of river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed performance when compared to those of ERA5, depending on geographical location and altitude. The description of the energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as reference MODIS data, mainly due to the contribution of coastal points where spatial resolution is important. Since January\uffc2\uffa02020, the ERA5-Land period available has extended from January\uffc2\uffa01981 to the near present, with a 2- to 3-month delay with respect to real time. The segment prior to 1981 is in production, aiming for a release of the whole dataset in summer/autumn\uffc2\uffa02021. The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies, to initialize NWP and climate models, and to support diverse applications dealing with water resource, land, and environmental management. The full ERA5-Land hourly (Mu\uffc3\uffb1oz-Sabater,\uffc2\uffa02019a) and monthly (Mu\uffc3\uffb1oz-Sabater,\uffc2\uffa02019b) averaged datasets presented in this paper are available through the C3S Climate Data Store at https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30, respectively.
", "keywords": ["QE1-996.5", "550", "IN-SITU", "LEAF-AREA", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Geology", "OPERATIONAL IMPLEMENTATION", "15. Life on land", "551", "SOIL-MOISTURE", "SURFACE-TEMPERATURE", "01 natural sciences", "LAKE PARAMETERIZATION", "[SDU] Sciences of the Universe [physics]", "Environmental sciences", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "Earth and Environmental Sciences", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "SNOW MODELS", "GE1-350", "WEST-AFRICA", "SATELLITE", "NUMERICAL WEATHER PREDICTION", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://centaur.reading.ac.uk/106796/1/essd-13-4349-2021.pdf"}, {"href": "https://essd.copernicus.org/articles/13/4349/2021/essd-13-4349-2021.pdf"}, {"href": "https://doi.org/10.5194/essd-13-4349-2021"}, {"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-13-4349-2021", "name": "item", "description": "10.5194/essd-13-4349-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-13-4349-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-15T00:00:00Z"}}, {"id": "10.5194/hess-22-4513-2018", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:21:50Z", "type": "Journal Article", "created": "2018-08-27", "title": "Exploring the merging of the global land evaporation WACMOS-ET products based on local tower measurements", "description": "Abstract. An inverse error variance weighting of the anomalies of three terrestrial evaporation (ET) products from the WACMOS-ET project based on FLUXNET sites is presented. The three ET models were run daily and at a resolution of 25\uffe2\uff80\uff89km for 2002\uffe2\uff80\uff932007, and based on common input data when possible. The local weights, derived based on the variance of the difference between the tower ET anomalies and the modelled ET anomalies, were made dynamic by estimating them using a 61-day running window centred on each day. These were then extrapolated from the tower locations to the global landscape by regressing them on the main model inputs and derived ET using a neural network. Over the stations, the weighted scheme usefully decreased the random error component, and the weighted ET correlated better with the tower data than a simple average. The global extrapolation produced weights displaying strong seasonal and geographical patterns, which translated into spatiotemporal differences between the ET weighted and simple average ET products. However, the uncertainty of the weights after the extrapolation remained large. Out-sample prediction tests showed that the tower data set, mostly located at temperate regions, had limitations with respect to the representation of different biome and climate conditions. Therefore, even if the local weighting was successful, the extrapolation to a global scale remains problematic, showing a limited added value over the simple average. Overall, this study suggests that merging tower observations and ET products at the timescales and spatial scales of this study is complicated by the tower spatial representativeness, the products' coarse spatial resolution, the nature of the error in both towers and gridded data sets, and how all these factors impact the weights extrapolation from the tower locations to the global landscape.
", "keywords": ["Technology", "550", "SPATIAL VARIABILITY", "0207 environmental engineering", "FLUX MEASUREMENTS", "02 engineering and technology", "SOIL-MOISTURE", "Environmental technology. Sanitary engineering", "01 natural sciences", "[PHYS] Physics [physics]", "G", "ENERGY-BALANCE CLOSURE", "Geography. Anthropology. Recreation", "GE1-350", "TD1-1066", "0105 earth and related environmental sciences", "[PHYS]Physics [physics]", "EVAPOTRANSPIRATION ESTIMATION", "CARBON-DIOXIDE EXCHANGE", "T", "DECIDUOUS FOREST", "EDDY-COVARIANCE", "Environmental sciences", "TEMPERATE FOREST", "13. Climate action", "Earth and Environmental Sciences", "[PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]", "INTERANNUAL VARIABILITY", "[PHYS.ASTR] Physics [physics]/Astrophysics [astro-ph]"]}, "links": [{"href": "https://hess.copernicus.org/articles/22/4513/2018/hess-22-4513-2018.pdf"}, {"href": "https://doi.org/10.5194/hess-22-4513-2018"}, {"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-22-4513-2018", "name": "item", "description": "10.5194/hess-22-4513-2018", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-22-4513-2018"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-08-27T00:00:00Z"}}, {"id": "10.5194/hess-26-3921-2022", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:21:51Z", "type": "Journal Article", "created": "2021-12-23", "title": "High-resolution satellite products improve hydrological modeling in northern Italy", "description": "Abstract. Satellite Earth observations (EO) are an accurate and reliable data source for atmospheric and environmental science. Their increasing spatial and temporal resolution, as well as the seamless availability over ungauged regions, make them appealing for hydrological modeling. This work shows recent advances in the use of high-resolution satellite-based Earth observation data in hydrological modelling. In a set of experiments, the distributed hydrological model Continuum is set up for the Po River Basin (Italy) and forced, in turn, by satellite precipitation and evaporation, while satellite-derived soil moisture and snow depths are ingested into the model structure through a data-assimilation scheme. Further, satellite-based estimates of precipitation, evaporation and river discharge are used for hydrological model calibration, and results are compared with those based on ground observations. Despite the high density of conventional ground measurements and the strong human influence in the focus region, all satellite products show strong potential for operational hydrological applications, with skillful estimates of river discharge throughout the model domain. Satellite-based evaporation and snow depths marginally improve (by 2 % and 4 %) the mean Kling-Gupta efficiency (KGE) at 27 river gauges, compared to a baseline simulation (KGEmean = 0.51) forced by high-quality conventional data. Precipitation has the largest impact on the model output, though the satellite dataset on average shows poorer skills compared to conventional data. Interestingly, a model calibration heavily relying on satellite data, as opposed to conventional data, provides a skillful reconstruction of river discharges, paving the way to fully satellite-driven hydrological applications.
", "keywords": ["Technology", "DATA", "ASSIMILATION", "Po River", "FLOOD RISK", "0211 other engineering and technologies", "0207 environmental engineering", "UNCERTAINTY", "02 engineering and technology", "high resolution satellite products", "Environmental technology. Sanitary engineering", "01 natural sciences", "G", "Geography. Anthropology. Recreation", "EARTH", "GE1-350", "continuum hydrological model", "RAINFALL", "TD1-1066", "0105 earth and related environmental sciences", "T", "RADAR ALTIMETRY DATA", "LAND-SURFACE", "6. Clean water", "Environmental sciences", "13. Climate action", "Earth and Environmental Sciences", "HYDRODYNAMIC MODEL", "OBSERVATION", "DISCHARGE ESTIMATION", "SOIL-MOISTURE PRODUCTS"]}, "links": [{"href": "https://hess.copernicus.org/articles/26/3921/2022/hess-26-3921-2022.pdf"}, {"href": "https://doi.org/10.5194/hess-26-3921-2022"}, {"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-26-3921-2022", "name": "item", "description": "10.5194/hess-26-3921-2022", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-26-3921-2022"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-23T00:00:00Z"}}, {"id": "1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:25:09Z", "type": "Journal Article", "created": "2023-05-13", "title": "Optimisation of AquaCrop backscatter simulations using Sentinel-1 observations", "description": "Open AccessIn preparation for active microwave-based data assimilation into a crop modeling system, the mapping of daily 1-km AquaCrop model (v6.1) biomass and surface soil moisture to backscatter was optimised, using two forward operators, i.e. the Water Cloud Model (WCM) and the Support Vector Regression (SVR). Both forward operators were calibrated (2014\u20132018) with 1-km Sentinel-1 backscatter (\u03d2\u00b0) observations in VV and VH polarisation, for three different study domains in Europe. For the validation period (2019\u20132021), the \u03d2\u00b0 simulations showed reasonable performances around Czech Republic and the Iberian Peninsula, to good performances over Belgium, but with strong variations within each domain. The domain-averaged root mean square difference between the model and Sentinel-1 \u03d2\u00b0 remained below 2 dB for both forward operators and all three study domains, and the mean bias for VV remained close to 0 dB, and close 0.5 dB for the VH polarisation. The WCM and SVR performed better in VV than VH and overall the SVR performed slightly better in mapping the AquaCrop soil moisture and vegetation to backscatter than the WCM. Additionally, the assumed linear relationship in the WCM between soil moisture and soil \u03d2\u00b0 holds better for VV than for VH. The remaining differences between WCM or SVR simulations and Sentinel-1 observations are mainly caused by AquaCrop model errors.", "keywords": ["Agriculture and Food Sciences", "Technology", "ASSIMILATION", "Sentine;-1", "Environmental Sciences & Ecology", "Geological & Geomatics Engineering", "BIOMASS", "Remote Sensing", "SAR BACKSCATTER", "SURFACE SOIL-MOISTURE", "SUPPORT", "0909 Geomatic Engineering", "WATER", "FAO CROP MODEL", "Imaging Science & Photographic Technology", "crop biomass", "Crop biomass", "YIELD RESPONSE", "Science & Technology", "backscatter modelling", "Backscatter modeling", "LEAF-AREA INDEX", "RADAR BACKSCATTER", "37 Earth sciences", "AquaCrop optimisation", "13. Climate action", "Earth and Environmental Sciences", "Sentinel-1", "Soil moisture", "0406 Physical Geography and Environmental Geoscience", "Life Sciences & Biomedicine", "Environmental Sciences"]}, "links": [{"href": "https://biblio.vub.ac.be/vubirfiles/112110259/108189295.pdf"}, {"href": "https://doi.org/1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing%20of%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4", "name": "item", "description": "1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-01T00:00:00Z"}}, {"id": "1854/LU-01JKX2FJKXN38WB8P6CAQC7AEH", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:25:09Z", "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": "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.