{"type": "FeatureCollection", "features": [{"id": "10.1016/j.jappgeo.2020.103987", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:17:56Z", "type": "Journal Article", "created": "2020-03-04", "title": "Paleotopography continues to drive surface to deep-layer interactions in a subtropical Critical Zone Observatory", "description": "Abstract   Subsurface critical zone structures (SCZS) refer to the spatial variation in the interactive layers underground. Although SCZS greatly affect terrestrial biogeochemical and hydrological cycles, underpinning mechanisms are poorly documented. Herein, we characterized the SCZS of a typical red soil in subtropical China, a type of soil with vast global distribution. The thickness information of three layers was derived from hand augers, boreholes and ground-penetrating radar (GPR) radargrams and incorporated into geographically weighted regression (GWR) models for the reconstruction of paleotopography (Cretaceous sandstone). The interpreted GPR results in terms of thicknesses and interfaces for the three layers were consistent with the borehole logs. The trained GWR models accounted for 43%\u201377% of the spatial variations in the three layers. The paleotopographic elevations were highly correlated with those of the current land surface (r\u00a0=\u00a00.85). Spatial analysis showed that the rougher paleotopography was inherited by the current landform. The SCZS evolution involving mainly the mantling covered by Quaternary red clay (QRC) was primarily driven by terrain attributes. These findings may enhance our understanding of the interaction between the paleoclimate and paleoenvironment. The combination of geophysical techniques, geochemical indicators and spatial prediction techniques provides an effective tool for understanding QRC landform evolution.", "keywords": ["paleotopography", "landscape evolution", "550", "01 natural sciences", "CHINA", "Ground-penetrating radar", "THICKNESS", "EARTH", "QE", "NE/N007611/1", "SOIL-WATER STORAGE", "GEOGRAPHICALLY WEIGHTED REGRESSION", "0105 earth and related environmental sciences", "critical zone", "ground-penetrating radar", "Natural Environment Research Council (NERC)", "Critical zone", "CONSTRAINTS", "15. Life on land", "Landscape evolution", "EVOLUTION", "SOUTHERN", "QE Geology", "Geophysics", "Paleotopography", "13. Climate action", "Red Soil Critical Zone Observatory", "QUATERNARY RED CLAY"]}, "links": [{"href": "https://doi.org/10.1016/j.jappgeo.2020.103987"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Applied%20Geophysics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jappgeo.2020.103987", "name": "item", "description": "10.1016/j.jappgeo.2020.103987", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jappgeo.2020.103987"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-04-01T00:00:00Z"}}, {"id": "10.1016/j.rse.2023.113621", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:18:11Z", "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": "10.1038/s41598-025-93658-2", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:19:28Z", "type": "Journal Article", "created": "2025-04-04", "title": "Plasticulture detection at the country scale by combining multispectral and SAR satellite data", "description": "Abstract           <p>The use of plastic films has been growing in agriculture, benefiting consumers and producers. However, concerns have been raised about the environmental impact of plastic film use, with mulching films posing a greater threat than greenhouse films. This calls for large-scale monitoring of different plastic film uses. We used cloud computing, freely available optical and radar satellite images, and machine learning to map plastic-mulched farmland (PMF) and plastic cover above vegetation (PCV) (e.g., greenhouse, tunnel) across Germany. The algorithm detected 103 103 ha of PMF and 37 103 ha of PCV in 2020, while a combination of agricultural statistics and surveys estimated a smaller plasticulture cover of around 100 103 ha in 2019. Based on ground observations, the overall accuracy of the classification is 85.3%. Optical and radar features had similar importance scores, and a distinct backscatter of PCV was related to metal frames underneath the plastic films. Overall, the algorithm achieved great results in the distinction between PCV and PMF. This study maps different plastic film uses at a country scale for the first time and sheds light on the high potential of freely available satellite data for continental monitoring.</p", "keywords": ["Science", "Optical remote sensing", "Q", "R", "Medicine", "Agriculture", "Synthetic aperture radar", "Plastic", "Sentinel", "Google earth engine", "Article"], "contacts": [{"organization": "Alessandro Fabrizi, Peter Fiener, Thomas Jagdhuber, Kristof Van Oost, Florian Wilken,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1038/s41598-025-93658-2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41598-025-93658-2", "name": "item", "description": "10.1038/s41598-025-93658-2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41598-025-93658-2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-04-02T00:00:00Z"}}, {"id": "10.1109/jstars.2019.2958847", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:20:23Z", "type": "Journal Article", "created": "2020-01-22", "title": "Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers", "description": "Open AccessThis article investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyzes the performance of this feature along with polarization and intensity products according to different classification strategies and algorithms. Seven different classification workflows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain\u2014interferometric coherence, backscattered intensities, and polarization. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximize diversity of land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on the study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Do\u00f1ana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%) obtained promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherence-based results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinel-1 A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1 A/B stacks, i.e., six-day sampling, are considered.", "keywords": ["Teledetecci\u00f3", "550", "Interferometric coherence", "Geophysics. Cosmic physics", "ta1171", "0211 other engineering and technologies", "02 engineering and technology", "01 natural sciences", "land cover mapping", "ta216", "TC1501-1800", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing", "SDG 15 - Life on Land", "0105 earth and related environmental sciences", "ta213", "QC801-809", "[SPI.ELEC] Engineering Sciences [physics]/Electromagnetism", "interferometric coherence", "Remote sensing", "synthetic aperture radar (SAR)", "15. Life on land", "[SPI.TRON] Engineering Sciences [physics]/Electronics", "SDG 11 - Sustainable Cities and Communities", "[SPI.TRON]Engineering Sciences [physics]/Electronics", "Ocean engineering", "Synthetic aperture radar (SAR)", "[SPI.ELEC]Engineering Sciences [physics]/Electromagnetism", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3", ":Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3 [\u00c0rees tem\u00e0tiques de la UPC]", "13. Climate action", "Teor\u00eda de la Se\u00f1al y Comunicaciones", "Sentinel-1", "[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing", "Land cover mapping", "Copernicus"]}, "links": [{"href": "https://doi.org/10.1109/jstars.2019.2958847"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Journal%20of%20Selected%20Topics%20in%20Applied%20Earth%20Observations%20and%20Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/jstars.2019.2958847", "name": "item", "description": "10.1109/jstars.2019.2958847", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/jstars.2019.2958847"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.1109/lgrs.2021.3073484", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:20:23Z", "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": "10.1117/12.2571722", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:21:10Z", "type": "Journal Article", "created": "2020-08-26", "title": "Remote sensing techniques for archaeology: a state of art analysis of SAR methods for land movement", "description": "The RESEARCH project (Remote Sensing techniques for Archaeology; H2020-MSCA-RISE, 2018-2022, grant agreement: 823987) addresses the design and development of a multi-task platform, combining advanced remote sensing technologies with Geographical Information System (GIS) application for mapping and long-term monitoring of Archaeological Heritage (AH) at risk, to identify changes due to climate change and anthropic pressures. The Earth Observation (EO) processing chain will address significant risks affecting AH including soil erosion, land movement and land-use change. The paper describes one of the main goals of RESEARCH project. It refers to a state of the art analysis of Synthetic Aperture Radar (SAR) methods applied to the land movement detection such as landslide and subsidence. Satellite SAR is a rapidly evolving remote sensing technology that offers a high potential for detecting, documenting and monitoring heritage targets. Satellite SAR interferometry (InSAR), Differential Interferometry (DinSAR) and Persistent Scatterer Interferometry (PSI) are different techniques that, depending on the available data and the required accuracy, can be used for deformation monitoring of AH.", "keywords": ["Synthetic Aperture Radar (SAR)", "Interferometry", "Land movement", "13. Climate action", "11. Sustainability", "Archaeological heritage", "0211 other engineering and technologies", "0202 electrical engineering", " electronic engineering", " information engineering", "Engineering and Technology", "02 engineering and technology", "15. Life on land", "Civil Engineering"]}, "links": [{"href": "https://doi.org/10.1117/12.2571722"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Eighth%20International%20Conference%20on%20Remote%20Sensing%20and%20Geoinformation%20of%20the%20Environment%20%28RSCy2020%29", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1117/12.2571722", "name": "item", "description": "10.1117/12.2571722", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1117/12.2571722"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-26T00:00:00Z"}}, {"id": "10.3390/rs12040654", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:33Z", "type": "Journal Article", "created": "2020-02-20", "title": "Joint Exploitation of SAR and GNSS for Atmospheric Phase Screens Retrieval Aimed at Numerical Weather Prediction Model Ingestion", "description": "<p>This paper proposes a simple and fast method to estimate Atmospheric Phase Screens (APSs) by jointly exploit a stack of Synthetic Aperture Radar (SAR) images and a dataset of GNSS-derived atmospheric product. The output of this processing is conceived to be ingested by Numerical Weather Prediction Models (NWPMs) to improve weather forecasts. In order to provide wide and dense area coverage and to respect requirements in terms of spatial resolution of ingestion products in NWPMs, both Permanent Scatterers (PSs) and Distributed Scatterers (DSs) are jointly exploited. While the formers are by definition stable targets, but unevenly distributed, the latter are ubiquitous but stable only within a certain temporal baseline that can vary depending on the operational frequency of the radar. The proposed method is thus particularly suited for C, L, and P band missions with low temporal baseline between two consecutive acquisitions of the same scene: these conditions, that are both necessary to provide the dense space-time coverage required by meteorologists, allow for a reliable and robust estimation of APSs thanks to the intrinsic limitation of temporal decorrelation. The proposed technique integrates Zenith Total Delay (ZTD) products computed on a very sparse grid from a network of GNSS stations to correct for SAR orbital errors and to provide the missing phase constant from the derived APS map. In this paper, the complete workflow is explained, and a comparison of the derived APSs is performed with phase screens derived from state-of-the-art SAR processing workflow (SqueeSAR\uffc2\uffae).</p>", "keywords": ["atmospheric phase screen", "gnss", "13. Climate action", "Science", "Q", "0211 other engineering and technologies", "synthetic aperture radar; atmospheric phase screen; GNSS", "02 engineering and technology", "01 natural sciences", "Atmospheric phase screen; GNSS; Synthetic aperture radar", "synthetic aperture radar", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/4/654/pdf"}, {"href": "https://re.public.polimi.it/bitstream/11311/1133358/1/remotesensing-12-00654-v2.pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/4/654/pdf"}, {"href": "https://doi.org/10.3390/rs12040654"}, {"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/rs12040654", "name": "item", "description": "10.3390/rs12040654", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12040654"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-02-17T00:00:00Z"}}, {"id": "10.2136/vzj2015.09.0131", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:22:48Z", "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", "QH301 Biology", "0208 environmental biotechnology", "SATURATED-UNSATURATED FLOW", "02 engineering and technology", "soil processes", "01 natural sciences", "Physical Geography and Environmental Geoscience", "Sciences de la Terre", "ARBUSCULAR MYCORRHIZAL FUNGI", "sciences du sol", "ANZSRC::3707 Hydrology", "SYNTHETIC-APERTURE RADAR", "ANZSRC::4106 Soil sciences", "SDG 13 - Climate Action", "2. Zero hunger", "GROUND-PENETRATING RADAR", "diffuse-reflectance spectroscopy", "ANZSRC::050399 Soil Sciences not elsewhere classified", "synthetic-aperture radar", "digital elevation model", "SDG 13 \u2013 Ma\u00dfnahmen zum Klimaschutz", "MULTIPLE ECOSYSTEM SERVICES", "knowledge integration", "Crop and Pasture Production", "101028 Mathematical modelling", "570", "DIFFUSE-REFLECTANCE SPECTROSCOPY", "Environmental Engineering", "international soil modeling consortium", "0207 environmental engineering", "Soil Science", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "arbuscular mycorrhizal fungi", "soil science", "ORGANIC-MATTER DYNAMICS", "QH301", "ANZSRC::0503 Soil Sciences", "Life Science", "SEDIMENT TRANSPORT MODELS", "data integration", "sediment transport models", "approche ecosyst\u00e9mique", "mod\u00e9lisation", "0105 earth and related environmental sciences", "ground-penetrating radar", "info:eu-repo/classification/ddc/550", "soil modeling", "ANZSRC::080110 Simulation and Modelling", "ROOT WATER-UPTAKE", "15. Life on land", "multiple ecosystem services", "root water-uptake", "13. Climate action", "Earth and Environmental Sciences", "Soil Sciences", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "Earth Sciences", "101028 Mathematische Modellierung", "saturated-unsaturated flow", "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.", "DIGITAL ELEVATION MODEL"]}, "links": [{"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/10.2136/vzj2015.09.0131"}, {"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": "10.2136/vzj2015.09.0131", "name": "item", "description": "10.2136/vzj2015.09.0131", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2136/vzj2015.09.0131"}, {"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": "10.5194/hess-26-3921-2022", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:24:35Z", "type": "Journal Article", "created": "2021-12-23", "title": "High-resolution satellite products improve hydrological modeling in northern Italy", "description": "<p>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.                         </p>", "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": "10.3390/rs9111155", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:36Z", "type": "Journal Article", "created": "2017-11-10", "title": "Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (\u03c3\u00b0). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of \u03c3\u00b0 and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of \u03c3\u00b0 ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of \u03c3\u00b0 where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD= 0.032 m3 m\u22123).</p></article>", "keywords": ["soil moisture and ocean salinity satellite (SMOS)", "Atmospheric Science", "Artificial intelligence", "Environmental Engineering", "550", "Science", "Soil Moisture", "0211 other engineering and technologies", "Aerospace Engineering", "FOS: Mechanical engineering", "02 engineering and technology", "01 natural sciences", "Environmental science", "[SDU] Sciences of the Universe [physics]", "Engineering", "Meteorology", "DISPATCH", "Image resolution", "Arctic Permafrost Dynamics and Climate Change", "14. Life underwater", "Moisture", "0105 earth and related environmental sciences", "Soil science", "Water content", "Radar", "Geography", "soil moisture and ocean salinity satellite (SMOS); DISPATCH; radar; Sentinel-1; disaggregation; soil moisture", "Soilmoisture and ocean salinity satellite (SMOS)", "Synthetic Aperture Radar Interferometry", "Q", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Earth and Planetary Sciences", "Groundwater Extraction", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "disaggregation", "Environmental Science", "Physical Sciences", "Sentinel-1", "soil moisture", "radar"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/11/1155/pdf"}, {"href": "https://doi.org/10.3390/rs9111155"}, {"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/rs9111155", "name": "item", "description": "10.3390/rs9111155", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs9111155"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-10T00:00:00Z"}}, {"id": "10.3390/geomatics1040024", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:23:25Z", "type": "Journal Article", "created": "2021-10-29", "title": "Precipitation Data Retrieval and Quality Assurance from Different Data Sources for the Namoi Catchment in Australia", "description": "<p>Within the Horizon 2020 Project WaterSENSE a modular approach was developed to provide different stakeholders with the required precipitation information. An operational high-quality rainfall grid was set up for the Namoi catchment in Australia based on rain gauge adjusted radar data. Data availability and processing considerations make it necessary to explore alternative precipitation approaches. The gauge adjusted radar data will serve as a benchmark for the alternative precipitation data. The two well established satellite-based precipitation datasets IMERG and GSMaP will be analyzed with the temporal and spatial requirements of the applications envisioned in WaterSENSE in mind. While first results appear promising, these datasets will need further refinements to meet the criteria of WaterSENSE, especially with respect to the spatial resolution. Inferring information from soil moisture-derived from EO observations to increase the spatial detail of the existing satellite-based datasets is a promising approach that will be investigated along with other alternatives.</p>", "keywords": ["QE1-996.5", "0207 environmental engineering", "Geology", "02 engineering and technology", "15. Life on land", "01 natural sciences", "precipitation measurement", "6. Clean water", "13. Climate action", "GSMaP", "soil moisture", "IMERG", "radar", "GPM", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Strehz, Alexander, Einfalt, Thomas,", "roles": ["creator"]}]}, "links": [{"href": "https://www.mdpi.com/2673-7418/1/4/24/pdf"}, {"href": "https://doi.org/10.3390/geomatics1040024"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geomatics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/geomatics1040024", "name": "item", "description": "10.3390/geomatics1040024", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/geomatics1040024"}, {"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": "10.3390/rs12010072", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:33Z", "type": "Journal Article", "created": "2019-12-24", "title": "Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data", "description": "<p>The main objective of this work was to retrieve surface soil moisture (SSM) by using scattering models and a support vector machine (SVM) technique driven by backscattering coefficients obtained from Sentinel-1 satellite images acquired over bare agricultural soil in the Tensfit basin of Morocco. Two backscattering models were selected in this study due to their wide use in inversion procedures: the theoretical integral equation model (IEM) and the semi-empirical model (Oh). To this end, the sensitivity of the SAR backscattering coefficients at     V V     (    \uffcf\uff83  v v  \uffe2\uff88\uff98    ) and     V H     (    \uffcf\uff83  v h  \uffe2\uff88\uff98    ) polarizations to in situ soil moisture data were analyzed first. As expected, the results showed that over bare soil the     \uffcf\uff83  v v  \uffe2\uff88\uff98     was well correlated with SSM compared to the     \uffcf\uff83  v h  \uffe2\uff88\uff98    , which showed more dispersion with correlation coefficients values (r) of about     0.84     and     0.61     for the     V V     and     V H     polarizations, respectively. Afterwards, these values of     \uffcf\uff83  v v  \uffe2\uff88\uff98     were compared to those simulated by the backscatter models. It was found that IEM driven by the measured length correlation L slightly underestimated SAR backscatter coefficients compared to the Oh model with a bias of about     \uffe2\uff88\uff92 0.7     dB and     \uffe2\uff88\uff92 1.2     dB and a root mean square (RMSE) of about     1.1     dB and     1.5     dB for Oh and IEM models, respectively. However, the use of an optimal value of L significantly improved the bias of IEM, which became near to zero, and the RMSE decreased to     0.9     dB. Then, a classical inversion approach of     \uffcf\uff83  v v  \uffe2\uff88\uff98     observations based on backscattering model is compared to a data driven retrieval technic (SVM). By comparing the retrieved soil moisture against ground truth measurements, it was found that results of SVM were very encouraging and were close to those obtained by IEM model. The bias and RMSE were about 0.28 vol.% and 2.77 vol.% and     \uffe2\uff88\uff92 0.13     vol.% and 2.71 vol.% for SVM and IEM, respectively. However, by taking into account the difficultly of obtaining roughness parameter at large scale, it was concluded that SVM is still a useful tool to retrieve soil moisture, and therefore, can be fairly used to generate maps at such scales.</p>", "keywords": ["[SDE] Environmental Sciences", "soil moisture; synthetic aperture radar (SAR); Sentinel-1; semi-empirical and theoretical backscatter models; support vector machine; bare soil", "550", "Science", "sentinel-1", "Q", "0211 other engineering and technologies", "0207 environmental engineering", "support vector", "02 engineering and technology", "synthetic aperture radar (SAR)", "15. Life on land", "543", "bare soil", "[SDE]Environmental Sciences", "Sentinel-1", "support vector machine", "soil moisture", "synthetic aperture radar (sar)", "semi-empirical and theoretical backscatter models", "machine"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/1/72/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/1/72/pdf"}, {"href": "https://doi.org/10.3390/rs12010072"}, {"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/rs12010072", "name": "item", "description": "10.3390/rs12010072", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12010072"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-12-24T00:00:00Z"}}, {"id": "10.3390/rs13071346", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:34Z", "type": "Journal Article", "created": "2021-04-01", "title": "A Low-Rank Group-Sparse Model for Eliminating Mixed Errors in Data for SRTM1", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The elimination of mixed errors is a key preprocessing technology for the area of digital elevation model data analysis, which is important for further applying data. We associated group sparsity with the low-rank uniqueness of local transformations of mixing errors to effectively remove mixing errors in data from Shuttle Radar Topography Mission 1 (SRTM 1) based on the sparseness of low-rank groups. First, the stripe-error structure that appeared globally in multiple directions was able to be better represented locally using group-sparse regularization and the uniqueness of the data in the low-rank direction of the local range and using variational ideas to constrain the gradient direction of the data to avoid redundant elimination. Second, the nonlocal self-similarity of the weighted kernel norm was used to remove random noise. Finally, the proposed model for eliminating mixed errors was solved using an algorithm based on the multiplier method of alternating direction. Experiments using simulated and real data found that the proposed low-rank group-sparse method (LRGS) eliminated mixed errors in both visual and quantitative evaluations better than the most recent processing methods and existing dataset products.</p></article>", "keywords": ["self-similarity", "digital elevation model", "Science", "Q", "0211 other engineering and technologies", "0202 electrical engineering", " electronic engineering", " information engineering", "group sparse", "02 engineering and technology", "mixed errors", "shuttle radar topography mission 1", "low-rank"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/7/1346/pdf"}, {"href": "https://doi.org/10.3390/rs13071346"}, {"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/rs13071346", "name": "item", "description": "10.3390/rs13071346", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13071346"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-01T00:00:00Z"}}, {"id": "10.3390/w12082160", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:23:41Z", "type": "Journal Article", "created": "2020-08-03", "title": "Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.</p></article>", "keywords": ["13. Climate action", "citizen science", "digital soil mapping", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "synthetic aperture radar (SAR)", " soil moisture", "04 agricultural and veterinary sciences", "15. Life on land"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/12/8/2160/pdf"}, {"href": "https://doi.org/10.3390/w12082160"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/w12082160", "name": "item", "description": "10.3390/w12082160", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/w12082160"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-30T00:00:00Z"}}, {"id": "10.5194/acp-22-535-2022", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:24:16Z", "type": "Journal Article", "created": "2022-01-14", "title": "Assimilating spaceborne lidar dust extinction can improve dust forecasts", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Atmospheric mineral dust has a rich tri-dimensional spatial and temporal structure that is poorly constrained in forecasts and analyses when only column-integrated aerosol optical depth (AOD) is assimilated. At present, this is the case of most operational global aerosol assimilation products. Aerosol vertical distributions obtained from spaceborne lidars can be assimilated in aerosol models, but questions about the extent of their benefit upon analyses and forecasts along with their consistency with AOD assimilation remain unresolved. Our study thoroughly explores the added value of assimilating spaceborne vertical dust profiles, with and without the joint assimilation of dust optical depth (DOD). We also discuss the consistency in the assimilation of both sources of information and analyse the role of the smaller footprint of the spaceborne lidar profiles in the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of 2 months over northern Africa, the Middle East, and Europe. We assimilate DOD derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-Orbiting Partnership (SUOMI-NPP) Deep Blue and for the first time Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP)-based LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AErosol RObotic NETwork (AERONET) Sun photometers and ground-based lidar dust extinction profiles from the Cyprus Clouds Aerosol and Rain Experiment (CyCARE) and PREparatory: does dust TriboElectrification affect our ClimaTe (Pre-TECT) field campaigns. Jointly assimilating LIVAS and Deep Blue data reduces the root mean square error (RMSE) in the DOD by 39\u2009% and in the dust extinction coefficient by 65\u2009% compared to a control simulation that excludes assimilation. We show that the assimilation of dust extinction coefficient profiles provides a strong added value to the analyses and forecasts. When only Deep Blue data are assimilated, the RMSE in the DOD is reduced further, by 42\u2009%. However, when only LIVAS data are assimilated, the RMSE in the dust extinction coefficient decreases by 72\u2009%, the largest improvement across experiments. We also show that the assimilation of dust extinction profiles yields better skill scores than the assimilation of DOD under an equivalent sensor footprint. Our results demonstrate the strong potential of future lidar space missions to improve desert dust forecasts, particularly if they foresee a depolarization lidar channel to allow discrimination of desert dust from other aerosol types.                     </p></article>", "keywords": ["Mineral dusts", "info:eu-repo/classification/ddc/550", "550", "ddc:550", "9. Industry and infrastructure", "Physics", "QC1-999", "Optical radar", "Aerosols atmosf\u00e8rics", "Atmospheric aerosols", "Radar \u00f2ptic", "01 natural sciences", ":Enginyeria qu\u00edmica::Qu\u00edmica del medi ambient::Qu\u00edmica atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "Earth sciences", "Chemistry", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3", ":Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3 [\u00c0rees tem\u00e0tiques de la UPC]", "13. Climate action", "Pols", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria qu\u00edmica::Qu\u00edmica del medi ambient::Qu\u00edmica atmosf\u00e8rica", "QD1-999", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://acp.copernicus.org/articles/22/535/2022/acp-22-535-2022.pdf"}, {"href": "https://doi.org/10.5194/acp-22-535-2022"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/acp-22-535-2022", "name": "item", "description": "10.5194/acp-22-535-2022", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/acp-22-535-2022"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-14T00:00:00Z"}}, {"id": "10.5194/essd-13-3707-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:24:30Z", "type": "Journal Article", "created": "2021-01-07", "title": "C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)", "description": "<p>Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in the semi-arid areas where the water resources are limited. Radar observations, available at high resolution and revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared to the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gathers soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/two weeks including above-ground fresh and dry biomasses, vegetation water content based on destructive measurements, cover fraction, leaf area index and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric effects-corrected products is also provided. This database, which is the first of its kind made available in open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation parameters retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely-accessible via the DOI:  https://doi.org/10.23708/8D6WQC  (Ouaadi et al., 2020a).                         </p>", "keywords": ["550", "Arid", "Soil Moisture", "0211 other engineering and technologies", "FOS: Mechanical engineering", "02 engineering and technology", "Digital Soil Mapping Techniques", "Normalized Difference Vegetation Index", "630", "Agricultural and Biological Sciences", "Engineering", "Pathology", "GE1-350", "2. Zero hunger", "QE1-996.5", "Vegetation Monitoring", "Water content", "Ecology", "Geography", "Statistics", "Life Sciences", "Hydrology (agriculture)", "Geology", "Remote Sensing in Vegetation Monitoring and Phenology", "04 agricultural and veterinary sciences", "Remote sensing", "Soil Erosion and Agricultural Sustainability", "6. Clean water", "Satellite Observations", "Archaeology", "Physical Sciences", "Leaf area index", "Telecommunications", "Medicine", "Vegetation (pathology)", "Environmental Engineering", "Data set", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Aerospace Engineering", "Soil Science", "Environmental science", "Digital Soil Mapping", "[SDU] Sciences of the Universe [physics]", "Global Soil Information", "FOS: Mathematics", "Biology", "Radar", "Synthetic Aperture Radar Interferometry", "Canopy", "FOS: Environmental engineering", "Soil Properties", "Paleontology", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Agronomy", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "0401 agriculture", " forestry", " and fisheries", "Mathematics"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/3707/2021/essd-13-3707-2021.pdf"}, {"href": "https://doi.org/10.5194/essd-13-3707-2021"}, {"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-13-3707-2021", "name": "item", "description": "10.5194/essd-13-3707-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-13-3707-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-07T00:00:00Z"}}, {"id": "2164/6134", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:29:32Z", "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": "10459.1/60556", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:28:28Z", "type": "Journal Article", "created": "2017-11-10", "title": "Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (\u03c3\u00b0). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of \u03c3\u00b0 and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of \u03c3\u00b0 ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of \u03c3\u00b0 where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD= 0.032 m3 m\u22123).</p></article>", "keywords": ["soil moisture and ocean salinity satellite (SMOS)", "Atmospheric Science", "Artificial intelligence", "Environmental Engineering", "550", "Science", "Soil Moisture", "0211 other engineering and technologies", "Aerospace Engineering", "FOS: Mechanical engineering", "02 engineering and technology", "01 natural sciences", "Environmental science", "[SDU] Sciences of the Universe [physics]", "Engineering", "Meteorology", "DISPATCH", "Image resolution", "Arctic Permafrost Dynamics and Climate Change", "14. Life underwater", "Moisture", "0105 earth and related environmental sciences", "Soil science", "Water content", "Radar", "Geography", "soil moisture and ocean salinity satellite (SMOS); DISPATCH; radar; Sentinel-1; disaggregation; soil moisture", "Soilmoisture and ocean salinity satellite (SMOS)", "Synthetic Aperture Radar Interferometry", "Q", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Earth and Planetary Sciences", "Groundwater Extraction", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "disaggregation", "Environmental Science", "Physical Sciences", "Sentinel-1", "soil moisture", "radar"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/11/1155/pdf"}, {"href": "https://doi.org/10459.1/60556"}, {"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": "10459.1/60556", "name": "item", "description": "10459.1/60556", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10459.1/60556"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-10T00:00:00Z"}}, {"id": "1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:28:58Z", "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": "2117/360820", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:29:28Z", "type": "Journal Article", "created": "2022-01-14", "title": "Assimilating spaceborne lidar dust extinction can improve dust forecasts", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Atmospheric mineral dust has a rich tri-dimensional spatial and temporal structure that is poorly constrained in forecasts and analyses when only column-integrated aerosol optical depth (AOD) is assimilated. At present, this is the case of most operational global aerosol assimilation products. Aerosol vertical distributions obtained from spaceborne lidars can be assimilated in aerosol models, but questions about the extent of their benefit upon analyses and forecasts along with their consistency with AOD assimilation remain unresolved. Our study thoroughly explores the added value of assimilating spaceborne vertical dust profiles, with and without the joint assimilation of dust optical depth (DOD). We also discuss the consistency in the assimilation of both sources of information and analyse the role of the smaller footprint of the spaceborne lidar profiles in the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of 2 months over northern Africa, the Middle East, and Europe. We assimilate DOD derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-Orbiting Partnership (SUOMI-NPP) Deep Blue and for the first time Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP)-based LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AErosol RObotic NETwork (AERONET) Sun photometers and ground-based lidar dust extinction profiles from the Cyprus Clouds Aerosol and Rain Experiment (CyCARE) and PREparatory: does dust TriboElectrification affect our ClimaTe (Pre-TECT) field campaigns. Jointly assimilating LIVAS and Deep Blue data reduces the root mean square error (RMSE) in the DOD by 39\u2009% and in the dust extinction coefficient by 65\u2009% compared to a control simulation that excludes assimilation. We show that the assimilation of dust extinction coefficient profiles provides a strong added value to the analyses and forecasts. When only Deep Blue data are assimilated, the RMSE in the DOD is reduced further, by 42\u2009%. However, when only LIVAS data are assimilated, the RMSE in the dust extinction coefficient decreases by 72\u2009%, the largest improvement across experiments. We also show that the assimilation of dust extinction profiles yields better skill scores than the assimilation of DOD under an equivalent sensor footprint. Our results demonstrate the strong potential of future lidar space missions to improve desert dust forecasts, particularly if they foresee a depolarization lidar channel to allow discrimination of desert dust from other aerosol types.</p></article>", "keywords": ["Mineral dusts", "info:eu-repo/classification/ddc/550", "550", "ddc:550", "9. Industry and infrastructure", "Physics", "QC1-999", "Optical radar", "Aerosols atmosf\u00e8rics", "Atmospheric aerosols", "Radar \u00f2ptic", "01 natural sciences", ":Enginyeria qu\u00edmica::Qu\u00edmica del medi ambient::Qu\u00edmica atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "Earth sciences", "Chemistry", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3", ":Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3 [\u00c0rees tem\u00e0tiques de la UPC]", "13. Climate action", "Pols", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria qu\u00edmica::Qu\u00edmica del medi ambient::Qu\u00edmica atmosf\u00e8rica", "QD1-999", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://acp.copernicus.org/articles/22/535/2022/acp-22-535-2022.pdf"}, {"href": "https://doi.org/2117/360820"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2117/360820", "name": "item", "description": "2117/360820", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/360820"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-14T00:00:00Z"}}, {"id": "2164/15968", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:29:31Z", "type": "Journal Article", "created": "2020-03-04", "title": "Paleotopography continues to drive surface to deep-layer interactions in a subtropical Critical Zone Observatory", "description": "Abstract   Subsurface critical zone structures (SCZS) refer to the spatial variation in the interactive layers underground. Although SCZS greatly affect terrestrial biogeochemical and hydrological cycles, underpinning mechanisms are poorly documented. Herein, we characterized the SCZS of a typical red soil in subtropical China, a type of soil with vast global distribution. The thickness information of three layers was derived from hand augers, boreholes and ground-penetrating radar (GPR) radargrams and incorporated into geographically weighted regression (GWR) models for the reconstruction of paleotopography (Cretaceous sandstone). The interpreted GPR results in terms of thicknesses and interfaces for the three layers were consistent with the borehole logs. The trained GWR models accounted for 43%\u201377% of the spatial variations in the three layers. The paleotopographic elevations were highly correlated with those of the current land surface (r\u00a0=\u00a00.85). Spatial analysis showed that the rougher paleotopography was inherited by the current landform. The SCZS evolution involving mainly the mantling covered by Quaternary red clay (QRC) was primarily driven by terrain attributes. These findings may enhance our understanding of the interaction between the paleoclimate and paleoenvironment. The combination of geophysical techniques, geochemical indicators and spatial prediction techniques provides an effective tool for understanding QRC landform evolution.", "keywords": ["critical zone", "paleotopography", "ground-penetrating radar", "landscape evolution", "550", "Natural Environment Research Council (NERC)", "CONSTRAINTS", "15. Life on land", "01 natural sciences", "CHINA", "EVOLUTION", "SOUTHERN", "QE Geology", "Geophysics", "13. Climate action", "Red Soil Critical Zone Observatory", "THICKNESS", "QUATERNARY RED CLAY", "EARTH", "QE", "NE/N007611/1", "SOIL-WATER STORAGE", "GEOGRAPHICALLY WEIGHTED REGRESSION", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/2164/15968"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Applied%20Geophysics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2164/15968", "name": "item", "description": "2164/15968", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2164/15968"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-04-01T00:00:00Z"}}, {"id": "2767588274", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:29:45Z", "type": "Journal Article", "created": "2017-11-10", "title": "Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (\u03c3\u00b0). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of \u03c3\u00b0 and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of \u03c3\u00b0 ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of \u03c3\u00b0 where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD= 0.032 m3 m\u22123).</p></article>", "keywords": ["soil moisture and ocean salinity satellite (SMOS)", "Atmospheric Science", "Artificial intelligence", "Environmental Engineering", "550", "Science", "Soil Moisture", "0211 other engineering and technologies", "Aerospace Engineering", "FOS: Mechanical engineering", "02 engineering and technology", "01 natural sciences", "Environmental science", "[SDU] Sciences of the Universe [physics]", "Engineering", "Meteorology", "DISPATCH", "Image resolution", "Arctic Permafrost Dynamics and Climate Change", "14. Life underwater", "Moisture", "0105 earth and related environmental sciences", "Soil science", "Water content", "Radar", "Geography", "soil moisture and ocean salinity satellite (SMOS); DISPATCH; radar; Sentinel-1; disaggregation; soil moisture", "Soilmoisture and ocean salinity satellite (SMOS)", "Synthetic Aperture Radar Interferometry", "Q", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Earth and Planetary Sciences", "Groundwater Extraction", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "disaggregation", "Environmental Science", "Physical Sciences", "Sentinel-1", "soil moisture", "radar"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/11/1155/pdf"}, {"href": "https://doi.org/2767588274"}, {"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": "2767588274", "name": "item", "description": "2767588274", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2767588274"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-10T00:00:00Z"}}, {"id": "3046085810", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:30:08Z", "type": "Journal Article", "created": "2020-08-03", "title": "Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.</p></article>", "keywords": ["13. Climate action", "citizen science", "digital soil mapping", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "synthetic aperture radar (SAR)", " soil moisture", "04 agricultural and veterinary sciences", "15. Life on land"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/12/8/2160/pdf"}, {"href": "https://doi.org/3046085810"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3046085810", "name": "item", "description": "3046085810", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3046085810"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-30T00:00:00Z"}}, {"id": "3146066833", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:30:17Z", "type": "Journal Article", "created": "2021-04-01", "title": "A Low-Rank Group-Sparse Model for Eliminating Mixed Errors in Data for SRTM1", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The elimination of mixed errors is a key preprocessing technology for the area of digital elevation model data analysis, which is important for further applying data. We associated group sparsity with the low-rank uniqueness of local transformations of mixing errors to effectively remove mixing errors in data from Shuttle Radar Topography Mission 1 (SRTM 1) based on the sparseness of low-rank groups. First, the stripe-error structure that appeared globally in multiple directions was able to be better represented locally using group-sparse regularization and the uniqueness of the data in the low-rank direction of the local range and using variational ideas to constrain the gradient direction of the data to avoid redundant elimination. Second, the nonlocal self-similarity of the weighted kernel norm was used to remove random noise. Finally, the proposed model for eliminating mixed errors was solved using an algorithm based on the multiplier method of alternating direction. Experiments using simulated and real data found that the proposed low-rank group-sparse method (LRGS) eliminated mixed errors in both visual and quantitative evaluations better than the most recent processing methods and existing dataset products.</p></article>", "keywords": ["self-similarity", "digital elevation model", "Science", "Q", "0211 other engineering and technologies", "0202 electrical engineering", " electronic engineering", " information engineering", "group sparse", "02 engineering and technology", "mixed errors", "shuttle radar topography mission 1", "low-rank"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/7/1346/pdf"}, {"href": "https://doi.org/3146066833"}, {"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": "3146066833", "name": "item", "description": "3146066833", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3146066833"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-01T00:00:00Z"}}, {"id": "40175409", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:30:48Z", "type": "Journal Article", "created": "2025-04-04", "title": "Plasticulture detection at the country scale by combining multispectral and SAR satellite data", "description": "Abstract           <p>The use of plastic films has been growing in agriculture, benefiting consumers and producers. However, concerns have been raised about the environmental impact of plastic film use, with mulching films posing a greater threat than greenhouse films. This calls for large-scale monitoring of different plastic film uses. We used cloud computing, freely available optical and radar satellite images, and machine learning to map plastic-mulched farmland (PMF) and plastic cover above vegetation (PCV) (e.g., greenhouse, tunnel) across Germany. The algorithm detected 103 103 ha of PMF and 37 103 ha of PCV in 2020, while a combination of agricultural statistics and surveys estimated a smaller plasticulture cover of around 100 103 ha in 2019. Based on ground observations, the overall accuracy of the classification is 85.3%. Optical and radar features had similar importance scores, and a distinct backscatter of PCV was related to metal frames underneath the plastic films. Overall, the algorithm achieved great results in the distinction between PCV and PMF. This study maps different plastic film uses at a country scale for the first time and sheds light on the high potential of freely available satellite data for continental monitoring.</p", "keywords": ["Science", "Optical remote sensing", "Q", "R", "Medicine", "Agriculture", "Synthetic aperture radar", "Plastic", "Sentinel", "Google earth engine", "Article"], "contacts": [{"organization": "Alessandro Fabrizi, Peter Fiener, Thomas Jagdhuber, Kristof Van Oost, Florian Wilken,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/40175409"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "40175409", "name": "item", "description": "40175409", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/40175409"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-04-02T00:00:00Z"}}, {"id": "ARLPA_TO:07.09.03_AD_2012-03-05-11:00", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[6.63, 44.06], [6.63, 46.46], [9.21, 46.46], [9.21, 44.06], [6.63, 44.06]]]}, "properties": {"themes": [{"concepts": [{"id": "geoscientificInformation"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "Suolo"}], "scheme": "http://www.eionet.europa.eu/gemet/inspire_themes"}, {"concepts": [{"id": "radar"}, {"id": "monitoraggio ambientale"}, {"id": "telemetria"}], "scheme": "https://www.eionet.europa.eu/gemet"}, {"concepts": [{"id": "Regionale"}], "scheme": "http://inspire.ec.europa.eu/metadata-codelist/SpatialScope"}], "license": "Licenza Creative Commons Attribuzione 4.0 Internazionale (attribuzione: Arpa Piemonte - Geoportale) - https://webgis.arpa.piemonte.it/w-metadoc/_Licenze/licenzaCCBY4.0_Geoportale_Arpa_Piemonte.pdf", "rights": "Ogni iniziativa di divulgazione delle informazioni contenute nel dataset o da esso derivate (cartogrammi, relazioni, servizi informativi), dovr\u00e0 sempre citare la fonte del dato originale (autori, proprietario). Per eventuali aggregazioni o rielaborazioni dei dati forniti finalizzate alla realizzazione di prodotti diversi dall'originale, pur permanendo l'obbligo di citazione della fonte, si declina ogni responsabilit\u00e0.", "updated": "2012-03-05", "type": "Dataset", "created": "2009", "language": "ita", "title": "Arpa Piemonte - Interferometric radar surveys (RADARSAT 2003-2009 campaign)", "description": "Dataset of points related to the results of the survey campaign using SqueeSAR radar-satellite technology. The SqueeSAR technique allows to detect the displacement over time of objects on the ground (typically manufactured or exposed rock) that are good radar reflectors. The calculations were carried out by the Europa TRE Remote Sensing of Milan (spin-off of the Politecnico di Milano) on radar images taken by the Radarsat satellite platform between 2003 and 2009; all the information provided therefore relates to this time interval. A correct use of the data made available by the service requires knowledge of the principles of the method, its limits and the methods used for processing. Uncritical use of data and lack of knowledge of the limitations of the method can lead to misinterpretations, in particular with regard to assessments of landslides. Speed data is expressed in mm/year", "formats": [{"name": "x-shapefile"}, {"name": "WWW:LINK-1.0-http--link"}, {"name": "WMS"}], "keywords": ["Suolo", "radar", "monitoraggio ambientale", "telemetria", "Regionale", "EU", "RNDT", "Radarsat", "Radar", "remote sensing", "SqueeSAR", "osservazione della Terra", "monitoraggio ambientale", "Earth observation"], "contacts": [{"name": null, "organization": "Agenzia Regionale per la Protezione dell'Ambiente del Piemonte", "position": null, "roles": ["pointOfContact"], "phones": [{"value": null}], "emails": [{"value": "webgis@arpa.piemonte.it"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": "http://www.arpa.piemonte.it", "protocol": null, "protocol_url": "", "name": null, "name_url": "", "description": null, "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}], "denominator": "10000"}, "links": [{"href": "https://webgis.arpa.piemonte.it/server/rest/services/rischi_naturali/PS_SqueeSAR_Radarsat/MapServer", "protocol": "WWW:LINK-1.0-http--link", "rel": null}, {"href": "https://webgis.arpa.piemonte.it/server/services/rischi_naturali/PS_SqueeSAR_Radarsat/MapServer/WMSServer?request=GetCapabilities&service=WMS", "protocol": "WMS", "rel": null}, {"href": "http://webgis.arpa.piemonte.it/w-metadoc/thumbnail/PS_Radarsat.jpg", "name": "preview", "description": "Web image thumbnail (URL)", "protocol": "WWW:LINK-1.0-http--image-thumbnail", "rel": "preview"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/ARLPA_TO%3A07.09.03_AD_2012-03-05-11%3A00", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "ARLPA_TO:07.09.03_AD_2012-03-05-11:00", "name": "item", "description": "ARLPA_TO:07.09.03_AD_2012-03-05-11:00", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/ARLPA_TO:07.09.03_AD_2012-03-05-11:00"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"interval": ["2003-06-26T00:00:00Z", "2009-06-26T00:00:00Z"]}}, {"id": "arlpa_to:07.09.02-D_2020-03-25-12:00", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[6.63, 44.06], [6.63, 46.46], [9.21, 46.46], [9.21, 44.06], [6.63, 44.06]]]}, "properties": {"themes": [{"concepts": [{"id": "geoscientificInformation"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "Geologia"}, {"id": "Suolo"}], "scheme": "http://www.eionet.europa.eu/gemet/inspire_themes"}, {"concepts": [{"id": "Radar"}, {"id": "monitoraggio ambientale"}], "scheme": "https://www.eionet.europa.eu/gemet"}, {"concepts": [{"id": "Regionale"}], "scheme": "http://inspire.ec.europa.eu/metadata-codelist/SpatialScope"}], "rights": "Ogni iniziativa di divulgazione delle informazioni contenute nel dataset o da esso derivate (cartogrammi, relazioni, servizi informativi), dovr\u00e0 sempre citare la fonte del dato originale (autori, proprietario) secondo quando indicato dalla licenza cc-BY 4.0 (https://creativeco mmons.org/licenses/by/4.0/). Per eventuali aggregazioni o rielaborazioni dei dati forniti finalizzate alla realizzazione di prodotti diversi dall'originale, pur permanendo l'obbligo di citazione della fonte, si declina ogni responsabilit\u00e0. Vincoli per il mapservice: http://webgis.arpa.piemonte.it/w-metadoc/_Licenze/Licenza_map_service.pdf Contains modified Copernicus Sentinel data (2014-2018)", "updated": "2020-03-25", "type": "Dataset", "created": "2019-01-01", "language": "ita", "title": "Arpa Piemonte - SqueeSAR Sentinel-1  (2014-2018)", "description": "Il dataset riporta le risultanze dell'analisi con tecnologia radar-satellitare SqueeSAR(TM) svoltenell'Ambito del Programma europeo di cooperazione transfrontaliera tra Francia e Italia INTERREG ALCOTRA - Progetto ADVITAM (http://www.interreg-alcotra.eu/it/decouvrir-alcotra/les-projets-finances/ad-vitam). Le analisi sono state effettuate dalla ditta TRE-Altmira per conto del Dipartimento Tematico Rischi Naturali e Ambientali di Arpa Piemonte, utilizzando i dati delle piattaforme satellitari SENTINEL-1B e SENTINEL-1B del programma europeo Copernicus (https://www.copernicus.eu/en/about-copernicus) per il periodo compreso tra ottobre 2014 e ottobre 2018.", "formats": [{"name": "x-shapefile"}, {"name": "WWW:LINK-1.0-http--link"}], "keywords": ["Geologia", "Suolo", "Radar", "monitoraggio ambientale", "Regionale", "EU", "RNDT", "SqueeSAR", "Sentinel", "Radar", "Piemonte", "Copernicus", "Earth Observation", "telerilevamento", "remote sensing", "osservazione della Terra"], "contacts": [{"name": null, "organization": "Arpa Piemonte", "position": null, "roles": ["pointOfContact"], "phones": [{"value": null}], "emails": [{"value": "webgis@arpa.piemonte.it"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": "http://www.arpa.piemonte.it", "protocol": null, "protocol_url": "", "name": null, "name_url": "", "description": null, "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}], "denominator": "10000"}, "links": [{"href": "https://webgis.arpa.piemonte.it/agportal/apps/webappviewer/index.html?id=27fb8f8dad884da7b01cfecd6d3ae61a", "protocol": "WWW:LINK-1.0-http--link", "rel": null}, {"href": "http://webgis.arpa.piemonte.it/w-metadoc/thumbnail/DS_SqueeSAR_Sentinel.jpg", "name": "preview", "description": "Web image thumbnail (URL)", "protocol": "WWW:LINK-1.0-http--image-thumbnail", "rel": "preview"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/arlpa_to%3A07.09.02-D_2020-03-25-12%3A00", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "arlpa_to:07.09.02-D_2020-03-25-12:00", "name": "item", "description": "arlpa_to:07.09.02-D_2020-03-25-12:00", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/arlpa_to:07.09.02-D_2020-03-25-12:00"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"interval": ["2014-10-01T00:00:00Z", "2018-10-01T00:00:00Z"]}}, {"id": "daily-soil-moisture-maps-for-the-uk-2016-2023-at-2-km-resolution", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:35:28Z", "type": "Dataset", "title": "Daily soil moisture maps for the UK (2016-2023) at 2 km resolution", "description": "The data consist of daily maps of volumetric soil moisture predicted by a model based on a network of cosmic-ray neutron sensors (COSMOS-UK), the National River Flow Archive (NRFA) and remotely-sensed data. Maps cover the UK and Ireland at 2 km resolution in the Ordnance Survey National Grid (OSGB) projection. Maps are produced in near-real time, lagging by about one week. Data are available from early 2016 to 2023, on a daily basis. The model was calibrated on a network of cosmic-ray neutron sensors (COSMOS-UK) and remotely-sensed soil moisture data. A key parameter was estimated from the national-scale spatial pattern in the catchment response to rainfall seen in the National River Flow Archive (NRFA) data. Precipitation and humidity data to drive the model came from the Met Office High Resolution Numerical Weather Prediction model (NWP-UKV) which incorporates the C-band rainfall radar network. The maps have a variety of uses in hydrology and elsewhere, for example as inputs to ecosystem models of greenhouse gas exchange, where soil moisture affects numerous processes. The modelling was carried out as part of UK-SCAPE Virtual Survey Lab, and the NERC project \"Detection and Attribution of Regional Emissions (DARE-UK)\". There are some gaps in the time series of meteorological and remote sensing inputs, and data are unavailable for these days. The NRFA data are only available for Great Britain, so estimates in Ireland and continental Europe will be less accurate. 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