{"type": "FeatureCollection", "features": [{"id": "10.1016/j.rse.2020.112050", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:17:08Z", "type": "Journal Article", "created": "2020-08-24", "title": "Monitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas. Remote Sensing of Environment, 251, 112050.", "description": "Abstract   Radar data at C-band has shown great potential for the monitoring of soil and canopy hydric conditions of wheat crops. In this study, the C-band Sentinel-1 time series including the backscattering coefficients \u03c30 at VV and VH polarization, the polarization ratio (PR) and the interferometric coherence \u03c1 are first analyzed with the support of experimental data gathered on three plots of irrigated winter wheat located in the Haouz plain in the center of Morocco covering five growing seasons. The results showed that \u03c1 and PR are tightly related to the canopy development. \u03c1 is also sensitive to soil preparation. By contrast, \u03c30 was found to be widely linked to changes in surface soil moisture (SSM) during the first growth stages when Leaf Area Index remains moderate (", "keywords": ["[SDE] Environmental Sciences", "2. Zero hunger", "Interferometric coherence", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "Surface soil moisture", "630", "Backscattering coefficient", "Winter wheat", "[SDE]Environmental Sciences", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "Semi-arid region", "C-band"]}, "links": [{"href": "https://doi.org/10.1016/j.rse.2020.112050"}, {"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.2020.112050", "name": "item", "description": "10.1016/j.rse.2020.112050", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.rse.2020.112050"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-01T00:00:00Z"}}, {"id": "10.1016/j.rse.2023.113621", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:17:08Z", "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": "10.1109/lgrs.2021.3073484", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:19:15Z", "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": "10.3390/rs12010072", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:21:58Z", "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": "10.5281/zenodo.7464242", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-27T16:24:43Z", "type": "Report", "title": "Extracting VV and VH polarization from Sentinel 1 at Soil Moisture measurement points", "description": "The presentation shows the key steps for extracting Sentinel 1 backscattering coefficient in VV (vertical transmit/vertical receive) and VH (vertical transmit/horizontal receive) polarization, in corrispondence with the Soil Moisture ground measurements. The script run in the Google Earth Engine (GEE) platform which offer the oppurtunity to process a huge amount of calibrated, ortho-corrected Sentinel 1 product.", "keywords": ["Sentinel 1", " backscattering coefficient", " VV co-polarization", " VH cross-polarization", "1"], "contacts": [{"organization": "Nino, Pasquale", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7464242"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7464242", "name": "item", "description": "10.5281/zenodo.7464242", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7464242"}, {"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": "10.5281/zenodo.7464243", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-27T16:24:43Z", "type": "Report", "title": "Extracting VV and VH polarization from Sentinel 1 at Soil Moisture measurement points", "description": "The presentation shows the key steps for extracting Sentinel 1 backscattering coefficient in VV (vertical transmit/vertical receive) and VH (vertical transmit/horizontal receive) polarization, in corrispondence with the Soil Moisture ground measurements. The script run in the Google Earth Engine (GEE) platform which offer the oppurtunity to process a huge amount of calibrated, ortho-corrected Sentinel 1 product.", "keywords": ["Sentinel 1", " backscattering coefficient", " VV co-polarization", " VH cross-polarization", "1"], "contacts": [{"organization": "Nino, Pasquale", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7464243"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7464243", "name": "item", "description": "10.5281/zenodo.7464243", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7464243"}, {"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": "10.5281/zenodo.7464300", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-27T16:24:43Z", "type": "Software", "title": "Extract_S1_VV_VV", "description": "Source script to run in Google Earth Engine platform.", "keywords": ["Sentinel 1", " backscattering coefficient", " VV co-polarization", " VH cross-polarization", " Soil Moisture"], "contacts": [{"organization": "Pasquale Nino", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7464300"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7464300", "name": "item", "description": "10.5281/zenodo.7464300", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7464300"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-20T00:00:00Z"}}, {"id": "10.5281/zenodo.7464481", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-27T16:24:43Z", "type": "Software", "title": "Extract_S1_VV_VH_GEE", "description": "Correct name of the GEE script", "keywords": ["Sentinel 1", " backscattering coefficient", " VV co-polarization", " VH cross-polarization", " Soil Moisture"], "contacts": [{"organization": "Pasquale Nino", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7464481"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7464481", "name": "item", "description": "10.5281/zenodo.7464481", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7464481"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-20T00:00:00Z"}}, {"id": "1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-27T16:26:28Z", "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"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Backscatter&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Backscatter&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Backscatter&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Backscatter&offset=9", "hreflang": "en-US"}], "numberMatched": 9, "numberReturned": 9, "distributedFeatures": [], "timeStamp": "2026-07-28T09:08:40.643586Z"}