{"type": "FeatureCollection", "features": [{"id": "10.1016/j.jag.2022.103101", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:16:34Z", "type": "Journal Article", "created": "2022-11-10", "title": "Forest foliage fuel load estimation from multi-sensor spatiotemporal features", "description": "Foliage fuel is the most flammable component in crown fires. Spatiotemporal dynamics of foliage fuel load (FFL) are important for fire managers to assess fire risk. Here, we integrated optical data from the Landsat 8 Operational Land Imager (OLI) with synthetic aperture radar (SAR) data from Sentinel-1 to estimate FFL. We first reconstructed seamless time series from the Landsat 8 and Sentinel-1 imagery by accounting for unequal time intervals between image observations and outliers. We then extracted temporal features that are proxies of the intra- and inter-annual dynamics from these time series. In addition, we derived spatial features from the imagery that quantify spatial context and therefore used varying window sizes. The random forest regression was implemented to assess the importance of the spatiotemporal features, reduce errors, and derive robust FFL estimates. The satellite estimates were validated against 96 field measurements from Pinus yunnanensis forests in the Liangshan Yi Autonomous Prefecture, Sichuan Province, China. Both the spatiotemporal features of SAR and optical data importantly contributed to FFL estimation. When only optical data was used, the model achieved a R2 of 0.75 (relative Root Mean Squared Error (rRMSE)\u00a0=\u00a025.3\u00a0%), while when only SAR data was used the R2 was 0.76 (rRMSE\u00a0=\u00a025.6\u00a0%). However, when optical and SAR data were combined, the R2 increased to 0.81 (rRMSE\u00a0=\u00a023.2\u00a0%). We also found that temporal features were more important predictors of FFL than features that captured spatial context. We demonstrated our FFL mapping method by a case study in the Chinese Sichuan Province, in relation to the occurrence of a fire. Our method needs additional validation over different tree species and forest types, yet has potential for mapping forest fuel loads and fire risk.", "keywords": ["Landsat 8", "Physical geography", "04 agricultural and veterinary sciences", "15. Life on land", "Fire risk", "01 natural sciences", "GB3-5030", "Spatiotemporal features", "Environmental sciences", "Forest foliage fuel load", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "GE1-350", "SDG 14 - Life Below Water", "Random forest", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.jag.2022.103101"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Applied%20Earth%20Observation%20and%20Geoinformation", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jag.2022.103101", "name": "item", "description": "10.1016/j.jag.2022.103101", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jag.2022.103101"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-01T00:00:00Z"}}, {"id": "10.1016/j.jag.2024.103718", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:16:34Z", "type": "Journal Article", "created": "2024-02-20", "title": "Interseasonal transfer learning for crop mapping using Sentinel-1 data", "description": "Crop maps are highly desired information in modern agriculture as they enable possessors to manage their business in the most optimal way. Usually in remote sensing, crop mapping is performed using satellite images and within-season ground truth samples that are collected in extensive survey campaigns every year, neglecting information and knowledge that past seasons\u2019 classification models provided. This paper assessed different temporal transferring approaches, including transfer learning, together with traditional crop mapping approach to provide an exhaustive comparison. Transferring approaches differed in portion of knowledge utilized from a historical model and that coming from a target season dataset. Three distinct algorithms, Random Forest, Convolutional Neural Network and Transformer, were employed and evaluated using highly dense time series of Sentinel-1 data. Source and target domain were respectively represented by two sets, 2017\u20132020 and 2021 season data, and 9 different crop types were classified. Results showcased that transferring a model has a great potential in crop mapping when little to no ground truth data is available for the target season. However, traditional approach catches up rather quickly and even surpasses transfer learning approach in terms of performance after a certain portion of target domain data is collected. Without target season ground truth data, model transferring can yield modest crop mapping performance of 78% for F1 score, between 84% and 86% F1 score with transfer learning employed in conjunction with limited target season ground truth (i.e. between 120 and 720 parcels), and 88% F1 score at best with traditional approach (ca. 720 parcels). Even though a good discriminatory is found between different crop types, there is still a room for improvement regarding the least represented classes in the dataset. The study significantly contributes to the area of agricultural monitoring and management by demonstrating the effectiveness of transfer learning while lessening the necessity for extensive and labor-intensive data collection, thereby fostering cost and time efficiency. Utilizing Sentinel-1 data, it provides a practical and efficient solution for agricultural analysis worldwide regardless of cloudiness.", "keywords": ["2. Zero hunger", "Physical geography", "Crop mapping", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "Transfer learning", "GB3-5030", "Environmental sciences", "Sentinel-1", "Pre-trained model", "0401 agriculture", " forestry", " and fisheries", "GE1-350", "Domain"]}, "links": [{"href": "https://doi.org/10.1016/j.jag.2024.103718"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Applied%20Earth%20Observation%20and%20Geoinformation", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jag.2024.103718", "name": "item", "description": "10.1016/j.jag.2024.103718", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jag.2024.103718"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-01T00:00:00Z"}}, {"id": "10.1016/j.isprsjprs.2019.02.004", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:16:33Z", "type": "Journal Article", "created": "2019-02-15", "title": "Including Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data", "description": "Abstract   The use of land surface temperature (LST) for monitoring the consumption and water status of crops requires data at fine spatial and temporal resolutions. Unfortunately, the current spaceborne thermal sensors provide data at either high temporal (e.g. MODIS: Moderate Resolution Imaging Spectro-radiometer) or high spatial (e.g. Landsat) resolution separately. Disaggregating low spatial resolution (LR) LST data using ancillary data available at high spatio-temporal resolution could compensate for the lack of high spatial resolution (HR) LST observations. Existing LST downscaling approaches generally rely on the fractional green vegetation cover (fgv) derived from HR reflectances but they do not take into account the soil water availability to explain the spatial variability in LST at HR. In this context, a new method is developed to disaggregate kilometric MODIS LST at 100\u202fm resolution by including the Sentinel-1 (S-1) backscatter, which is indirectly linked to surface soil moisture, in addition to the Landsat-7 and Landsat-8 (L-7 & L-8) reflectances. The approach is tested over two different sites \u2013 an 8\u202fkm by 8\u202fkm irrigated crop area named \u201cR3\u201d and a 12\u202fkm by 12\u202fkm rainfed area named \u201cSidi Rahal\u201d in central Morocco (Marrakech) \u2013 on the seven dates when S-1, and L-7 or L-8 acquisitions coincide with a one-day precision during the 2015\u20132016 growing season. The downscaling methods are applied to the 1\u202fkm resolution MODIS-Terra LST data, and their performance is assessed by comparing the 100\u202fm disaggregated LST to Landsat LST in three cases: no disaggregation, disaggregation using Landsat fgv only, disaggregation using both Landsat fgv and S-1 backscatter. When including fgv only in the disaggregation procedure, the mean root mean square error in LST decreases from 4.20 to 3.60\u202f\u00b0C and the mean correlation coefficient (R) increases from 0.45 to 0.69 compared to the non-disaggregated case within R3. The new methodology including the S-1 backscatter as input to the disaggregation is found to be systematically more accurate on the available dates with a disaggregation mean error decreasing to 3.35\u202f\u00b0C and a mean R increasing to 0.75.", "keywords": ["LST", "2. Zero hunger", "550", "0211 other engineering and technologies", "02 engineering and technology", "15. Life on land", "01 natural sciences", "333", "6. Clean water", "MODIS/Terra", "Disaggregation", "disaggregation", "[SDE.ES] Environmental Sciences/Environment and Society", "MODIS/Terra Landsat", "MODISTerra Landsat", "Sentinel-1", "Soil moisture", "soil moisture", "[SDE.ES]Environmental Sciences/Environment and Society", "Landsat", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.isprsjprs.2019.02.004"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/ISPRS%20Journal%20of%20Photogrammetry%20and%20Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.isprsjprs.2019.02.004", "name": "item", "description": "10.1016/j.isprsjprs.2019.02.004", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.isprsjprs.2019.02.004"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-01T00:00:00Z"}}, {"id": "10.1016/j.rse.2018.04.013", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:16:47Z", "type": "Journal Article", "created": "2018-04-24", "title": "Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil", "description": "Radar data have been used to retrieve and monitor the surface soil moisture (SM) changes in various conditions. However, the calibration of radar models whether empirically or physically-based, is still subject to large uncertainties especially at high-spatial resolution. To help calibrate radar-based retrieval approaches to supervising SM at high resolution, this paper presents an innovative synergistic method combining Sentinel-1 (S1) microwave and Landsat-7/8 (L7/8) thermal data. First, the S1 backscatter coefficient was normalized by its maximum and minimum values obtained during 2015\u20132016 agriculture season. Second, the normalized S1 backscatter coefficient was calibrated from reference points provided by a thermal-derived SM proxy named soil evaporative efficiency (SEE, defined as the ratio of actual to potential soil evaporation). SEE was estimated as the radiometric soil temperature normalized by its minimum and maximum values reached in a water-saturated and dry soil, respectively. We estimated both soil temperature endmembers by using a soil energy balance model forced by available meteorological forcing. The proposed approach was evaluated against in situ SM measurements collected over three bare soil fields in a semi-arid region in Morocco and we compared it against a classical approach based on radar data only. The two polarizations VV (vertical transmit and receive) and VH (vertical transmit and horizontal receive) of the S1 data available over the area are tested to analyse the sensitivity of radar signal to SM at high incidence angles (39\u00b0\u201343\u00b0). We found that the VV polarization was better correlated to SM than the VH polarization with a determination coefficient of 0.47 and 0.28, respectively. By combining S1 (VV) and L7/8 data, we reduced the root mean square difference between satellite and in situ SM to 0.03\u202fm3\u202fm\u22123, which is far smaller than 0.16\u202fm3\u202fm\u22123 when using S1 (VV) only.", "keywords": ["550", "[SDE.IE]Environmental Sciences/Environmental Engineering", "Sentinel-1 (A/B)", "near surface soil moisture", "Bare soil", "0211 other engineering and technologies", "Sentinel-1 (AB)", "02 engineering and technology", "15. Life on land", "Landsat-78", "01 natural sciences", "Energy balance modelling", "Near surface soil moisture", "Landsat-7/8", "bare soil", "13. Climate action", "energy balance modelling", "soil evaporation", "[SDE.IE] Environmental Sciences/Environmental Engineering", "Soil evaporation", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://hal.archives-ouvertes.fr/hal-01912888/file/Amazirh%20et%20al_2018%20%281%29.pdf"}, {"href": "https://doi.org/10.1016/j.rse.2018.04.013"}, {"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.2018.04.013", "name": "item", "description": "10.1016/j.rse.2018.04.013", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.rse.2018.04.013"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-06-01T00:00:00Z"}}, {"id": "10.1016/j.rse.2020.112050", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:16:47Z", "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": "2026-05-29T16:16:47Z", "type": "Journal Article", "created": "2023-05-13", "title": "Optimisation of AquaCrop backscatter simulations using Sentinel-1 observations", "description": "In preparation for active microwave-based data assimilation into a crop modeling system, the mapping of daily 1-km AquaCrop model (v6.1) biomass and surface soil moisture to backscatter was optimised, using two forward operators, i.e. the Water Cloud Model (WCM) and the Support Vector Regression (SVR). Both forward operators were calibrated (2014\u20132018) with 1-km Sentinel-1 backscatter ( ) observations in VV and VH polarisation, for three different study domains in Europe. For the validation period (2019\u20132021), the simulations showed reasonable performances around Czech Republic and the Iberian Peninsula, to good performances over Belgium, but with strong variations within each domain. The domain-averaged root mean square difference between the model and Sentinel-1 remained below 2 dB for both forward operators and all three study domains, and the mean bias for VV remained close to 0 dB, and close 0.5 dB for the VH polarisation. The WCM and SVR performed better in VV than VH and overall the SVR performed slightly better in mapping the AquaCrop soil moisture and vegetation to backscatter than the WCM. Additionally, the assumed linear relationship in the WCM between soil moisture and soil holds better for VV than for VH. The remaining differences between WCM or SVR simulations and Sentinel-1 observations are mainly caused by AquaCrop model errors.", "keywords": ["Agriculture and Food Sciences", "Crop biomass", "YIELD RESPONSE", "ASSIMILATION", "Backscatter modeling", "LEAF-AREA INDEX", "RADAR BACKSCATTER", "BIOMASS", "SAR BACKSCATTER", "AquaCrop optimisation", "13. Climate action", "SURFACE SOIL-MOISTURE", "Earth and Environmental Sciences", "SUPPORT", "Sentinel-1", "WATER", "Soil moisture", "FAO CROP MODEL"]}, "links": [{"href": "https://doi.org/10.1016/j.rse.2023.113621"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing%20of%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.rse.2023.113621", "name": "item", "description": "10.1016/j.rse.2023.113621", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.rse.2023.113621"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-01T00:00:00Z"}}, {"id": "10.1029/2024jg008231", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:17:43Z", "type": "Journal Article", "created": "2024-10-17", "title": "Assimilation of Sentinel\u20101 Backscatter to Update AquaCrop Estimates of Soil Moisture and Crop Biomass", "description": "Abstract<p>This study assesses the potential of regional microwave backscatter data assimilation (DA) in AquaCrop for the first time, using NASA's Land Information System. The objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates. SSM and crop biomass simulations from AquaCrop were updated using Sentinel\uffe2\uff80\uff901 synthetic aperture radar observations, over three regions in Europe in two separate DA experiments. The first experiment concerned updating SSM using VV\uffe2\uff80\uff90polarized backscatter and the corrections were propagated via the model to the biomass. In the second experiment, the DA setup was extended by also updating the biomass with VH\uffe2\uff80\uff90polarized backscatter. SSM was evaluated with local in situ data and with downscaled Soil Moisture Active Passive (SMAP) retrievals for all cropland grid cells, whereas crop biomass was compared to SMAP vegetation optical depth and the Copernicus dry matter productivity. The assimilation showed mixed results for root mean square error and Pearson's correlation, with slight overall improvements in the (anomaly) correlations of updated SSM relative to independent in situ and satellite data. By contrast, the biomass estimates obtained with backscatter DA did not agree better with reference data sets. Overall, the SSM evaluation showed that there is potential in using Sentinel\uffe2\uff80\uff901 backscatter for assimilation in AquaCrop, but the present setup was not able to improve crop biomass estimates. Our study reveals how the complex interaction between SSM, crop biomass and backscatter affect the impact and performance of DA, offering insight into ways to optimize DA for crop growth estimation.</p", "keywords": ["SURFACE", "SIMULATE YIELD RESPONSE", "LAND INFORMATION-SYSTEM", "FRAMEWORK", "AquaCrop", "MODEL", "Earth and Environmental Sciences", "IRRIGATION", "Sentinel-1 SAR", "NETWORK", "soil moisture", "data assimilation", "SATELLITE", "crop biomass"]}, "links": [{"href": "https://doi.org/10.1029/2024jg008231"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1029/2024jg008231", "name": "item", "description": "10.1029/2024jg008231", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1029/2024jg008231"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-01T00:00:00Z"}}, {"id": "10.3390/land11091397", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:09Z", "type": "Journal Article", "created": "2022-08-26", "title": "Evaluation of Accuracy Enhancement in European-Wide Crop Type Mapping by Combining Optical and Microwave Time Series", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This investigation evaluates the potential of combining Copernicus Sentinel-1 (S1) and Sentinel-2 (S2) satellite data in producing a detailed Land Use and Land Cover (LULC) map with 19 crop type classes and 2 broader categories containing Woodland/Shrubland and Grassland over 28 Member States of Europe (EU-28). The Eurostat Land Use and Coverage Area Frame Survey (LUCAS) 2018 dataset is employed as ground truth for model training and validation. Monthly and yearly optical features from S2 spectral reflectance and spectral indices, alongside decadal (10-days) composites from an S1 microwave sensor, are extracted for the EU-28 territory for 2018 using Google Earth Engine (GEE). Five different feature sets using a mixture of indicators were created as input training data. A Random Forest (RF) machine learning algorithm was applied to classify these feature sets, and the generated classification models were compared using an identical validation dataset. Results show that S1 and S2 yearly features together are able to provide a full coverage map less dependent on cloud effects and having appropriate overall accuracy (OA). Based on this feature set, the 21 classes could be classified with an OA of 78.3% using the independent validation data set. The OA increases to 82.7% by grouping 21 classes into 8 broader categories. The comparison with similar studies using individual S1 and S2 data indicates that combining S1 and S2 time series can attain slightly better results while enhancing spatial coverage.</p></article>", "keywords": ["LUCAS 2018", "S", "0211 other engineering and technologies", "Agriculture", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "crop type classification", "machine learning", "13. Climate action", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "time series", "Google Earth Engine"]}, "links": [{"href": "https://www.mdpi.com/2073-445X/11/9/1397/pdf"}, {"href": "https://doi.org/10.3390/land11091397"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/land11091397", "name": "item", "description": "10.3390/land11091397", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/land11091397"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-08-25T00:00:00Z"}}, {"id": "10.1109/igarss.2018.8518170", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:18:39Z", "type": "Journal Article", "created": "2018-11-16", "title": "Sentinel-1 & Sentinel-2 for SOIL Moisture Retrieval at Field Scale", "description": "Soil moisture content is an essential climate variable that is operationally delivered at low resolution (e.g. 36-9 km) by earth observation missions, such as ESA/SMOS, NASA/SMAP and EUMETSAT/ASCAT. However numerous land applications would benefit from the availability of soil moisture maps at higher resolution. For this reason, there is a large research effort to develop soil moisture products at higher resolution using, for instance, data acquired by the new ESA's Sentinel missions. The objective of this study is twofold. First, it presents the validation status of a pre-operational soil moisture product derived from Sentinel-1 at 1 km resolution. Second, it assesses the possibility of integrating Sentinel-2 data and additional ancillary information, such as parcel borders and high resolution soil texture maps, in order to obtain soil moisture maps at 'field scale' resolution, i.e. similar to 0.1 km Case studies concerning agricultural sites located in Europe are presented.", "keywords": ["ASCAT", "high resolution", "13. Climate action", "0211 other engineering and technologies", "Sentinel-1", "SMAP", "02 engineering and technology", "Soil moisture content", "Sentinel-2", "15. Life on land", "01 natural sciences", "SMOS", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/8496405/8517275/08518170.pdf?arnumber=8518170"}, {"href": "https://doi.org/10.1109/igarss.2018.8518170"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IGARSS%202018%20-%202018%20IEEE%20International%20Geoscience%20and%20Remote%20Sensing%20Symposium", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/igarss.2018.8518170", "name": "item", "description": "10.1109/igarss.2018.8518170", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/igarss.2018.8518170"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-07-01T00:00:00Z"}}, {"id": "10.1109/igarss.2018.8519103", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:18:39Z", "type": "Journal Article", "created": "2018-11-16", "title": "Sentinel-1 & Sentinel-2 Data for Soil Tillage Change Detection", "description": "In this paper, an algorithm using Sentinel-1 (S-1) and Sentinel-2 (S-2) data to identify changes of tillage over agricultural fields at approximately similar to 100m resolution is presented. The methodology implements a multiscale temporal change detection on S-1 VH backscatter in order to single out VH changes due to agricultural practices only. The algorithm can be applied over bare or scarcely vegetated agricultural fields, which are identified from S-2 NDVI measurements. An initial assessment at farm scale using in situ and S-1 and SPOT5-Take5 data, acquired over the Apulian Tavoliere in southern Italy in 2015, is illustrated. A full validation of the approach is in progress over three European agricultural areas located in Italy, Spain and France. Results will be further reported in the paper.", "keywords": ["0106 biological sciences", "0301 basic medicine", "2. Zero hunger", "03 medical and health sciences", "soil tillage change identification", "Sentinel-1", "Sentinel-2", "01 natural sciences", "3. Good health"]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/8496405/8517275/08519103.pdf?arnumber=8519103"}, {"href": "https://doi.org/10.1109/igarss.2018.8519103"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IGARSS%202018%20-%202018%20IEEE%20International%20Geoscience%20and%20Remote%20Sensing%20Symposium", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/igarss.2018.8519103", "name": "item", "description": "10.1109/igarss.2018.8519103", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/igarss.2018.8519103"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-07-01T00:00:00Z"}}, {"id": "10.1109/igarss.2019.8899164", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:18:39Z", "type": "Journal Article", "created": "2019-11-25", "title": "Sensitivity of Sentinel-1 Interferometric Coherence to Crop Structure and Soil Moisture", "description": "This paper investigates the sensitivity of Sentinel-1 (S-1) interferometric coherence to crop structure and near surface soil moisture (SSM) content. The study analyzes a data set collected in 2017 over the Apulian Tavoliere agricultural site (Southern Italy). The data set includes: i) in situ data over more than 600 agricultural fields monitored during the 2017 winter and spring growing seasons; ii) time-series of S-1 IW VV & VH backscatter & interferometric coherence; iii) time series of S-1 SSM maps. The temporal behavior of S-1 coherence and VH backscatter has been assessed over the monitored agricultural fields. Initial results indicate a stronger sensitivity of S-1 coherence than VH backscatter to crop geometric structure. In addition, an analysis at site scale, conducted before and after an important rain event, indicates a change of SSM from 0.18 to 0.30 m3/m3 along with a change of S-1 coherence from 0.61 to 0.53.", "keywords": ["2. Zero hunger", "crop segmentation", "crop segmentation; interferometric coherence; Sentinel-1; soil moisture", "0211 other engineering and technologies", "0202 electrical engineering", " electronic engineering", " information engineering", "Sentinel-1", "interferometric coherence", "02 engineering and technology", "soil moisture", "15. Life on land"]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/8891871/8897702/08899164.pdf?arnumber=8899164"}, {"href": "https://doi.org/10.1109/igarss.2019.8899164"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IGARSS%202019%20-%202019%20IEEE%20International%20Geoscience%20and%20Remote%20Sensing%20Symposium", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/igarss.2019.8899164", "name": "item", "description": "10.1109/igarss.2019.8899164", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/igarss.2019.8899164"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-01T00:00:00Z"}}, {"id": "10.1109/jstars.2019.2958847", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:18:40Z", "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": "10.3390/rs13142667", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:17Z", "type": "Journal Article", "created": "2021-07-07", "title": "Irrigation amounts and timing retrieval through data assimilation of surface soil moisture into the FAO-56 approach in the South Mediterranean region", "description": "<p>Agricultural water use represents more than 70% of the world\uffe2\uff80\uff99s freshwater through irrigation water inputs that are poorly known at the field scale. Irrigation monitoring is thus an important issue for optimizing water use in particular with regards to the water scarcity that the semi-arid regions are already facing. In this context, the aim of this study is to develop and evaluate a new approach to predict seasonal to daily irrigation timing and amounts at the field scale. The method is based on surface soil moisture (SSM) data assimilated into a simple land surface (FAO-56) model through a particle filter technique based on an ensemble of irrigation scenarios. The approach is implemented in three steps. First, synthetic experiments are designed to assess the impact of the frequency of observation, the errors on SSM and the a priori constraints on the irrigation scenarios for different irrigation techniques (flooding and drip). In a second step, the method is evaluated using in situ SSM measurements with different revisit times (3, 6 and 12 days) to mimic the available SSM product derived from remote sensing observation. Finally, SSM estimates from Sentinel-1 are used. Data are collected on different wheat fields grown in Morocco, for both flood and drip irrigation techniques in addition to rainfed fields used for an indirect evaluation of the method performance. Using in situ data, accurate results are obtained. With an observation every 6 days to mimic the Sentinel-1 revisit time, the seasonal amounts are retrieved with R &gt; 0.98, RMSE &lt; 32 mm and bias &lt; 2.5 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation as more than 70% of the detected irrigation events have a time difference from actual irrigation events shorter than 4 days. Over the drip irrigated fields, the statistical metrics are R = 0.74, RMSE = 24.8 mm and bias = 2.3 mm for irrigation amounts cumulated over 15 days. When using SSM products derived from Sentinel-1 data, the statistical metrics on 15-day cumulated amounts slightly dropped to R = 0.64, RMSE = 28.7 mm and bias = 1.9 mm. The metrics on the seasonal amount retrievals are close to assimilating in situ observations with R = 0.99, RMSE = 33.5 mm and bias = \uffe2\uff88\uff9218.8 mm. Finally, among four rainfed seasons, only one false event was detected. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas.</p>", "keywords": ["550", "Science", "particle filters", "0207 environmental engineering", "02 engineering and technology", "01 natural sciences", "irrigation timing and amounts", "Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture irrigation timing and amounts", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "semi-arid Mediterranean region", "data assimilation", "0105 earth and related environmental sciences", "FAO-56", "2. Zero hunger", "Q", "15. Life on land", "surface soil moisture", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "winter wheat", "irrigation timing and amounts; surface soil moisture; data assimilation; particle filters; FAO-56; Sentinel-1; semi-arid Mediterranean region; winter wheat", "13. Climate action", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "ZONE MEDITERRANEENNE", "Sentinel-1", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/14/2667/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/14/2667/pdf"}, {"href": "https://doi.org/10.3390/rs13142667"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13142667", "name": "item", "description": "10.3390/rs13142667", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13142667"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-07T00:00:00Z"}}, {"id": "10.3390/s21217406", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:19Z", "type": "Journal Article", "created": "2021-11-09", "title": "A Calibration/Disaggregation Coupling Scheme for Retrieving Soil Moisture at High Spatio-Temporal Resolution: Synergy between SMAP Passive Microwave, MODIS/Landsat Optical/Thermal and Sentinel-1 Radar Data", "description": "<p>Soil moisture (SM) data are required at high spatio-temporal resolution\uffe2\uff80\uff94typically the crop field scale every 3\uffe2\uff80\uff936 days\uffe2\uff80\uff94for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66\uffe2\uff80\uff930.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration.</p>", "keywords": ["550", "Chemical technology", "0211 other engineering and technologies", "synergy", "SMAP", "TP1-1185", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "630", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "Article", "DISPATCH", "disaggregation", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "soil moisture", "environment", "Landsat"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/21/21/7406/pdf"}, {"href": "https://www.mdpi.com/1424-8220/21/21/7406/pdf"}, {"href": "https://doi.org/10.3390/s21217406"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/s21217406", "name": "item", "description": "10.3390/s21217406", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s21217406"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-08T00:00:00Z"}}, {"id": "10.3390/rs10091495", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:15Z", "type": "Journal Article", "created": "2018-09-19", "title": "Irrigation Mapping Using Sentinel-1 Time Series at Field Scale", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical\u2013vertical) and VH (vertical\u2013horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change.</p></article>", "keywords": ["[SDE] Environmental Sciences", "550", "Science", "IMAGE SATELLITE", "irrigated farming", "0211 other engineering and technologies", "0207 environmental engineering", "02 engineering and technology", "630", "irrigation", "remote sensing", "cartography", "CULTURE IRRIGUEE", "TELEDETECTION", "CARTOGRAPHIE", "2. Zero hunger", "HUMIDITE DU SOL", "Q", "soil water content", "15. Life on land", "6. Clean water", "classification", "[SDE]Environmental Sciences", "Sentinel-1", "soil moisture", "soil moisture; SAR; Sentinel-1; irrigation; classification", "SAR"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/10/9/1495/pdf"}, {"href": "https://doi.org/10.3390/rs10091495"}, {"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/rs10091495", "name": "item", "description": "10.3390/rs10091495", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs10091495"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-09-18T00:00:00Z"}}, {"id": "10.3390/w12082160", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:23Z", "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": "10.3390/rs9111155", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:18Z", "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/rs12010072", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:16Z", "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/rs13163181", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:17Z", "type": "Journal Article", "created": "2021-08-11", "title": "Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Monitoring agricultural crops is of paramount importance for preserving water resources and increasing water efficiency over semi-arid areas. This can be achieved by modelling the water resources all along the growing season through the coupled water\u2013surface energy balance. Surface albedo is a key land surface variable to constrain the surface radiation budget and hence the coupled water\u2013surface energy balance. In order to capture the hydric status changes over the growing season, optical remote sensing becomes impractical due to cloud cover in some periods, especially over irrigated winter crops in semi-arid regions. To fill the gap, this paper aims to generate cloudless surface albedo product from Sentinel-1 data that offers a source of high spatio-temporal resolution images. This can help to better capture the vegetation development along the growth season through the surface radiation budget. Random Forest (RF) algorithm was implemented using Sentinel-1 backscatters as input. The approach was tested over an irrigated semi-arid zone in Morocco, which is known by its heterogeneity in term of soil conditions and crop types. The obtained results are evaluated against Landsat-derived albedo with quasi-concurrent Landsat/Sentinel-1 overpasses (up to one day offset), while a further validation was investigated using in situ field scale albedo data. The best model-hyperparameters selection was dependent on two validation approaches (K-fold cross-validation \u2018k = 10\u2019, and holdout). The more robust and accurate model parameters are those that represent the best statistical metrics (root mean square error \u2018RMSE\u2019, bias and correlation coefficient \u2018R\u2019). Coefficient values ranging from 0.70 to 0.79 and a RMSE value between 0.0002 and 0.00048 were obtained comparing Landsat and predicted albedo by RF method. The relative error ratio equals 4.5, which is acceptable to predict surface albedo.</p></article>", "keywords": ["[SDE] Environmental Sciences", "crop vegetation", "550", "Science", "Q", "500", "surface albedo", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water", "13. Climate action", "[SDE]Environmental Sciences", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "Landsat", "random forest", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/16/3181/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/16/3181/pdf"}, {"href": "https://doi.org/10.3390/rs13163181"}, {"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/rs13163181", "name": "item", "description": "10.3390/rs13163181", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13163181"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-11T00:00:00Z"}}, {"id": "10.3390/s17091966", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:19Z", "type": "Journal Article", "created": "2017-08-28", "title": "Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The recent deployment of ESA\u2019s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015\u2013November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.</p></article>", "keywords": ["[SDE] Environmental Sciences", "NDVI", "Chemical technology", "HUMIDITE DU SOL", "soil moisture; SAR; Sentinel-1; NDVI; Sentinel-2; change detection", "0211 other engineering and technologies", "soil water content", "TP1-1185", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Article", "remote sensing", "Sentinel-1", "cartography", "soil moisture", "Sentinel-2", "TELEDETECTION", "change detection", "CARTOGRAPHIE", "SAR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/17/9/1966/pdf"}, {"href": "https://doi.org/10.3390/s17091966"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/s17091966", "name": "item", "description": "10.3390/s17091966", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s17091966"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-08-26T00:00:00Z"}}, {"id": "10.3390/s25072239", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:19Z", "type": "Journal Article", "created": "2025-04-02", "title": "Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Information on the harvest date of crops can help with logistics management in the agricultural industry, planning machinery operations and also with yield prediction modelling. In this study, the determination and prediction of harvest dates for different crops were performed by applying machine learning techniques on C-band synthetic aperture radar (SAR) data. Ground truth data were provided for the Vojvodina region (Serbia), an area with intensive agricultural production, considering winter wheat, maize and soybean fields with exact harvest dates, for the period 2017\u20132020, including 592 samples in total. Data from the Sentinel-1 satellite were used in the study. Time series of backscattering coefficients for vertical\u2013horizontal (VH) and vertical\u2013vertical (VV) polarisations, both from ascending and descending orbits, were collected from Google Earth Engine. Clustering of harvested and unharvested fields was performed with Principal Component Analysis, multidimensional scaling and t-distributed Stochastic Neighbour Embedding, for initial cluster visualization. It is shown that the separability of unharvested and harvested data in two-dimensional space does not depend on the selected method but more on the crop itself. Support Vector Machine and Multi-layer Perceptron were used as classification algorithms for harvest detection, with the former achieving higher accuracies of 79.65% for wheat, 83.41% for maize and 95.97% for soybean. Finally, regression models were developed for the prediction of the harvest date using Random Forest and the long short-term memory network, with the latter achieving better results: an R2 score of 0.72, mean absolute error of 6.80 days and root mean squared error of 9.25 days, for all crops considered together.</p></article>", "keywords": ["machine learning", "agricultural production", "Chemical technology", "Sentinel-1", "TP1-1185", "harvest dates", "Google Earth Engine", "Article", "SAR"], "contacts": [{"organization": "Gordan Mimi\u0107, Amit Kumar Mishra, Miljana Markovi\u0107, Branislav \u017divaljevi\u0107, Dejan Pavlovi\u0107, Oskar Marko,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3390/s25072239"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/s25072239", "name": "item", "description": "10.3390/s25072239", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s25072239"}, {"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": "1854/LU-01JM1T576ZX50W7293M9RBH0RG", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:25:29Z", "type": "Journal Article", "created": "2024-10-17", "title": "Assimilation of Sentinel\u20101 Backscatter to Update AquaCrop Estimates of Soil Moisture and Crop Biomass", "description": "Abstract<p>This study assesses the potential of regional microwave backscatter data assimilation (DA) in AquaCrop for the first time, using NASA's Land Information System. The objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates. SSM and crop biomass simulations from AquaCrop were updated using Sentinel\uffe2\uff80\uff901 synthetic aperture radar observations, over three regions in Europe in two separate DA experiments. The first experiment concerned updating SSM using VV\uffe2\uff80\uff90polarized backscatter and the corrections were propagated via the model to the biomass. In the second experiment, the DA setup was extended by also updating the biomass with VH\uffe2\uff80\uff90polarized backscatter. SSM was evaluated with local in situ data and with downscaled Soil Moisture Active Passive (SMAP) retrievals for all cropland grid cells, whereas crop biomass was compared to SMAP vegetation optical depth and the Copernicus dry matter productivity. The assimilation showed mixed results for root mean square error and Pearson's correlation, with slight overall improvements in the (anomaly) correlations of updated SSM relative to independent in situ and satellite data. By contrast, the biomass estimates obtained with backscatter DA did not agree better with reference data sets. Overall, the SSM evaluation showed that there is potential in using Sentinel\uffe2\uff80\uff901 backscatter for assimilation in AquaCrop, but the present setup was not able to improve crop biomass estimates. Our study reveals how the complex interaction between SSM, crop biomass and backscatter affect the impact and performance of DA, offering insight into ways to optimize DA for crop growth estimation.</p", "keywords": ["Science & Technology", "SURFACE", "SIMULATE YIELD RESPONSE", "Environmental Sciences & Ecology", "Geology", "LAND INFORMATION-SYSTEM", "0404 Geophysics", "FRAMEWORK", "AquaCrop", "MODEL", "1158423N#56471461", "Earth and Environmental Sciences", "IRRIGATION", "Physical Sciences", "Sentinel-1 SAR", "NETWORK", "Geosciences", " Multidisciplinary", "soil moisture", "Life Sciences & Biomedicine", "data assimilation", "3706 Geophysics", "Environmental Sciences", "SATELLITE", "crop biomass"]}, "links": [{"href": "https://doi.org/1854/LU-01JM1T576ZX50W7293M9RBH0RG"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-01JM1T576ZX50W7293M9RBH0RG", "name": "item", "description": "1854/LU-01JM1T576ZX50W7293M9RBH0RG", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-01JM1T576ZX50W7293M9RBH0RG"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8091840", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:23:55Z", "type": "Journal Article", "created": "2022-08-26", "title": "Evaluation of Accuracy Enhancement in European-Wide Crop Type Mapping by Combining Optical and Microwave Time Series", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This investigation evaluates the potential of combining Copernicus Sentinel-1 (S1) and Sentinel-2 (S2) satellite data in producing a detailed Land Use and Land Cover (LULC) map with 19 crop type classes and 2 broader categories containing Woodland/Shrubland and Grassland over 28 Member States of Europe (EU-28). The Eurostat Land Use and Coverage Area Frame Survey (LUCAS) 2018 dataset is employed as ground truth for model training and validation. Monthly and yearly optical features from S2 spectral reflectance and spectral indices, alongside decadal (10-days) composites from an S1 microwave sensor, are extracted for the EU-28 territory for 2018 using Google Earth Engine (GEE). Five different feature sets using a mixture of indicators were created as input training data. A Random Forest (RF) machine learning algorithm was applied to classify these feature sets, and the generated classification models were compared using an identical validation dataset. Results show that S1 and S2 yearly features together are able to provide a full coverage map less dependent on cloud effects and having appropriate overall accuracy (OA). Based on this feature set, the 21 classes could be classified with an OA of 78.3% using the independent validation data set. The OA increases to 82.7% by grouping 21 classes into 8 broader categories. The comparison with similar studies using individual S1 and S2 data indicates that combining S1 and S2 time series can attain slightly better results while enhancing spatial coverage.</p></article>", "keywords": ["LUCAS 2018", "S", "0211 other engineering and technologies", "Agriculture", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "crop type classification", "machine learning", "13. Climate action", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "time series", "Google Earth Engine"]}, "links": [{"href": "https://www.mdpi.com/2073-445X/11/9/1397/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8091840"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8091840", "name": "item", "description": "10.5281/zenodo.8091840", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8091840"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-08-25T00:00:00Z"}}, {"id": "10.5281/zenodo.5597222", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:23:37Z", "type": "Report", "title": "Accuracy assessment of early- and late-season crop classification using optical and SAR imagery", "description": "<em>Reliable early-season crop classification provides necessary input for storage planning, logistics optimization, sales forecasting and setting adequate government policies. The objective of this research was to evaluate crop classification models at different points in time using SAR (Synthetic Aperture Radar) and optical satellite images. SAR and optical images used in the study came from ESA's Sentinel-1 and Sentinel-2 satellites, respectively. A total of 7 cloud-free Sentinel-2 and 50 Sentinel-1 images were used to obtain the time-series necessary for seasonal crop classification. For the classifier training purpose ground truth was collected through the conducted data collection campaign, which consisted of information about the location and crop type for over 1400 parcels of 5 most important crops on the plains of Vojvodina in northern Serbia, where the study was conducted. These are: maize, wheat, soybeans, sugar beet and sunflower, which represented labels for classification. Pixel-based Random Forest (RF) algorithm proved to be a very good solution for the classification problem of the large scale datasets. The final labeled dataset was used for training and testing of RF classifier in different classification scenarios. Early-season crop classification was performed at the end of May, when the crops of interest reached the desired growth stage, while the late-season crop classification was performed at the end of August. Fusion of SAR and optical images gave the overall accuracy of 75.93% for early-season crop classification, while the late-season crop classification accuracy was 89.75%. Using individual sources, accuracies were 68.23% and 75.29%, for SAR and optical images, while the late-season accuracies were 88.37% and 88.88%, respectively. In order to achieve more accurate classification results, various vegetation indices can be integrated in further research.</em>", "keywords": ["2. Zero hunger", "Classification", " Random Forest", " Sentinel-1", " Sentinel-2", "15. Life on land"], "contacts": [{"organization": "Branislav Pejak, Milos Pandzic, Predrag Lugonja, Oskar Marko,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5597222"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5597222", "name": "item", "description": "10.5281/zenodo.5597222", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5597222"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-03-21T00:00:00Z"}}, {"id": "10.5281/zenodo.5597223", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:23:37Z", "type": "Report", "title": "Accuracy assessment of early- and late-season crop classification using optical and SAR imagery", "description": "<em>Reliable early-season crop classification provides necessary input for storage planning, logistics optimization, sales forecasting and setting adequate government policies. The objective of this research was to evaluate crop classification models at different points in time using SAR (Synthetic Aperture Radar) and optical satellite images. SAR and optical images used in the study came from ESA's Sentinel-1 and Sentinel-2 satellites, respectively. A total of 7 cloud-free Sentinel-2 and 50 Sentinel-1 images were used to obtain the time-series necessary for seasonal crop classification. For the classifier training purpose ground truth was collected through the conducted data collection campaign, which consisted of information about the location and crop type for over 1400 parcels of 5 most important crops on the plains of Vojvodina in northern Serbia, where the study was conducted. These are: maize, wheat, soybeans, sugar beet and sunflower, which represented labels for classification. Pixel-based Random Forest (RF) algorithm proved to be a very good solution for the classification problem of the large scale datasets. The final labeled dataset was used for training and testing of RF classifier in different classification scenarios. Early-season crop classification was performed at the end of May, when the crops of interest reached the desired growth stage, while the late-season crop classification was performed at the end of August. Fusion of SAR and optical images gave the overall accuracy of 75.93% for early-season crop classification, while the late-season crop classification accuracy was 89.75%. Using individual sources, accuracies were 68.23% and 75.29%, for SAR and optical images, while the late-season accuracies were 88.37% and 88.88%, respectively. In order to achieve more accurate classification results, various vegetation indices can be integrated in further research.</em>", "keywords": ["2. Zero hunger", "Classification", " Random Forest", " Sentinel-1", " Sentinel-2", "15. Life on land"], "contacts": [{"organization": "Pejak, Branislav, Pandzic, Milos, Lugonja, Predrag, Marko, Oskar,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5597223"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5597223", "name": "item", "description": "10.5281/zenodo.5597223", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5597223"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-03-21T00:00:00Z"}}, {"id": "10.5281/zenodo.6669643", "type": "Feature", "geometry": null, "properties": {"license": "Embargo", "updated": "2026-05-29T16:23:41Z", "type": "Software", "title": "Script to derive and apply crop classification based on Sentinel 1 satellite radar images in Google Earth Engine platform", "description": "For the derivation of crop maps a method has been developed with which time series crop information can be predicted based on remote sensing data. The training of the crop classification model has been performed on the cropland data of the LUCAS Land Use / Cover Area Frame Survey of year 2015 and 2018 \u2013 revised by d\u2019Andrimont et al. (2020) \u2013 merged with the Sentinel-1A and -1B satellite radar images based on d\u2019Andrimont et al. (2021). The pixel based crop classification has been derived using a random forest algorithm on Google Earth Engine platform. The method can be applied for 2015 and all following years. By adding a map of field boundaries the pixel based prediction can be overwritten by the majority of the predicted crop. References: d\u2019Andrimont, R., Verhegghen, A., Lemoine, G., Kempeneers, P., Meroni, M. &amp; van der Velde, M. 2021. From parcel to continental scale \u2013 A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations. <em>Remote Sensing of Environment</em>, <strong>266</strong>. d\u2019Andrimont, R., Yordanov, M., Martinez-Sanchez, L., Eiselt, B., Palmieri, A., Dominici, P., Gallego, J., Reuter, H.I., Joebges, C., Lemoine, G. &amp; van der Velde, M. 2020. Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union. <em>Scientific Data</em>, <strong>7</strong>, 1\u201315.", "keywords": ["2. Zero hunger", "machine learning", "cropmap", "Sentinel-1", "15. Life on land"], "contacts": [{"organization": "M\u00e9sz\u00e1ros, J\u00e1nos, Szab\u00f3, Brigitta,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6669643"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6669643", "name": "item", "description": "10.5281/zenodo.6669643", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6669643"}, {"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.6700122", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-29T16:23:41Z", "type": "Software", "title": "Script to derive and apply crop classification based on Sentinel 1 satellite radar images in Google Earth Engine platform", "description": "For the derivation of crop maps a method has been developed with which time series crop information can be predicted based on remote sensing data. The training of the crop classification model has been performed on the cropland data of the LUCAS Land Use / Cover Area Frame Survey of year 2015 and 2018 \u2013 revised by d\u2019Andrimont et al. (2020) \u2013 merged with the Sentinel-1A and -1B satellite radar images based on d\u2019Andrimont et al. (2021). The pixel based crop classification has been derived using a random forest algorithm on Google Earth Engine platform. The method can be applied for 2015 and all following years. By adding a map of field boundaries the pixel based prediction can be overwritten by the majority of the predicted crop. References: d\u2019Andrimont, R., Verhegghen, A., Lemoine, G., Kempeneers, P., Meroni, M. &amp; van der Velde, M. 2021. From parcel to continental scale \u2013 A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations. <em>Remote Sensing of Environment</em>, <strong>266</strong>. d\u2019Andrimont, R., Yordanov, M., Martinez-Sanchez, L., Eiselt, B., Palmieri, A., Dominici, P., Gallego, J., Reuter, H.I., Joebges, C., Lemoine, G. &amp; van der Velde, M. 2020. Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union. <em>Scientific Data</em>, <strong>7</strong>, 1\u201315.", "keywords": ["2. Zero hunger", "machine learning", "cropmap", "Sentinel-1", "15. Life on land"], "contacts": [{"organization": "M\u00e9sz\u00e1ros, J\u00e1nos, Szab\u00f3, Brigitta,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6700122"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6700122", "name": "item", "description": "10.5281/zenodo.6700122", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6700122"}, {"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": "3046085810", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:26:25Z", "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": "10459.1/60556", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:25:00Z", "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": "10754/627861", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:25:04Z", "type": "Journal Article", "created": "2018-04-24", "title": "Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil", "description": "Radar data have been used to retrieve and monitor the surface soil moisture (SM) changes in various conditions. However, the calibration of radar models whether empirically or physically-based, is still subject to large uncertainties especially at high-spatial resolution. To help calibrate radar-based retrieval approaches to supervising SM at high resolution, this paper presents an innovative synergistic method combining Sentinel-1 (S1) microwave and Landsat-7/8 (L7/8) thermal data. First, the S1 backscatter coefficient was normalized by its maximum and minimum values obtained during 2015\u20132016 agriculture season. Second, the normalized S1 backscatter coefficient was calibrated from reference points provided by a thermal-derived SM proxy named soil evaporative efficiency (SEE, defined as the ratio of actual to potential soil evaporation). SEE was estimated as the radiometric soil temperature normalized by its minimum and maximum values reached in a water-saturated and dry soil, respectively. We estimated both soil temperature endmembers by using a soil energy balance model forced by available meteorological forcing. The proposed approach was evaluated against in situ SM measurements collected over three bare soil fields in a semi-arid region in Morocco and we compared it against a classical approach based on radar data only. The two polarizations VV (vertical transmit and receive) and VH (vertical transmit and horizontal receive) of the S1 data available over the area are tested to analyse the sensitivity of radar signal to SM at high incidence angles (39\u00b0\u201343\u00b0). We found that the VV polarization was better correlated to SM than the VH polarization with a determination coefficient of 0.47 and 0.28, respectively. By combining S1 (VV) and L7/8 data, we reduced the root mean square difference between satellite and in situ SM to 0.03\u202fm3\u202fm\u22123, which is far smaller than 0.16\u202fm3\u202fm\u22123 when using S1 (VV) only.", "keywords": ["550", "[SDE.IE]Environmental Sciences/Environmental Engineering", "Sentinel-1 (A/B)", "near surface soil moisture", "Bare soil", "0211 other engineering and technologies", "Sentinel-1 (AB)", "02 engineering and technology", "15. Life on land", "Landsat-78", "01 natural sciences", "Energy balance modelling", "Near surface soil moisture", "Landsat-7/8", "bare soil", "13. Climate action", "energy balance modelling", "soil evaporation", "[SDE.IE] Environmental Sciences/Environmental Engineering", "Soil evaporation", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://hal.archives-ouvertes.fr/hal-01912888/file/Amazirh%20et%20al_2018%20%281%29.pdf"}, {"href": "https://doi.org/10754/627861"}, {"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": "10754/627861", "name": "item", "description": "10754/627861", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10754/627861"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-06-01T00:00:00Z"}}, {"id": "1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:25:29Z", "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": "1871.1/505fa0c0-6587-48f4-a8b1-4f1ad19d6bb8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:25:30Z", "type": "Journal Article", "created": "2022-11-10", "title": "Forest foliage fuel load estimation from multi-sensor spatiotemporal features", "description": "Foliage fuel is the most flammable component in crown fires. Spatiotemporal dynamics of foliage fuel load (FFL) are important for fire managers to assess fire risk. Here, we integrated optical data from the Landsat 8 Operational Land Imager (OLI) with synthetic aperture radar (SAR) data from Sentinel-1 to estimate FFL. We first reconstructed seamless time series from the Landsat 8 and Sentinel-1 imagery by accounting for unequal time intervals between image observations and outliers. We then extracted temporal features that are proxies of the intra- and inter-annual dynamics from these time series. In addition, we derived spatial features from the imagery that quantify spatial context and therefore used varying window sizes. The random forest regression was implemented to assess the importance of the spatiotemporal features, reduce errors, and derive robust FFL estimates. The satellite estimates were validated against 96 field measurements from Pinus yunnanensis forests in the Liangshan Yi Autonomous Prefecture, Sichuan Province, China. Both the spatiotemporal features of SAR and optical data importantly contributed to FFL estimation. When only optical data was used, the model achieved a R2 of 0.75 (relative Root Mean Squared Error (rRMSE)\u00a0=\u00a025.3\u00a0%), while when only SAR data was used the R2 was 0.76 (rRMSE\u00a0=\u00a025.6\u00a0%). However, when optical and SAR data were combined, the R2 increased to 0.81 (rRMSE\u00a0=\u00a023.2\u00a0%). We also found that temporal features were more important predictors of FFL than features that captured spatial context. We demonstrated our FFL mapping method by a case study in the Chinese Sichuan Province, in relation to the occurrence of a fire. Our method needs additional validation over different tree species and forest types, yet has potential for mapping forest fuel loads and fire risk.", "keywords": ["Landsat 8", "Physical geography", "04 agricultural and veterinary sciences", "15. Life on land", "Fire risk", "01 natural sciences", "GB3-5030", "Spatiotemporal features", "Environmental sciences", "Forest foliage fuel load", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "GE1-350", "SDG 14 - Life Below Water", "Random forest", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/1871.1/505fa0c0-6587-48f4-a8b1-4f1ad19d6bb8"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Applied%20Earth%20Observation%20and%20Geoinformation", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/505fa0c0-6587-48f4-a8b1-4f1ad19d6bb8", "name": "item", "description": "1871.1/505fa0c0-6587-48f4-a8b1-4f1ad19d6bb8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/505fa0c0-6587-48f4-a8b1-4f1ad19d6bb8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-01T00:00:00Z"}}, {"id": "2160/0069afd7-a0c3-4bc2-aff8-aae8953cfc0d", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:25:56Z", "type": "Journal Article", "created": "2025-04-02", "title": "Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Information on the harvest date of crops can help with logistics management in the agricultural industry, planning machinery operations and also with yield prediction modelling. In this study, the determination and prediction of harvest dates for different crops were performed by applying machine learning techniques on C-band synthetic aperture radar (SAR) data. Ground truth data were provided for the Vojvodina region (Serbia), an area with intensive agricultural production, considering winter wheat, maize and soybean fields with exact harvest dates, for the period 2017\u20132020, including 592 samples in total. Data from the Sentinel-1 satellite were used in the study. Time series of backscattering coefficients for vertical\u2013horizontal (VH) and vertical\u2013vertical (VV) polarisations, both from ascending and descending orbits, were collected from Google Earth Engine. Clustering of harvested and unharvested fields was performed with Principal Component Analysis, multidimensional scaling and t-distributed Stochastic Neighbour Embedding, for initial cluster visualization. It is shown that the separability of unharvested and harvested data in two-dimensional space does not depend on the selected method but more on the crop itself. Support Vector Machine and Multi-layer Perceptron were used as classification algorithms for harvest detection, with the former achieving higher accuracies of 79.65% for wheat, 83.41% for maize and 95.97% for soybean. Finally, regression models were developed for the prediction of the harvest date using Random Forest and the long short-term memory network, with the latter achieving better results: an R2 score of 0.72, mean absolute error of 6.80 days and root mean squared error of 9.25 days, for all crops considered together.</p></article>", "keywords": ["machine learning", "agricultural production", "Chemical technology", "Sentinel-1", "NDC 2 - Dim Newyn", "TP1-1185", "harvest dates", "SDG 2 - Zero Hunger", "Google Earth Engine", "Article", "SAR"]}, "links": [{"href": "https://www.mdpi.com/1424-8220/25/7/2239/pdf"}, {"href": "https://doi.org/2160/0069afd7-a0c3-4bc2-aff8-aae8953cfc0d"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2160/0069afd7-a0c3-4bc2-aff8-aae8953cfc0d", "name": "item", "description": "2160/0069afd7-a0c3-4bc2-aff8-aae8953cfc0d", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2160/0069afd7-a0c3-4bc2-aff8-aae8953cfc0d"}, {"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": "2889759488", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:26:11Z", "type": "Journal Article", "created": "2018-09-19", "title": "Irrigation Mapping Using Sentinel-1 Time Series at Field Scale", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical\u2013vertical) and VH (vertical\u2013horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change.</p></article>", "keywords": ["[SDE] Environmental Sciences", "550", "Science", "IMAGE SATELLITE", "irrigated farming", "0211 other engineering and technologies", "0207 environmental engineering", "02 engineering and technology", "630", "irrigation", "remote sensing", "cartography", "CULTURE IRRIGUEE", "TELEDETECTION", "CARTOGRAPHIE", "2. Zero hunger", "HUMIDITE DU SOL", "Q", "soil water content", "15. Life on land", "6. Clean water", "classification", "[SDE]Environmental Sciences", "Sentinel-1", "soil moisture", "soil moisture; SAR; Sentinel-1; irrigation; classification", "SAR"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/10/9/1495/pdf"}, {"href": "https://doi.org/2889759488"}, {"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": "2889759488", "name": "item", "description": "2889759488", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2889759488"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-09-18T00:00:00Z"}}, {"id": "2747196278", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:26:07Z", "type": "Journal Article", "created": "2017-08-28", "title": "Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The recent deployment of ESA\u2019s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015\u2013November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.</p></article>", "keywords": ["[SDE] Environmental Sciences", "NDVI", "Chemical technology", "HUMIDITE DU SOL", "soil moisture; SAR; Sentinel-1; NDVI; Sentinel-2; change detection", "0211 other engineering and technologies", "soil water content", "TP1-1185", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Article", "remote sensing", "Sentinel-1", "cartography", "soil moisture", "Sentinel-2", "TELEDETECTION", "change detection", "CARTOGRAPHIE", "SAR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/17/9/1966/pdf"}, {"href": "https://doi.org/2747196278"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2747196278", "name": "item", "description": "2747196278", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2747196278"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-08-26T00:00:00Z"}}, {"id": "2767588274", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:26:07Z", "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": "2914631759", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:26:13Z", "type": "Journal Article", "created": "2019-02-15", "title": "Including Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data", "description": "Abstract   The use of land surface temperature (LST) for monitoring the consumption and water status of crops requires data at fine spatial and temporal resolutions. Unfortunately, the current spaceborne thermal sensors provide data at either high temporal (e.g. MODIS: Moderate Resolution Imaging Spectro-radiometer) or high spatial (e.g. Landsat) resolution separately. Disaggregating low spatial resolution (LR) LST data using ancillary data available at high spatio-temporal resolution could compensate for the lack of high spatial resolution (HR) LST observations. Existing LST downscaling approaches generally rely on the fractional green vegetation cover (fgv) derived from HR reflectances but they do not take into account the soil water availability to explain the spatial variability in LST at HR. In this context, a new method is developed to disaggregate kilometric MODIS LST at 100\u202fm resolution by including the Sentinel-1 (S-1) backscatter, which is indirectly linked to surface soil moisture, in addition to the Landsat-7 and Landsat-8 (L-7 & L-8) reflectances. The approach is tested over two different sites \u2013 an 8\u202fkm by 8\u202fkm irrigated crop area named \u201cR3\u201d and a 12\u202fkm by 12\u202fkm rainfed area named \u201cSidi Rahal\u201d in central Morocco (Marrakech) \u2013 on the seven dates when S-1, and L-7 or L-8 acquisitions coincide with a one-day precision during the 2015\u20132016 growing season. The downscaling methods are applied to the 1\u202fkm resolution MODIS-Terra LST data, and their performance is assessed by comparing the 100\u202fm disaggregated LST to Landsat LST in three cases: no disaggregation, disaggregation using Landsat fgv only, disaggregation using both Landsat fgv and S-1 backscatter. When including fgv only in the disaggregation procedure, the mean root mean square error in LST decreases from 4.20 to 3.60\u202f\u00b0C and the mean correlation coefficient (R) increases from 0.45 to 0.69 compared to the non-disaggregated case within R3. The new methodology including the S-1 backscatter as input to the disaggregation is found to be systematically more accurate on the available dates with a disaggregation mean error decreasing to 3.35\u202f\u00b0C and a mean R increasing to 0.75.", "keywords": ["LST", "2. Zero hunger", "550", "0211 other engineering and technologies", "02 engineering and technology", "15. Life on land", "01 natural sciences", "333", "6. Clean water", "MODIS/Terra", "Disaggregation", "disaggregation", "[SDE.ES] Environmental Sciences/Environment and Society", "MODIS/Terra Landsat", "MODISTerra Landsat", "Sentinel-1", "Soil moisture", "soil moisture", "[SDE.ES]Environmental Sciences/Environment and Society", "Landsat", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/2914631759"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/ISPRS%20Journal%20of%20Photogrammetry%20and%20Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2914631759", "name": "item", "description": "2914631759", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2914631759"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-01T00:00:00Z"}}, {"id": "3197830923", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:26:37Z", "type": "Journal Article", "created": "2021-08-11", "title": "Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Monitoring agricultural crops is of paramount importance for preserving water resources and increasing water efficiency over semi-arid areas. This can be achieved by modelling the water resources all along the growing season through the coupled water\u2013surface energy balance. Surface albedo is a key land surface variable to constrain the surface radiation budget and hence the coupled water\u2013surface energy balance. In order to capture the hydric status changes over the growing season, optical remote sensing becomes impractical due to cloud cover in some periods, especially over irrigated winter crops in semi-arid regions. To fill the gap, this paper aims to generate cloudless surface albedo product from Sentinel-1 data that offers a source of high spatio-temporal resolution images. This can help to better capture the vegetation development along the growth season through the surface radiation budget. Random Forest (RF) algorithm was implemented using Sentinel-1 backscatters as input. The approach was tested over an irrigated semi-arid zone in Morocco, which is known by its heterogeneity in term of soil conditions and crop types. The obtained results are evaluated against Landsat-derived albedo with quasi-concurrent Landsat/Sentinel-1 overpasses (up to one day offset), while a further validation was investigated using in situ field scale albedo data. The best model-hyperparameters selection was dependent on two validation approaches (K-fold cross-validation \u2018k = 10\u2019, and holdout). The more robust and accurate model parameters are those that represent the best statistical metrics (root mean square error \u2018RMSE\u2019, bias and correlation coefficient \u2018R\u2019). Coefficient values ranging from 0.70 to 0.79 and a RMSE value between 0.0002 and 0.00048 were obtained comparing Landsat and predicted albedo by RF method. The relative error ratio equals 4.5, which is acceptable to predict surface albedo.</p></article>", "keywords": ["[SDE] Environmental Sciences", "crop vegetation", "550", "Science", "Q", "500", "surface albedo", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water", "13. Climate action", "[SDE]Environmental Sciences", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "Landsat", "random forest", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/16/3181/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/16/3181/pdf"}, {"href": "https://doi.org/3197830923"}, {"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": "3197830923", "name": "item", "description": "3197830923", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3197830923"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-11T00:00:00Z"}}, {"id": "PMC11990955", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:28:41Z", "type": "Journal Article", "created": "2025-04-02", "title": "Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Information on the harvest date of crops can help with logistics management in the agricultural industry, planning machinery operations and also with yield prediction modelling. In this study, the determination and prediction of harvest dates for different crops were performed by applying machine learning techniques on C-band synthetic aperture radar (SAR) data. Ground truth data were provided for the Vojvodina region (Serbia), an area with intensive agricultural production, considering winter wheat, maize and soybean fields with exact harvest dates, for the period 2017\u20132020, including 592 samples in total. Data from the Sentinel-1 satellite were used in the study. Time series of backscattering coefficients for vertical\u2013horizontal (VH) and vertical\u2013vertical (VV) polarisations, both from ascending and descending orbits, were collected from Google Earth Engine. Clustering of harvested and unharvested fields was performed with Principal Component Analysis, multidimensional scaling and t-distributed Stochastic Neighbour Embedding, for initial cluster visualization. It is shown that the separability of unharvested and harvested data in two-dimensional space does not depend on the selected method but more on the crop itself. Support Vector Machine and Multi-layer Perceptron were used as classification algorithms for harvest detection, with the former achieving higher accuracies of 79.65% for wheat, 83.41% for maize and 95.97% for soybean. Finally, regression models were developed for the prediction of the harvest date using Random Forest and the long short-term memory network, with the latter achieving better results: an R2 score of 0.72, mean absolute error of 6.80 days and root mean squared error of 9.25 days, for all crops considered together.</p></article>", "keywords": ["machine learning", "agricultural production", "Chemical technology", "Sentinel-1", "TP1-1185", "harvest dates", "Google Earth Engine", "Article", "SAR"]}, "links": [{"href": "https://www.mdpi.com/1424-8220/25/7/2239/pdf"}, {"href": "https://doi.org/PMC11990955"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC11990955", "name": "item", "description": "PMC11990955", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC11990955"}, {"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": "PMC5621168", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:28:42Z", "type": "Journal Article", "created": "2017-08-28", "title": "Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The recent deployment of ESA\u2019s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015\u2013November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.</p></article>", "keywords": ["[SDE] Environmental Sciences", "NDVI", "Chemical technology", "HUMIDITE DU SOL", "soil moisture; SAR; Sentinel-1; NDVI; Sentinel-2; change detection", "0211 other engineering and technologies", "soil water content", "TP1-1185", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Article", "remote sensing", "Sentinel-1", "cartography", "soil moisture", "Sentinel-2", "TELEDETECTION", "change detection", "CARTOGRAPHIE", "SAR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/17/9/1966/pdf"}, {"href": "https://doi.org/PMC5621168"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC5621168", "name": "item", "description": "PMC5621168", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC5621168"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-08-26T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Sentinel-1&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=Sentinel-1&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=Sentinel-1&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Sentinel-1&offset=40", "hreflang": "en-US"}], "numberMatched": 40, "numberReturned": 40, "distributedFeatures": [], "timeStamp": "2026-05-30T11:08:44.019808Z"}