{"type": "FeatureCollection", "features": [{"id": "10.1016/j.jag.2024.103659", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:16:52Z", "type": "Journal Article", "created": "2024-01-21", "title": "Automatized Sentinel-2 mosaicking for large area forest mapping", "description": "Creating maps of forest inventory variables is commonly taking advantage of satellite images, which are mosaicked together for gaining larger coverage. Recently, mosaicking has increasingly shifted towards user friendly cloud-based online environments such as Google Earth Engine (GEE), which are equipped with huge image repositories and extensive processing capabilities. This enables the easy transferability of workflows into new image sets and diversifies the range of methodological options for mosaicking. The quality control of the output mosaic, ensuring that the reflectance values are representative to the targeted land cover, is however primarily based on certain assumptions or pre-set rules which may not always produce an optimal result. Our study focuses on assessing and comparing the performance of three different mosaicking algorithms for predicting forest inventory variables, based on an extensive set of field data on the main site type, fertility class, and volume and biomass of growing stock. One of the compared mosaics derives from manual image selection, thus enabling rigorous visual quality control, and two others are resting on GEE-assisted automatized methods which include applying a percentile-based statistic over all the input reflectance values and selecting the best pixels using predefined quality indicators. The results indicate that the manual and the percentile-based mosaics are generally providing the best and relatively equal performance levels. Compared to them, the quality-based mosaic has slightly lower accuracy particularly when predicting continuous variables (i.e., the volume and biomass of growing stock) and it suffers from minor image defects. For the total volume of growing stock, for example, the RMS errors are 56.22 % for the manual, 56.33 % for the percentile-based, and 59.47 % for the quality-based mosaics, respectively. These results indicate that from the perspective of large area forest mapping, automatically generated mosaics may provide approximately similar accuracy as compared to manually controlled workflow at a fraction of the workload.", "keywords": ["Image mosaicking", "Physical geography", "791", "forest research", "04 agricultural and veterinary sciences", "15. Life on land", "Feature prediction", "01 natural sciences", "GB3-5030", "Environmental sciences", "0401 agriculture", " forestry", " and fisheries", "GE1-350", "Sentinel-2", "Google Earth Engine", "satellite images", "Forest inventory", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Balazs Andras, Tuominen Sakari, Pitk\u00e4nen Timo P.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.jag.2024.103659"}, {"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.2024.103659", "name": "item", "description": "10.1016/j.jag.2024.103659", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jag.2024.103659"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-01T00:00:00Z"}}, {"id": "10.1007/978-3-030-84144-7_7", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:14:30Z", "type": "Report", "created": "2022-04-11", "title": "Potential of Sentinel-2 Satellite and Novel Proximal Sensor Data Fusion for Agricultural Applications", "description": "Open AccessConsidering the importance of crop production for the growing population of the world, timely and accurate information about crop development is essential for successful agricultural monitoring. With an increasing interest of the agricultural community in precision agriculture, there is also a growing interest for using different spectral vegetation indices derived by different sensor devices. They can offer a valuable perspective both at the field-scale and at the plant level. In order to better utilize the spectral reflectance measurements from different sensors for agricultural applications, as well as to promote synergistic use of proximal and remote sensing sensors in this area, this paper aims to compare two novel sensing approaches for crop monitoring; a) the recently developed active multispectral proximal sensor named Plant-O-Meter and b) Sentinel-2 satellite, which carries a multispectral optical instrument. Both sensors and sensing methods are suitable for agricultural applications, following the same basic measurement principles. In general, their operation is based on the estimation of the proportion of radiation that is reflected from the target, which in agricultural systems refers to plants or the soil, at different wavelengths of the spectrum of light. However, each of the two sensing systems shows pros and cons regarding the spatial, spectral and temporal resolutions, the need for corrections and calibrations and the dependency from external parameters such as the weather or illumination conditions. Therefore, their complementary use is expected to bring added value comparing to information retrieved by each sensor separately. In order to correctly address the problem of data fusion, compatibility studies between the two sensors (passive remote and active proximal) are necessary. In this study, a maize field was sensed on several dates in 2018 growing season using both the Plant-O-Meter active proximal sensor and images acquired by Sentinel-2. Numerous vegetation indices based on different spectral channel combinations were calculated and the results were compared using linear regression analysis. First results showed good positive correlations between the indices obtained by the two sensors which signify their joint potential, hence further development and research on this topic are appreciated and expected.", "keywords": ["2. Zero hunger", "crop monitoring", " proximal sensing", " Sentinel-2", " vegetation indices", " correlation", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.1007/978-3-030-84144-7_7"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/978-3-030-84144-7_7", "name": "item", "description": "10.1007/978-3-030-84144-7_7", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/978-3-030-84144-7_7"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.1016/j.jag.2022.103101", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:16:52Z", "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": "2026-07-27T16:16:52Z", "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.agee.2022.108124", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:15:47Z", "type": "Journal Article", "created": "2022-08-18", "title": "Assessing almond response to irrigation and soil management practices using vegetation indexes time-series and plant water status measurements", "description": "Open AccessThis research was funded in the frame of the projects PRECIRIEGO RTC-2017\u20136365-2 financed by Agencia Estatal de Investigaci\u00f3n with European Regional Development Fund co-funds; and the European Union H2020 project SHUI GA 773903. The research was supported also by the CajaMar Caja Rural Contract \u201cEfficient use of water resources under climate change scenarios\u201d. I. Buesa and J.M. Ram\u00edrez-Cuesta acknowledge the postdoctoral financial support received from Juan de la Cierva Spanish Postdoctoral Program (FJC2019\u2013042122-I and IJC2020\u2013043601-I, respectively). Authors acknowledge David Hortelano and Jos\u00e9 Luis Ru\u00edz Garc\u00eda for the help provided in the field measurements acquisition. This work represents a contribution to CSIC Thematic Interdisciplinary Platform PTI TELEDETECT.", "keywords": ["0106 biological sciences", "Soil management", "Almonds", "F06 Irrigation", "01 natural sciences", "12. Responsible consumption", "Vegetation index", "Sentinel 2", "Remote sensing sustainable agriculture", "P33 Soil chemistry and physics", "F40 Plant ecology", "2. Zero hunger", "precision agriculture", "Precision agriculture", "Sustainable agriculture", "Water use efficiency", "Vegetation cover", "F07 Soil cultivation", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "Tree canopy", "F60 Plant physiology and biochemistry", "6. Clean water", "Water management", "P30 Soil science and management", "P10 Water resources and management", "0401 agriculture", " forestry", " and fisheries", "Remote sensing", " sustainable agriculture", "Sentinel-2"]}, "links": [{"href": "https://www.iris.unict.it/bitstream/20.500.11769/552491/2/Agriculture%2c%20ecosystems%20and%20environment%202022.pdf"}, {"href": "https://doi.org/10.1016/j.agee.2022.108124"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agee.2022.108124", "name": "item", "description": "10.1016/j.agee.2022.108124", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agee.2022.108124"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-01T00:00:00Z"}}, {"id": "10.1016/j.rse.2018.04.013", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:17:07Z", "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.geoderma.2022.115935", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:16:48Z", "type": "Journal Article", "created": "2022-05-20", "title": "Improving remote sensing of salinity on topsoil with crop residues using novel indices of optical and microwave bands", "description": "Remote sensing indices have been proposed to characterize soil salinity. However, the sensitivity of these indicators is unstable owing to differences in geographic environment and vegetation type. This study investigated the performance of several existing indices to estimate the salinity of topsoil with residues in southern Xinjiang, China. The results showed that these indices were not satisfactory. In order to construct an index that can be used to directly indicate soil salinity in a specific area, novel salinity indices were calculated using optical bands (blue, green, red, vegetation red edge, and shortwave infrared bands) derived from Sentinel-2 multispectral data and Sentinel-1 radar data (backscattering coefficient VV, VH). To enhance the sensitivity of the optical bands, five transformation methods (logarithmic, reciprocal, first-, second-, and third-derivative) were applied to the original spectra. Based on previous studies, statistical methods were used to construct two-, three-, and four-bands indices. One constructed three-bands index with the second-derivative transformation, called the Enhanced Residues Soil Salinity Index (ERSSI), showed the highest correlation with topsoil salinity (r = 0.65 and 0.68 in training and testing). ERSSI establishes a linear relationship in soil salinity estimation with an R2 of 0.53 and a LCCC of 0.65 in training dataset, with an R2 of 0.51 and a LCCC of 0.73 in testing dataset. And it shows contribution in random forest regression with an R2 of 0.80 and a LCCC of 0.86 in training dataset, with an R2 of 0.77 and a LCCC of 0.81 in testing dataset. The ERSSI consisted of the B, G, and SWIR1 bands, and was sensitive to salinity variations in the residues remaining in farmland soils. This study provides a novel index and method for the accurate and robust assessment and mapping of salinity in farmland covered by crop residues.", "keywords": ["2. Zero hunger", "Soil salinity", "Residues remained soil", "Indices constructions", "Spectral response", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Sentinel-2", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.geoderma.2022.115935"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.geoderma.2022.115935", "name": "item", "description": "10.1016/j.geoderma.2022.115935", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geoderma.2022.115935"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-01T00:00:00Z"}}, {"id": "10.1016/j.isprsjprs.2017.10.016", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:16:51Z", "type": "Journal Article", "created": "2017-11-06", "title": "Estimation And Mapping Of Above-Ground Biomass Of Mangrove Forests And Their Replacement Land Uses In The Philippines Using Sentinel Imagery", "description": "Abstract The recent launch of the Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity for land-based biomass mapping and monitoring especially in the tropics where deforestation is highest. Yet, unlike in agriculture and inland land uses, the use of Sentinel imagery has not been evaluated for biomass retrieval in mangrove forest and the non-forest land uses that replaced mangroves. In this study, we evaluated the ability of Sentinel imagery for the retrieval and predictive mapping of above-ground biomass of mangroves and their replacement land uses. We used Sentinel SAR and multispectral imagery to develop biomass prediction models through the conventional linear regression and novel Machine Learning algorithms. We developed models each from SAR raw polarisation backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables. The results show that the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall above-ground biomass. In contrast, the model which utilised optical bands had the lowest accuracy. However, the SAR-based model was more accurate in predicting the biomass in the usually deficient to low vegetation cover non-forest replacement land uses such as abandoned aquaculture pond, cleared mangrove and abandoned salt pond. These models had 0.82\u20130.83 correlation/agreement of observed and predicted value, and root mean square error of 27.8\u201328.5\u202fMg\u202fha \u22121 . Among the Sentinel-2 multispectral bands, the red and red edge bands (bands 4, 5 and 7), combined with elevation data, were the best variable set combination for biomass prediction. The red edge-based Inverted Red-Edge Chlorophyll Index had the highest prediction accuracy among the vegetation indices. Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of the above-ground biomass of mangroves and the replacement non-forest land uses, especially with the inclusion of elevation data. The study demonstrates encouraging results in biomass mapping of mangroves and other coastal land uses in the tropics using the freely accessible and relatively high-resolution Sentinel imagery.", "keywords": ["land use change", "580", "sentinel imagery", "mangrove", "biomass", "550", "Philippines", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "biomass mapping", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "https://doi.org/10.1016/j.isprsjprs.2017.10.016"}, {"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.2017.10.016", "name": "item", "description": "10.1016/j.isprsjprs.2017.10.016", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.isprsjprs.2017.10.016"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-12-01T00:00:00Z"}}, {"id": "10.1016/j.isprsjprs.2019.02.004", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:16:51Z", "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.2020.112050", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:17:08Z", "type": "Journal Article", "created": "2020-08-24", "title": "Monitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas. Remote Sensing of Environment, 251, 112050.", "description": "Abstract Radar data at C-band has shown great potential for the monitoring of soil and canopy hydric conditions of wheat crops. In this study, the C-band Sentinel-1 time series including the backscattering coefficients \u03c30 at VV and VH polarization, the polarization ratio (PR) and the interferometric coherence \u03c1 are first analyzed with the support of experimental data gathered on three plots of irrigated winter wheat located in the Haouz plain in the center of Morocco covering five growing seasons. The results showed that \u03c1 and PR are tightly related to the canopy development. \u03c1 is also sensitive to soil preparation. By contrast, \u03c30 was found to be widely linked to changes in surface soil moisture (SSM) during the first growth stages when Leaf Area Index remains moderate (", "keywords": ["[SDE] Environmental Sciences", "2. Zero hunger", "Interferometric coherence", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "Surface soil moisture", "630", "Backscattering coefficient", "Winter wheat", "[SDE]Environmental Sciences", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "Semi-arid region", "C-band"]}, "links": [{"href": "https://doi.org/10.1016/j.rse.2020.112050"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing%20of%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.rse.2020.112050", "name": "item", "description": "10.1016/j.rse.2020.112050", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.rse.2020.112050"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-01T00:00:00Z"}}, {"id": "10.1016/j.rse.2023.113621", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:17:08Z", "type": "Journal Article", "created": "2023-05-13", "title": "Optimisation of AquaCrop backscatter simulations using Sentinel-1 observations", "description": "In preparation for active microwave-based data assimilation into a crop modeling system, the mapping of daily 1-km AquaCrop model (v6.1) biomass and surface soil moisture to backscatter was optimised, using two forward operators, i.e. the Water Cloud Model (WCM) and the Support Vector Regression (SVR). Both forward operators were calibrated (2014\u20132018) with 1-km Sentinel-1 backscatter ( ) observations in VV and VH polarisation, for three different study domains in Europe. For the validation period (2019\u20132021), the simulations showed reasonable performances around Czech Republic and the Iberian Peninsula, to good performances over Belgium, but with strong variations within each domain. The domain-averaged root mean square difference between the model and Sentinel-1 remained below 2 dB for both forward operators and all three study domains, and the mean bias for VV remained close to 0 dB, and close 0.5 dB for the VH polarisation. The WCM and SVR performed better in VV than VH and overall the SVR performed slightly better in mapping the AquaCrop soil moisture and vegetation to backscatter than the WCM. Additionally, the assumed linear relationship in the WCM between soil moisture and soil holds better for VV than for VH. The remaining differences between WCM or SVR simulations and Sentinel-1 observations are mainly caused by AquaCrop model errors.", "keywords": ["Agriculture and Food Sciences", "Crop biomass", "YIELD RESPONSE", "ASSIMILATION", "Backscatter modeling", "LEAF-AREA INDEX", "RADAR BACKSCATTER", "BIOMASS", "SAR BACKSCATTER", "AquaCrop optimisation", "13. Climate action", "SURFACE SOIL-MOISTURE", "Earth and Environmental Sciences", "SUPPORT", "Sentinel-1", "WATER", "Soil moisture", "FAO CROP MODEL"]}, "links": [{"href": "https://doi.org/10.1016/j.rse.2023.113621"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing%20of%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.rse.2023.113621", "name": "item", "description": "10.1016/j.rse.2023.113621", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.rse.2023.113621"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-01T00:00:00Z"}}, {"id": "10.3389/fenvs.2021.555216", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:21:34Z", "type": "Journal Article", "created": "2021-03-16", "title": "Extending the spatio-temporal applicability of DISPATCH soil moisture downscaling algorithm: A study case using SMAP, MODIS and Sentinel-3 data", "description": "

DISPATCH is a disaggregation algorithm of the low-resolution soil moisture (SM) estimates derived from passive microwave observations. It provides disaggregated SM data at typically 1\uffc2\uffa0km resolution by using the soil evaporative efficiency (SEE) estimated from optical/thermal data collected around solar noon. DISPATCH is based on the relationship between the evapo-transpiration rate and the surface SM under non-energy-limited conditions and hence is well adapted for semi-arid regions with generally low cloud cover and sparse vegetation. The objective of this paper is to extend the spatio-temporal coverage of DISPATCH data by 1) including more densely vegetated areas and 2) assessing the usefulness of thermal data collected earlier in the morning. Especially, we evaluate the performance of the Temperature Vegetation Dryness Index (TVDI) instead of SEE in the DISPATCH algorithm over vegetated areas (called vegetation-extended DISPATCH) and we quantify the increase in coverage using Sentinel-3 (overpass at around 09:30 am) instead of MODIS (overpass at around 10:30 am and 1:30 pm for Terra and Aqua, respectively) data. In this study, DISPATCH is applied to 36\uffc2\uffa0km resolution Soil Moisture Active and Passive SM data over three 50\uffc2\uffa0km by 50\uffc2\uffa0km areas in Spain and France to assess the effectiveness of the approach over temperate and semi-arid regions. The use of TVDI within DISPATCH increases the coverage of disaggregated images by 9 and 14% over the temperate and semi-arid sites, respectively. Moreover, including the vegetated pixels in the validation areas increases the overall correlation between satellite and in situ SM from 0.36 to 0.43 and from 0.41 to 0.79 for the temperate and semi-arid regions, respectively. The use of Sentinel-3 can increase the spatio-temporal coverage by up to 44% over the considered MODIS tile, while the overlapping disaggregated data sets derived from Sentinel-3 and MODIS land surface temperature data are strongly correlated (around 0.7). Additionally, the correlation between satellite and in situ SM is significantly better for DISPATCH (0.39\uffe2\uff80\uff930.80) than for the Copernicus Sentinel-1-based (\uffe2\uff88\uff920.03 to 0.69) and SMAP/S1 (0.37\uffe2\uff80\uff930.74) product over the three studies (temperate and semi-arid) areas, with an increase in yearly valid retrievals for the vegetation-extended DISPATCH algorithm.

", "keywords": ["550", "0211 other engineering and technologies", "TVDI", "SMAP", "02 engineering and technology", "EVI", "15. Life on land", "01 natural sciences", "333", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "Environmental sciences", "DISPATCH", "13. Climate action", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "GE1-350", "Sentinel-3", "14. Life underwater", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "soil moisture", "environment", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.3389/fenvs.2021.555216"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Environmental%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fenvs.2021.555216", "name": "item", "description": "10.3389/fenvs.2021.555216", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fenvs.2021.555216"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-16T00:00:00Z"}}, {"id": "10.1016/j.still.2024.106125", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:17:47Z", "type": "Journal Article", "created": "2024-04-26", "title": "On the impact of soil texture on local scale organic carbon quantification: From airborne to spaceborne sensing domains", "description": "Soil organic carbon (SOC) distribution and interaction with light is influenced by soil texture parameters (clay, silt and sand), which makes SOC prediction complicated, especially in samples with considerable pedological variability. Hence, understanding the relationship between SOC and soil texture is important within the context of SOC prediction using remote sensing data. The main objective of this study was to find the impact of soil texture on the performance of local SOC prediction models that were developed on Sentinel-2 (S2) multispectral and CASI/SASI (CS) hyperspectral airborne data as the main predictor variables. One approach to that objective was to lower the texture variance by stratification of the samples. Therefore, soil samples collected from four agricultural sites in the Czech Republic were segregated based on the i) site-based and ii) texture-based stratification strategies. Random forest (RF) models were then developed on all stratified classes with and without considering the soil texture parameters as predictor variables and results were compared with those obtained by the RF models developed on the non-stratified (NS) samples. Both stratification strategies provided more homogeneous classes, which enhanced the accuracy of SOC prediction, compared to using the NS samples. In addition, the texture-based RF models yielded higher accuracy predictions than the site-based ones. Except for sand, adding texture parameters to the main predictors improved the accuracy of the models, so that the highest prediction performance was obtained by a texture-based model developed on clay-added CS data. Overall, texture-based stratification could significantly enhance the SOC prediction, when the texture parameters were added to the S2 and CS data as the main predictor variables.", "keywords": ["EJP SOIL", "550", "Airborne hyperspectral data", "STEROPES", "Soil organic carbon", "Soil texture", "EJPSOIL", "Sentinel-2", "Stratification"]}, "links": [{"href": "https://doi.org/10.1016/j.still.2024.106125"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20and%20Tillage%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.still.2024.106125", "name": "item", "description": "10.1016/j.still.2024.106125", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.still.2024.106125"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-09-01T00:00:00Z"}}, {"id": "10.1029/2024jg008231", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:18:15Z", "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

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.The use of plastic films has been growing in agriculture, benefiting consumers and producers. However, concerns have been raised about the environmental impact of plastic film use, with mulching films posing a greater threat than greenhouse films. This calls for large-scale monitoring of different plastic film uses. We used cloud computing, freely available optical and radar satellite images, and machine learning to map plastic-mulched farmland (PMF) and plastic cover above vegetation (PCV) (e.g., greenhouse, tunnel) across Germany. The algorithm detected 103 103 ha of PMF and 37 103 ha of PCV in 2020, while a combination of agricultural statistics and surveys estimated a smaller plasticulture cover of around 100 103 ha in 2019. Based on ground observations, the overall accuracy of the classification is 85.3%. Optical and radar features had similar importance scores, and a distinct backscatter of PCV was related to metal frames underneath the plastic films. Overall, the algorithm achieved great results in the distinction between PCV and PMF. This study maps different plastic film uses at a country scale for the first time and sheds light on the high potential of freely available satellite data for continental monitoring.

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.

", "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": "10.12688/openreseurope.13135.2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:20:06Z", "type": "Journal Article", "created": "2021-09-06", "title": "A Google Earth Engine-enabled Python approach for the identification of anthropogenic palaeo-landscape features", "description": "The necessity of sustainable development for landscapes has emerged as an important theme in recent decades. Current methods take a holistic approach to landscape heritage and promote an interdisciplinary dialogue to facilitate complementary landscape management strategies. With the socio-economic values of the \u201cnatural\u201d and \u201ccultural\u201d landscape heritage increasingly recognised worldwide, remote sensing tools are being used more and more to facilitate the recording and management of landscape heritage. The advent of freeware cloud computing services has enabled significant improvements in landscape research allowing the rapid exploration and processing of satellite imagery such as the Landsat and Copernicus Sentinel datasets. This research represents one of the first applications of the Google Earth Engine (GEE) \u00a0Python application programming interface (API) in studies of historic landscapes. The complete free and open-source software (FOSS) cloud protocol proposed here consists of a Python code script developed in Google Colab, which could be adapted and replicated in different areas of the world. A multi-temporal approach has been adopted to investigate the potential of Sentinel-2 satellite imagery to detect buried hydrological and anthropogenic features along with spectral index and spectral decomposition analysis. The protocol's effectiveness in identifying palaeo-riverscape features has been tested in the Po Plain (N Italy).", "keywords": ["FOS: Computer and information sciences", "Landscape Archaeology", "Computer Vision and Pattern Recognition (cs.CV)", "Computer Science - Computer Vision and Pattern Recognition", "0211 other engineering and technologies", "Articles", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Fluvial and Alluvial Archaeology", "12. Responsible consumption", "Multispectral analysis", "Computer Science - Computers and Society", "Buried features", "Multispectral analysis;Sentinel-2;Spectral decomposition;Python;Riverscape;Fluvial and Alluvial Archaeology;Landscape Archaeology;Buried features", "13. Climate action", "Computers and Society (cs.CY)", "11. Sustainability", "Spectral decomposition", "Sentinel-2", "Riverscape", "Python", "Research Article", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://air.unimi.it/bitstream/2434/878015/4/Brandolini%2bet%2bal_ORE_2021_compressed%20%282%29.pdf"}, {"href": "https://eprints.ncl.ac.uk/fulltext.aspx?url=272362/A22B27B6-9486-4FBF-91B1-B06594F968F1.pdf&pub_id=272362"}, {"href": "https://doi.org/10.12688/openreseurope.13135.2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Open%20Research%20Europe", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.12688/openreseurope.13135.2", "name": "item", "description": "10.12688/openreseurope.13135.2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.12688/openreseurope.13135.2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-24T00:00:00Z"}}, {"id": "10.13140/rg.2.2.23926.55365", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:20:09Z", "type": "Report", "title": "Predicting Soil Organic Matter Content using Machine Learning Models based on Sentinel-2 Imagery", "description": "We used machine learning to process Sentinel 2 multispectral images and infer the amount of soil organic matter using satellite soil indices.", "keywords": ["Machine Learning", "Soil Organic Matter", "Sentinel 2", "15. Life on land"], "contacts": [{"organization": "Vladimir \u0106iri\u0107, Sanja Brdar, Predrag Lugonja, Oskar Marko, Vladimir Crnojevi\u0107,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.13140/rg.2.2.23926.55365"}, {"rel": "self", "type": "application/geo+json", "title": "10.13140/rg.2.2.23926.55365", "name": "item", "description": "10.13140/rg.2.2.23926.55365", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.13140/rg.2.2.23926.55365"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.3390/rs11091106", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:21:58Z", "type": "Journal Article", "created": "2019-05-09", "title": "Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion", "description": "

Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region.

", "keywords": ["Landsat 8", "2. Zero hunger", "soil erosion", "550", "Science", "Q", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "01 natural sciences", "630", "field spectroscopy", "6. Clean water", "soil erosion; remote sensing; Sentinel-2; Landsat 8; ANN; RUSLE; field spectroscopy; OLSR; GWR", "remote sensing", "Field spectroscopy", "OLSR", "13. Climate action", "Soil erosion", "0401 agriculture", " forestry", " and fisheries", "RUSLE", "Sentinel-2", "ANN", "GWR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/11/9/1106/pdf"}, {"href": "https://doi.org/10.3390/rs11091106"}, {"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/rs11091106", "name": "item", "description": "10.3390/rs11091106", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs11091106"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-09T00:00:00Z"}}, {"id": "10.3390/rs12142299", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:21:58Z", "type": "Journal Article", "created": "2020-07-20", "title": "Feasibility of Using the Two-Source Energy Balance Model (TSEB) with Sentinel-2 and Sentinel-3 Images to Analyze the Spatio-Temporal Variability of Vine Water Status in a Vineyard", "description": "

In viticulture, detailed spatial information about actual evapotranspiration (ETa) and vine water status within a vineyard may be of particular utility when applying site-specific, precision irrigation management. Over recent decades, extensive research has been carried out in the use of remote sensing energy balance models to estimate and monitor ETa at the field level. However, one of the major limitations remains the coarse spatial resolution in the thermal infrared (TIR) domain. In this context, the recent advent of the Sentinel missions of the European Space Agency (ESA) has greatly improved the possibility of monitoring crop parameters and estimating ETa at higher temporal and spatial resolutions. In order to bridge the gap between the coarse-resolution Sentinel-3 thermal and the fine-resolution Sentinel-2 shortwave data, sharpening techniques have been used to downscale the Sentinel-3 land surface temperature (LST) from 1 km to 20 m. However, the accurate estimates of high-resolution LST through sharpening techniques are still unclear, particularly when intended to be used for detecting crop water stress. The goal of this study was to assess the feasibility of the two-source energy balance model (TSEB) using sharpened LST images from Sentinel-2 and Sentinel-3 (TSEB-PTS2+3) to estimate the spatio-temporal variability of actual transpiration (T) and water stress in a vineyard. T and crop water stress index (CWSI) estimates were evaluated against a vine water consumption model and regressed with in situ stem water potential (\uffce\uffa8stem). Two different TSEB approaches, using very high-resolution airborne thermal imagery, were also included in the analysis as benchmarks for TSEB-PTS2+3. One of them uses aggregated TIR data at the vine+inter-row level (TSEB-PTairb), while the other is based on a contextual method that directly, although separately, retrieves soil and canopy temperatures (TSEB-2T). The results obtained demonstrated that when comparing airborne Trad and sharpened S2+3 LST, the latter tend to be underestimated. This complicates the use of TSEB-PTS2+3 to detect crop water stress. TSEB-2T appeared to outperform all the other methods. This was shown by a higher R2 and slightly lower RMSD when compared with modelled T. In addition, regressions between T and CWSI-2T with \uffce\uffa8stem also produced the highest R2.

", "keywords": ["evapotranspiration; TSEB; Sentinel-2; Sentinel-3; crop water stress index; vine water status; grapevines", "2. Zero hunger", "crop water stress index", "Science", "Q", "evapotranspiration", "634", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Sentinel-3", "Sentinel-2", "TSEB", "vine water status"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/14/2299/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/14/2299/pdf"}, {"href": "https://doi.org/10.3390/rs12142299"}, {"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/rs12142299", "name": "item", "description": "10.3390/rs12142299", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12142299"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-17T00:00:00Z"}}, {"id": "10.3390/rs13020305", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:21:59Z", "type": "Journal Article", "created": "2021-01-20", "title": "Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China", "description": "

Accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of arid regions. As a branch of artificial intelligence, machine learning acquires new knowledge through self-learning and continuously improves its own performance. The purpose of this study is to combine Sentinel-2 Multispectral Imager (MSI) data and MSI-derived covariates with measured soil salinity data and to apply three machine learning algorithms in modeling to estimate and map the soil salinity in the study sample area. According to the convenient transportation conditions, the study area and sampling quadrat were set up, and the 5-point method was used to collect the soil mixed samples, and 160 soil mixed samples were collected. Kennard\u2013Stone (K\u2013S) algorithm was used for sample classification, 70% for modeling and 30% for verification. The machine learning algorithm uses Support Vector Machines (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The results showed that (1) the average reflectance of each band of the MSI data ranged from 0.21\u20130.28. According to the spectral characteristics corresponding to different soil electrical conductivity (EC) levels (1.07\u201379.6 dS m\u22121), the spectral reflectance of salinized soil in the MSI data ranged from 0.09\u20130.35. (2) The correlation coefficient between the MSI data and MSI-derived covariates and soil EC was moderate, and the correlation between certain MSI data sets and soil EC was not significant. (3) The SVM soil EC estimation model established with the MSI data set attained a higher performance and accuracy (R2 = 0.88, root mean square error (RMSE) = 4.89 dS m\u22121, and ratio of the performance to the interquartile range (RPIQ) = 1.96, standard error of the laboratory measurements to the standard error of the predictions (SEL/SEP) = 1.11) than those attained with the soil EC estimation models established with the RF and ANN models. (4) We applied the SVM soil EC estimation model to map the soil salinity in the study area, which showed that the farmland with higher altitudes discharged a large amount of salt to the surroundings due to long-term irrigation, and the secondary salinization of the farmland also caused a large amount of salt accumulation. This research provides a scientific basis for the simulation of soil salinization scenarios in arid areas in the future.

", "keywords": ["2. Zero hunger", "soil salinization; Sentinel-2 MSI; remote sensing; machine learning; arid area", "Science", "soil salinization", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "Sentinel-2 MSI", "6. Clean water", "remote sensing", "machine learning", "arid area", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/2/305/pdf"}, {"href": "https://doi.org/10.3390/rs13020305"}, {"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/rs13020305", "name": "item", "description": "10.3390/rs13020305", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13020305"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-17T00:00:00Z"}}, {"id": "10.3390/rs13142667", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:21:59Z", "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": "

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 > 0.98, RMSE < 32 mm and bias < 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.

", "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/land11091397", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:21:52Z", "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": "

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.

", "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": "10.3390/rs14030714", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:22:00Z", "type": "Journal Article", "created": "2022-02-07", "title": "Evaluation of Agricultural Bare Soil Properties Retrieval from Landsat 8, Sentinel-2 and PRISMA Satellite Data", "description": "

The PRISMA satellite is equipped with an advanced hyperspectral Earth observation technology capable of improving the accuracy of quantitative estimation of bio-geophysical variables in various Earth Science Applications and in particular for soil science. The purpose of this research was to evaluate the ability of the PRISMA hyperspectral imager to estimate topsoil properties (i.e., organic carbon, clay, sand, silt), in comparison with current satellite multispectral sensors. To investigate this expectation, a test was carried out using topsoil data collected in Italy following two approaches. Firstly, PRISMA, Sentinel-2 and Landsat 8 spectral simulated datasets were obtained from the spectral resampling of a laboratory soil library. Subsequently, bare soil reflectance data were obtained from two experimental areas in Italy, using real satellites images, at dates close to each other. The estimation models of soil properties were calibrated employing both Partial Least Square Regression and Cubist Regression algorithms. The results of the study revealed that the best accuracies in retrieving topsoil properties were obtained by PRISMA data, using both laboratory and real datasets. Indeed, the resampled spectra of the hyperspectral imager provided the best Ratio of Performance to Inter-Quartile distance (RPIQ) for clay (4.87), sand (3.80), and organic carbon (2.59) estimation, for the spectral soil library datasets. For the bare soil reflectance obtained from real satellite imagery, a higher level of prediction accuracy was obtained from PRISMA data, with RPIQ \u00b1 SE values of 2.32 \u00b1 0.07 for clay, 3.85 \u00b1 0.19 for silt, and 3.51 \u00b1 0.16 for soil organic carbon. The results for the PRISMA hyperspectral satellite imagery with the Cubist Regression provided the best performance in the prediction of silt, sand, clay and SOC. The same variables were better estimated using PLSR models in the case of the resampled hyperspectral data. The statistical accuracy in the retrieval of SOC from real and resampled PRISMA data revealed the potential of the actual hyperspectral satellite. The results supported the expected good ability of the PRISMA imager to estimate topsoil properties.

", "keywords": ["Landsat 8", "Sentinel\u20102", "Multispectral", "multispectral", "Science", "hyperspectral; multispectral; PRISMA; soil properties; bare soil; SOC; soil texture; Sentinel-2; Landsat 8; PLSR; Cubist", "Q", "Bare soil", "Cubist", "PRISMA", "04 agricultural and veterinary sciences", "15. Life on land", "hyperspectral", "Hyperspectral", "PLSR", "bare soil", "soil properties", "Soil texture", "Bare soil; Cubist; Hyperspectral; Landsat 8; Multispectral; PLSR; PRISMA; Sentinel\u20102; SOC; Soil properties; Soil texture", "0401 agriculture", " forestry", " and fisheries", "SOC", "Soil properties", "Sentinel-2"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/3/714/pdf"}, {"href": "https://iris.cnr.it/bitstream/20.500.14243/413305/1/prod_473291-doc_192827_compressed.pdf"}, {"href": "https://www.iris.unina.it/bitstream/11588/948571/1/Evaluation%20of%20Agricultural%20Bare%20Soil%20Properties%20Retrieval%20from%20Landsat%208%2c%20Sentinel-2%20and%20PRISMA%20Satellite%20Data%20Enhanced%20Reader.pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/3/714/pdf"}, {"href": "https://doi.org/10.3390/rs14030714"}, {"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/rs14030714", "name": "item", "description": "10.3390/rs14030714", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs14030714"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-02T00:00:00Z"}}, {"id": "10.3390/rs16091510", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:22:01Z", "type": "Journal Article", "created": "2024-04-25", "title": "Remote Quantification of Soil Organic Carbon: Role of Topography in the Intra-Field Distribution", "description": "

Soil organic carbon (SOC) measurements are an indicator of soil health and an important parameter for the study of land-atmosphere carbon fluxes. Field sampling provides precise measurements at the sample location but entails high costs and cannot provide detailed maps unless the sampling density is very high. Remote sensing offers the possibility to quantify SOC over large areas in a cost-effective way. As a result, numerous studies have sought to quantify SOC using Earth observation data with a focus on inter-field or regional distributions. This study took a different angle and aimed to map the spatial distribution of SOC at the intra-field scale, since this distribution provides important insights into the biophysiochemical processes involved in the retention of SOC. Instead of solely using spectral measurements to quantify SOC, topographic and spectral features act as predictor variables. The necessary data on study fields in South-East England was acquired through a detailed SOC sampling campaign, including a LiDAR survey flight. Multi-spectral Sentinel-2 data of the study fields were acquired for the exact day of the sampling campaign, and for an interval of 18 months before and after this date. Random Forest (RF) and Support Vector Regression (SVR) models were trained and tested on the spectral and topographical data of the fields to predict the observed SOC values. Five different sets of model predictors were assessed, by using independently and in combination, single and multidate spectral data, and topographical features for the SOC sampling points. Both, RF and SVR models performed best when trained on multi-temporal Sentinel-2 data together with topographic features, achieving validation root-mean-square errors (RMSEs) of 0.29% and 0.23% SOC, respectively. These RMSEs are competitive when compared with those found in the literature for similar models. The topographic wetness index (TWI) exhibited the highest permutation importance for virtually all models. Given that farming practices within each field are the same, this result suggests an important role of soil moisture in SOC retention. Contrary to findings in dryer climates or in studies encompassing larger areas, TWI was negatively related to SOC levels in the study fields, suggesting a different role of soil wetness in the SOC storage in climates characterized by excess rainfall and poorly drained soils.

", "keywords": ["2. Zero hunger", "Science", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "soil organic carbon", "topographic wetness index", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "support vector machine", "Sentinel-2", "random forest", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.mdpi.com/2072-4292/16/9/1510/pdf"}, {"href": "https://doi.org/10.3390/rs16091510"}, {"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/rs16091510", "name": "item", "description": "10.3390/rs16091510", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs16091510"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-25T00:00:00Z"}}, {"id": "10.3390/rs8110938", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:22:01Z", "type": "Journal Article", "created": "2016-11-11", "title": "Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples", "description": "

This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth. Using the European Space Agency\uffe2\uff80\uff99s (ESA) Sen2Cor algorithm, the platform processes ESA\uffe2\uff80\uff99s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data. Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).

", "keywords": ["550", "reflectance", "t\u00e9l\u00e9d\u00e9tection", "Science", "0211 other engineering and technologies", "02 engineering and technology", "7. Clean energy", "remote sensing", "Traitement du signal et de l'image", "atmospheric correction", "remote sensing;sentinel-2;atmospheric correction;Sen2Cor;LAI;broadband HDRF", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing", "9. Industry and infrastructure", "sentinel-2", "Q", "Signal and Image processing", "04 agricultural and veterinary sciences", "broadband HDRF", "620", "LAI", "atmosph\u00e8re", "Sen2Cor", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing", "donn\u00e9e satellitaire"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/8/11/938/pdf"}, {"href": "https://doi.org/10.3390/rs8110938"}, {"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/rs8110938", "name": "item", "description": "10.3390/rs8110938", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs8110938"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-11-11T00:00:00Z"}}, {"id": "10.3390/rs9111155", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:22:01Z", "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": "

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).

", "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": "10.5194/isprs-archives-XLII-4-W2-121-2017", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:22:52Z", "type": "Journal Article", "created": "2017-07-17", "title": "FLOWERED-GEODBAPP: AN APPLICATION BASED ON CROWD-GENERATING DATA USING SENTINEL2 IMAGERY", "description": "

Abstract. This study is part of the EU H2020 research Project FLOWERED (de-FLuoridation technologies for imprOving quality of WatEr and agRo-animal products along the East African Rift Valley in the context of aDaptation to climate change). FLOWERED project aims to develop technologies and methodologies at cross-boundary catchment scales to manage the risks associated with high Fluoride water supply in Africa, focusing on three representative test areas along the African Rift Valley (i.e. Ethiopia, Kenya and Tanzania), characterized by high fluoride contents in waters and soils, water scarcity, overexploitation of groundwater and high vulnerability to risks arising from climate change, as drought and desertification. It also is empowering local communities to take responsibility for the integrated-sustainability of the natural resources, growing national and international environmental priorities, enhancing transboundary cooperation and promoting local ownership based on a scientific and technological approach. Within the FLOWERED project, the transition from the land cover to the land use and water use maps is provided through the development of a mobile application (FLOWERED-GeoDBapp ). It is dedicated to the collection of local geo-information on land use, water uses, irrigation systems, household features, use of drinking water and the other information needful for the specific knowledge of water supply involving local communities through participative approach. This system is structured to be populated, through an action of crowd-generating data by local communities (students and people involved mainly by NGOs). The SHAREGEODBapp is proposed as an innovative tool for water management and agriculture institutions at regional and local level.

", "keywords": ["2. Zero hunger", "Technology", "Land cover", " ESA Sentinel", " Crowd-generating data", " Rift Valley", " Fluoride", "T", "0207 environmental engineering", "1. No poverty", "02 engineering and technology", "15. Life on land", "Engineering (General). Civil engineering (General)", "01 natural sciences", "6. Clean water", "TA1501-1820", "12. Responsible consumption", "13. Climate action", "11. Sustainability", "Applied optics. Photonics", "TA1-2040", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://iris.unica.it/bitstream/11584/219983/1/FOSS4G-EU_2017_paper_31%20%283%29.pdf"}, {"href": "https://isprs-archives.copernicus.org/articles/XLII-4-W2/121/2017/isprs-archives-XLII-4-W2-121-2017.pdf"}, {"href": "https://doi.org/10.5194/isprs-archives-XLII-4-W2-121-2017"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20International%20Archives%20of%20the%20Photogrammetry%2C%20Remote%20Sensing%20and%20Spatial%20Information%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/isprs-archives-XLII-4-W2-121-2017", "name": "item", "description": "10.5194/isprs-archives-XLII-4-W2-121-2017", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/isprs-archives-XLII-4-W2-121-2017"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-07-05T00:00:00Z"}}, {"id": "10.3390/agronomy11050946", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:21:43Z", "type": "Journal Article", "created": "2021-05-11", "title": "Estimating Farm Wheat Yields from NDVI and Meteorological Data", "description": "

Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (Triticum aestivum) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R2 = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment.

", "keywords": ["yield estimation", "PREDICTION", "NDVI", "Triticum aestivum", "0703 Crop and Pasture Production", "3002 Agriculture", " land and farm management", "3004 Crop and pasture production", "Belgium", "0502 Environmental Science and Management", "Triticum aestivum", "2. Zero hunger", "Science & Technology", "S", "Plant Sciences", "Agriculture", "weather impact", "04 agricultural and veterinary sciences", "WINTER-WHEAT", "15. Life on land", "Agronomy", "winter wheat", "MODEL", "RESOLUTION", "SENTINEL-2", "0401 agriculture", " forestry", " and fisheries", "LANDSAT 8", "Life Sciences & Biomedicine"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/5/946/pdf"}, {"href": "https://doi.org/10.3390/agronomy11050946"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agronomy11050946", "name": "item", "description": "10.3390/agronomy11050946", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agronomy11050946"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-11T00:00:00Z"}}, {"id": "10.3390/agronomy9050255", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:21:45Z", "type": "Journal Article", "created": "2019-05-21", "title": "Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation", "description": "

Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield using a re-calibration data assimilation approach. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields. LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The assimilation of LAI in EPIC provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed. The diverging results obtained in the two years indicated that the assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.

", "keywords": ["2. Zero hunger", "yield estimation", "S", "Leaf Area Index", "EPIC model", "Agriculture", "Crop growth model", "04 agricultural and veterinary sciences", "15. Life on land", "crop growth model", "Yield estimation", "13. Climate action", "Leaf area index", "Data assimilation", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "data assimilation"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://doi.org/10.3390/agronomy9050255"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agronomy9050255", "name": "item", "description": "10.3390/agronomy9050255", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agronomy9050255"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-21T00:00:00Z"}}, {"id": "10.3390/ijgi10020102", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:21:51Z", "type": "Journal Article", "created": "2021-02-23", "title": "Machine Learning-Based Processing Proof-of-Concept Pipeline for Semi-Automatic Sentinel-2 Imagery Download, Cloudiness Filtering, Classifications, and Updates of Open Land Use/Land Cover Datasets", "description": "

Land use and land cover are continuously changing in today\u2019s world. Both domains, therefore, have to rely on updates of external information sources from which the relevant land use/land cover (classification) is extracted. Satellite images are frequent candidates due to their temporal and spatial resolution. On the contrary, the extraction of relevant land use/land cover information is demanding in terms of knowledge base and time. The presented approach offers a proof-of-concept machine-learning pipeline that takes care of the entire complex process in the following manner. The relevant Sentinel-2 images are obtained through the pipeline. Later, cloud masking is performed, including the linear interpolation of merged-feature time frames. Subsequently, four-dimensional arrays are created with all potential training data to become a basis for estimators from the scikit-learn library; the LightGBM estimator is then used. Finally, the classified content is applied to the open land use and open land cover databases. The verification of the provided experiment was conducted against detailed cadastral data, to which Shannon\u2019s entropy was applied since the number of cadaster information classes was naturally consistent. The experiment showed a good overall accuracy (OA) of 85.9%. It yielded a classified land use/land cover map of the study area consisting of 7188 km2 in the southern part of the South Moravian Region in the Czech Republic. The developed proof-of-concept machine-learning pipeline is replicable to any other area of interest so far as the requirements for input data are met.

", "keywords": ["Geography (General)", "0211 other engineering and technologies", "land use", "cloud masking", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "satellite imagery", "machine learning", "land cover", "Sentinel 2", "machine learning; land use; land cover; satellite imagery; Sentinel 2; image classification; cloud masking; LightGBM estimator", "G1-922", "0401 agriculture", " forestry", " and fisheries", "LightGBM estimator", "image classification"]}, "links": [{"href": "http://www.mdpi.com/2220-9964/10/2/102/pdf"}, {"href": "https://www.mdpi.com/2220-9964/10/2/102/pdf"}, {"href": "https://doi.org/10.3390/ijgi10020102"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/ISPRS%20International%20Journal%20of%20Geo-Information", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/ijgi10020102", "name": "item", "description": "10.3390/ijgi10020102", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/ijgi10020102"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-23T00:00:00Z"}}, {"id": "10.3390/plants13091212", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:21:57Z", "type": "Journal Article", "created": "2024-04-29", "title": "Precision Estimation of Crop Coefficient for Maize Cultivation Using High-Resolution Satellite Imagery to Enhance Evapotranspiration Assessment in Agriculture", "description": "

The estimation of crop evapotranspiration (ETc) is crucial for irrigation water management, especially in arid regions. This can be particularly relevant in the Po Valley (Italy), where arable lands suffer from drought damages on an annual basis, causing drastic crop yield losses. This study presents a novel approach for vegetation-based estimation of crop evapotranspiration (ETc) for maize. Three years of high-resolution multispectral satellite (Sentinel-2)-based Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Leaf Area Index (LAI) time series data were used to derive crop coefficients of maize in nine plots at the Acqua Campus experimental farm of Irrigation Consortium for the Emilia Romagna Canal (CER), Italy. Since certain vegetation indices (VIs) (such as NDVI) have an exponential nature compared to the other indices, both linear and power regression models were evaluated to estimate the crop coefficient (Kc). In the context of linear regression, the correlations between Food and Agriculture Organization (FAO)-based Kc and NDWI, NDRE, NDVI, and LAI-based Kc were 0.833, 0.870, 0.886, and 0.771, respectively. Strong correlation values in the case of power regression (NDWI: 0.876, NDRE: 0.872, NDVI: 0.888, LAI: 0.746) indicated an alternative approach to provide crop coefficients for the vegetation period. The VI-based ETc values were calculated using reference evapotranspiration (ET0) and VI-based Kc. The weather station data of CER were used to calculate ET0 based on Penman-Monteith estimation. Out of the Vis, NDWI and NDVI-based ETc performed the best both in the cases of linear (NDWI RMSE: 0.43 \u00b1 0.12; NDVI RMSE: 0.43 \u00b1 0.095) and power (NDWI RMSE: 0.44 \u00b1 0.116; NDVI RMSE: 0.44 \u00b1 0.103) approaches. The findings affirm the efficacy of the developed methodology in accurately assessing the evapotranspiration rate. Consequently, it offers a more refined temporal estimation of water requirements for maize cultivation in the region.

", "keywords": ["2. Zero hunger", "Botany", "04 agricultural and veterinary sciences", "vegetation index-based Kc", "15. Life on land", "01 natural sciences", "Article", "6. Clean water", "maize water demand", "QK1-989", "vegetation index-based crop evapotranspiration", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.mdpi.com/2223-7747/13/9/1212/pdf"}, {"href": "https://doi.org/10.3390/plants13091212"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Plants", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/plants13091212", "name": "item", "description": "10.3390/plants13091212", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/plants13091212"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-27T00:00:00Z"}}, {"id": "10.3390/rs12010072", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:21:58Z", "type": "Journal Article", "created": "2019-12-24", "title": "Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data", "description": "

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.

", "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/rs13091616", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:21:59Z", "type": "Journal Article", "created": "2021-04-22", "title": "Potential of Sentinel-2 Satellite Images for Monitoring Green Waste Compost and Manure Amendments in Temperate Cropland", "description": "

Increasing attention has been placed on the agroecological impact of applying exogenous organic matter (EOM) amendments, such as green waste compost (GWC) and livestock manure, to agricultural landscapes. However, monitoring the frequency and locality of this practice poses a major challenge, as these events are typically unreported. The purpose of this study is to evaluate the utility of Sentinel-2 imagery for the detection of EOM amendments. Specifically, we investigated the spectral shift resulting from GWC and manure application at two spatial scales, satellite and proximal. At the satellite scale, multispectral Sentinel-2 image pairs were analyzed before and after EOM application to six cultivated fields in the Versailles Plain, France. At the proximal scale, multi-temporal spectral field measurements were taken of experimental plots consisting of 14 total treatments: EOM variety, amendment quantity (15, 30 and 60 t.ha\u22121) and tillage. The Sentinel-2 images showed significant spectral differences before and after EOM application. Exogenous Organic Matter Indices (EOMI) were developed and analyzed for separative performance. The best performing index was EOMI2, using the B4 and B12 Sentinel-2 spectral bands. At the proximal scale, simulated Sentinel-2 reflectance spectra, which were created using field measurements, successfully monitored all EOM treatments for three days, except for the buried green waste compost at a rate of 15 t.ha\u22121.

", "keywords": ["agroecology", "reflectance", "[SPI] Engineering Sciences [physics]", "amendments", "Science", "[SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy", "[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study", "01 natural sciences", "7. Clean energy", "630", "[SPI]Engineering Sciences [physics]", "11. Sustainability", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study", "0105 earth and related environmental sciences", "[SDV.SA.AGRO] Life Sciences [q-bio]/Agricultural sciences/Agronomy", "2. Zero hunger", "[SDV.SA] Life Sciences [q-bio]/Agricultural sciences", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "soil organic carbon", "13. Climate action", "tillage", "0401 agriculture", " forestry", " and fisheries", "[SDE.IE] Environmental Sciences/Environmental Engineering", "Sentinel-2", "exogenous organic matter"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/9/1616/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/9/1616/pdf"}, {"href": "https://doi.org/10.3390/rs13091616"}, {"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/rs13091616", "name": "item", "description": "10.3390/rs13091616", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13091616"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-21T00:00:00Z"}}, {"id": "10.3390/rs13163181", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-27T16:22:00Z", "type": "Journal Article", "created": "2021-08-11", "title": "Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach", "description": "

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\uffe2\uff80\uff93surface energy balance. Surface albedo is a key land surface variable to constrain the surface radiation budget and hence the coupled water\uffe2\uff80\uff93surface 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 \uffe2\uff80\uff98k = 10\uffe2\uff80\uff99, and holdout). The more robust and accurate model parameters are those that represent the best statistical metrics (root mean square error \uffe2\uff80\uff98RMSE\uffe2\uff80\uff99, bias and correlation coefficient \uffe2\uff80\uff98R\uffe2\uff80\uff99). 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.

", "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/rs13173355", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:22:00Z", "type": "Journal Article", "created": "2021-08-25", "title": "Reviewing the Potential of Sentinel-2 in Assessing the Drought", "description": "

This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth\u2019s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.

", "keywords": ["land use and land cover analysis", "vegetation response", "Sentinel-2; drought; soil moisture; evapotranspiration; vegetation response; surface water and wetland analysis; land use and land cover analysis", "Science", "Q", "evapotranspiration", "0207 environmental engineering", "drought", "02 engineering and technology", "15. Life on land", "01 natural sciences", "6. Clean water", "surface water and wetland analysis", "13. Climate action", "Sentinel-2; drought", "Sentinel-2", "soil moisture", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.mdpi.com/2072-4292/13/17/3355/pdf"}, {"href": "https://doi.org/10.3390/rs13173355"}, {"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/rs13173355", "name": "item", "description": "10.3390/rs13173355", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13173355"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-24T00:00:00Z"}}, {"id": "10.3390/rs13214195", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:22:00Z", "type": "Journal Article", "created": "2021-10-20", "title": "Sentinel-2 Recognition of Uncovered and Plastic Covered Agricultural Soil", "description": "

Medium resolution satellite data, such as Sentinel-2 of the Copernicus programme, offer great new opportunities for the agricultural sector, and provide insights on soil surface characteristics and their management. Soil monitoring requires a high-quality dataset of uncovered and plastic covered agricultural soil. We developed a methodology to identify uncovered soil pixels in agricultural parcels during seedbed preparation and considered the impacts of clouds and shadows, vegetation cover, and artificial covers, such as those of greenhouses and plastic mulch films. We preserved the spatial and temporal integrity of parcels in the process and analysed spectral anomalies and their sources. The approach is based on freely available tools, namely Google Earth Engine and R Programming packages. We tested the methodology on the northern region of Belgium, which is characterised by small, fragmented parcels. We selected a period between mid-April to end-May, when active agricultural management practices leave the soil bare in preparation for the main cropping season. The spectral angle mapper was used to identify soil covered by non-plastic greenhouses or temporary soil covers, such as plastic mulch films. The effect of underlying soil on temporary covers was considered. The retrogressive plastic greenhouse index was used for detecting plastic greenhouses. The result was a high quality dataset of potential bare uncovered agricultural soil that allows further soil surface characterisation. This offered an improved understanding of the use of artificial covers, their spatial distribution, and their corresponding crops during the considered period. Artificial covers occurred most frequently in maize parcels. The approach resulted in precision values exceeding 0.9 for the detection of temporary covers and non-plastic greenhouses and a sensitivity value exceeding 0.95 for non-plastic and plastic greenhouses.

", "keywords": ["Technology", "SURFACE", "Science", "Environmental Sciences & Ecology", "TEXTURE", "artificial cover", "ALMERIA", "0203 Classical Physics", "soil", "Remote Sensing", "SUPPORT", "0909 Geomatic Engineering", "Geosciences", " Multidisciplinary", "Imaging Science & Photographic Technology", "agriculture", "2. Zero hunger", "plastic mulch", "Science & Technology", "IDENTIFICATION", "soil; agriculture; Sentinel-2; artificial cover; plastic mulch", "Q", "Geology", "04 agricultural and veterinary sciences", "15. Life on land", "CLOUD", "REFLECTANCE", "RESOLUTION", "13. Climate action", "Physical Sciences", "0401 agriculture", " forestry", " and fisheries", "4013 Geomatic engineering", "Sentinel-2", "GREENHOUSE", "0406 Physical Geography and Environmental Geoscience", "Life Sciences & Biomedicine", "3701 Atmospheric sciences", "Environmental Sciences", "3709 Physical geography and environmental geoscience"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/21/4195/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/21/4195/pdf"}, {"href": "https://doi.org/10.3390/rs13214195"}, {"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/rs13214195", "name": "item", "description": "10.3390/rs13214195", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13214195"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-20T00:00:00Z"}}, {"id": "10.3390/rs13245115", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:22:00Z", "type": "Journal Article", "created": "2021-12-16", "title": "Using Sentinel-2 Images for Soil Organic Carbon Content Mapping in Croplands of Southwestern France. The Usefulness of Sentinel-1/2 Derived Moisture Maps and Mismatches between Sentinel Images and Sampling Dates", "description": "

In agronomy, soil organic carbon (SOC) content is important for the development and growth of crops. From an environmental monitoring viewpoint, SOC sequestration is essential for mitigating the emission of greenhouse gases into the atmosphere. SOC dynamics in cropland soils should be further studied through various approaches including remote sensing. In order to predict SOC content over croplands in southwestern France (area of 22,177 km\u00b2), this study addresses (i) the influence of the dates on which Sentinel-2 (S2) images were acquired in the springs of 2017\u20132018 as well as the influence of the soil sampling period of a set of samples collected between 2005 and 2018, (ii) the use of soil moisture products (SMPs) derived from Sentinel-1/2 satellites to analyze the influence of surface soil moisture on model performance when included as a covariate, and (iii) whether the spatial distribution of SOC as mapped using S2 is related to terrain-derived attributes. The influences of S2 image dates and soil sampling periods were analyzed for bare topsoil. The dates of the S2 images with the best performance (RPD \u2265 1.7) were 6 April and 26 May 2017, using soil samples collected between 2016 and 2018. The soil sampling dates were also analyzed using SMP values. Soil moisture values were extracted for each sample and integrated into partial least squares regression (PLSR) models. The use of soil moisture as a covariate had no effect on the prediction performance of the models; however, SMP values were used to select the driest dates, effectively mapping topsoil organic carbon. S2 was able to predict high SOC contents in the specific soil types located on the old terraces (mesas) shaped by rivers flowing from the southwestern Pyr\u00e9n\u00e9es.

", "keywords": ["2. Zero hunger", "550", "soil organic carbon; sentinel-2; soil moisture; croplands; digital soil mapping; southwestern france; topographic wetness index; slaking crust sensitivity index", "sentinel-2", "Science", "Q", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "15. Life on land", "croplands", "630", "soil organic carbon", "southwestern france", "topographic wetness index", "13. Climate action", "digital soil mapping", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "soil moisture", "slaking crust sensitivity index"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/24/5115/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/24/5115/pdf"}, {"href": "https://doi.org/10.3390/rs13245115"}, {"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/rs13245115", "name": "item", "description": "10.3390/rs13245115", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13245115"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-16T00:00:00Z"}}, {"id": "10.3390/rs14246331", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:22:00Z", "type": "Journal Article", "created": "2022-12-15", "title": "Remote Sensing of Poplar Phenophase and Leaf Miner Attack in Urban Forests", "description": "

Remote sensing of phenology is adopted as the practice in greenery monitoring. Now research is turned towards the fusion of data from various sensors to fill in the gap in time series and allow monitoring of pests and disturbances. Poplar species were monitored for the determination of the best approach for detecting phenology and disturbances. With the adjustments that include a choice of indices, wavelengths, and a setup, a multispectral camera may be used to calibrate satellite images. The image processing pipeline included different denoising and interpolation methods. The correlation of the changes in a signal of top and lateral imaging proved that the contribution of the whole canopy is reflected in satellite images. Normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE) successfully distinguished among phenophases and detected leaf miner presence, unlike enhanced vegetation index (EVI). Changes in the indices were registered before, during, and after the development of the disease. NDRE is the most sensitive as it distinguished among the different intensities of damage caused by pests but it was not able to forecast its occurrence. An efficient and accurate system for detection and monitoring of phenology enables the improvement of the phenological models\u2019 quality and creates the basis for a forecast that allows planning in various disciplines.

", "keywords": ["data fusion", "Populus sp.", "Science", "Q", "multispectral imaging", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "Sentinel-2", "Fenusella hortulana (Klug\uff1b1818)", "15. Life on land", "phenology", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.mdpi.com/2072-4292/14/24/6331/pdf"}, {"href": "https://doi.org/10.3390/rs14246331"}, {"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/rs14246331", "name": "item", "description": "10.3390/rs14246331", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs14246331"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-14T00:00:00Z"}}, {"id": "10.3390/s17091966", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:22:01Z", "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": "

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.

", "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/s19040904", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:22:01Z", "type": "Journal Article", "created": "2019-02-22", "title": "Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI)", "description": "

The spatial quantification of green leaf area index (LAIgreen), the total green photosynthetically active leaf area per ground area, is a crucial biophysical variable for agroecosystem monitoring. The Sentinel-2 mission is with (1) a temporal resolution lower than a week, (2) a spatial resolution of up to 10 m, and (3) narrow bands in the red and red-edge region, a highly promising mission for agricultural monitoring. The aim of this work is to define an easy implementable LAIgreen index for the Sentinel-2 mission. Two large and independent multi-crop datasets of in situ collected LAIgreen measurements were used. Commonly used LAIgreen indices applied on the Sentinel-2 10 m \uffc3\uff97 10 m pixel resulted in a validation R2 lower than 0.6. By calculating all Sentinel-2 band combinations to identify high correlation and physical basis with LAIgreen, the new Sentinel-2 LAIgreen Index (SeLI) was defined. SeLI is a normalized index that uses the 705 nm and 865 nm centered bands, exploiting the red-edge region for low-saturating absorption sensitivity to photosynthetic vegetation. A R2 of 0.708 (root mean squared error (RMSE) = 0.67) and a R2 of 0.732 (RMSE = 0.69) were obtained with a linear fitting for the calibration and validation datasets, respectively, outperforming established indices. Sentinel-2 LAIgreen maps are presented.

", "keywords": ["2. Zero hunger", "leaf area index", "Chemical technology", "0211 other engineering and technologies", "TP1-1185", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "crops", "7. Clean energy", "Article", "remote sensing", "13. Climate action", "vegetation indices", "red-edge", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/19/4/904/pdf"}, {"href": "https://doi.org/10.3390/s19040904"}, {"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/s19040904", "name": "item", "description": "10.3390/s19040904", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s19040904"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-02-21T00:00:00Z"}}, {"id": "10.3390/s21217406", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:22:01Z", "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": "

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.

", "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/s25072239", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-27T16:22:02Z", "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": "

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.

", "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"}}], "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&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&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&", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Sentinel&offset=50", "hreflang": "en-US"}], "numberMatched": 119, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-07-28T04:00:50.623897Z"}