{"type": "FeatureCollection", "features": [{"id": "10.1007/978-3-030-84144-7_7", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:14:46Z", "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.2024.103659", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:17:04Z", "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": "10.1016/j.agee.2022.108124", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:16:02Z", "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.geoderma.2022.115935", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:17:00Z", "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 R<sup>2</sup> of 0.53 and a LCCC of 0.65 in training dataset, with an R<sup>2</sup> of 0.51 and a LCCC of 0.73 in testing dataset. And it shows contribution in random forest regression with an R<sup>2</sup> of 0.80 and a LCCC of 0.86 in training dataset, with an R<sup>2</sup> 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.still.2024.106125", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:17:54Z", "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": "10.1109/igarss.2018.8518170", "type": "Feature", "geometry": null, "properties": {"license": "Restricted", "updated": "2026-06-23T16:19:15Z", "type": "Journal Article", "created": "2018-11-16", "title": "Sentinel-1 & Sentinel-2 for SOIL Moisture Retrieval at Field Scale", "description": "Soil moisture content is an essential climate variable that is operationally delivered at low resolution (e.g. 36-9 km) by earth observation missions, such as ESA/SMOS, NASA/SMAP and EUMETSAT/ASCAT. However numerous land applications would benefit from the availability of soil moisture maps at higher resolution. For this reason, there is a large research effort to develop soil moisture products at higher resolution using, for instance, data acquired by the new ESA's Sentinel missions. The objective of this study is twofold. First, it presents the validation status of a pre-operational soil moisture product derived from Sentinel-1 at 1 km resolution. Second, it assesses the possibility of integrating Sentinel-2 data and additional ancillary information, such as parcel borders and high resolution soil texture maps, in order to obtain soil moisture maps at 'field scale' resolution, i.e. similar to 0.1 km Case studies concerning agricultural sites located in Europe are presented.", "keywords": ["ASCAT", "high resolution", "13. Climate action", "0211 other engineering and technologies", "Sentinel-1", "SMAP", "02 engineering and technology", "Soil moisture content", "Sentinel-2", "15. Life on land", "01 natural sciences", "SMOS", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/8496405/8517275/08518170.pdf?arnumber=8518170"}, {"href": "https://doi.org/10.1109/igarss.2018.8518170"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IGARSS%202018%20-%202018%20IEEE%20International%20Geoscience%20and%20Remote%20Sensing%20Symposium", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/igarss.2018.8518170", "name": "item", "description": "10.1109/igarss.2018.8518170", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/igarss.2018.8518170"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-07-01T00:00:00Z"}}, {"id": "10.1109/igarss.2018.8519103", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:19:15Z", "type": "Journal Article", "created": "2018-11-16", "title": "Sentinel-1 & Sentinel-2 Data for Soil Tillage Change Detection", "description": "In this paper, an algorithm using Sentinel-1 (S-1) and Sentinel-2 (S-2) data to identify changes of tillage over agricultural fields at approximately similar to 100m resolution is presented. The methodology implements a multiscale temporal change detection on S-1 VH backscatter in order to single out VH changes due to agricultural practices only. The algorithm can be applied over bare or scarcely vegetated agricultural fields, which are identified from S-2 NDVI measurements. An initial assessment at farm scale using in situ and S-1 and SPOT5-Take5 data, acquired over the Apulian Tavoliere in southern Italy in 2015, is illustrated. A full validation of the approach is in progress over three European agricultural areas located in Italy, Spain and France. Results will be further reported in the paper.", "keywords": ["0106 biological sciences", "0301 basic medicine", "2. Zero hunger", "03 medical and health sciences", "soil tillage change identification", "Sentinel-1", "Sentinel-2", "01 natural sciences", "3. Good health"]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/8496405/8517275/08519103.pdf?arnumber=8519103"}, {"href": "https://doi.org/10.1109/igarss.2018.8519103"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IGARSS%202018%20-%202018%20IEEE%20International%20Geoscience%20and%20Remote%20Sensing%20Symposium", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/igarss.2018.8519103", "name": "item", "description": "10.1109/igarss.2018.8519103", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/igarss.2018.8519103"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-07-01T00:00:00Z"}}, {"id": "10.12688/openreseurope.13135.2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:20:08Z", "type": "Journal Article", "created": "2021-09-06", "title": "A Google Earth Engine-enabled Python approach for the identification of anthropogenic palaeo-landscape features", "description": "<ns4:p>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).</ns4:p>", "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": "10.3390/rs11091106", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:50Z", "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": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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/rs13020305", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:51Z", "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": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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/rs14030714", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:52Z", "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": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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/rs8110938", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:52Z", "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": "<p>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).</p>", "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/plants13091212", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:21:49Z", "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": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "keywords": ["2. Zero hunger", "Botany", "04 agricultural and veterinary sciences", "vegetation index-based K<sub>c</sub>", "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/agronomy11050946", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:38Z", "type": "Journal Article", "created": "2021-05-11", "title": "Estimating Farm Wheat Yields from NDVI and Meteorological Data", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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", "<i>Triticum aestivum</i>", "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": {"license": "Open Access", "updated": "2026-06-23T16:21:39Z", "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": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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/land11091397", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:45Z", "type": "Journal Article", "created": "2022-08-26", "title": "Evaluation of Accuracy Enhancement in European-Wide Crop Type Mapping by Combining Optical and Microwave Time Series", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This investigation evaluates the potential of combining Copernicus Sentinel-1 (S1) and Sentinel-2 (S2) satellite data in producing a detailed Land Use and Land Cover (LULC) map with 19 crop type classes and 2 broader categories containing Woodland/Shrubland and Grassland over 28 Member States of Europe (EU-28). The Eurostat Land Use and Coverage Area Frame Survey (LUCAS) 2018 dataset is employed as ground truth for model training and validation. Monthly and yearly optical features from S2 spectral reflectance and spectral indices, alongside decadal (10-days) composites from an S1 microwave sensor, are extracted for the EU-28 territory for 2018 using Google Earth Engine (GEE). Five different feature sets using a mixture of indicators were created as input training data. A Random Forest (RF) machine learning algorithm was applied to classify these feature sets, and the generated classification models were compared using an identical validation dataset. Results show that S1 and S2 yearly features together are able to provide a full coverage map less dependent on cloud effects and having appropriate overall accuracy (OA). Based on this feature set, the 21 classes could be classified with an OA of 78.3% using the independent validation data set. The OA increases to 82.7% by grouping 21 classes into 8 broader categories. The comparison with similar studies using individual S1 and S2 data indicates that combining S1 and S2 time series can attain slightly better results while enhancing spatial coverage.</p></article>", "keywords": ["LUCAS 2018", "S", "0211 other engineering and technologies", "Agriculture", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "crop type classification", "machine learning", "13. Climate action", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "time series", "Google Earth Engine"]}, "links": [{"href": "https://www.mdpi.com/2073-445X/11/9/1397/pdf"}, {"href": "https://doi.org/10.3390/land11091397"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/land11091397", "name": "item", "description": "10.3390/land11091397", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/land11091397"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-08-25T00:00:00Z"}}, {"id": "10.3390/rs12142299", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:51Z", "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": "<p>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.</p>", "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/rs13091616", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:51Z", "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": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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/rs13173355", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:21:51Z", "type": "Journal Article", "created": "2021-08-25", "title": "Reviewing the Potential of Sentinel-2 in Assessing the Drought", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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": "2026-06-23T16:21:51Z", "type": "Journal Article", "created": "2021-10-20", "title": "Sentinel-2 Recognition of Uncovered and Plastic Covered Agricultural Soil", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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": "2026-06-23T16:21:52Z", "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": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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": {"license": "Open Access", "updated": "2026-06-23T16:21:52Z", "type": "Journal Article", "created": "2022-12-15", "title": "Remote Sensing of Poplar Phenophase and Leaf Miner Attack in Urban Forests", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "keywords": ["data fusion", "<i>Populus</i> sp.", "Science", "Q", "multispectral imaging", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "Sentinel-2", "<i>Fenusella hortulana</i> (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/rs16091510", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:52Z", "type": "Journal Article", "created": "2024-04-25", "title": "Remote Quantification of Soil Organic Carbon: Role of Topography in the Intra-Field Distribution", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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/s17091966", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:53Z", "type": "Journal Article", "created": "2017-08-28", "title": "Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The recent deployment of ESA\u2019s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015\u2013November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.</p></article>", "keywords": ["[SDE] Environmental Sciences", "NDVI", "Chemical technology", "HUMIDITE DU SOL", "soil moisture; SAR; Sentinel-1; NDVI; Sentinel-2; change detection", "0211 other engineering and technologies", "soil water content", "TP1-1185", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Article", "remote sensing", "Sentinel-1", "cartography", "soil moisture", "Sentinel-2", "TELEDETECTION", "change detection", "CARTOGRAPHIE", "SAR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/17/9/1966/pdf"}, {"href": "https://doi.org/10.3390/s17091966"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/s17091966", "name": "item", "description": "10.3390/s17091966", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s17091966"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-08-26T00:00:00Z"}}, {"id": "10.3390/s19040904", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:53Z", "type": "Journal Article", "created": "2019-02-22", "title": "Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI)", "description": "<p>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.</p>", "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": "10.5194/egusphere-egu2020-21951", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:22:32Z", "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": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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", "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/10.5194/egusphere-egu2020-21951"}, {"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": "10.5194/egusphere-egu2020-21951", "name": "item", "description": "10.5194/egusphere-egu2020-21951", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/egusphere-egu2020-21951"}, {"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": "10.5281/zenodo.11188379", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-06-23T16:23:03Z", "type": "Dataset", "title": "Paired Vegetation and Soil Burn Severity Metrics and Associated Climate, Weather, Topographical, and Land Cover Attributes", "description": "unspecifiedThis dataset pairs differenced Normalized Burn Ratio (dNBR) and soil burn severity (SBS) for 254 large (>400 ha in size) fires across the western US. Dataset also includes climate, weather, topography, physical and chemical soil characteristics, and land cover attributes of each burned pixel at the time of fire. This effort provided a table of 16.3 million burned pixels and their associated characteristics including dNBR, SBS, and 94 biological and physical covariates. After removing correlated features, the final data includes 18 fire covariates namely: dNBR, elevation, slope, aspect, land cover type, wind speed, energy release component, vapor pressure deficit, annual precipitation, and annual average daily max temperature, as well as the clay, sand and silt content of the soil and volumetric fraction of coarse fragments and soil organic carbon content. We also included spatial coherence metrices for dNBR, including DVAR, SHADE and SAVG. This data is provided as CSV files in Xtrain, Xvalidation, Xtest, as well as Ytrain, Yvalidation, and Ytest; in which X files (model input) provide all features except for SBS and Y files (model output) include SBS. We also provided this data for an additional 16 large fires across the western US ('Extra Test' folder, including Dataset \u2013 X file \u2013 and Label \u2013 Y file). Finally, the trained XGBoost model to translate dNBR to SBS using the associated features is also provided in this folder.", "keywords": ["Remote Sensing", "Soil Burn Severity Metrics", "13. Climate action", "Vegetation Burn Severity", "Climate", "DEM", "15. Life on land", "Wildfire", "Sentinel-2", "Fire", "Land Cover", "Weather", "Landsat"], "contacts": [{"organization": "Seydi, Seyd Teymoor", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.11188379"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.11188379", "name": "item", "description": "10.5281/zenodo.11188379", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.11188379"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-09-11T00:00:00Z"}}, {"id": "2944731604", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:27:00Z", "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": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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/2944731604"}, {"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": "2944731604", "name": "item", "description": "2944731604", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2944731604"}, {"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": "10.5281/zenodo.4384105", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:24:20Z", "type": "Software", "title": "A Colab-Python script code to identify palaeo-landscape features", "description": "Open Access{'references': ['1. Python Software Foundation. Python Language Reference. 2020. Available: http://www.python.org', '2. Wu Q. geemap: A Python package for interactive mapping with Google Earth Engine. Journal of Open Source Software. 2020;5: 2305', '3. Bisong E. Google Colaboratory. In: Bisong E, editor. Building Machine Learning and Deep Learning Models on Google Cloud Platform: u00a0 u00a0  u00a0A Comprehensive Guide for Beginners. Berkeley, CA: Apress; 2019. pp. 59 u201364', '4. Project Jupyter. Jupyter Notebook. 2020. Available: https://jupyter.org/', '5. QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. 2019. u00a0  u00a0  u00a0Available: https://www.qgis.org/en/site/index.html', '6. Gillies S et al. Rasterio: geospatial raster I/O for Python programmers. Mapbox; 2013. Available: https://github.com/mapbox/rasterio', '7. Hunter JD. Matplotlib: A 2D Graphics Environment. Comput Sci Eng. 2007;9: 90 u201395.']}", "keywords": ["Remote Sensing", "Multispectral analysis", "Landscape Archaeology", "Spectral decomposition", "15. Life on land", "Sentinel-2", "Riverscape", "Fluvial and Alluvial Archaeology", "12. Responsible consumption", "Python"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4384105"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4384105", "name": "item", "description": "10.5281/zenodo.4384105", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4384105"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-22T00:00:00Z"}}, {"id": "10.5281/zenodo.5235030", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:24:23Z", "type": "Software", "title": "A Colab-Python script code to identify palaeo-landscape features", "description": "Open Access{'references': ['1. Python Software Foundation. Python Language Reference. 2020. Available: http://www.python.org', '2. Wu Q. geemap: A Python package for interactive mapping with Google Earth Engine. Journal of Open Source Software. 2020;5: 2305', '3. Bisong E. Google Colaboratory. In: Bisong E, editor. Building Machine Learning and Deep Learning Models on Google Cloud Platform: u00a0 u00a0  u00a0A Comprehensive Guide for Beginners. Berkeley, CA: Apress; 2019. pp. 59 u201364', '4. Project Jupyter. Jupyter Notebook. 2020. Available: https://jupyter.org/', '5. QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. 2019. u00a0  u00a0  u00a0Available: https://www.qgis.org/en/site/index.html', '6. Gillies S et al. Rasterio: geospatial raster I/O for Python programmers. Mapbox; 2013. Available: https://github.com/mapbox/rasterio', '7. Hunter JD. Matplotlib: A 2D Graphics Environment. Comput Sci Eng. 2007;9: 90 u201395.']}", "keywords": ["Remote Sensing", "Multispectral analysis", "Landscape Archaeology", "Spectral decomposition", "15. Life on land", "Sentinel-2", "Riverscape", "Fluvial and Alluvial Archaeology", "12. Responsible consumption", "Python"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5235030"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5235030", "name": "item", "description": "10.5281/zenodo.5235030", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5235030"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-22T00:00:00Z"}}, {"id": "10.5281/zenodo.5509889", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:24:23Z", "type": "Journal Article", "created": "2021-08-24", "title": "Reviewing the Potential of Sentinel-2 in Assessing the Drought", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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/10.5281/zenodo.5509889"}, {"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": "10.5281/zenodo.5509889", "name": "item", "description": "10.5281/zenodo.5509889", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5509889"}, {"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": "10.5281/zenodo.5597222", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:24:24Z", "type": "Report", "title": "Accuracy assessment of early- and late-season crop classification using optical and SAR imagery", "description": "<em>Reliable early-season crop classification provides necessary input for storage planning, logistics optimization, sales forecasting and setting adequate government policies. The objective of this research was to evaluate crop classification models at different points in time using SAR (Synthetic Aperture Radar) and optical satellite images. SAR and optical images used in the study came from ESA's Sentinel-1 and Sentinel-2 satellites, respectively. A total of 7 cloud-free Sentinel-2 and 50 Sentinel-1 images were used to obtain the time-series necessary for seasonal crop classification. For the classifier training purpose ground truth was collected through the conducted data collection campaign, which consisted of information about the location and crop type for over 1400 parcels of 5 most important crops on the plains of Vojvodina in northern Serbia, where the study was conducted. These are: maize, wheat, soybeans, sugar beet and sunflower, which represented labels for classification. Pixel-based Random Forest (RF) algorithm proved to be a very good solution for the classification problem of the large scale datasets. The final labeled dataset was used for training and testing of RF classifier in different classification scenarios. Early-season crop classification was performed at the end of May, when the crops of interest reached the desired growth stage, while the late-season crop classification was performed at the end of August. Fusion of SAR and optical images gave the overall accuracy of 75.93% for early-season crop classification, while the late-season crop classification accuracy was 89.75%. Using individual sources, accuracies were 68.23% and 75.29%, for SAR and optical images, while the late-season accuracies were 88.37% and 88.88%, respectively. In order to achieve more accurate classification results, various vegetation indices can be integrated in further research.</em>", "keywords": ["2. Zero hunger", "Classification", " Random Forest", " Sentinel-1", " Sentinel-2", "15. Life on land"], "contacts": [{"organization": "Branislav Pejak, Milos Pandzic, Predrag Lugonja, Oskar Marko,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5597222"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5597222", "name": "item", "description": "10.5281/zenodo.5597222", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5597222"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-03-21T00:00:00Z"}}, {"id": "10.5281/zenodo.5597223", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:24:24Z", "type": "Report", "title": "Accuracy assessment of early- and late-season crop classification using optical and SAR imagery", "description": "<em>Reliable early-season crop classification provides necessary input for storage planning, logistics optimization, sales forecasting and setting adequate government policies. The objective of this research was to evaluate crop classification models at different points in time using SAR (Synthetic Aperture Radar) and optical satellite images. SAR and optical images used in the study came from ESA's Sentinel-1 and Sentinel-2 satellites, respectively. A total of 7 cloud-free Sentinel-2 and 50 Sentinel-1 images were used to obtain the time-series necessary for seasonal crop classification. For the classifier training purpose ground truth was collected through the conducted data collection campaign, which consisted of information about the location and crop type for over 1400 parcels of 5 most important crops on the plains of Vojvodina in northern Serbia, where the study was conducted. These are: maize, wheat, soybeans, sugar beet and sunflower, which represented labels for classification. Pixel-based Random Forest (RF) algorithm proved to be a very good solution for the classification problem of the large scale datasets. The final labeled dataset was used for training and testing of RF classifier in different classification scenarios. Early-season crop classification was performed at the end of May, when the crops of interest reached the desired growth stage, while the late-season crop classification was performed at the end of August. Fusion of SAR and optical images gave the overall accuracy of 75.93% for early-season crop classification, while the late-season crop classification accuracy was 89.75%. Using individual sources, accuracies were 68.23% and 75.29%, for SAR and optical images, while the late-season accuracies were 88.37% and 88.88%, respectively. In order to achieve more accurate classification results, various vegetation indices can be integrated in further research.</em>", "keywords": ["2. Zero hunger", "Classification", " Random Forest", " Sentinel-1", " Sentinel-2", "15. Life on land"], "contacts": [{"organization": "Pejak, Branislav, Pandzic, Milos, Lugonja, Predrag, Marko, Oskar,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5597223"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5597223", "name": "item", "description": "10.5281/zenodo.5597223", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5597223"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-03-21T00:00:00Z"}}, {"id": "10.5281/zenodo.7079708", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:24:34Z", "type": "Report", "title": "Sentinel-2 and Landsat-8 for High-Resolution Land Cover Mapping in Sustainable Agriculture", "description": "Land cover mapping has become an increasingly important source of information in agriculture. Farmers use it for on-field decision-making, retailers and stock traders for planning and governments for making agricultural strategies and setting subsidy levels. Besides agricultural stakeholders, there are biologists and environmental scientists who use this kind of information for monitoring the quality of habitats.There are a number of optical EO satellites which offer images free of charge, with Sentinel-2, a part of ESA\u2019s Copernicus programme, and Landsat-8, launched by NASA and USGS, being the most popular ones. This work focused on their application in land cover mapping in northern Serbia. Using joint information from these satellites we improved the system in many aspects. Data fusion allowed us to have images from more dates available. In this way we decreased the risk of misclassification due to missing data caused by high cloud coverage. Also, it allowed us to train a more accurate classifier compared to those trained on individual satellites. Finally, the spatial resolution of resulting maps was higher than the resolution of input images. This is extremely important for the observed region which is mostly constituted of small fields, 400 x 60 m in average. The database included more than 3 billion pixels with around 200 features each, i.e. all image channels from key dates between January and September. Classifiers were trained to distinguish between following crops: maize, wheat, sunflower, sugar beet and soybean, as well as forest and water bodies. Random forest proved to be the best classification algorithm, in terms of accuracy, speed and ability to deal with missing data. In classification of forest and water bodies, accuracy of up to 97 - 98% was achieved even without data fusion. However, since crop classification is a more difficult problem, performances of Sentinel and Landsat based classifiers could not match the performance of the joint classifier. Data fusion increased the overall system accuracy through the increase of average accuracy over all classes, as well as through more equal distribution of accuracy values over categories, in addition to higher spatial resolution of final decisions. The most significant improvement was observed in soybean classification, where Sentinel, Landsat and joint classifiers achieved accuracies of 84%, 87%, 89%, respectively. Other crops, such as sugar beet and wheat, which could be accurately classified with Sentinel and Landsat, were not improved further. This work is a step towards next year\u2019s case, when besides these two satellites, Sentinel-2b will be available. It will cut the revisit time of Sentinels to only 6 days meaning that there will be even more data available and even better classification performance can be expected. The system developed in this research is intended to be a part of a broader geo service. This service would offer solutions customised for a vast variety of users, utilising the full potential of land cover mapping.", "keywords": ["2. Zero hunger", "Land cover mapping", " Sentinel-2", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Predrag Lugonja, Oskar Marko, Marko Pani\u0107, Branko Brklja\u010d, Sanja Brdar, Vladimir Crnojevi\u0107,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7079708"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7079708", "name": "item", "description": "10.5281/zenodo.7079708", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7079708"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-03-16T00:00:00Z"}}, {"id": "10.5281/zenodo.7079709", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:24:34Z", "type": "Report", "title": "Sentinel-2 and Landsat-8 for High-Resolution Land Cover Mapping in Sustainable Agriculture", "description": "Land cover mapping has become an increasingly important source of information in agriculture. Farmers use it for on-field decision-making, retailers and stock traders for planning and governments for making agricultural strategies and setting subsidy levels. Besides agricultural stakeholders, there are biologists and environmental scientists who use this kind of information for monitoring the quality of habitats.There are a number of optical EO satellites which offer images free of charge, with Sentinel-2, a part of ESA\u2019s Copernicus programme, and Landsat-8, launched by NASA and USGS, being the most popular ones. This work focused on their application in land cover mapping in northern Serbia. Using joint information from these satellites we improved the system in many aspects. Data fusion allowed us to have images from more dates available. In this way we decreased the risk of misclassification due to missing data caused by high cloud coverage. Also, it allowed us to train a more accurate classifier compared to those trained on individual satellites. Finally, the spatial resolution of resulting maps was higher than the resolution of input images. This is extremely important for the observed region which is mostly constituted of small fields, 400 x 60 m in average. The database included more than 3 billion pixels with around 200 features each, i.e. all image channels from key dates between January and September. Classifiers were trained to distinguish between following crops: maize, wheat, sunflower, sugar beet and soybean, as well as forest and water bodies. Random forest proved to be the best classification algorithm, in terms of accuracy, speed and ability to deal with missing data. In classification of forest and water bodies, accuracy of up to 97 - 98% was achieved even without data fusion. However, since crop classification is a more difficult problem, performances of Sentinel and Landsat based classifiers could not match the performance of the joint classifier. Data fusion increased the overall system accuracy through the increase of average accuracy over all classes, as well as through more equal distribution of accuracy values over categories, in addition to higher spatial resolution of final decisions. The most significant improvement was observed in soybean classification, where Sentinel, Landsat and joint classifiers achieved accuracies of 84%, 87%, 89%, respectively. Other crops, such as sugar beet and wheat, which could be accurately classified with Sentinel and Landsat, were not improved further. This work is a step towards next year\u2019s case, when besides these two satellites, Sentinel-2b will be available. It will cut the revisit time of Sentinels to only 6 days meaning that there will be even more data available and even better classification performance can be expected. The system developed in this research is intended to be a part of a broader geo service. This service would offer solutions customised for a vast variety of users, utilising the full potential of land cover mapping.", "keywords": ["2. Zero hunger", "Land cover mapping", " Sentinel-2", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Lugonja, Predrag, Marko, Oskar, Pani\u0107, Marko, Brklja\u010d, Branko, Brdar, Sanja, Crnojevi\u0107, Vladimir,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7079709"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7079709", "name": "item", "description": "10.5281/zenodo.7079709", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7079709"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-03-16T00:00:00Z"}}, {"id": "10.5281/zenodo.7079582", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:24:34Z", "type": "Report", "title": "Satellite based estimation of the arable topsoil texture at regional scale using Sentinel-2 data", "description": "Although satellite imaging has been present as a source of valuable spatial data for a long time, it was not until very recently that high quality satellite imagery products produced by high resolution multispectral instruments became affordable and broadly available. On the other hand, information contained in such measurements proved to have significant impact on the overall improvement of the best practices in agricultural production and environmental monitoring. One of the applications that could benefit from the large scale satellite based measurements is characterization of topsoil properties of arable land. More exactly, bare soil spectra acquired by multispectral instruments can directly provide information about soil texture, represented by the content of clay, sand, or silt, over the observed vegetation free area. There have been a few attempts to investigate such possibilities in the context of the current and forthcoming multispectral and hyperspectral imagers. In a recently published study, a comprehensive evaluation of the capabilities of several imagers in the task of soil texture estimation was performed. However, those findings were based only on the simulated and resampled spectral responses derived from the soil spectral signature libraries acquired under controlled laboratory conditions using high precision hyperspectral instruments. Among the simulated imagers was also Sentinel-2 MSI. In line with these efforts, aim of this paper is to further investigate applicability of this instrument in the real working environment, characterized by the challenging factors introduced by the atmosphere, tillage and plant remains, missing data due to cloud coverage, variable soil moisture as a consequence of climate and volatile weather conditions, as well as natural soil spatial variability, due to the large spatial extent of the performed analysis.", "keywords": ["2. Zero hunger", "13. Climate action", "Sentinel-2", " Satellite imaging", " Topsoil texture", " Estimation", "15. Life on land"], "contacts": [{"organization": "Predrag Lugonja, Branko Brklja\u010d, Vladimir \u0106iri\u0107, Pavel Benka, Vladimir Crnojevi\u0107,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7079582"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7079582", "name": "item", "description": "10.5281/zenodo.7079582", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7079582"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-09-28T00:00:00Z"}}, {"id": "10.5281/zenodo.7079583", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:24:34Z", "type": "Report", "title": "Satellite based estimation of the arable topsoil texture at regional scale using Sentinel-2 data", "description": "Although satellite imaging has been present as a source of valuable spatial data for a long time, it was not until very recently that high quality satellite imagery products produced by high resolution multispectral instruments became affordable and broadly available. On the other hand, information contained in such measurements proved to have significant impact on the overall improvement of the best practices in agricultural production and environmental monitoring. One of the applications that could benefit from the large scale satellite based measurements is characterization of topsoil properties of arable land. More exactly, bare soil spectra acquired by multispectral instruments can directly provide information about soil texture, represented by the content of clay, sand, or silt, over the observed vegetation free area. There have been a few attempts to investigate such possibilities in the context of the current and forthcoming multispectral and hyperspectral imagers. In a recently published study, a comprehensive evaluation of the capabilities of several imagers in the task of soil texture estimation was performed. However, those findings were based only on the simulated and resampled spectral responses derived from the soil spectral signature libraries acquired under controlled laboratory conditions using high precision hyperspectral instruments. Among the simulated imagers was also Sentinel-2 MSI. In line with these efforts, aim of this paper is to further investigate applicability of this instrument in the real working environment, characterized by the challenging factors introduced by the atmosphere, tillage and plant remains, missing data due to cloud coverage, variable soil moisture as a consequence of climate and volatile weather conditions, as well as natural soil spatial variability, due to the large spatial extent of the performed analysis.", "keywords": ["2. Zero hunger", "13. Climate action", "Sentinel-2", " Satellite imaging", " Topsoil texture", " Estimation", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7079583"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7079583", "name": "item", "description": "10.5281/zenodo.7079583", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7079583"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-09-28T00:00:00Z"}}, {"id": "10.5281/zenodo.7079648", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:24:34Z", "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/10.5281/zenodo.7079648"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7079648", "name": "item", "description": "10.5281/zenodo.7079648", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7079648"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8091840", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:24:42Z", "type": "Journal Article", "created": "2022-08-26", "title": "Evaluation of Accuracy Enhancement in European-Wide Crop Type Mapping by Combining Optical and Microwave Time Series", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This investigation evaluates the potential of combining Copernicus Sentinel-1 (S1) and Sentinel-2 (S2) satellite data in producing a detailed Land Use and Land Cover (LULC) map with 19 crop type classes and 2 broader categories containing Woodland/Shrubland and Grassland over 28 Member States of Europe (EU-28). The Eurostat Land Use and Coverage Area Frame Survey (LUCAS) 2018 dataset is employed as ground truth for model training and validation. Monthly and yearly optical features from S2 spectral reflectance and spectral indices, alongside decadal (10-days) composites from an S1 microwave sensor, are extracted for the EU-28 territory for 2018 using Google Earth Engine (GEE). Five different feature sets using a mixture of indicators were created as input training data. A Random Forest (RF) machine learning algorithm was applied to classify these feature sets, and the generated classification models were compared using an identical validation dataset. Results show that S1 and S2 yearly features together are able to provide a full coverage map less dependent on cloud effects and having appropriate overall accuracy (OA). Based on this feature set, the 21 classes could be classified with an OA of 78.3% using the independent validation data set. The OA increases to 82.7% by grouping 21 classes into 8 broader categories. The comparison with similar studies using individual S1 and S2 data indicates that combining S1 and S2 time series can attain slightly better results while enhancing spatial coverage.</p></article>", "keywords": ["LUCAS 2018", "S", "0211 other engineering and technologies", "Agriculture", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "crop type classification", "machine learning", "13. Climate action", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "time series", "Google Earth Engine"]}, "links": [{"href": "https://www.mdpi.com/2073-445X/11/9/1397/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8091840"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8091840", "name": "item", "description": "10.5281/zenodo.8091840", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8091840"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-08-25T00:00:00Z"}}, {"id": "10.5281/zenodo.7763668", "type": "Feature", "geometry": null, "properties": {"license": "Restricted", "updated": "2026-06-23T16:24:39Z", "type": "Dataset", "title": "Sentinel-2 based maps of irrigated fields in Vojvodina, Serbia", "description": "RestrictedThis dataset consists of classified irrigated fields of the three most irrigated crops in Vojvodina (Serbia): maize, soybean, and sugar beet. Maps were generated for three years (2020, 2021, 2022), characterized by different climate conditions using Sentinel-2 satellite data and machine learning algorithms. All maps are in GIS format (.tiff) where label 0 corresponds to non-irrigated fields while label 1 corresponds to irrigated fields in Vojvodina and as if could be used for further research.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "Sentinel-2", "Irrigation"], "contacts": [{"organization": "Radulovi\u0107 Mirjana, Brdar Sanja, Pejak Branislav, Lugonja Predrag, Athanasiadis Ioannis, Pajevi\u0107 Nina, Pavi\u0107 Dragoslav, Crnojevi\u0107 Vladimir,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7763668"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7763668", "name": "item", "description": "10.5281/zenodo.7763668", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7763668"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8091638", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:24:42Z", "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": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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/10.5281/zenodo.8091638"}, {"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": "10.5281/zenodo.8091638", "name": "item", "description": "10.5281/zenodo.8091638", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8091638"}, {"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": "10.5281/zenodo.8092676", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:24:43Z", "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 R<sup>2</sup> of 0.53 and a LCCC of 0.65 in training dataset, with an R<sup>2</sup> of 0.51 and a LCCC of 0.73 in testing dataset. And it shows contribution in random forest regression with an R<sup>2</sup> of 0.80 and a LCCC of 0.86 in training dataset, with an R<sup>2</sup> 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/10.5281/zenodo.8092676"}, {"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": "10.5281/zenodo.8092676", "name": "item", "description": "10.5281/zenodo.8092676", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8092676"}, {"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": "15e1c633a10b24550f29f930a53fc295", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:26:10Z", "type": "Report", "title": "The European space agency world soils monitoring system", "keywords": ["remote sensing", "Soil organic carbon", "Sentinel-2", "soil spectral libraries"], "contacts": [{"organization": "Yag\u00fce, M.J., Sanz, Adrian, Poggio, Laura, Wesemael, Bas van, Tziolas, Nikos, Chabrillat, Sabine, Heiden, Uta, Gholizadeh, Asa, Ben-Dor, Eyal,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/15e1c633a10b24550f29f930a53fc295"}, {"rel": "self", "type": "application/geo+json", "title": "15e1c633a10b24550f29f930a53fc295", "name": "item", "description": "15e1c633a10b24550f29f930a53fc295", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/15e1c633a10b24550f29f930a53fc295"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "20.500.11769/552491", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:26:26Z", "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": "Current water scarcity scenario has led to the implementation of sustainable agricultural practices intended to improve water use efficiency. The present work evaluates during three agricultural campaigns (2018-2020) the response of a young almond orchard to two management practices in terms by combining remote sensing indexes (Normalized Difference Vegetation Index, NDVI; and Soil Adjusted Vegetation Indexes, SAVI) and physiological/ morphological measurement (stem water potential, \u03a8stem; trunk perimeter and canopy diameter). The management practices included (I) sustained deficit irrigation and (II) soil management. Severe deficit irrigation resulted in lower vegetation indexes (VI) values, \u03a8stem and tree dimensions (13 %, 23 % and 14 % lower, respectively) than those obtained for full irrigation strategy; whereas moderate deficit irrigation did not affect any of the parameters analysed. The presence of vegetation cover in the inter-row resulted in a VIs increase (19-42 %) and in lower tree dimensions (reductions of 7-8 % for trunk perimeter and 0.34-0.37 m for canopy diameter) when compared to bare soil treatment, but did not have any influence on \u03a8stem. The present study proves the suitability of remote sensing and physiological measurements for assessing almond response to the different management practices.", "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", "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/20.500.11769/552491"}, {"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": "20.500.11769/552491", "name": "item", "description": "20.500.11769/552491", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11769/552491"}, {"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": "20.500.14243/413305", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:26:33Z", "type": "Journal Article", "created": "2022-02-06", "title": "Evaluation of Agricultural Bare Soil Properties Retrieval from Landsat 8, Sentinel-2 and PRISMA Satellite Data", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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/20.500.14243/413305"}, {"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": "20.500.14243/413305", "name": "item", "description": "20.500.14243/413305", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.14243/413305"}, {"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": "2434/878015", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:26:49Z", "type": "Journal Article", "created": "2021-09-06", "title": "A Google Earth Engine-enabled Python approach for the identification of anthropogenic palaeo-landscape features", "description": "<ns4:p>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).</ns4:p>", "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://doi.org/2434/878015"}, {"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": "2434/878015", "name": "item", "description": "2434/878015", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2434/878015"}, {"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": "2945065301", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:27:00Z", "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": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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/2945065301"}, {"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": "2945065301", "name": "item", "description": "2945065301", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2945065301"}, {"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": "2747196278", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:26:53Z", "type": "Journal Article", "created": "2017-08-28", "title": "Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The recent deployment of ESA\u2019s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015\u2013November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.</p></article>", "keywords": ["[SDE] Environmental Sciences", "NDVI", "Chemical technology", "HUMIDITE DU SOL", "soil moisture; SAR; Sentinel-1; NDVI; Sentinel-2; change detection", "0211 other engineering and technologies", "soil water content", "TP1-1185", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Article", "remote sensing", "Sentinel-1", "cartography", "soil moisture", "Sentinel-2", "TELEDETECTION", "change detection", "CARTOGRAPHIE", "SAR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/17/9/1966/pdf"}, {"href": "https://doi.org/2747196278"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2747196278", "name": "item", "description": "2747196278", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2747196278"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-08-26T00:00:00Z"}}, {"id": "301c0949-d502-4a49-ba3d-cc7405240164", "type": "Feature", "geometry": null, "properties": {"license": "http://dcat-ap.de/def/licenses/cc-by/4.0", "updated": "2025-09-11T11:47:11", "type": "Dataset", "language": "en", "title": "SoilSuite \u2013 Sentinel-2 \u2013 Europe, 5 year composite (2018-2022)", "description": "The SoilSuite contains a collection of different image data products that provide information about the spectral and statistical properties of European soils and other bare surfaces such as rocks. It is created using DLR's Soil Composite Mapping Processor (ScMAP), which utilises the Sentinel-2 data archive. SCMaP is a specialised processing chain for detecting and analysing bare soils/surfaces on a large (continental) scale. Bare surface and soil pixels are selected using a combined NDVI and NBR index (PVIR2) that optimises the exclusion of photosynthetically active and non-active vegetation. The index is calculated and applied for each individual pixel. All SoilSuite products are calculated based on the available Sentinel-2 scenes recorded between January 2018 and December 2022 in Europe. The data package excludes all scenes with a cloud cover of > 80 % and a sun elevation of < 20\u00b0. The spectral composite products are calculated from the mean value after extensive removal of clouds, haze and snow effects at both scene and pixel level. The spectral data products are available at a pixel size of 20 m and contain 10 Sentinel-2 bands (B02, B03, B04, B05, B06, B07, B08, B08a, B11, B12). The SoilSuite comprises: (a) \u201cBare Surface Reflectance Composite \u2013 Mean\u201d that provides the spectral properties of soils that vary due to different soil organic carbon (SOC) content, soil moisture and soil minerology. This product is often used for spectral and digital soil mapping approaches, (b) \u201cBare Surface Reflectance Composite - Standard deviation\u201d informing about the spectral dynamic of bare surfaces and soils, (c) \u201cBare Surface Reflectance Composite \u2013 95% Confidence\u201d contains information about the reliability of the spectral information due to the number of valid observations per pixel, (d) \u201cBare Surface Statistics Product\u201d provides the number of bare soil occurrences over the total number of valid observations (Band 1), the number of bare soil occurrences (Band 2) and the total number of valid observations (Band 3), (e) \u201cMask\u201d is a product that aggregates simple landcover classes that occur during the time period between 2018 - 2022 (Sentinel-2). The three-class Mask contains bare surface occurrences (1), permanent vegetation (2) and other surfaces such as water bodies, urban areas, roads (3). Additionally, the SoilSuite provides (f) \u201cReflectance Composite \u2013 Mean\u201d that represents the mean reflectance of all valid Sentinel-2 observations between 2018 \u2013 2022 including vegetation, bare and other surfaces, and (g) \u201cReflectance Composite \u2013 Standard deviation\u201d, which contains the standard deviation per band for all valid Sentinel-2 observations between 2018 \u2013 2022.", "formats": [{"name": "Download"}], "keywords": ["bare-soil-coverage", "bare-soil-frequency", "de", "dlr", "eoc", "europe", "land", "opendata", "reflectance-composite", "scmap", "sentinel-2", "soil", "soil-composite-mapping-processor", "soil-coverage", "surface-reflectance-composite", "temporal-composites"], "contacts": [{"organization": "German Aerospace Center (DLR)", "roles": ["creator"]}]}, "links": [{"href": "https://download.geoservice.dlr.de/SOILSUITE/files/EUROPE_5Y/"}, {"href": "https://geoservice.dlr.de/eoc/land/wms?"}, {"href": "https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S2-soilsuite-europe-2018-2022-P5Y"}, {"href": "http://data.europa.eu/88u/dataset/301c0949-d502-4a49-ba3d-cc7405240164"}, {"rel": "self", "type": "application/geo+json", "title": "301c0949-d502-4a49-ba3d-cc7405240164", "name": "item", "description": "301c0949-d502-4a49-ba3d-cc7405240164", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/301c0949-d502-4a49-ba3d-cc7405240164"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "3122338430", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:27:18Z", "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": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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/3122338430"}, {"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": "3122338430", "name": "item", "description": "3122338430", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3122338430"}, {"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"}}], "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-2&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-2&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-2&", "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-2&offset=50", "hreflang": "en-US"}], "numberMatched": 59, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-06-24T09:27:11.534097Z"}