{"type": "FeatureCollection", "features": [{"id": "10.1007/978-3-030-84144-7_7", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:14:31Z", "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": "10.5281/zenodo.7079709", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:23:39Z", "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": "10.3390/rs12142299", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:07Z", "type": "Journal Article", "created": "2020-07-20", "title": "Feasibility of Using the Two-Source Energy Balance Model (TSEB) with Sentinel-2 and Sentinel-3 Images to Analyze the Spatio-Temporal Variability of Vine Water Status in a Vineyard", "description": "

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

", "keywords": ["evapotranspiration; TSEB; Sentinel-2; Sentinel-3; crop water stress index; vine water status; grapevines", "2. Zero hunger", "crop water stress index", "Science", "Q", "evapotranspiration", "634", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Sentinel-3", "Sentinel-2", "TSEB", "vine water status"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/14/2299/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/14/2299/pdf"}, {"href": "https://doi.org/10.3390/rs12142299"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs12142299", "name": "item", "description": "10.3390/rs12142299", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12142299"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-17T00:00:00Z"}}, {"id": "10.3390/rs11091106", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:07Z", "type": "Journal Article", "created": "2019-05-09", "title": "Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion", "description": "

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

", "keywords": ["Landsat 8", "2. Zero hunger", "soil erosion", "550", "Science", "Q", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "01 natural sciences", "630", "field spectroscopy", "6. Clean water", "soil erosion; remote sensing; Sentinel-2; Landsat 8; ANN; RUSLE; field spectroscopy; OLSR; GWR", "remote sensing", "Field spectroscopy", "OLSR", "13. Climate action", "Soil erosion", "0401 agriculture", " forestry", " and fisheries", "RUSLE", "Sentinel-2", "ANN", "GWR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/11/9/1106/pdf"}, {"href": "https://doi.org/10.3390/rs11091106"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs11091106", "name": "item", "description": "10.3390/rs11091106", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs11091106"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-09T00:00:00Z"}}, {"id": "10.3390/rs13020305", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:07Z", "type": "Journal Article", "created": "2021-01-20", "title": "Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China", "description": "

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

", "keywords": ["2. Zero hunger", "soil salinization; Sentinel-2 MSI; remote sensing; machine learning; arid area", "Science", "soil salinization", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "Sentinel-2 MSI", "6. Clean water", "remote sensing", "machine learning", "arid area", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/2/305/pdf"}, {"href": "https://doi.org/10.3390/rs13020305"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13020305", "name": "item", "description": "10.3390/rs13020305", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13020305"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-17T00:00:00Z"}}, {"id": "10.3390/rs14030714", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:08Z", "type": "Journal Article", "created": "2022-02-07", "title": "Evaluation of Agricultural Bare Soil Properties Retrieval from Landsat 8, Sentinel-2 and PRISMA Satellite Data", "description": "

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

", "keywords": ["Landsat 8", "Sentinel\u20102", "Multispectral", "multispectral", "Science", "hyperspectral; multispectral; PRISMA; soil properties; bare soil; SOC; soil texture; Sentinel-2; Landsat 8; PLSR; Cubist", "Q", "Bare soil", "Cubist", "PRISMA", "04 agricultural and veterinary sciences", "15. Life on land", "hyperspectral", "Hyperspectral", "PLSR", "bare soil", "soil properties", "Soil texture", "Bare soil; Cubist; Hyperspectral; Landsat 8; Multispectral; PLSR; PRISMA; Sentinel\u20102; SOC; Soil properties; Soil texture", "0401 agriculture", " forestry", " and fisheries", "SOC", "Soil properties", "Sentinel-2"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/3/714/pdf"}, {"href": "https://iris.cnr.it/bitstream/20.500.14243/413305/1/prod_473291-doc_192827_compressed.pdf"}, {"href": "https://www.iris.unina.it/bitstream/11588/948571/1/Evaluation%20of%20Agricultural%20Bare%20Soil%20Properties%20Retrieval%20from%20Landsat%208%2c%20Sentinel-2%20and%20PRISMA%20Satellite%20Data%20Enhanced%20Reader.pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/3/714/pdf"}, {"href": "https://doi.org/10.3390/rs14030714"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs14030714", "name": "item", "description": "10.3390/rs14030714", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs14030714"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-02T00:00:00Z"}}, {"id": "10.3390/rs13214195", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:08Z", "type": "Journal Article", "created": "2021-10-20", "title": "Sentinel-2 Recognition of Uncovered and Plastic Covered Agricultural Soil", "description": "

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

", "keywords": ["Technology", "SURFACE", "Science", "Environmental Sciences & Ecology", "TEXTURE", "artificial cover", "ALMERIA", "0203 Classical Physics", "soil", "Remote Sensing", "SUPPORT", "0909 Geomatic Engineering", "Geosciences", " Multidisciplinary", "Imaging Science & Photographic Technology", "agriculture", "2. Zero hunger", "plastic mulch", "Science & Technology", "IDENTIFICATION", "soil; agriculture; Sentinel-2; artificial cover; plastic mulch", "Q", "Geology", "04 agricultural and veterinary sciences", "15. Life on land", "CLOUD", "REFLECTANCE", "RESOLUTION", "13. Climate action", "Physical Sciences", "0401 agriculture", " forestry", " and fisheries", "4013 Geomatic engineering", "Sentinel-2", "GREENHOUSE", "0406 Physical Geography and Environmental Geoscience", "Life Sciences & Biomedicine", "3701 Atmospheric sciences", "Environmental Sciences", "3709 Physical geography and environmental geoscience"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/21/4195/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/21/4195/pdf"}, {"href": "https://doi.org/10.3390/rs13214195"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13214195", "name": "item", "description": "10.3390/rs13214195", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13214195"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-20T00:00:00Z"}}, {"id": "10.3390/rs13245115", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:08Z", "type": "Journal Article", "created": "2021-12-16", "title": "Using Sentinel-2 Images for Soil Organic Carbon Content Mapping in Croplands of Southwestern France. The Usefulness of Sentinel-1/2 Derived Moisture Maps and Mismatches between Sentinel Images and Sampling Dates", "description": "

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

", "keywords": ["2. Zero hunger", "550", "soil organic carbon; sentinel-2; soil moisture; croplands; digital soil mapping; southwestern france; topographic wetness index; slaking crust sensitivity index", "sentinel-2", "Science", "Q", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "15. Life on land", "croplands", "630", "soil organic carbon", "southwestern france", "topographic wetness index", "13. Climate action", "digital soil mapping", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "soil moisture", "slaking crust sensitivity index"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/24/5115/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/24/5115/pdf"}, {"href": "https://doi.org/10.3390/rs13245115"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13245115", "name": "item", "description": "10.3390/rs13245115", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13245115"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-16T00:00:00Z"}}, {"id": "10.3390/s17091966", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-25T16:21:09Z", "type": "Journal Article", "created": "2017-08-28", "title": "Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution", "description": "

The recent deployment of ESA\u2019s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015\u2013November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.

", "keywords": ["[SDE] Environmental Sciences", "NDVI", "Chemical technology", "HUMIDITE DU SOL", "soil moisture; SAR; Sentinel-1; NDVI; Sentinel-2; change detection", "0211 other engineering and technologies", "soil water content", "TP1-1185", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Article", "remote sensing", "Sentinel-1", "cartography", "soil moisture", "Sentinel-2", "TELEDETECTION", "change detection", "CARTOGRAPHIE", "SAR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/17/9/1966/pdf"}, {"href": "https://doi.org/10.3390/s17091966"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/s17091966", "name": "item", "description": "10.3390/s17091966", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s17091966"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-08-26T00:00:00Z"}}, {"id": "2747196278", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-25T16:25:54Z", "type": "Journal Article", "created": "2017-08-28", "title": "Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution", "description": "

The recent deployment of ESA\u2019s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015\u2013November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.

", "keywords": ["[SDE] Environmental Sciences", "NDVI", "Chemical technology", "HUMIDITE DU SOL", "soil moisture; SAR; Sentinel-1; NDVI; Sentinel-2; change detection", "0211 other engineering and technologies", "soil water content", "TP1-1185", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Article", "remote sensing", "Sentinel-1", "cartography", "soil moisture", "Sentinel-2", "TELEDETECTION", "change detection", "CARTOGRAPHIE", "SAR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/17/9/1966/pdf"}, {"href": "https://doi.org/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": "10.5281/zenodo.5597222", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-25T16:23:30Z", "type": "Report", "title": "Accuracy assessment of early- and late-season crop classification using optical and SAR imagery", "description": "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.", "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": {"license": "Open Access", "updated": "2026-07-25T16:23:31Z", "type": "Report", "title": "Accuracy assessment of early- and late-season crop classification using optical and SAR imagery", "description": "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.", "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": "2026-07-25T16:23:39Z", "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.7079648", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-25T16:23:39Z", "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": "2944731604", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:26:03Z", "type": "Journal Article", "created": "2019-05-09", "title": "Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion", "description": "

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

", "keywords": ["Landsat 8", "2. Zero hunger", "soil erosion", "550", "Science", "Q", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "01 natural sciences", "630", "field spectroscopy", "6. Clean water", "soil erosion; remote sensing; Sentinel-2; Landsat 8; ANN; RUSLE; field spectroscopy; OLSR; GWR", "remote sensing", "Field spectroscopy", "OLSR", "13. Climate action", "Soil erosion", "0401 agriculture", " forestry", " and fisheries", "RUSLE", "Sentinel-2", "ANN", "GWR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/11/9/1106/pdf"}, {"href": "https://doi.org/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.8091638", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:23:47Z", "type": "Journal Article", "created": "2021-01-20", "title": "Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China", "description": "

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

", "keywords": ["2. Zero hunger", "soil salinization; Sentinel-2 MSI; remote sensing; machine learning; arid area", "Science", "soil salinization", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "Sentinel-2 MSI", "6. Clean water", "remote sensing", "machine learning", "arid area", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/2/305/pdf"}, {"href": "https://doi.org/10.5281/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": "20.500.14243/413305", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-25T16:25:33Z", "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": "

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

", "keywords": ["Landsat 8", "Sentinel\u20102", "Multispectral", "multispectral", "Science", "hyperspectral; multispectral; PRISMA; soil properties; bare soil; SOC; soil texture; Sentinel-2; Landsat 8; PLSR; Cubist", "Q", "Bare soil", "Cubist", "PRISMA", "04 agricultural and veterinary sciences", "15. Life on land", "hyperspectral", "Hyperspectral", "PLSR", "bare soil", "soil properties", "Soil texture", "Bare soil; Cubist; Hyperspectral; Landsat 8; Multispectral; PLSR; PRISMA; Sentinel\u20102; SOC; Soil properties; Soil texture", "0401 agriculture", " forestry", " and fisheries", "SOC", "Soil properties", "Sentinel-2"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/3/714/pdf"}, {"href": "https://iris.cnr.it/bitstream/20.500.14243/413305/1/prod_473291-doc_192827_compressed.pdf"}, {"href": "https://www.iris.unina.it/bitstream/11588/948571/1/Evaluation%20of%20Agricultural%20Bare%20Soil%20Properties%20Retrieval%20from%20Landsat%208%2c%20Sentinel-2%20and%20PRISMA%20Satellite%20Data%20Enhanced%20Reader.pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/3/714/pdf"}, {"href": "https://doi.org/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": "3122338430", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:26:18Z", "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": "

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

", "keywords": ["2. Zero hunger", "soil salinization; Sentinel-2 MSI; remote sensing; machine learning; arid area", "Science", "soil salinization", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "Sentinel-2 MSI", "6. Clean water", "remote sensing", "machine learning", "arid area", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/2/305/pdf"}, {"href": "https://doi.org/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"}}, {"id": "3207901193", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:26:25Z", "type": "Journal Article", "created": "2021-10-21", "title": "Sentinel-2 Recognition of Uncovered and Plastic Covered Agricultural Soil", "description": "

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

", "keywords": ["Technology", "SURFACE", "Science", "Environmental Sciences & Ecology", "TEXTURE", "artificial cover", "ALMERIA", "0203 Classical Physics", "soil", "Remote Sensing", "SUPPORT", "0909 Geomatic Engineering", "Geosciences", " Multidisciplinary", "Imaging Science & Photographic Technology", "agriculture", "2. Zero hunger", "plastic mulch", "Science & Technology", "IDENTIFICATION", "soil; agriculture; Sentinel-2; artificial cover; plastic mulch", "Q", "Geology", "04 agricultural and veterinary sciences", "15. Life on land", "CLOUD", "REFLECTANCE", "RESOLUTION", "13. Climate action", "Physical Sciences", "0401 agriculture", " forestry", " and fisheries", "4013 Geomatic engineering", "Sentinel-2", "GREENHOUSE", "0406 Physical Geography and Environmental Geoscience", "Life Sciences & Biomedicine", "3701 Atmospheric sciences", "Environmental Sciences", "3709 Physical geography and environmental geoscience"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/21/4195/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/21/4195/pdf"}, {"href": "https://doi.org/3207901193"}, {"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": "3207901193", "name": "item", "description": "3207901193", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3207901193"}, {"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": "PMC5621168", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-25T16:28:10Z", "type": "Journal Article", "created": "2017-08-28", "title": "Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution", "description": "

The recent deployment of ESA\u2019s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015\u2013November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.

", "keywords": ["[SDE] Environmental Sciences", "NDVI", "Chemical technology", "HUMIDITE DU SOL", "soil moisture; SAR; Sentinel-1; NDVI; Sentinel-2; change detection", "0211 other engineering and technologies", "soil water content", "TP1-1185", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Article", "remote sensing", "Sentinel-1", "cartography", "soil moisture", "Sentinel-2", "TELEDETECTION", "change detection", "CARTOGRAPHIE", "SAR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/17/9/1966/pdf"}, {"href": "https://doi.org/PMC5621168"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC5621168", "name": "item", "description": "PMC5621168", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC5621168"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-08-26T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=+Sentinel-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": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=+Sentinel-2&offset=20", "hreflang": "en-US"}], "numberMatched": 20, "numberReturned": 20, "distributedFeatures": [], "timeStamp": "2026-07-26T00:05:05.544428Z"}