{"type": "FeatureCollection", "features": [{"id": "10.1109/jstars.2019.2958847", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:18:40Z", "type": "Journal Article", "created": "2020-01-22", "title": "Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers", "description": "Open AccessThis article investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyzes the performance of this feature along with polarization and intensity products according to different classification strategies and algorithms. Seven different classification workflows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain\u2014interferometric coherence, backscattered intensities, and polarization. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximize diversity of land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on the study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Do\u00f1ana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%) obtained promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherence-based results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinel-1 A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1 A/B stacks, i.e., six-day sampling, are considered.", "keywords": ["Teledetecci\u00f3", "550", "Interferometric coherence", "Geophysics. Cosmic physics", "ta1171", "0211 other engineering and technologies", "02 engineering and technology", "01 natural sciences", "land cover mapping", "ta216", "TC1501-1800", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing", "SDG 15 - Life on Land", "0105 earth and related environmental sciences", "ta213", "QC801-809", "[SPI.ELEC] Engineering Sciences [physics]/Electromagnetism", "interferometric coherence", "Remote sensing", "synthetic aperture radar (SAR)", "15. Life on land", "[SPI.TRON] Engineering Sciences [physics]/Electronics", "SDG 11 - Sustainable Cities and Communities", "[SPI.TRON]Engineering Sciences [physics]/Electronics", "Ocean engineering", "Synthetic aperture radar (SAR)", "[SPI.ELEC]Engineering Sciences [physics]/Electromagnetism", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3", ":Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3 [\u00c0rees tem\u00e0tiques de la UPC]", "13. Climate action", "Teor\u00eda de la Se\u00f1al y Comunicaciones", "Sentinel-1", "[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing", "Land cover mapping", "Copernicus"]}, "links": [{"href": "https://doi.org/10.1109/jstars.2019.2958847"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Journal%20of%20Selected%20Topics%20in%20Applied%20Earth%20Observations%20and%20Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/jstars.2019.2958847", "name": "item", "description": "10.1109/jstars.2019.2958847", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/jstars.2019.2958847"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.1117/12.2571722", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:19:18Z", "type": "Journal Article", "created": "2020-08-26", "title": "Remote sensing techniques for archaeology: a state of art analysis of SAR methods for land movement", "description": "The RESEARCH project (Remote Sensing techniques for Archaeology; H2020-MSCA-RISE, 2018-2022, grant agreement: 823987) addresses the design and development of a multi-task platform, combining advanced remote sensing technologies with Geographical Information System (GIS) application for mapping and long-term monitoring of Archaeological Heritage (AH) at risk, to identify changes due to climate change and anthropic pressures. The Earth Observation (EO) processing chain will address significant risks affecting AH including soil erosion, land movement and land-use change. The paper describes one of the main goals of RESEARCH project. It refers to a state of the art analysis of Synthetic Aperture Radar (SAR) methods applied to the land movement detection such as landslide and subsidence. Satellite SAR is a rapidly evolving remote sensing technology that offers a high potential for detecting, documenting and monitoring heritage targets. Satellite SAR interferometry (InSAR), Differential Interferometry (DinSAR) and Persistent Scatterer Interferometry (PSI) are different techniques that, depending on the available data and the required accuracy, can be used for deformation monitoring of AH.", "keywords": ["Synthetic Aperture Radar (SAR)", "Interferometry", "Land movement", "13. Climate action", "11. Sustainability", "Archaeological heritage", "0211 other engineering and technologies", "0202 electrical engineering", " electronic engineering", " information engineering", "Engineering and Technology", "02 engineering and technology", "15. Life on land", "Civil Engineering"]}, "links": [{"href": "https://doi.org/10.1117/12.2571722"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Eighth%20International%20Conference%20on%20Remote%20Sensing%20and%20Geoinformation%20of%20the%20Environment%20%28RSCy2020%29", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1117/12.2571722", "name": "item", "description": "10.1117/12.2571722", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1117/12.2571722"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-26T00:00:00Z"}}, {"id": "10.3390/w12082160", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:23Z", "type": "Journal Article", "created": "2020-08-03", "title": "Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.</p></article>", "keywords": ["13. Climate action", "citizen science", "digital soil mapping", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "synthetic aperture radar (SAR)", " soil moisture", "04 agricultural and veterinary sciences", "15. Life on land"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/12/8/2160/pdf"}, {"href": "https://doi.org/10.3390/w12082160"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/w12082160", "name": "item", "description": "10.3390/w12082160", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/w12082160"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-30T00:00:00Z"}}, {"id": "10.3390/rs12010072", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:16Z", "type": "Journal Article", "created": "2019-12-24", "title": "Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data", "description": "<p>The main objective of this work was to retrieve surface soil moisture (SSM) by using scattering models and a support vector machine (SVM) technique driven by backscattering coefficients obtained from Sentinel-1 satellite images acquired over bare agricultural soil in the Tensfit basin of Morocco. Two backscattering models were selected in this study due to their wide use in inversion procedures: the theoretical integral equation model (IEM) and the semi-empirical model (Oh). To this end, the sensitivity of the SAR backscattering coefficients at     V V     (    \uffcf\uff83  v v  \uffe2\uff88\uff98    ) and     V H     (    \uffcf\uff83  v h  \uffe2\uff88\uff98    ) polarizations to in situ soil moisture data were analyzed first. As expected, the results showed that over bare soil the     \uffcf\uff83  v v  \uffe2\uff88\uff98     was well correlated with SSM compared to the     \uffcf\uff83  v h  \uffe2\uff88\uff98    , which showed more dispersion with correlation coefficients values (r) of about     0.84     and     0.61     for the     V V     and     V H     polarizations, respectively. Afterwards, these values of     \uffcf\uff83  v v  \uffe2\uff88\uff98     were compared to those simulated by the backscatter models. It was found that IEM driven by the measured length correlation L slightly underestimated SAR backscatter coefficients compared to the Oh model with a bias of about     \uffe2\uff88\uff92 0.7     dB and     \uffe2\uff88\uff92 1.2     dB and a root mean square (RMSE) of about     1.1     dB and     1.5     dB for Oh and IEM models, respectively. However, the use of an optimal value of L significantly improved the bias of IEM, which became near to zero, and the RMSE decreased to     0.9     dB. Then, a classical inversion approach of     \uffcf\uff83  v v  \uffe2\uff88\uff98     observations based on backscattering model is compared to a data driven retrieval technic (SVM). By comparing the retrieved soil moisture against ground truth measurements, it was found that results of SVM were very encouraging and were close to those obtained by IEM model. The bias and RMSE were about 0.28 vol.% and 2.77 vol.% and     \uffe2\uff88\uff92 0.13     vol.% and 2.71 vol.% for SVM and IEM, respectively. However, by taking into account the difficultly of obtaining roughness parameter at large scale, it was concluded that SVM is still a useful tool to retrieve soil moisture, and therefore, can be fairly used to generate maps at such scales.</p>", "keywords": ["[SDE] Environmental Sciences", "soil moisture; synthetic aperture radar (SAR); Sentinel-1; semi-empirical and theoretical backscatter models; support vector machine; bare soil", "550", "Science", "sentinel-1", "Q", "0211 other engineering and technologies", "0207 environmental engineering", "support vector", "02 engineering and technology", "synthetic aperture radar (SAR)", "15. Life on land", "543", "bare soil", "[SDE]Environmental Sciences", "Sentinel-1", "support vector machine", "soil moisture", "synthetic aperture radar (sar)", "semi-empirical and theoretical backscatter models", "machine"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/1/72/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/1/72/pdf"}, {"href": "https://doi.org/10.3390/rs12010072"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs12010072", "name": "item", "description": "10.3390/rs12010072", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12010072"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-12-24T00:00:00Z"}}, {"id": "3046085810", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:26:25Z", "type": "Journal Article", "created": "2020-08-03", "title": "Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.</p></article>", "keywords": ["13. Climate action", "citizen science", "digital soil mapping", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "synthetic aperture radar (SAR)", " soil moisture", "04 agricultural and veterinary sciences", "15. 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