{"type": "FeatureCollection", "features": [{"id": "10.24057/2071-9388-2019-10", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:22:57Z", "type": "Journal Article", "created": "2019-11-26", "title": "Simultaneous assessment of the summer urban heat island in Moscow megacity based on in situ observations, thermal satellite images and mesoscale modeling", "description": "<p>This study compares three popular approaches to quantify the urban heat island (UHI) effect in Moscow megacity in a summer season (June-August 2015). The first approach uses the measurements of the near-surface air temperature obtained from weather stations, the second is based on remote sensing from thermal imagery of MODIS satellites, and the third is based on the numerical simulations with the mesoscale atmospheric model COSMO-CLM coupled with the urban canopy scheme TERRA_URB. The first approach allows studying the canopy-layer UHI (CLUHI, or anomaly of a near- surface air temperature), while the second allows studying the surface UHI (SUHI, or anomaly of a land surface temperature), and both types of the UHI could be simulated by the atmospheric model. These approaches were compared in the daytime, evening and nighttime conditions. The results of the study highlight a substantial difference between the SUHI and CLUHI in terms of the diurnal variation and spatial structure. The strongest differences are found at the daytime, at which the SUHI reaches the maximal intensity (up to 10\uffc2\uffb0\uffd0\uffa1) whereas the CLUHI reaches the minimum intensity (1.5\uffc2\uffb0\uffd0\uffa1). However, there is a stronger consistency between CLUHU and SUHI at night, when their intensities converge to 5\uffe2\uff80\uff936\uffc2\uffb0\uffd0\uffa1. In addition, the nighttime CLUHI and SUHI have similar monocentric spatial structure with a temperature maximum in the city center. The presented findings should be taken into account when interpreting and comparing the results of UHI studies, based on the different approaches. The mesoscale model reproduces the CLUHI-SUHI relationships and provides good agreement with in situ observations on the CLUHI spatiotemporal variations (with near-zero biases for daytime and nighttime CLUHI intensity and correlation coefficients more than 0.8 for CLUHI spatial patterns). However, the agreement of the simulated SUHI with the remote sensing data is lower than agreement of the simulated CLUHI with in situ measurements. Specifically, the model tends to overestimate the daytime SUHI intensity. These results indicate a need for further in-depth investigation of the model behavior and SUHI\uffe2\uff80\uff93CLUHI relationships in general.</p>", "keywords": ["modis", "Geography (General)", "COSMO", "suhi", "0207 environmental engineering", "uhi", "land surface temperature", "UHI", "urban heat island", "moscow", "02 engineering and technology", "Moscow", "01 natural sciences", "thermal satellite images", "remote sensing", "MODIS", "13. Climate action", "Earth and Environmental Sciences", "SUHI", "cosmo", "urban climate", "11. Sustainability", "G1-922", "mesoscale modelling", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Varentsov, Mikhail I., Grishchenko, Mikhail Y., Wouters, Hendrik,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.24057/2071-9388-2019-10"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/GEOGRAPHY%2C%20ENVIRONMENT%2C%20SUSTAINABILITY", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.24057/2071-9388-2019-10", "name": "item", "description": "10.24057/2071-9388-2019-10", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.24057/2071-9388-2019-10"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-12-31T00:00:00Z"}}, {"id": "10.5194/hess-2018-94", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:24:34Z", "type": "Journal Article", "created": "2018-04-05", "title": "The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Soil moisture measurements are needed in a large number of applications such as climate change, watershed water balance and irrigation management. One of the main characteristics of this property is that soil moisture is highly variable with both space and time, hindering the estimation of a representative value. Deciding how to measure soil moisture before undertaking any type of study is therefore an important issue that needs to be addressed correctly. Nowadays, different kinds of methodologies exist for measuring soil moisture; Remote Sensing, soil moisture sensors or gravimetric measurements. This work is focused on how to measure soil moisture for irrigation scheduling, where soil moisture sensors are the main methodology for monitoring soil moisture. One of its disadvantages, however, is that soil moisture sensors measure a small volume of soil, and do not take into account the existing variability in the field. In contrast, Remote Sensing techniques are able to estimate soil moisture with a low spatial resolution, and thus it is not possible to apply these estimations to agricultural applications. In order to solve this problem, different kinds of algorithms have been developed for downscaling these estimations from low to high resolution. The DISPATCH algorithm downscales soil moisture estimations from 40\u2009km to 1\u2009km resolution using SMOS satellite soil moisture, NDVI and LST from MODIS sensor estimations. In this work, DISPATCH estimations are compared with soil moisture sensors and gravimetric measurements to validate the DISPATCH algorithm in two different hydrologic scenarios; (1) when wet conditions are maintained around the field for rainfall events, and (2) when it is local irrigation that maintains wet conditions. Results show that the DISPATCH algorithm is sensitive when soil moisture is homogenized during general rainfall events, but not when local irrigation generates occasional heterogeneity. In order to explain these different behaviours, we have examined the spatial variability scales of NDVI and LST data, which are the variables involved in the downscaling process provided by the MODIS sensor. Sample variograms show that the spatial scales associated with the NDVI and LST properties are too large to represent the variations of the average water content at the site, and this could be a reason for why the DISPATCH algorithm is unable to detect soil moisture increments caused by local irrigation.                         </p></article>", "keywords": ["2. Zero hunger", "Technology", ":Enginyeria civil::Geologia::Hidrologia [\u00c0rees tem\u00e0tiques de la UPC]", "T", "15. Life on land", "Environmental technology. Sanitary engineering", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria civil::Geologia::Hidrologia", "01 natural sciences", "6. Clean water", "S\u00f2ls -- Humitat -- Mesurament", "G", "Environmental sciences", "13. Climate action", "Geography. Anthropology. Recreation", "GE1-350", "Soil moisture--Measurement--Remote sensing", "TD1-1066", "0105 earth and related environmental sciences"], "contacts": [{"organization": "M. Fontanet, M. Fontanet, M. Fontanet, D. Fern\u00e0ndez-Garcia, D. Fern\u00e0ndez-Garcia, F. Ferrer,", "roles": ["creator"]}]}, "links": [{"href": "https://hess.copernicus.org/articles/22/5889/2018/hess-22-5889-2018.pdf"}, {"href": "https://doi.org/10.5194/hess-2018-94"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/hess-2018-94", "name": "item", "description": "10.5194/hess-2018-94", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-2018-94"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-04-05T00:00:00Z"}}, {"id": "10.3390/app12031330", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:20Z", "type": "Journal Article", "created": "2022-01-26", "title": "Implementing a GIS-Based Digital Atlas of Agricultural Plastics to Reduce Their Environmental Footprint; Part I: A Deductive Approach", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The agricultural sector has benefitted over the last century from several factors that have led to an exponential increase in its productive efficiency. The increasing use of new materials, such as plastics, has been one of the most important factors, as they have allowed for increased production in a simpler and more economical way. Various polymer types are used in different phases of the agricultural production cycle, but when their use is incorrectly managed, it can lead to different environmental impacts. In this study, an applied and simplified methodology to manage agricultural plastics monitoring and planning is proposed. The techniques used are based on quantification through the use of different datasets (orthophotos and satellite images) of the areas covered by plastics used for crop protection. The study area chosen is a part of the Ionian Coast of Southern Italy, which includes the most important municipalities of the Basilicata Region for fruit and vegetable production. The use of geographical techniques and observation methodologies, developed in an open-source GIS environment, enabled accurate location of about 2000 hectares of agricultural land covered by plastics, as well as identification of the areas most susceptible to the accumulation of plastic waste. The techniques and the model implemented, due to its simplicity of use and reliability, can be applied by different local authorities in order to realize an Atlas of agricultural plastics, which would be applied for continuous monitoring, thereby enabling the upscaling of future social and ecological impact assessments, identification of new policy impacts, market searches, etc.</p></article>", "keywords": ["Technology", "remote sensing indice", "Microplastics", "sustainable plasticulture", "0211 other engineering and technologies", "Plastic greenhouse", "02 engineering and technology", "remote sensing indices", "01 natural sciences", "630", "RPGI", "11. Sustainability", "0202 electrical engineering", " electronic engineering", " information engineering", "Biology (General)", "Agro-plastics", "plastic footprint", "2. Zero hunger", "T", "Physics", "04 agricultural and veterinary sciences", "Engineering (General). Civil engineering (General)", "plastic greenhouse", "6. Clean water", "Sustainable plasticulture", "Chemistry", "agricultural plastic surface", "Agricultural plastic surface", "agro-plastics; digital Atlas; agricultural plastic surface; remote sensing indices; RPGI; plastic footprint", "agro\u2010plastic", "TA1-2040", "microplastic", "microplastics", "330", "QH301-705.5", "Soil pollution", "QC1-999", "Plastic footprint", "digital Atla", "Agro\u2010plastic", "12. Responsible consumption", "Agricultural plastic coefficient", "QD1-999", "agro-plastics", "0105 earth and related environmental sciences", "soil pollution", "Mulching film", "mulching film", "plastic greenhouse; mulching film; microplastics; soil pollution; agricultural plastic coefficient; sustainable plasticulture", "15. Life on land", "Remote sensing indices", "agricultural plastic coefficient", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Digital Atlas", "digital Atlas"]}, "links": [{"href": "http://www.mdpi.com/2076-3417/12/3/1330/pdf"}, {"href": "http://www.mdpi.com/2076-3417/12/15/7545/pdf"}, {"href": "https://www.mdpi.com/2076-3417/12/3/1330/pdf"}, {"href": "https://www.mdpi.com/2076-3417/12/15/7545/pdf"}, {"href": "https://doi.org/10.3390/app12031330"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/app12031330", "name": "item", "description": "10.3390/app12031330", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/app12031330"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-26T00:00:00Z"}}, {"id": "10.3390/app12126068", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:23:20Z", "type": "Journal Article", "created": "2022-06-16", "title": "Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Land surface temperature (LST) is a vital parameter associated with the land\u2013atmosphere interface. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST product can provide precise LST with high time resolution, and is widely applied in various remote sensing temperature research. However, due to its inability to penetrate the cloud and fog, its quality is not able to meet the requirements of actual research. Hence, obtaining continuous and cloudless MODIS LST datasets remains challenging for researchers. The critical point is to reconstruct missing pixels. To compare the performance of different methods, first, three kinds of methods were used to reconstruct the missing pixels, namely, temporal, spatial, and spatiotemporal methods. The predicted values using these methods were validated by the automatic weather system data (AWS) in the Heihe river basin of China. The results demonstrated that, compared with other methods, linear temporal interpolation using Aqua data had the best performance in MODIS LST reconstruction in the Heihe river basin, with an RMSE of 7.13 K and an R2 of 0.82, and the NSE and PBias were 0.78 and \u22120.76%, respectively. Furthermore, the interpolation method was improved using adaptive windows and robust regression. First, the international Geosphere\u2013Biosphere Program (IGBP) classification was employed to distinguish the different land surface types. Then, the invalid LST values were reconstructed using adjacent days\u2019 effective LST values combined with a robust regression. Finally, a mean filter was applied to eliminate outliers. The overall results combined with ERA5 data were validated by AWS, with an RMSE of 6.96 K and an R2 of 0.79 and the NSE and PBias were 0.77 and \u22120.20%, respectively. The validation demonstrated that the scheme proposed in this paper is able to accurately reconstruct the missing values and improve the accuracy of the interpolation method to a certain extent when reconstructing MODIS LST.</p></article>", "keywords": ["Technology", "land surface temperature (LST)", "reconstruction", "land surface temperature (LST); remote sensing; interpolation; reconstruction; MODIS", "QH301-705.5", "T", "Physics", "QC1-999", "Engineering (General). Civil engineering (General)", "01 natural sciences", "interpolation", "6. Clean water", "Chemistry", "remote sensing", "MODIS", "13. Climate action", "TA1-2040", "Biology (General)", "QD1-999", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2076-3417/12/12/6068/pdf"}, {"href": "https://doi.org/10.3390/app12126068"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/app12126068", "name": "item", "description": "10.3390/app12126068", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/app12126068"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-15T00:00:00Z"}}, {"id": "10.3390/rs71114708", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:36Z", "type": "Journal Article", "created": "2015-11-05", "title": "Estimation of Evapotranspiration and Crop Coefficients of Tendone Vineyards Using Multi-Sensor Remote Sensing Data in a Mediterranean Environment", "description": "<p>The sustainable management of water resources plays a key role in Mediterranean viticulture, characterized by scarcity and competition of available water. This study focuses on estimating the evapotranspiration and crop coefficients of table grapes vineyards trained on overhead \uffe2\uff80\uff9ctendone\uffe2\uff80\uff9d systems in the Apulia region (Italy). Maximum vineyard transpiration was estimated by adopting the \uffe2\uff80\uff9cdirect\uffe2\uff80\uff9d methodology for ETp proposed by the Food and Agriculture Organization in Irrigation and Drainage Paper No. 56, with crop parameters estimated from Landsat 8 and RapidEye satellite data in combination with ground-based meteorological data. The modeling results of two growing seasons (2013 and 2014) indicated that canopy growth, seasonal and 10-day sums evapotranspiration values were strictly related to thermal requirements and rainfall events. The estimated values of mean seasonal daily evapotranspiration ranged between 4.2 and 4.1 mm\uffc2\uffb7d\uffe2\uff88\uff921, while midseason estimated values of crop coefficients ranged from 0.88 to 0.93 in 2013,  and 1.02 to 1.04 in 2014, respectively. The experimental evapotranspiration values calculated represent the maximum value in absence of stress, so the resulting crop coefficients should be used with some caution. It is concluded that the retrieval of crop parameters and evapotranspiration derived from remotely-sensed data could be helpful for downscaling to the field the local weather conditions and agronomic practices and thus may be the basis for supporting grape growers and irrigation managers.</p>", "keywords": ["Landsat 8", "2. Zero hunger", "0106 biological sciences", "Evapotranspiration", "leaf area index", "Science", "Q", "evapotranspiration", "table grapes", "Remote sensing", "15. Life on land", "Vineyards", "01 natural sciences", "6. Clean water", "evapotranspiration; crop coefficient; leaf area index; Landsat 8; RapidEye; remote sensing; vineyards; table grapes", "Crop coefficient; Evapotranspiration; Landsat 8; Leaf area index; RapidEye; Remote sensing; Table grapes; Vineyards; Earth and Planetary Sciences (all)", "remote sensing", "vineyards", "Table grapes", "Crop coefficient", "Leaf area index", "RapidEye", "Earth and Planetary Sciences (all)", "crop coefficient"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/7/11/14708/pdf"}, {"href": "https://www.iris.unina.it/bitstream/11588/637935/1/remotesensing2015-07-14708.pdf"}, {"href": "https://doi.org/10.3390/rs71114708"}, {"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/rs71114708", "name": "item", "description": "10.3390/rs71114708", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs71114708"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-11-05T00:00:00Z"}}, {"id": "10.3390/rs14122917", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:35Z", "type": "Journal Article", "created": "2022-06-20", "title": "Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km2: dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of ~15 g\u00b7kg\u22121 and a range of 30 g\u00b7kg\u22121 in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information.</p></article>", "keywords": ["2. Zero hunger", "550", "Science", "Q", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Environmental Sciences (social aspects to be 507)", "Geology", "04 agricultural and veterinary sciences", "[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study", "910", "15. Life on land", "satellite imagery", "630", "Remote Sensing", "soil organic carbon", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "spectral models"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/532033/1/remotesensing-steropes%20review.pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/12/2917/pdf"}, {"href": "https://pub.epsilon.slu.se/28706/1/vaoudour-e-et-al-220809.pdf"}, {"href": "https://doi.org/10.3390/rs14122917"}, {"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/rs14122917", "name": "item", "description": "10.3390/rs14122917", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs14122917"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-18T00:00:00Z"}}, {"id": "10.3390/rs8020156", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:23:36Z", "type": "Journal Article", "created": "2016-02-19", "title": "Impacts of Re-Vegetation on Surface Soil Moisture over the Chinese Loess Plateau Based on Remote Sensing Datasets", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>A large-scale re-vegetation supported by the Grain for Green Project (GGP) has greatly changed local eco-hydrological systems, with an impact on soil moisture conditions for the Chinese Loess Plateau. It is important to know how, exactly, re-vegetation influences soil moisture conditions, which not only crucially constrain growth and distribution of vegetation, and hence, further re-vegetation, but also determine the degree of soil desiccation and, thus, erosion risk in the region. In this study, three eco-environmental factors, which are Soil Water Index (SWI), the Normalized Difference Vegetation Index (NDVI), and precipitation, were used to investigate the response of soil moisture in the one-meter layer of top soil to the re-vegetation during the GGP. SWI was estimated based on the backscatter coefficient produced by the European Remote Sensing Satellite (ERS-1/2) and Meteorological Operational satellite program (MetOp), while NDVI was derived from SPOT imageries. Two separate periods, which are 1998\u20132000 and 2008\u20132010, were selected to examine the spatiotemporal pattern of the chosen eco-environmental factors. It has been shown that the amount of precipitation in 1998\u20132000 was close to that of 2008\u20132010 (the difference being 13.10 mm). From 1998\u20132000 to 2008\u20132010, the average annual NDVI increased for 80.99%, while the SWI decreased for 72.64% of the area on the Loess Plateau. The average NDVI over the Loess Plateau increased rapidly by 17.76% after the 10-year GGP project. However, the average SWI decreased by 4.37% for two-thirds of the area. More specifically, 57.65% of the area on the Loess Plateau experienced an increased NDVI and decreased SWI, 23.34% of the area had an increased NDVI and SWI. NDVI and SWI decreased simultaneously for 14.99% of the area, and the decreased NDVI and increased SWI occurred at the same time for 4.02% of the area. These results indicate that re-vegetation, human activities, and climate change have impacts on soil moisture. However, re-vegetation, which consumes a large quantity of soil water, may be the major factor for soil moisture change in most areas of the Loess Plateau. It is, therefore, suggested that Soil Moisture Content (SMC) should be kept in mind when carrying out re-vegetation in China\u2019s arid and semi-arid regions.</p></article>", "keywords": ["2. Zero hunger", "China", "Science", "Q", "Soil Water Index (SWI)", "precipitation", "15. Life on land", "01 natural sciences", "6. Clean water", "remote sensing", "the Loess Plateau", "13. Climate action", "11. Sustainability", "Normalized Difference Vegetation Index (NDVI)", "Grain for Green Project (GGP)", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Qiao Jiao, Rui Li, Fei Wang, Xingmin Mu, Pengfei Li, Chunchun An,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2072-4292/8/2/156/pdf"}, {"href": "https://doi.org/10.3390/rs8020156"}, {"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/rs8020156", "name": "item", "description": "10.3390/rs8020156", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs8020156"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-02-19T00:00:00Z"}}, {"id": "10.3390/rs8110938", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:36Z", "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/rs9111155", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:36Z", "type": "Journal Article", "created": "2017-11-10", "title": "Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (\u03c3\u00b0). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of \u03c3\u00b0 and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of \u03c3\u00b0 ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of \u03c3\u00b0 where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD= 0.032 m3 m\u22123).</p></article>", "keywords": ["soil moisture and ocean salinity satellite (SMOS)", "Atmospheric Science", "Artificial intelligence", "Environmental Engineering", "550", "Science", "Soil Moisture", "0211 other engineering and technologies", "Aerospace Engineering", "FOS: Mechanical engineering", "02 engineering and technology", "01 natural sciences", "Environmental science", "[SDU] Sciences of the Universe [physics]", "Engineering", "Meteorology", "DISPATCH", "Image resolution", "Arctic Permafrost Dynamics and Climate Change", "14. Life underwater", "Moisture", "0105 earth and related environmental sciences", "Soil science", "Water content", "Radar", "Geography", "soil moisture and ocean salinity satellite (SMOS); DISPATCH; radar; Sentinel-1; disaggregation; soil moisture", "Soilmoisture and ocean salinity satellite (SMOS)", "Synthetic Aperture Radar Interferometry", "Q", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Earth and Planetary Sciences", "Groundwater Extraction", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "disaggregation", "Environmental Science", "Physical Sciences", "Sentinel-1", "soil moisture", "radar"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/11/1155/pdf"}, {"href": "https://doi.org/10.3390/rs9111155"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs9111155", "name": "item", "description": "10.3390/rs9111155", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs9111155"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-10T00:00:00Z"}}, {"id": "10.3389/fpls.2021.658357", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:14Z", "type": "Journal Article", "created": "2021-04-16", "title": "Performance of the Two-Source Energy Balance (TSEB) Model as a Tool for Monitoring the Response of Durum Wheat to Drought by High-Throughput Field Phenotyping", "description": "<p>The current lack of efficient methods for high throughput field phenotyping is a constraint on the goal of increasing durum wheat yields. This study illustrates a comprehensive methodology for phenotyping this crop's water use through the use of the two-source energy balance (TSEB) model employing very high resolution imagery. An unmanned aerial vehicle (UAV) equipped with multispectral and thermal cameras was used to phenotype 19 durum wheat cultivars grown under three contrasting irrigation treatments matching crop evapotranspiration levels (ETc): 100%ETc treatment meeting all crop water requirements (450 mm), 50%ETc treatment meeting half of them (285 mm), and a rainfed treatment (122 mm). Yield reductions of 18.3 and 48.0% were recorded in the 50%ETc and rainfed treatments, respectively, in comparison with the 100%ETc treatment. UAV flights were carried out during jointing (April 4th), anthesis (April 30th), and grain-filling (May 22nd). Remotely-sensed data were used to estimate: (1) plant height from a digital surface model (H, R2 = 0.95, RMSE = 0.18m), (2) leaf area index from multispectral vegetation indices (LAI, R2 = 0.78, RMSE = 0.63), and (3) actual evapotranspiration (ETa) and transpiration (T) through the TSEB model (R2 = 0.50, RMSE = 0.24 mm/h). Compared with ground measurements, the four traits estimated at grain-filling provided a good prediction of days from sowing to heading (DH, r = 0.58\uffe2\uff80\uff930.86), to anthesis (DA, r = 0.59\uffe2\uff80\uff930.85) and to maturity (r = 0.67\uffe2\uff80\uff930.95), grain-filling duration (GFD, r = 0.54\uffe2\uff80\uff930.74), plant height (r = 0.62\uffe2\uff80\uff930.69), number of grains per spike (NGS, r = 0.41\uffe2\uff80\uff930.64), and thousand kernel weight (TKW, r = 0.37\uffe2\uff80\uff930.42). The best trait to estimate yield, DH, DA, and GFD was ETa at anthesis or during grain filling. Better forecasts for yield-related traits were recorded in the irrigated treatments than in the rainfed one. These results show a promising perspective in the use of energy balance models for the phenotyping of large numbers of durum wheat genotypes under Mediterranean conditions.</p>", "keywords": ["2. Zero hunger", "grain weight", "Plant culture", "633", "Plant Science", "04 agricultural and veterinary sciences", "15. Life on land", "yield", "6. Clean water", "transpiration", "plant height", "SB1-1110", "631", "remote sensing", "0401 agriculture", " forestry", " and fisheries", "grain number"], "contacts": [{"organization": "G\u00f3mez-Cand\u00f3n, David, Bellvert, Joaquim, Royo, Conxita,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3389/fpls.2021.658357"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fpls.2021.658357", "name": "item", "description": "10.3389/fpls.2021.658357", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fpls.2021.658357"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-16T00:00:00Z"}}, {"id": "10.3390/app12157545", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:21Z", "type": "Journal Article", "created": "2022-01-26", "title": "Implementing a GIS-Based Digital Atlas of Agricultural Plastics to Reduce Their Environmental Footprint: Part II, an Inductive Approach", "description": "<p>The agricultural sector has benefitted over the last century from several factors that have led to an exponential increase in its productive efficiency. The increasing use of new materials, such as plastics, has been one of the most important factors, as they have allowed for increased production in a simpler and more economical way. Various polymer types are used in different phases of the agricultural production cycle, but when their use is incorrectly managed, it can lead to different environmental impacts. In this study, an applied and simplified methodology to manage agricultural plastics monitoring and planning is proposed. The techniques used are based on quantification through the use of different datasets (orthophotos and satellite images) of the areas covered by plastics used for crop protection. The study area chosen is a part of the Ionian Coast of Southern Italy, which includes the most important municipalities of the Basilicata Region for fruit and vegetable production. The use of geographical techniques and observation methodologies, developed in an open-source GIS environment, enabled accurate location of about 2000 hectares of agricultural land covered by plastics, as well as identification of the areas most susceptible to the accumulation of plastic waste. The techniques and the model implemented, due to its simplicity of use and reliability, can be applied by different local authorities in order to realize an Atlas of agricultural plastics, which would be applied for continuous monitoring, thereby enabling the upscaling of future social and ecological impact assessments, identification of new policy impacts, market searches, etc.</p>", "keywords": ["Technology", "remote sensing indice", "Microplastics", "sustainable plasticulture", "0211 other engineering and technologies", "Plastic greenhouse", "02 engineering and technology", "remote sensing indices", "01 natural sciences", "630", "RPGI", "11. Sustainability", "0202 electrical engineering", " electronic engineering", " information engineering", "Biology (General)", "Agro-plastics", "plastic footprint", "2. Zero hunger", "T", "Physics", "04 agricultural and veterinary sciences", "Engineering (General). Civil engineering (General)", "plastic greenhouse", "6. Clean water", "Sustainable plasticulture", "Chemistry", "agricultural plastic surface", "Agricultural plastic surface", "agro-plastics; digital Atlas; agricultural plastic surface; remote sensing indices; RPGI; plastic footprint", "agro\u2010plastic", "TA1-2040", "microplastic", "microplastics", "330", "QH301-705.5", "Soil pollution", "QC1-999", "Plastic footprint", "digital Atla", "Agro\u2010plastic", "12. Responsible consumption", "Agricultural plastic coefficient", "QD1-999", "agro-plastics", "0105 earth and related environmental sciences", "soil pollution", "Mulching film", "mulching film", "plastic greenhouse; mulching film; microplastics; soil pollution; agricultural plastic coefficient; sustainable plasticulture", "15. Life on land", "Remote sensing indices", "agricultural plastic coefficient", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Digital Atlas", "digital Atlas"]}, "links": [{"href": "http://www.mdpi.com/2076-3417/12/3/1330/pdf"}, {"href": "http://www.mdpi.com/2076-3417/12/15/7545/pdf"}, {"href": "https://www.mdpi.com/2076-3417/12/3/1330/pdf"}, {"href": "https://www.mdpi.com/2076-3417/12/15/7545/pdf"}, {"href": "https://doi.org/10.3390/app12157545"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/app12157545", "name": "item", "description": "10.3390/app12157545", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/app12157545"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-26T00:00:00Z"}}, {"id": "10.3390/s22020645", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:37Z", "type": "Journal Article", "created": "2022-01-17", "title": "Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. In this paper, we evaluated the performances of five clustering algorithms including k-means, fuzzy C-means (FCM), hierarchical, mean shift, and density-based spatial clustering of applications with noise (DBSCAN) in different scenarios and assessed the impacts of input data format and feature selection on MZ delineation quality. We used key soil fertility attributes (moisture content (MC), organic carbon (OC), calcium (Ca), cation exchange capacity (CEC), exchangeable potassium (K), magnesium (Mg), sodium (Na), exchangeable phosphorous (P), and pH) collected with an online visible and near-infrared (vis-NIR) spectrometer along with Sentinel2 and yield data of five commercial fields in Belgium. We demonstrated that k-means is the optimal clustering method for MZ delineation, and the input data should be normalized (range normalization). Feature selection was also shown to be positively effective. Furthermore, we proposed an algorithm based on DBSCAN for smoothing the MZs maps to allow smooth actuating during variable rate application by agricultural machinery. Finally, the whole process of MZ delineation was integrated in a clustering and smoothing pipeline (CaSP), which automatically performs the following steps sequentially: (1) range normalization, (2) feature selection based on cross-correlation analysis, (3) k-means clustering, and (4) smoothing. It is recommended to adopt the developed platform for automatic MZ delineation for variable rate applications of farming inputs.</p></article>", "keywords": ["Agriculture and Food Sciences", "2. Zero hunger", "Spatial Analysis", "precision agriculture", "ACCURACY", "Chemical technology", "management zone delineation", "TP1-1185", "04 agricultural and veterinary sciences", "15. Life on land", "Article", "VARIABILITY", "Soil", "YIELD", "FUSION", "feature selection", "ATTRIBUTES", "clustering; feature selection; management zone delineation; precision agriculture", "Remote Sensing Technology", "Cluster Analysis", "0401 agriculture", " forestry", " and fisheries", "FIELD", "SOIL-PHOSPHORUS", "Algorithms", "clustering"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/22/2/645/pdf"}, {"href": "https://www.mdpi.com/1424-8220/22/2/645/pdf"}, {"href": "https://doi.org/10.3390/s22020645"}, {"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/s22020645", "name": "item", "description": "10.3390/s22020645", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s22020645"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-14T00:00:00Z"}}, {"id": "10.3390/rs11091138", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:33Z", "type": "Journal Article", "created": "2019-05-13", "title": "Advances in the Remote Sensing of Terrestrial Evaporation", "description": "<p>Characterizing the terrestrial carbon, water, and energy cycles depends strongly on a capacity to accurately reproduce the spatial and temporal dynamics of land surface evaporation. For this, and many other reasons, monitoring terrestrial evaporation across multiple space and time scales has been an area of focused research for a number of decades. Much of this activity has been supported by developments in satellite remote sensing, which have been leveraged to deliver new process insights, model development and methodological improvements. In this Special Issue, published contributions explored a range of research topics directed towards the enhanced estimation of terrestrial evaporation. Here we summarize these cutting-edge efforts and provide an overview of some of the state-of-the-art approaches for retrieving this key variable. Some perspectives on outstanding challenges, issues, and opportunities are also presented.</p>", "keywords": ["Atmospheric sciences", "CubeSats", "Life on Land", "Classical Physics", "Science", "0207 environmental engineering", "02 engineering and technology", "high-resolution", "01 natural sciences", "Physical Geography and Environmental Geoscience", "Article", "evaporation", "land surface modeling", "remote sensing", "Engineering", "novel sensing", "Physical geography and environmental geoscience", "0105 earth and related environmental sciences", "Earth observation", "Q", "Geomatic engineering", "15. Life on land", "Geomatic Engineering", "land surface flux", "13. Climate action", "cubesats"]}, "links": [{"href": "https://www.mdpi.com/2072-4292/11/9/1138/pdf"}, {"href": "https://escholarship.org/content/qt1sh5v7hp/qt1sh5v7hp.pdf"}, {"href": "https://doi.org/10.3390/rs11091138"}, {"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/rs11091138", "name": "item", "description": "10.3390/rs11091138", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs11091138"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-13T00:00:00Z"}}, {"id": "10.3390/rs13061133", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:34Z", "type": "Journal Article", "created": "2021-03-16", "title": "Assessing Irrigation Water Use with Remote Sensing-Based Soil Water Balance at an Irrigation Scheme Level in a Semi-Arid Region of Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This study aims to evaluate a remote sensing-based approach to allow estimation of the temporal and spatial distribution of crop evapotranspiration (ET) and irrigation water requirements over irrigated areas in semi-arid regions. The method is based on the daily step FAO-56 Soil Water Balance model combined with a time series of basal crop coefficients and the fractional vegetation cover derived from high-resolution satellite Normalized Difference Vegetation Index (NDVI) imagery. The model was first calibrated and validated at plot scale using ET measured by eddy-covariance systems over wheat fields and olive orchards representing the main crops grown in the study area of the Haouz plain (central Morocco). The results showed that the model provided good estimates of ET for wheat and olive trees with a root mean square error (RMSE) of about 0.56 and 0.54 mm/day respectively. The model was then used to compare remotely sensed estimates of irrigation requirements (RS-IWR) and irrigation water supplied (WS) at plot scale over an irrigation district in the Haouz plain through three growing seasons. The comparison indicated a large spatio-temporal variability in irrigation water demands and supplies; the median values of WS and RS-IWR were 130 (175), 117 (175) and 118 (112) mm respectively in the 2002\u20132003, 2005\u20132006 and 2008\u20132009 seasons. This could be attributed to inadequate irrigation supply and/or to farmers\u2019 socio-economic considerations and management practices. The findings demonstrate the potential for irrigation managers to use remote sensing-based models to monitor irrigation water usage for efficient and sustainable use of water resources.</p></article>", "keywords": ["0106 biological sciences", "2. Zero hunger", "FAO-56 soil water balance", "550", "[SDE.MCG]Environmental Sciences/Global Changes", "Science", "water", "Q", "evapotranspiration", "balance", "15. Life on land", "01 natural sciences", "630", "irrigation", "6. Clean water", "[SDE.MCG] Environmental Sciences/Global Changes", "remote sensing", "evapotranspiration; irrigation; water; remote sensing; FAO-56 soil water balance; NDVI time series", "FAO-56 soil water", "NDVI time series"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/6/1133/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/6/1133/pdf"}, {"href": "https://doi.org/10.3390/rs13061133"}, {"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/rs13061133", "name": "item", "description": "10.3390/rs13061133", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13061133"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-16T00:00:00Z"}}, {"id": "10.3390/rs13163101", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:34Z", "type": "Journal Article", "created": "2021-08-06", "title": "Cereal yield forecasting with satellite drought-based indices, weather data and regional climate indices using machine learning in Morocco.", "description": "<p>Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000\uffe2\uff80\uff932017 (i.e., 15 \uffc3\uff97 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha\uffe2\uff88\uff921. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting.</p>", "keywords": ["[SDE] Environmental Sciences", "330", "Science", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "[INFO] Computer Science [cs]", "crop yield forecasting", "01 natural sciences", "630", "indices", "[INFO]Computer Science [cs]", "Climate indices", "remote sensing drought indices", "weather data", "0105 earth and related environmental sciences", "2. Zero hunger", "Remote sensing drought indices", "climate indices", "remote sensing drought", "Q", "Crop yield forecasting", "04 agricultural and veterinary sciences", "semiarid region", "15. Life on land", "6. Clean water", "machine learning", "13. Climate action", "[SDE]Environmental Sciences", "crop yield forecasting; machine learning; remote sensing drought indices; climate indices; weather data; semiarid region", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "0401 agriculture", " forestry", " and fisheries", "Semiarid region"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/16/3101/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/16/3101/pdf"}, {"href": "https://doi.org/10.3390/rs13163101"}, {"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/rs13163101", "name": "item", "description": "10.3390/rs13163101", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13163101"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-06T00:00:00Z"}}, {"id": "10.3390/rs13214195", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:35Z", "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/rs14030634", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:35Z", "type": "Journal Article", "created": "2022-01-29", "title": "4D U-Nets for Multi-Temporal Remote Sensing Data Classification", "description": "<p>Multispectral sensors constitute a core earth observation imaging technology generating massive high-dimensional observations acquired across multiple time instances. The collected multi-temporal remote sensed data contain rich information for Earth monitoring applications, from flood detection to crop classification. To easily classify such naturally multidimensional data, conventional low-order deep learning models unavoidably toss away valuable information residing across the available dimensions. In this work, we extend state-of-the-art convolutional network models based on the U-Net architecture to their high-dimensional analogs, which can naturally capture multi-dimensional dependencies and correlations. We introduce several model architectures, both of low as well as of high order, and we quantify the achieved classification performance vis-\uffc3\uffa0-vis the latest state-of-the-art methods. The experimental analysis on observations from Landsat-8 reveals that approaches based on low-order U-Net models exhibit poor classification performance and are outperformed by our proposed high-dimensional U-Net scheme.</p>", "keywords": ["remote sensing", "multi-temporal data classification", "Science", "Q", "0211 other engineering and technologies", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "02 engineering and technology", "u-nets", "higher-order convolutional neural networks"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/3/634/pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/3/634/pdf"}, {"href": "https://doi.org/10.3390/rs14030634"}, {"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/rs14030634", "name": "item", "description": "10.3390/rs14030634", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs14030634"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-28T00:00:00Z"}}, {"id": "10.3390/rs14092106", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:35Z", "type": "Journal Article", "created": "2022-04-28", "title": "Accounting for Almond Crop Water Use under Different Irrigation Regimes with a Two-Source Energy Balance Model and Copernicus-Based Inputs", "description": "<p>Accounting for water use in agricultural fields is of vital importance for the future prospects for enhancing water use efficiency. Remote sensing techniques, based on modelling surface energy fluxes, such as the two-source energy balance (TSEB), were used to estimate actual evapotranspiration (ETa) on the basis of shortwave and thermal data. The lack of high temporal and spatial resolution of satellite thermal infrared (TIR) missions has led to new approaches to obtain higher spatial resolution images with a high revisit time. These new approaches take advantage of the high spatial resolution of Sentinel-2 (10\uffe2\uff80\uff9320 m), and the high revisit time of Sentinel-3 (daily). The use of the TSEB model with sharpened temperature (TSEBS2+S3) has recently been applied and validated in several study sites. However, none of these studies has applied it in heterogeneous row crops under different water status conditions within the same orchard. This study assessed the TSEBS2+S3 modelling approach to account for almond crop water use under four different irrigation regimes and over four consecutive growing seasons (2017\uffe2\uff80\uff932020). The energy fluxes were validated with an eddy covariance system and also compared with a soil water balance model. The former reported errors of 90 W/m2 and 87 W/m2 for the sensible (H) and latent heat flux (LE), respectively. The comparison of ETa with the soil water balance model showed a root-mean-square deviation (RMSD) ranging from 0.6 to 2.5 mm/day. Differences in cumulative ETa between the irrigation treatments were estimated, with maximum differences obtained in 2019 of 20% to 13% less in the most water-limited treatment compared to the most well-watered one. Therefore, this study demonstrates the feasibility of using the TSEBS2+S3 for monitoring ETa in almond trees under different water regimes.</p>", "keywords": ["2. Zero hunger", "Evapotranspiration", "Science", "Q", "evapotranspiration", "633", "04 agricultural and veterinary sciences", "Almond", "Remote sensing", "15. Life on land", "almond", "6. Clean water", "remote sensing", "evapotranspiration; almond; TSEB; remote sensing", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "TSEB"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/9/2106/pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/9/2106/pdf"}, {"href": "https://doi.org/10.3390/rs14092106"}, {"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/rs14092106", "name": "item", "description": "10.3390/rs14092106", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs14092106"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-27T00:00:00Z"}}, {"id": "10.3390/s17091966", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:36Z", "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/s22051851", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:37Z", "type": "Journal Article", "created": "2022-02-28", "title": "Embedded Temporal Convolutional Networks for Essential Climate Variables Forecasting", "description": "<p>Forecasting the values of essential climate variables like land surface temperature and soil moisture can play a paramount role in understanding and predicting the impact of climate change. This work concerns the development of a deep learning model for analyzing and predicting spatial time series, considering both satellite derived and model-based data assimilation processes. To that end, we propose the Embedded Temporal Convolutional Network (E-TCN) architecture, which integrates three different networks, namely an encoder network, a temporal convolutional network, and a decoder network. The model accepts as input satellite or assimilation model derived values, such as land surface temperature and soil moisture, with monthly periodicity, going back more than fifteen years. We use our model and compare its results with the state-of-the-art model for spatiotemporal data, the ConvLSTM model. To quantify performance, we explore different cases of spatial resolution, spatial region extension, number of training examples and prediction windows, among others. The proposed approach achieves better performance in terms of prediction accuracy, while using a smaller number of parameters compared to the ConvLSTM model. Although we focus on two specific environmental variables, the method can be readily applied to other variables of interest.</p>", "keywords": ["deep learning; time-series forecasting; remote sensing; climate variables; surface temperature; soil moisture", "Chemical technology", "Temperature", "0211 other engineering and technologies", "deep learning", "climate variables", "TP1-1185", "02 engineering and technology", "surface temperature", "time-series forecasting", "Article", "remote sensing", "Soil", "13. Climate action", "0202 electrical engineering", " electronic engineering", " information engineering", "soil moisture"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/22/5/1851/pdf"}, {"href": "https://www.mdpi.com/1424-8220/22/5/1851/pdf"}, {"href": "https://doi.org/10.3390/s22051851"}, {"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/s22051851", "name": "item", "description": "10.3390/s22051851", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s22051851"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-26T00:00:00Z"}}, {"id": "10.3390/s19040904", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:36Z", "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": "10.3390/su17115042", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:23:39Z", "type": "Journal Article", "created": "2025-06-02", "title": "Citizen Science for Soil Monitoring and Protection in Europe: Insights from the PREPSOIL Project Under the European Soil Mission", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Citizen science (CS) is increasingly recognized as a complementary approach for addressing soil health challenges\u2014including erosion, pollution, nutrient imbalances, and biodiversity loss\u2014by harnessing public participation to broaden spatial and temporal data collection. This review synthesizes findings from the following: (i) a systematic analysis of peer-reviewed literature and grey sources, (ii) a database of 96 CS initiatives compiled by the European PREPSOIL project, and (iii) questionnaire surveys and workshops conducted in five Living Labs across Europe. Our analysis indicates that volunteer-driven monitoring can enhance the volume and granularity of soil data, providing critical insights into parameters such as organic carbon content, nutrient levels, and pollutant concentrations. However, persistent challenges remain, including inconsistencies in data validation, volunteer attrition, and concerns regarding digital literacy and data privacy. Despite these challenges, ongoing efforts to standardize protocols, integrate remote sensing and sensor-based validation methods, and employ feedback mechanisms improve data reliability and participant engagement. We conclude that sustained capacity-building, transparent data governance, and stakeholder collaboration, from local communities to governmental bodies, are essential for fully realizing the potential of citizen science in soil conservation. This work is framed within the context of the European Soil Mission, and CS is demonstrated to meaningfully support sustainable land management and evidence-based policymaking by aligning public-generated observations with established scientific frameworks.</p></article>", "keywords": ["community stewardship", "remote sensing", "[SDV.EE] Life Sciences [q-bio]/Ecology", " environment", "volunteer engagement", "soil health", "soil monitoring", "citizen science", "open data", "data validation", "policy integration", "biodiversity"]}, "links": [{"href": "https://www.mdpi.com/2071-1050/17/11/5042/pdf"}, {"href": "https://doi.org/10.3390/su17115042"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/su17115042", "name": "item", "description": "10.3390/su17115042", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/su17115042"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-30T00:00:00Z"}}, {"id": "10.4995/cigeo2021.2021.12694", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:57Z", "type": "Journal Article", "created": "2021-10-11", "title": "A review of the use of remote sensing for monitoring and quantifying carbon sequestration in marginal lands", "description": "<p>In recent years, Remote Sensing (RS) and its derived products have been used as a key tool for the detection, monitoring,management and future use of Marginal Lands (ML). Currently, there is no single, universally accepted definition of theterm and there is a wide variety of synonyms. In this paper, we conduct a compilation of synonyms and meanings thatencompass the term, as well as propose a definition. To reach this objective, an overview of the state of the art of ML isdone, visualising trends by science maps, based on bibliographic data of established research journals, found in GoogleScholar, Web of Science (WoS) and Scopus search engines. The bibliographic review carried out shows that the study ofML has traditionally been carried out with an ad hoc basis focused on the objective to be achieved, this aspect and otherknowledge gaps are discussed to analyse the global study of ML. Due to the broad spectrum of uses in which ML havebeen studied, the work has been focused on RS for monitoring and characterizing ML, focusing on two different aspects:(i) satellite monitoring of marginal lands; and (ii) determining carbon sequestration potential of marginal lands using remotesensing.</p>", "keywords": ["Cartography", "Carbon sequestration", "Earth observation", "Uso del suelo", "Cultural Heritage", "Marginal lands", "Remote sensing", "15. Life on land", "12. Responsible consumption", "3D Modelling", "Geophysics", "Captura de carbono", "13. Climate action", "Land use", "11. Sustainability", "Teledetecci\u00f3n", "Tierras marginales", "marginal lands", " remote sensing", " carbon sequestration", " land use", "Geocomputing", "Environmental applications", "Geodesy"]}, "links": [{"href": "https://doi.org/10.4995/cigeo2021.2021.12694"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20-%203rd%20Congress%20in%20Geomatics%20Engineering%20-%20CIGeo", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.4995/cigeo2021.2021.12694", "name": "item", "description": "10.4995/cigeo2021.2021.12694", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.4995/cigeo2021.2021.12694"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-07T00:00:00Z"}}, {"id": "10.4995/cigeo2021.2021.12729", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:57Z", "type": "Journal Article", "created": "2021-10-11", "title": "Methodological proposal for the identification of marginal lands with remote sensing-derived products and ancillary data", "description": "<p>The concept of marginal land (ML) is dynamic and depends on various factors related to the environment, climate, scale,culture, and economic sector. The current methods for identifying ML are diverse, they employ multiple parameters andvariables derived from land use and land cover, and mostly reflect specific management purposes. A methodologicalapproach for the identification of marginal lands using remote sensing and ancillary data products and validated on samplesfrom four European countries (i.e., Germany, Spain, Greece, and Poland) is presented in this paper. The methodologyproposed combines land use and land cover data sets as excluding indicators (forest, croplands, protected areas,impervious areas, land-use change, water bodies, and permanent snow areas) and environmental constraints informationas marginality indicators: (i) physical soil properties, in terms of slope gradient, erosion, soil depth, soil texture, percentageof coarse soil texture fragments, etc.; (ii) climatic factors e.g. aridity index; (iii) chemical soil properties, including soil pH,cation exchange capacity, contaminants, and toxicity, among others. This provides a common vision of marginality thatintegrates a multidisciplinary approach. To determine the ML, we first analyzed the excluding indicators used to delimit theareas with defined land use. Then, thresholds were determined for each marginality indicator through which the landproductivity progressively decreases. Finally, the marginality indicator layers were combined in Google Earth Engine. Theresult was categorized into 3 levels of productivity of ML: high productivity, low productivity, and potentially unsuitable land.The results obtained indicate that the percentage of marginal land per country is 11.64% in Germany, 19.96% in Spain,18.76% in Greece, and 7.18% in Poland. The overall accuracies obtained per country were 60.61% for Germany, 88.87%for Spain, 71.52% for Greece, and 90.97% for Poland.</p>", "keywords": ["Cartography", "Land cover", "Cultural Heritage", "Cobertura de suelo", "3D Modelling", "11. Sustainability", "Teledetecci\u00f3n", "Environmental applications", "Uso de suelo", "2. Zero hunger", "Earth observation", "Tierra abandonada", "Remote sensing", "15. Life on land", "GIS", "SIG", "Geophysics", "Idle land", "13. Climate action", "Degradaci\u00f3n del suelo", "Land use", "Land degradation", "land use", " land cover", " idle land", " land degradation", " GIS", " remote sensing", " Google Earth Engine", "Geocomputing", "Google Earth Engine", "Geodesy"]}, "links": [{"href": "https://doi.org/10.4995/cigeo2021.2021.12729"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20-%203rd%20Congress%20in%20Geomatics%20Engineering%20-%20CIGeo", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.4995/cigeo2021.2021.12729", "name": "item", "description": "10.4995/cigeo2021.2021.12729", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.4995/cigeo2021.2021.12729"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-07T00:00:00Z"}}, {"id": "10.5061/dryad.cvdncjt89", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:24:05Z", "type": "Dataset", "title": "Data for: Soil organic carbon contents of collected soil samples from China's black soil region", "description": "The long-term use of cropland and cropland reclamation from natural  ecosystems led to soil degradation. This study investigated the effect of  the long-term use of cropland and cropland reclamation from natural  ecosystems on soil organic carbon (SOC) content and density over the past  35 years. Altogether, 2140 topsoil samples (0\u201320 cm) were collected across  Northeast China. Landsat images were acquired from 1985 to 2020 through  Google Earth Engine, and the reflectance of each soil sample was extracted  from the Landsat image that its time was consistent with sampling. The  hybrid model that included two individual SOC prediction models for two  clustering regions was built for accurate estimation after k-means  clustering. The probability hybrid model, a combination between the hybrid  model and classification probabilities of pixels, was introduced to  enhance the accuracy of SOC mapping. Cropland reclamation results were  extracted from the land cover time series dataset at a 5-year interval.  Our study indicated that: (1) Long-term use of cropland led to a 3.07 g  kg-1 and 6.71 Mg C ha-1 decrease in SOC content and density, respectively,  and the decrease of SOC stock was 0.32 Pg over the past 35 years; (2)  Nearly 64% of cropland had a negative change in terms of SOC content from  1985 to 2020; (3) Cropland reclamation track changed from high to low SOC  content, and almost no cropland was reclaimed on the \u2018Black soils\u2019 after  2005; (4) Cropland reclamation from wetlands resulted in the highest  decrease, and reclamation period of years 31\u201335 decreased when SOC density  and SOC stock were 16.05 Mg C ha-1 and 0.005 Pg, respectively, while  reclamation period of years 26\u201330 from forest witnessed SOC density and  stock decreases of 8.33 Mg C ha-1 and 0.01 Pg, respectively. Our research  results provide a reference for SOC change in the black soil region of  Northeast China and can attract more attention to the area of the  protection of \u2018Black soils\u2019 and natural ecosystems.", "keywords": ["2. Zero hunger", "soil organic carbon", "Black soil region", "FOS: Agricultural sciences", "15. Life on land", "Remote sensing", "6. Clean water"], "contacts": [{"organization": "Wang, Xiang, Li, Sijia, Wang, Liping, Zheng, Miao, Wang, Zongming, Song, Kaishan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.cvdncjt89"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.cvdncjt89", "name": "item", "description": "10.5061/dryad.cvdncjt89", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.cvdncjt89"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-06-18T00:00:00Z"}}, {"id": "10.5194/essd-13-3707-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:24:30Z", "type": "Journal Article", "created": "2021-01-07", "title": "C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)", "description": "<p>Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in the semi-arid areas where the water resources are limited. Radar observations, available at high resolution and revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared to the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gathers soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/two weeks including above-ground fresh and dry biomasses, vegetation water content based on destructive measurements, cover fraction, leaf area index and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric effects-corrected products is also provided. This database, which is the first of its kind made available in open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation parameters retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely-accessible via the DOI:  https://doi.org/10.23708/8D6WQC  (Ouaadi et al., 2020a).                         </p>", "keywords": ["550", "Arid", "Soil Moisture", "0211 other engineering and technologies", "FOS: Mechanical engineering", "02 engineering and technology", "Digital Soil Mapping Techniques", "Normalized Difference Vegetation Index", "630", "Agricultural and Biological Sciences", "Engineering", "Pathology", "GE1-350", "2. Zero hunger", "QE1-996.5", "Vegetation Monitoring", "Water content", "Ecology", "Geography", "Statistics", "Life Sciences", "Hydrology (agriculture)", "Geology", "Remote Sensing in Vegetation Monitoring and Phenology", "04 agricultural and veterinary sciences", "Remote sensing", "Soil Erosion and Agricultural Sustainability", "6. Clean water", "Satellite Observations", "Archaeology", "Physical Sciences", "Leaf area index", "Telecommunications", "Medicine", "Vegetation (pathology)", "Environmental Engineering", "Data set", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Aerospace Engineering", "Soil Science", "Environmental science", "Digital Soil Mapping", "[SDU] Sciences of the Universe [physics]", "Global Soil Information", "FOS: Mathematics", "Biology", "Radar", "Synthetic Aperture Radar Interferometry", "Canopy", "FOS: Environmental engineering", "Soil Properties", "Paleontology", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Agronomy", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "0401 agriculture", " forestry", " and fisheries", "Mathematics"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/3707/2021/essd-13-3707-2021.pdf"}, {"href": "https://doi.org/10.5194/essd-13-3707-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-13-3707-2021", "name": "item", "description": "10.5194/essd-13-3707-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-13-3707-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-07T00:00:00Z"}}, {"id": "10.5194/egusphere-egu25-13513", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:24:29Z", "type": "Report", "created": "2025-03-15", "title": "Integrating Remote Sensing and AI modelling in Mediterranean Agroforestry and Croplands systems: A Methodological Perspective for spatial SOC monitoring in the MRV4SOC project, Spain", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This study presents a robust framework for spatially explicit monitoring of soil properties and Above Ground Biomass (AGB) estimation in Mediterranean agroforestry and cropland systems by integrating remote sensing (RS) and artificial intelligence (AI). These variables are critical for assimilation into process-based models for Soil Organic Carbon (SOC) dynamics monitoring within a Monitoring, Reporting, and Verification (MRV) system. The framework was developed as part of the MRV4SOC project in Spain, aimed at designing a comprehensive, robust, and cost-effective Tier-3 approach. The primary goal is to produce high-quality geospatial layers of topsoil properties and AGB estima tion, which serve as key inputs for SOC dynamics modeling.The methodology was tested at two long-term demonstration sites in Spain: Quercus ilex Dehesas in Extremadura (SW Spain) and rainfed cereal crops at La Canaleja experimental farm in central Spain. These agroecosystems provide diverse testing grounds for scalable and transferable SOC assessment methodologies within an MRV framework. The approach integrates multi-temporal remote sensing data (2018&amp;#8211;2022) from Sentinel-2 and Landsat satellites with machine learning models to predict essential soil properties (SOC, Sand, Silt, Clay, pH, and Total N) and AGB. Ground truth data for AGB estimation were sourced from the Spanish National Forest Inventory (SNFI), while soil property predictions utilized the LUCAS 2018 topsoil libraries due to limited site-specific datasets for model training. A bare soil reflectance composite (2018&amp;#8211;2022) derived from Sentinel-2 bands (B02&amp;#8211;B12) at 20-meter resolution was employed for geospatial soil property mapping.Given the limited availability of ground truth data, simpler models like Quantile Regression Forests (QRF) and XGBoost were selected. QRF achieved better accuracy for soil texture properties, with R&amp;#178; = 0.62 for clay and outperforming XGBoost for SOC (R&amp;#178; = 0.63) and pH (R&amp;#178; = 0.76) in the agroforestry site. However, XGBoost performed better for SOC (R&amp;#178; = 0.54) and total nitrogen in croplands, as well as for sand, silt, clay, and total nitrogen in the agroforestry site (R&amp;#178; = 0.61 for clay). For AGB estimation in the Dehesas area, a machine learning approach was implemented using SNFI data and remote sensing-derived transformation features. A gradient boosting algorithm (LightGBM) resulted in an R&amp;#178; value of 0.8. In La Canaleja, a bare soil reflectance composite was similarly employed for soil property mapping. Further analysis will be carried out to develop a bottom-up approach for monitoring SOC using these products and process-based modelsUncertainty analysis using Prediction Interval Ratio (PIR) assessment was conducted separately for landscape (L) and sub-landscape (SL) levels. While most properties showed medium to low uncertainty, sand and silt exhibited higher variability in croplands, and SOC displayed the highest uncertainty in the agroforestry site across L and SL levels.This methodology contributes significantly to improving MRV systems by delivering high-quality geospatial layers for SOC dynamics monitoring in complex environments. Increasing ground truth data availability is essential for enhancing model accuracy and minimizing prediction uncertainties further.</p></article>", "keywords": ["Cropland management", "Artificial Intelligence", "Remote Sensing Technology", "Agroforestry"]}, "links": [{"href": "https://doi.org/10.5194/egusphere-egu25-13513"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/egusphere-egu25-13513", "name": "item", "description": "10.5194/egusphere-egu25-13513", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/egusphere-egu25-13513"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-18T00:00:00Z"}}, {"id": "10.5194/hess-2019-105", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:24:34Z", "type": "Journal Article", "created": "2019-04-23", "title": "An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1\u2009km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1\u2009km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014\u20132018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014\u20132018). The field was seeded for the 2014\u20132015 (S1), 2016\u20132017 (S2) and 2017\u20132018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015\u20132016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated \u03b1PT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved \u03b1PT remains at a mostly constant value (\u223c\u20090.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181\u2009W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62\u2009W/m2 for S1, S2, S3 and B1 respectively.                         </p></article>", "keywords": ["Technology", "Atmospheric sciences", "550", "Soil Moisture", "0208 environmental biotechnology", "02 engineering and technology", "Environmental technology. Sanitary engineering", "01 natural sciences", "Engineering", "Geography. Anthropology. Recreation", "Pathology", "GE1-350", "TD1-1066", "2. Zero hunger", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "T", "Soil Water Retention", "Moderate-resolution imaging spectroradiometer", "Hydrology (agriculture)", "Geology", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "Aerospace engineering", "Physical Sciences", "Medicine", "environment", "Vegetation (pathology)", "Latent heat", "Mechanics and Transport in Unsaturated Soils", "Land cover", "Environmental Engineering", "0207 environmental engineering", "Energy balance", "Thermal Effects on Soil", "Environmental science", "[SDU] Sciences of the Universe [physics]", "G", "Meteorology", "Civil engineering", "14. Life underwater", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Biology", "Civil and Structural Engineering", "0105 earth and related environmental sciences", "Global Forest Drought Response and Climate Change", "FOS: Environmental engineering", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "Satellite", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Land use", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "FOS: Civil engineering"]}, "links": [{"href": "https://hess.copernicus.org/articles/24/1781/2020/hess-24-1781-2020.pdf"}, {"href": "https://doi.org/10.5194/hess-2019-105"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/hess-2019-105", "name": "item", "description": "10.5194/hess-2019-105", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-2019-105"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-23T00:00:00Z"}}, {"id": "10.5194/hess-22-5889-2018", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:24:35Z", "type": "Journal Article", "created": "2018-04-05", "title": "The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields", "description": "<p>Abstract. Soil moisture measurements are needed in a large number of applications such as climate change, watershed water balance and irrigation management. One of the main characteristics of this property is that soil moisture is highly variable with both space and time, hindering the estimation of a representative value. Deciding how to measure soil moisture before undertaking any type of study is therefore an important issue that needs to be addressed correctly. Nowadays, different kinds of methodologies exist for measuring soil moisture; Remote Sensing, soil moisture sensors or gravimetric measurements. This work is focused on how to measure soil moisture for irrigation scheduling, where soil moisture sensors are the main methodology for monitoring soil moisture. One of its disadvantages, however, is that soil moisture sensors measure a small volume of soil, and do not take into account the existing variability in the field. In contrast, Remote Sensing techniques are able to estimate soil moisture with a low spatial resolution, and thus it is not possible to apply these estimations to agricultural applications. In order to solve this problem, different kinds of algorithms have been developed for downscaling these estimations from low to high resolution. The DISPATCH algorithm downscales soil moisture estimations from 40\uffe2\uff80\uff89km to 1\uffe2\uff80\uff89km resolution using SMOS satellite soil moisture, NDVI and LST from MODIS sensor estimations. In this work, DISPATCH estimations are compared with soil moisture sensors and gravimetric measurements to validate the DISPATCH algorithm in two different hydrologic scenarios; (1) when wet conditions are maintained around the field for rainfall events, and (2) when it is local irrigation that maintains wet conditions. Results show that the DISPATCH algorithm is sensitive when soil moisture is homogenized during general rainfall events, but not when local irrigation generates occasional heterogeneity. In order to explain these different behaviours, we have examined the spatial variability scales of NDVI and LST data, which are the variables involved in the downscaling process provided by the MODIS sensor. Sample variograms show that the spatial scales associated with the NDVI and LST properties are too large to represent the variations of the average water content at the site, and this could be a reason for why the DISPATCH algorithm is unable to detect soil moisture increments caused by local irrigation.                         </p>", "keywords": ["2. Zero hunger", "Technology", ":Enginyeria civil::Geologia::Hidrologia [\u00c0rees tem\u00e0tiques de la UPC]", "T", "15. Life on land", "Environmental technology. Sanitary engineering", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria civil::Geologia::Hidrologia", "01 natural sciences", "6. Clean water", "S\u00f2ls -- Humitat -- Mesurament", "G", "Environmental sciences", "13. Climate action", "Geography. Anthropology. Recreation", "GE1-350", "Soil moisture--Measurement--Remote sensing", "TD1-1066", "0105 earth and related environmental sciences"], "contacts": [{"organization": "M. Fontanet, M. Fontanet, M. Fontanet, D. Fern\u00e0ndez-Garcia, D. Fern\u00e0ndez-Garcia, F. Ferrer,", "roles": ["creator"]}]}, "links": [{"href": "https://hess.copernicus.org/articles/22/5889/2018/hess-22-5889-2018.pdf"}, {"href": "https://doi.org/10.5194/hess-22-5889-2018"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/hess-22-5889-2018", "name": "item", "description": "10.5194/hess-22-5889-2018", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-22-5889-2018"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-04-05T00:00:00Z"}}, {"id": "10.5194/hess-24-1781-2020", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:24:35Z", "type": "Journal Article", "created": "2019-04-23", "title": "An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data", "description": "<p>Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1\uffe2\uff80\uff89km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1\uffe2\uff80\uff89km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014\uffe2\uff80\uff932018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014\uffe2\uff80\uff932018). The field was seeded for the 2014\uffe2\uff80\uff932015 (S1), 2016\uffe2\uff80\uff932017 (S2) and 2017\uffe2\uff80\uff932018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015\uffe2\uff80\uff932016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated \uffce\uffb1PT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved \uffce\uffb1PT remains at a mostly constant value (\uffe2\uff88\uffbc\uffe2\uff80\uff890.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181\uffe2\uff80\uff89W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62\uffe2\uff80\uff89W/m2 for S1, S2, S3 and B1 respectively.                         </p>", "keywords": ["Technology", "Atmospheric sciences", "550", "Soil Moisture", "0208 environmental biotechnology", "02 engineering and technology", "Environmental technology. Sanitary engineering", "01 natural sciences", "Engineering", "Geography. Anthropology. Recreation", "Pathology", "GE1-350", "TD1-1066", "2. Zero hunger", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "T", "Soil Water Retention", "Moderate-resolution imaging spectroradiometer", "Hydrology (agriculture)", "Geology", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "Aerospace engineering", "Physical Sciences", "Medicine", "environment", "Vegetation (pathology)", "Latent heat", "Mechanics and Transport in Unsaturated Soils", "Land cover", "Environmental Engineering", "0207 environmental engineering", "Energy balance", "Thermal Effects on Soil", "Environmental science", "[SDU] Sciences of the Universe [physics]", "G", "Meteorology", "Civil engineering", "14. Life underwater", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Biology", "Civil and Structural Engineering", "0105 earth and related environmental sciences", "Global Forest Drought Response and Climate Change", "FOS: Environmental engineering", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "Satellite", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Land use", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "FOS: Civil engineering"]}, "links": [{"href": "https://hess.copernicus.org/articles/24/1781/2020/hess-24-1781-2020.pdf"}, {"href": "https://doi.org/10.5194/hess-24-1781-2020"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/hess-24-1781-2020", "name": "item", "description": "10.5194/hess-24-1781-2020", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-24-1781-2020"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-23T00:00:00Z"}}, {"id": "10.5194/hess-25-5749-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:24:35Z", "type": "Journal Article", "created": "2021-11-09", "title": "The International Soil Moisture Network: serving  Earth system science for over a decade", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. In\u00a02009, the International Soil Moisture Network\u00a0(ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et\u00a0al.,\u00a02011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28\u00a0October\u00a02021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000\u00a0active users and over 1000\u00a0scientific publications referencing the data sets provided by the network. As of July\u00a02021, the ISMN now contains the data of 71\u00a0networks and 2842\u00a0stations located all over the globe, with a time period spanning from\u00a01952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70\u2009% of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.                     </p></article>", "keywords": ["[SDE] Environmental Sciences", "Technology", "Atmospheric Science", "550", "Soil Moisture", "TA Engineering (General). Civil engineering (General)", "02 engineering and technology", "Soil Moisture; ISMN; IMA_CAN1; swc; STEMS", "Spatial variability", "Environmental technology. Sanitary engineering", "01 natural sciences", "Agency (philosophy)", "remote sensing", "Antecedent wetness conditions", "Engineering", "Geography. Anthropology. Recreation", "GE1-350", "TD1-1066", "Smos brightness temperature", "Heihe river-basin", "T", "Soil Water Retention", "Leaf-area index", "004", "FOS: Philosophy", " ethics and religion", "Programming language", "Earth and Planetary Sciences", "Physical Sciences", "name=Water Science and Technology", "/dk/atira/pure/subjectarea/asjc/1900/1901", "Medicine", "name=Earth and Planetary Sciences (miscellaneous)", "Mechanics and Transport in Unsaturated Soils", "Environmental Engineering", "Soil Moisture International Network", "0207 environmental engineering", "Epistemology", "Environmental science", "G", "Database", "Soil Moisture; network", "Arctic Permafrost Dynamics and Climate Change", "Scope (computer science)", "Land data assimilation", "Civil and Structural Engineering", "0105 earth and related environmental sciences", "info:eu-repo/classification/ddc/550", "Consecutive dry days", "in situ", "FOS: Environmental engineering", "AMSR-E", "15. Life on land", "Remote Sensing of Soil Moisture", "Globe", "Computer science", "Environmental sciences", "QE Geology", "Philosophy", "Ophthalmology", "In-situ measurements", "13. Climate action", "ITC-ISI-JOURNAL-ARTICLE", "global scale", "Environmental Science", "G70.212-70.215 Geographic information system", "soil moisture", "ITC-GOLD", "/dk/atira/pure/subjectarea/asjc/2300/2312", "Wireless sensor network"]}, "links": [{"href": "https://iris.polito.it/bitstream/11583/2998914/1/prod_447100-doc_161016.pdf"}, {"href": "https://iris.polito.it/bitstream/11583/2998914/2/prod_447100-doc_178365.pdf"}, {"href": "https://research.unipg.it/bitstream/11391/1498417/2/2021_The%20international%20soil_OA.pdf"}, {"href": "https://cris.unibo.it/bitstream/11585/910145/1/Dourigo_etal_2021.pdf"}, {"href": "https://doi.org/10.5194/hess-25-5749-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/hess-25-5749-2021", "name": "item", "description": "10.5194/hess-25-5749-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-25-5749-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-09T00:00:00Z"}}, {"id": "10.5194/isprs-archives-xlii-3-w6-9-2019", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:24:35Z", "type": "Journal Article", "created": "2019-07-29", "title": "EVAPOTRANSPIRATION AND EVAPORATION/TRANSPIRATION RETRIEVAL USING DUAL-SOURCE SURFACE ENERGY BALANCE MODELS INTEGRATING VIS/NIR/TIR DATA WITH SATELLITE SURFACE SOIL MOISTURE INFORMATION", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Evapotranspiration is an important component of the water cycle. For the agronomic management and ecosystem health monitoring, it is also important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data are thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data. Flux data at an experimental plot in semi-arid Morocco is used to assess this potentiality and shows the increased robustness of both the total evapotranspiration and partitioning retrieval performances. This work is realized within the frame of the Phase A activities for the TRISHNA CNES/ISRO Thermal Infra-Red satellite mission.                     </p></article>", "keywords": ["Technology", "Environmental Engineering", "550", "Ecosystem Resilience", "Soil Moisture", "Evaporation", "Energy balance", "Biochemistry", "Environmental science", "Transpiration", "Meteorology", "Artificial Intelligence", "Soil water", "Thermal Infrared", "Applied optics. Photonics", "Machine Learning Methods for Solar Radiation Forecasting", "Photosynthesis", "TRISHNA", "Water balance", "Biology", "Soil science", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "Global Forest Drought Response and Climate Change", "T", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "15. Life on land", "Engineering (General). Civil engineering (General)", "Remote Sensing of Soil Moisture", "6. Clean water", "TA1501-1820", "[SDE.MCG] Environmental Sciences/Global Changes", "Chemistry", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Computer Science", "TA1-2040", "Water cycle"]}, "links": [{"href": "https://doi.org/10.5194/isprs-archives-xlii-3-w6-9-2019"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20International%20Archives%20of%20the%20Photogrammetry%2C%20Remote%20Sensing%20and%20Spatial%20Information%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/isprs-archives-xlii-3-w6-9-2019", "name": "item", "description": "10.5194/isprs-archives-xlii-3-w6-9-2019", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/isprs-archives-xlii-3-w6-9-2019"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-26T00:00:00Z"}}, {"id": "10.5281/zenodo.11188379", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:25:00Z", "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": "10.5281/zenodo.13951144", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:25:17Z", "type": "Dataset", "title": "SERENA EJP Soil - Map of Soil Sealing of Italy", "description": "Open AccessThe map of soil sealing of Italy was based on semi-automatic classification process of multitemporal satellite images and photointerpretation. The methodology uses vegetation and backscatter indices calculated over time series of images, and decision rules. The semi-automatic classification supports the subsequent photointerpretation phase based on national orthophotos and other free VHR satellite images. The final map is the product of a binary classification (sealed/not-sealed) having 10 m spatial resolution. The selected indicator was degree of soil sealing.", "keywords": ["ITALY", "soil threat", "soil sealing", "remote sensing", "SERENA EJPSOIL", "WP3", "Task 3.2", "H2020", "Grant n. 862695", "satellite images", "photointerpretation"], "contacts": [{"organization": "Smiraglia, Daniela, Congedo, Luca, Marinosci, Ines, De Fioravante, Paolo, ASSENNATO, FRANCESCA, Munaf\u00f2, Michele, Riitano, Nicola,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13951144"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13951144", "name": "item", "description": "10.5281/zenodo.13951144", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13951144"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-18T00:00:00Z"}}, {"id": "10.5281/zenodo.13951145", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:25:17Z", "type": "Dataset", "title": "SERENA EJP Soil - Map of Soil Sealing of Italy", "description": "Open AccessThe map of soil sealing of Italy was based on semi-automatic classification process of multitemporal satellite images and photointerpretation. The methodology uses vegetation and backscatter indices calculated over time series of images, and decision rules. The semi-automatic classification supports the subsequent photointerpretation phase based on national orthophotos and other free VHR satellite images. The final map is the product of a binary classification (sealed/not-sealed) having 10 m spatial resolution. The selected indicator was degree of soil sealing.", "keywords": ["ITALY", "soil threat", "soil sealing", "remote sensing", "SERENA EJPSOIL", "WP3", "Task 3.2", "H2020", "Grant n. 862695", "satellite images", "photointerpretation"], "contacts": [{"organization": "Smiraglia, Daniela, Congedo, Luca, Marinosci, Ines, De Fioravante, Paolo, ASSENNATO, FRANCESCA, Munaf\u00f2, Michele, Riitano, Nicola,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13951145"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13951145", "name": "item", "description": "10.5281/zenodo.13951145", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13951145"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-18T00:00:00Z"}}, {"id": "10.5281/zenodo.13969874", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:25:17Z", "type": "Dataset", "created": "2024-10-20", "title": "SERENA_EJPSOIL_AT_SOILSEALING_SEALINGDEGREE", "description": "The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. \u00a0  This data was prepared according to the methodology of SERENA Soil Sealing cookbook. For Austria, the application was carried out at regional scale. The map of soil sealing (soil threat) was based on classification of multitemporal satellite images and photointerpretation. The methodology uses vegetation and backscatter indices calculated over time series of images, and decision rules. The semi-automatic classification supports the subsequent photointerpretation phase based on national orthophotos and other free VHR satellite images.", "keywords": ["EJP SOIL", "soil threat", "soil sealing", "remote sensing", "Austria", "2018", "D3.3", "SERENA"], "contacts": [{"organization": "Foldal, Cecilie", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13969874"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13969874", "name": "item", "description": "10.5281/zenodo.13969874", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13969874"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-22T00:00:00Z"}}, {"id": "10.5281/zenodo.13969875", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:25:17Z", "type": "Dataset", "created": "2024-10-20", "title": "SERENA_EJPSOIL_AT_SOILSEALING_SEALINGDEGREE", "description": "The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. \u00a0  This data was prepared according to the methodology of SERENA Soil Sealing cookbook. For Austria, the application was carried out at regional scale. The map of soil sealing (soil threat) was based on classification of multitemporal satellite images and photointerpretation. The methodology uses vegetation and backscatter indices calculated over time series of images, and decision rules. The semi-automatic classification supports the subsequent photointerpretation phase based on national orthophotos and other free VHR satellite images.", "keywords": ["EJP SOIL", "soil threat", "soil sealing", "remote sensing", "Austria", "2018", "D3.3", "SERENA"], "contacts": [{"organization": "Foldal, Cecilie", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13969875"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13969875", "name": "item", "description": "10.5281/zenodo.13969875", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13969875"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-22T00:00:00Z"}}, {"id": "10.5281/zenodo.14008412", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:25:21Z", "type": "Dataset", "title": "SERENA EJPSOIL BE Flanders soil sealing cookbook", "description": "Open AccessThe internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national and European scales.The data was prepared according to the Level 2 methodology of the SERENA soil sealing cookbook. For Belgium, the application was carried out at the regional scale for the Flanders region. \u00a0The automatically generated yearly soil sealing maps (1 m resolution GeoTIFF rasters)\u00a0combine \u201cknown\u201d sealing from administrative databases (buildings and transport infrastructure) with modelled sealing based on artificial intelligence. Administrative databases do not (adequately) cover parking lots, private driveways and garden terraces, which are a substantial part of the sealed area in Flanders. Hence, a machine learning model was built for deriving this remaining sealing from aerial imagery. For this purpose, an assessor manually labeled the sealed parts on a subset of the images. Based on this training set, a convolutional neural network model was used to produce a sealing probability map, which was converted to a binary modelled sealing map. Finally, a continuity correction was applied to ensure a temporally consistent result across the yearly maps. \u00a0The objective of the SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. The selected indicator was the degree of soil sealing. By evaluating this degree at two moments in time, the change in soil sealing can be determined. \u00a0\u00a0The following data were used:\u00a0         Large-scale Reference Database (Grootschalig Referentiebestand or Basiskaart), the digital topographic reference map for Flanders (vector)\u00a0           Medium-scale annual winter aerial images of Flanders (15 or 25 cm raster resolution)    This dataset is originally hosted at Geopunt (www.geopunt.be). For the most up-to-date version of the dataset, please access the data from the Geopunt repository.", "keywords": ["soil sealing", "remote sensing", "BELGIUM (FLANDERS)", "aerial images", "SERENA", "EJP-Soil", "photointerpretation"], "contacts": [{"organization": "Cockx, Kasper, Oorts, Katrien,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14008412"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14008412", "name": "item", "description": "10.5281/zenodo.14008412", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14008412"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-30T00:00:00Z"}}, {"id": "10.5281/zenodo.14044657", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:25:23Z", "type": "Dataset", "title": "SERENA EJPSOIL BE Flanders soil sealing cookbook", "description": "Open AccessThe internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national and European scales.The data was prepared according to the Level 2 methodology of the SERENA soil sealing cookbook. For Belgium, the application was carried out at the regional scale for the Flanders region. \u00a0The automatically generated yearly soil sealing maps (1 m resolution GeoTIFF rasters)\u00a0combine \u201cknown\u201d sealing from administrative databases (buildings and transport infrastructure) with modelled sealing based on artificial intelligence. Administrative databases do not (adequately) cover parking lots, private driveways and garden terraces, which are a substantial part of the sealed area in Flanders. Hence, a machine learning model was built for deriving this remaining sealing from aerial imagery. For this purpose, an assessor manually labeled the sealed parts on a subset of the images. Based on this training set, a convolutional neural network model was used to produce a sealing probability map, which was converted to a binary modelled sealing map. Finally, a continuity correction was applied to ensure a temporally consistent result across the yearly maps. \u00a0The objective of the SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. The selected indicator was the degree of soil sealing. By evaluating this degree at two moments in time, the change in soil sealing can be determined. \u00a0\u00a0The following data were used:\u00a0         Large-scale Reference Database (Grootschalig Referentiebestand or Basiskaart), the digital topographic reference map for Flanders (vector)\u00a0           Medium-scale annual winter aerial images of Flanders (15 or 25 cm raster resolution)    This dataset is originally hosted at Geopunt (www.geopunt.be). For the most up-to-date version of the dataset, please access the data from the Geopunt repository.", "keywords": ["soil sealing", "remote sensing", "BELGIUM (FLANDERS)", "aerial images", "SERENA", "EJP-Soil", "photointerpretation"], "contacts": [{"organization": "Cockx, Kasper, Oorts, Katrien,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14044657"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14044657", "name": "item", "description": "10.5281/zenodo.14044657", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14044657"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-30T00:00:00Z"}}, {"id": "10.5281/zenodo.14055243", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:25:24Z", "type": "Dataset", "title": "Plasticulture detection at the country scale by combining multispectral and SAR satellite data", "description": "Open AccessThe data is split into 4 parts:  Part 1 \u2013 Regions for training plasticulture detection: the shapefile contains polygonal areas collected in Western Germany and used to train a random forest algorithm for plasticulture detection. The data is stored in file ESR02_11.zip while meta data can be found in ESR02_11.pdf.  Part 2 \u2013 Google Earth Engine scripts for plasticulture detection: the folder contains 5 Jupyter Notebook files, representing the scripts used to detect plasticulture in Germany, and to export and evaluate the results. The data is stored in file ESR02_12.zip while meta data can be found in ESR02_12.pdf.  Part 3 \u2013 Plasticulture area in Germany on a hexagonal grid: the shapefile contains a hexagonal grid covering the German territory, where the area covered by plastic free farmland, plastic mulched farmland, and plastic covers above vegetation was calculated for each hexagon. The data is stored in file ESR02_13.zip while meta data can be found in ESR02_13.pdf.  Part 4 \u2013 Plasticulture area in Germany on a hexagonal grid - false hotspots unmasked: the shapefile contains a hexagonal grid covering the German territory, where the area covered by plastic free farmland, plastic mulched farmland, and plastic covers above vegetation was calculated for each hexagon, before identifying the regions of false plastic detection. The data is stored in file ESR02_14.zip while meta data can be found in ESR02_14.pdf.", "keywords": ["Machine learning", "Mulching", "Agriculture", "Greenhouses", "Remote sensing", "Plastic", "Plasticulture"], "contacts": [{"organization": "Fabrizi, Alessandro, Fiener, Peter, Jagdhuber, Thomas, Van Oost, Kristof, Wilken, Florian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14055243"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14055243", "name": "item", "description": "10.5281/zenodo.14055243", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14055243"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-08T00:00:00Z"}}, {"id": "10.5281/zenodo.14055244", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:25:24Z", "type": "Dataset", "title": "Plasticulture detection at the country scale by combining multispectral and SAR satellite data", "description": "Open AccessThe data is split into 4 parts:  Part 1 \u2013 Regions for training plasticulture detection: the shapefile contains polygonal areas collected in Western Germany and used to train a random forest algorithm for plasticulture detection. The data is stored in file ESR02_11.zip while meta data can be found in ESR02_11.pdf.  Part 2 \u2013 Google Earth Engine scripts for plasticulture detection: the folder contains 5 Jupyter Notebook files, representing the scripts used to detect plasticulture in Germany, and to export and evaluate the results. The data is stored in file ESR02_12.zip while meta data can be found in ESR02_12.pdf.  Part 3 \u2013 Plasticulture area in Germany on a hexagonal grid: the shapefile contains a hexagonal grid covering the German territory, where the area covered by plastic free farmland, plastic mulched farmland, and plastic covers above vegetation was calculated for each hexagon. The data is stored in file ESR02_13.zip while meta data can be found in ESR02_13.pdf.  Part 4 \u2013 Plasticulture area in Germany on a hexagonal grid - false hotspots unmasked: the shapefile contains a hexagonal grid covering the German territory, where the area covered by plastic free farmland, plastic mulched farmland, and plastic covers above vegetation was calculated for each hexagon, before identifying the regions of false plastic detection. The data is stored in file ESR02_14.zip while meta data can be found in ESR02_14.pdf.", "keywords": ["Machine learning", "Mulching", "Agriculture", "Greenhouses", "Remote sensing", "Plastic", "Plasticulture"], "contacts": [{"organization": "Fabrizi, Alessandro, Fiener, Peter, Jagdhuber, Thomas, Van Oost, Kristof, Wilken, Florian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14055244"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14055244", "name": "item", "description": "10.5281/zenodo.14055244", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14055244"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-08T00:00:00Z"}}, {"id": "10.5281/zenodo.14055260", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:25:24Z", "type": "Other", "title": "Influence of soil texture on the estimation of soil organic carbon from Sentinel-2 temporal mosaics at 34 European sites", "description": "The aim of this study was to investigate the influence of soil texture on SOC predictions using Sentinel-2 temporal mosaics. The study analysed how local within-site variability and correlations in SOC and soil texture influence the possibility of predicting SOC from satellite data using local models at sites in different pedo-climatic zones across Europe. Analyses of 34 individual sites in 10 European countries were carried out within the framework of the STEROPES project of the European Joint H2020 Programme, EJP SOIL.  The manuscript is curently under review in European journal of Soil Science.", "keywords": ["EJP SOIL", "remote sensing", "STEROPES", "satellite", "SOC", "clay", "soil moisture", "time series", "field scale"], "contacts": [{"organization": "Wetterlind, J, Simmler, M, Castaldi, F, Bor\u016fvka, L, Gabriel, J.L., Gomes, L.C., Khosravi, V, K\u0131vrak, C, Koparan, M.H., L\u00e1zaro-L\u00f3pez, A, Liebisch, F, Rodriguez, J.A., Sava\u015f, A.\u00d6., Stenberg, B, Tun\u00e7ay, T, Vinci, I, Volungevi\u010dius, J, \u017dydelis, R, Vaudour, E,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14055260"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14055260", "name": "item", "description": "10.5281/zenodo.14055260", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14055260"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-08T00:00:00Z"}}, {"id": "10.5281/zenodo.14056687", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:25:24Z", "type": "Report", "title": "STEROPES synthesis report", "description": "STEROPES, named after a cyclop from the Ancient Greek mythology, stands for \u201cstimulating novel technologies from earth remote sensing to predict European soil carbon\u201d. The overall context of this project (36 months duration plus 6 months-extension, started on 1st February 2021) stems from the need to spatially estimate and monitor soil properties, and especially soil organic carbon (SOC) content, for decision support and land management, notably regarding carbon balance assessment and the feasibility of carbon credits, and more generally in relation to soil health and ecosystem services assessment.  The main aim of STEROPES was to elaborate maps from satellite time series, such as those\u00a0made available by the European Space Agency through the Copernicus data portal. The STEROPES consortium was composed of 19 public institutes from 14 countries spread over Europe, hence with highly diverse agropedoclimatic contexts.  The first phase of the project consisted of constructing reflectance image spectra of optical satellite series, notably Sentinel-2 (ESA), based on a number of diversified areas for which SOC samples were already available. Over the course of the project, several datasets have been collected focusing on small regions of some hundreds of km\u00b2 or on detailed scales of farms or catchments of some km\u00b2, for which SOC samples were already available with an areal density higher than 1-3 samples/km\u00b2.  The second phase of the project was dedicated to analysing the influence of various factors on SOC prediction performance: soil moisture, texture, green and dry vegetation due to management practices, salinity. Then, for the sites where satellite information may not enable to derive acceptable predictions, other ancillary data such as gamma-ray data layers were considered.  The third phase of the project included field campaigns that were carried out in order to complement or to provide datasets; the processing of the data and the launch of an additional work package dedicated to emerging research gaps.  This final report compiles the overall results of the work packages and their joint analysis in WP6 of\u00a0STEROPES.", "keywords": ["EJP SOIL", "remote sensing", "STEROPES", "satellite", "SOC", "time series"], "contacts": [{"organization": "Vaudour, Emmanuelle, Wetterlind, Johanna, van Egmond, Fenny, Bor\u016fvka, Lubo\u0161, Castaldi, Fabio, Dik, Pim, Farzamian, Mohammad, Fouad, Youssef, Liebisch, Frank, Lopatka, Artur, Michot, Didier, Nino, Pasquale, Saberioon, Mehdi, Simmler, Michael, Vanderhasselt, Adrian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14056687"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14056687", "name": "item", "description": "10.5281/zenodo.14056687", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14056687"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-08T00:00:00Z"}}, {"id": "10.5281/zenodo.14163614", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:25:27Z", "type": "Dataset", "title": "STEROPES_dataset-29", "description": "Dataset 29: Multispectral UAV data, ILVO\u00a0\u00a0  Origin: Multispectral UAV remote sensing data of soil surface were collected across the ILVO field trails (s15 - BOPACT & S2 - Gavere) in Flanders (Belgium). \u00a0  Purpose: Used in WP 4.\u00a0\u00a0  Format: raster files.\u00a0\u00a0  Data utility:\u00a0 For fusing with sentinel-2 data in order to improve the spatial resolution of sentinel-2 images in evaluating the impact of plant residual on SOC prediction.", "keywords": ["EJP SOIL", "remote sensing", "STEROPES", "UAV", "SENTINEL2"], "contacts": [{"organization": "De Boever, Maarten, saberioon, mohammadmehdi, Van Beek, Jonathan, Callens, Bert,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14163614"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14163614", "name": "item", "description": "10.5281/zenodo.14163614", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14163614"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-14T00:00:00Z"}}, {"id": "10.5281/zenodo.14169022", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:25:27Z", "type": "Dataset", "title": "STEROPES - Dataset 19: Collected soil data, ILVO", "description": "Dataset 19: Collected soil data, ILVO (Belgium)  Origin: First field trial on BOPACT LTE (S15) and on field trial S2 in Gavere, 16 samples from topsoil 0-10 cm; in 5m-radius around center of each plot): soil carbon and soil moisture). Second field trial, 20 samples from top soil 0-10 cm during 2021 and 2022 collected and soil organic carbon measured.\u00a0\u00a0  Purpose and objectives: to analyse the input of plant residue in SOC prediction from remote sensing data under WP4.\u00a0  Format: Data in the format of CSV .\u00a0\u00a0  Data utility: Data is collected based on the experiment where each plot has different amount of plant residual from without plant residual till completely covered with plant residual.", "keywords": ["EJP SOIL", "sampling", "remote sensing", "STEROPES", "SENTINEL2", "soil"], "contacts": [{"organization": "De Boever, Maarten, saberioon, mohammadmehdi,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14169022"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14169022", "name": "item", "description": "10.5281/zenodo.14169022", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14169022"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-14T00:00:00Z"}}, {"id": "10.5281/zenodo.14184825", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:25:28Z", "type": "Report", "title": "The EJP SOIL ProbeField Session of seven presentations at the GPSS workshop in Gent October 2024", "description": "This presentation is a compilation of seven presentations covering a large part of the work and findings of the EJP SOIL project ProbeField - A novel protocol for robust in field monitoring of carbon stock and soil fertility based on proximal sensors and existing soil spectral libraries given at a special session during\u00a0The Sixth Global Proximal Soil Sensing Workshop (https://www.gpss-2024.com/)\u00a0 in Gent in October 2024. ProbeField is a joint project involving 14 partners from 12 countries:\u00a0  Swedish University of Agricultural Sciences (SLU), Aarhus University (AU), Austrian Agency for Health and Food Safety Ltd. (AGES), Agroscope (AGS), University of Natural Resources and Life Sciences Vienna (BOKU), National Research Council of Italy (CNR), Council for Agricultural Research and Economics (CREA), Spanish National Research Council (CSIC), Czech University of Life Sciences Prague (CZU), National Research Institute for Agriculture, Food and Environment (INRAE), Institute of Soil Science and Plant Cultivation - State Research Institute (IUNG-PIB), General Directorate of Agricultural Research and Policies (TAGEM), University of Maribor, Faculty of Agriculture and Life Sciences (UM-FKBV) and Wageningen Environmental Research (WR).", "keywords": ["Proximal soil sensing", "ProbeField", "EJP SOIL", "Soil sciences", "Remote sensing"], "contacts": [{"organization": "Stenberg, Bo, Metzger, Konrad, Liebisch, Frank, Castaldi, Fabio, van Egmond, Fenny, Lozano Fond\u00f3n, Carlos, Ben Dor, Eyal,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14184825"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14184825", "name": "item", "description": "10.5281/zenodo.14184825", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14184825"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-19T00:00:00Z"}}, {"id": "10.5281/zenodo.14184826", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:25:28Z", "type": "Report", "title": "The EJP SOIL ProbeField Session of seven presentations at the GPSS workshop in Gent October 2024", "description": "This presentation is a compilation of seven presentations covering a large part of the work and findings of the EJP SOIL project ProbeField - A novel protocol for robust in field monitoring of carbon stock and soil fertility based on proximal sensors and existing soil spectral libraries given at a special session during\u00a0The Sixth Global Proximal Soil Sensing Workshop (https://www.gpss-2024.com/)\u00a0 in Gent in October 2024. ProbeField is a joint project involving 14 partners from 12 countries:\u00a0  Swedish University of Agricultural Sciences (SLU), Aarhus University (AU), Austrian Agency for Health and Food Safety Ltd. (AGES), Agroscope (AGS), University of Natural Resources and Life Sciences Vienna (BOKU), National Research Council of Italy (CNR), Council for Agricultural Research and Economics (CREA), Spanish National Research Council (CSIC), Czech University of Life Sciences Prague (CZU), National Research Institute for Agriculture, Food and Environment (INRAE), Institute of Soil Science and Plant Cultivation - State Research Institute (IUNG-PIB), General Directorate of Agricultural Research and Policies (TAGEM), University of Maribor, Faculty of Agriculture and Life Sciences (UM-FKBV) and Wageningen Environmental Research (WR).", "keywords": ["Proximal soil sensing", "ProbeField", "EJP SOIL", "Soil sciences", "Remote sensing"], "contacts": [{"organization": "Stenberg, Bo, Metzger, Konrad, Liebisch, Frank, Castaldi, Fabio, van Egmond, Fenny, Lozano Fond\u00f3n, Carlos, Ben Dor, Eyal,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14184826"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14184826", "name": "item", "description": "10.5281/zenodo.14184826", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14184826"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.14336252", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:25:34Z", "type": "Dataset", "title": "Hyperspectral and multispectral reflectance of agricultural plastic films", "description": "Open AccessThe dataset contains hyperspectral and multispectral reflectance measurements of various agricultural plastic films on two soil backgrounds, captured using proximal sensing techniques.", "keywords": ["Hyperspectral", "Multispectral", "Agriculture", "Spectral library", "Plastic", "Remote sensing", "Plasticulture"], "contacts": [{"organization": "Fabrizi, Alessandro, Fiener, Peter, Van Oost, Kristof, Wilken, Florian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14336252"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14336252", "name": "item", "description": "10.5281/zenodo.14336252", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14336252"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-12-09T00:00:00Z"}}, {"id": "10.5281/zenodo.14285685", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:25:33Z", "type": "Dataset", "title": "Soil Health Index and Soil Function maps for Latin America and the Caribbean", "description": "Description:This repository contains 90-meter resolution raster maps generated as part of the study titled \u201cSoil Health in Latin America and the Caribbean\u201d. These datasets provide geospatial information on soil health and its five primary functions across the Latin America and Caribbean (LAC) region. The data aim to support research, policy-making, and land management practices by offering insights into soil health conditions and functionality at a continental scale.  Data Included:      Soil Health Index (SHI):\u00a0      LAC_SHI: Comprehensive index integrating physical, chemical, and biological soil attributes to assess soil health across LAC (Size 3.19 Gb).       Soil Functions (f):      LAC_fi: Storage and regulation of nutrient fluxes and availability (Size 2.12 Gb).     LAC_fii: Regulation of water fluxes, storage, and availability (Size 2.59 Gb).     LAC_fiii: Soil organic carbon sequestration and biodiversity support (Size 1.94 Gb).     LAC_fiv: Physical support for plant growth (Size 2.48 Gb).     LAC_fv: Resistance to erosion and degradation (Size 2.42 Gb).      Format:      Raster maps in GeoTIFF format (*.tif).     Spatial resolution: 90 meters.     Coordinate reference system: EPSG:4326 (WGS 84).     Scale factor: 0.01    Use and applications:      Environmental research and modeling.     Policy development for soil conservation and sustainable land management.     Educational purposes in soil science and geospatial studies.    Visualization and other sources:Additionally, the Soil Health Index (SHI) and soil functions (SF) maps can be visualized via the Earth Engine application at https://geocis.users.earthengine.app/view/lac-soil-health and downloaded from https://geocis.users.earthengine.app/view/lac-soil-health-download. For more information, access it on the GeoCiS website, available at https://esalqgeocis.wixsite.com/english/thematic-products.  Acknowledgments:We thank the S\u00e3o Paulo Research Foundation (FAPESP, process 2014/22262-0; 2021/05129-8), the Center for Carbon Research in Tropical Agriculture (CCARBON/USP, process 2021/10573-4) and the Geotechnologies in Soil Science research group (GeoCiS, https://esalqgeocis.wixsite.com/english) for supporting this work.", "keywords": ["Soil sciences", "Machine learning", "Geotechnology", "Remote sensing", "Soil quality", "Environmental Policy"], "contacts": [{"organization": "Poppiel, Ra\u00fal Roberto, Cherubin, Maur\u00edcio Roberto, Novais, Jean Jesus Macedo, Dematte, Jose A. 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