{"type": "FeatureCollection", "features": [{"id": "10.3390/rs13061133", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:01Z", "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": "

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.

", "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": "10.1038/s41598-025-93658-2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:17:43Z", "type": "Journal Article", "created": "2025-04-04", "title": "Plasticulture detection at the country scale by combining multispectral and SAR satellite data", "description": "Abstract

The use of plastic films has been growing in agriculture, benefiting consumers and producers. However, concerns have been raised about the environmental impact of plastic film use, with mulching films posing a greater threat than greenhouse films. This calls for large-scale monitoring of different plastic film uses. We used cloud computing, freely available optical and radar satellite images, and machine learning to map plastic-mulched farmland (PMF) and plastic cover above vegetation (PCV) (e.g., greenhouse, tunnel) across Germany. The algorithm detected 103 103 ha of PMF and 37 103 ha of PCV in 2020, while a combination of agricultural statistics and surveys estimated a smaller plasticulture cover of around 100 103 ha in 2019. Based on ground observations, the overall accuracy of the classification is 85.3%. Optical and radar features had similar importance scores, and a distinct backscatter of PCV was related to metal frames underneath the plastic films. Overall, the algorithm achieved great results in the distinction between PCV and PMF. This study maps different plastic film uses at a country scale for the first time and sheds light on the high potential of freely available satellite data for continental monitoring.Archaeological Heritage, naturally endangered by environmental processes and anthropogenic pressures, is today increasingly at risk, because of intense human activities and climate change, and their impact on atmosphere and soil. European research is increasingly dedicated to the development of good practices for monitoring archaeological sites and their preservation. One of the running projects about these topics is RESEARCH (Remote Sensing techniques for Archaeology; H2020-MSCA-RISE, grant agreement: 823987), started in 2018 and ending in 2022. RESEARCH aims at testing risk assessment methodology using an integrated system of documentation and research in the fields of archaeology and environmental studies. It will introduce a strategy and select the most efficient tools for the harmonization of different data, criteria, and indicators in order to produce an effective risk assessment. These will be used to assess and monitor the impact of soil erosion, land movement, and land-use change on tangible archaeological heritage assets. As a final product, the Project addresses the development of a multi-task thematic platform, combining advanced remote sensing technologies with GIS application. The demonstration and validation of the Platform will be conducted on six case studies located in Italy, Greece, Cyprus, and Poland, and variously affected by the threats considered by the Project. In the frame of RISE (Research and Innovation Staff Exchange), RESEARCH will coordinate the existing expertise and research efforts of seven beneficiaries into a synergetic plan of collaborations and exchanges of personnel (Ph.D. students and research staff), to offer a comprehensive transfer of knowledge and training environment for the researchers in the specific area. This paper aims at illustrating the results of the activities conducted during the first year of the Project, which consisted in developing an effective risk assessment methodology for soil-related threats affecting archaeological heritage, and defining the scientific requirements and the user requirements of the Platform. The activities have been conducted in synergy with all the Partners and were supported by the possibility of staff exchange allowed by the funding frame MSCA-RISE.

", "keywords": ["13. Climate action", "11. Sustainability", "Research; Remote sensing; environmental", "15. Life on land", "12. Responsible consumption"], "contacts": [{"organization": "de Angeli S., Battistin F., Serpetti M., Iorio A. D., Moresi F. V.,", "roles": ["creator"]}]}, "links": [{"href": "https://iris.uniroma1.it/bitstream/11573/1481228/2/Moresi_Research-project_2020.pdf"}, {"href": "https://doi.org/10.1088/1757-899x/949/1/012058"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IOP%20Conference%20Series%3A%20Materials%20Science%20and%20Engineering", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1088/1757-899x/949/1/012058", "name": "item", "description": "10.1088/1757-899x/949/1/012058", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1088/1757-899x/949/1/012058"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-01T00:00:00Z"}}, {"id": "10.1080/00438243.2021.1891963", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:18:06Z", "type": "Journal Article", "created": "2021-03-23", "title": "European agricultural terraces and lynchets: from archaeological theory to heritage management", "description": "Terraces are highly productive, culturally distinctive socioecological systems. Although they form part of time/place-specific debates, terraces per se have been neglected - fields on slopes or landscape elements. We argue that this is due to mapping and dating problems, and lack of artefacts/ecofacts. However, new techniques can overcome some of these constraints, allowing us to re-engage with theoretical debates around agricultural intensification. Starting from neo-Broserupian propositions, we can engage with the sociopolitical and environmental aspects of terrace emergence, maintenance and abandonment. Non-reductionist avenues include identifying and dating different phases of development within single terrace systems, identifying a full crop-range, and other activities not generally associated with terraces (e.g. metallurgy). The proposition here is that terraces are a multi-facetted investment that includes both intensification and diversification and can occur under a range of social conditions but which constitutes a response to demographic pressure in the face to fluctuating environmental conditions.", "keywords": ["2. Zero hunger", "550", "11. Sustainability", "VDP::Humanities: 000::Archeology: 090", "0601 history and archaeology", "Articles", "06 humanities and the arts", "VDP::Humaniora: 000::Arkeologi: 090", "15. Life on land", "Agricultural intensification; agricultural sustainability; landscape change; population density; remote sensing; terrace classification"]}, "links": [{"href": "https://eprints.whiterose.ac.uk/172476/1/European_agricultural_terraces_and_lynchets_from_archaeological_theory_to_heritage_management.pdf"}, {"href": "https://www.research.unipd.it/bitstream/11577/3390089/5/Brown%20et%20al.%20%282020%29.pdf"}, {"href": "https://eprints.soton.ac.uk/448979/1/European_agricultural_terraces_and_lynchets_from_archaeological_theory_to_heritage_management.pdf"}, {"href": "https://www.tandfonline.com/doi/pdf/10.1080/00438243.2021.1891963"}, {"href": "https://doi.org/10.1080/00438243.2021.1891963"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/World%20Archaeology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1080/00438243.2021.1891963", "name": "item", "description": "10.1080/00438243.2021.1891963", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1080/00438243.2021.1891963"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-07T00:00:00Z"}}, {"id": "10.1109/JURSE.2017.7924592", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:18:27Z", "type": "Journal Article", "created": "2017-05-12", "title": "EO-based products in support of urban heat fluxes estimation", "description": "Presently, there is a growing need for information suitable to effectively characterize the Urban Energy Budget (UEB) and, hence, to properly estimate the magnitude of the anthropogenic heat flux Q F . Indeed, a precise knowledge of Q F - whose implications for urban planners are still prone to large uncertainties - is fundamental for implementing effective strategies to improve thermal comfort and energy efficiency. To address this challenging issue, the Horizon 2020 URBANFLUXES project aims at developing a novel methodology for accurately estimating the different terms of the UEB based on the use of Earth Observation (EO) data and, hence, at reliably characterizing the Q F spatiotemporal patterns and its implications on urban climate. In this paper, we aim at giving an overview of the EO-based products which have been identified as the most useful in the framework of the considered study. In particular, the suite which has been implemented so far in the first phase of the project includes biophysical parameters, morphology parameters as well as land-cover maps.", "keywords": ["Anthropogenic Heat Flux", "H2020 URBANFLUXES", "13. Climate action", "11. Sustainability", "0211 other engineering and technologies", "Earth Observation", "Urban Remote Sensing", "02 engineering and technology", "01 natural sciences", "7. Clean energy", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/7919506/7924526/07924592.pdf?arnumber=7924592"}, {"href": "https://doi.org/10.1109/JURSE.2017.7924592"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2017%20Joint%20Urban%20Remote%20Sensing%20Event%20%28JURSE%29", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/JURSE.2017.7924592", "name": "item", "description": "10.1109/JURSE.2017.7924592", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/JURSE.2017.7924592"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-03-01T00:00:00Z"}}, {"id": "10.1109/JURSE.2017.7924594", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:18:27Z", "type": "Journal Article", "created": "2017-05-12", "title": "Spatial distribution of sensible and latent heat flux in the URBANFLUXES case study city Basel (Switzerland)", "description": "Turbulent sensible and latent heat fluxes are calculated by a combined method using micrometeorological approaches (the Aerodynamic Resistance Method ARM), Earth Observation (EO) data and GIS-Techniques. The spatial distributions of turbulent heat fluxes were analyzed for 22 for the city of Basel (Switzerland), covering all seasons and different meteorological conditions. Seasonal variations in heat fluxes are strongly dependent on meteorological conditions, i.e. air temperature, water vapor saturation deficit and wind speed. The agreement of measured fluxes (by the Eddy Covariance method) with modeled fluxes in the weighted source area of the flux towers is moderate due to known drawbacks in the modelling approach and uncertainties inherent to EC measurements, particularly also in urban areas.", "keywords": ["H2020 URBANFLUXES", "13. Climate action", "Sensible Heat Flux", "11. Sustainability", "0211 other engineering and technologies", "Urban Remote Sensing", "02 engineering and technology", "Latent Heat Flux", "7. Clean energy", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/7919506/7924526/07924594.pdf?arnumber=7924594"}, {"href": "https://doi.org/10.1109/JURSE.2017.7924594"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2017%20Joint%20Urban%20Remote%20Sensing%20Event%20%28JURSE%29", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/JURSE.2017.7924594", "name": "item", "description": "10.1109/JURSE.2017.7924594", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/JURSE.2017.7924594"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-03-01T00:00:00Z"}}, {"id": "10.1111/ejss.70054", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:18:35Z", "type": "Journal Article", "created": "2025-02-05", "title": "Influence of Soil Texture on the Estimation of Soil Organic Carbon From Sentinel\u20102 Temporal Mosaics at\u00a034\u00a0European Sites", "description": "ABSTRACT

Multispectral imaging satellites such as Sentinel\uffe2\uff80\uff902 are considered a possible tool to assist in the mapping of soil organic carbon (SOC) using images of bare soil. However, the reported results are variable. The measured reflectance of the soil surface is not only related to SOC but also to several other environmental and edaphic factors. Soil texture is one such factor that strongly affects soil reflectance. Depending on the spatial correlation with SOC, the influence of soil texture may improve or hinder the estimation of SOC from spectral data. This study aimed to investigate these influences using local models at 34 sites in different pedo\uffe2\uff80\uff90climatic zones across 10 European countries. The study sites were individual agricultural fields or a few fields in close proximity. For each site, local models to predict SOC and the clay particle size fraction were developed using the Sentinel\uffe2\uff80\uff902 temporal mosaics of bare soil images. Overall, predicting SOC and clay was difficult, and prediction performances with a ratio of performance to deviation (RPD) >\uffe2\uff80\uff891.5 were observed at 8 and 12 of the 34 sites for SOC and clay, respectively. A general relationship between SOC prediction performance and the correlation of SOC and clay in soil was evident but explained only a small part of the large variability we observed in SOC prediction performance across the sites. Adding information on soil texture as additional predictors improved SOC prediction on average, but the additional benefit varied strongly between the sites. The average relative importance of the different Sentinel\uffe2\uff80\uff902 bands in the SOC and clay models indicated that spectral information in the red and far\uffe2\uff80\uff90red regions of the visible spectrum was more important for SOC prediction than for clay prediction. The opposite was true for the region around 2200\uffe2\uff80\uff89nm, which was more important in the clay models.This study dynamically monitors ecosystem performance (EP) to identify grasslands potentially suitable for cellulosic feedstock crops (e.g., switchgrass) within the Greater Platte River Basin (GPRB). We computed grassland site potential and EP anomalies using 9\uffe2\uff80\uff90year (2000\uffe2\uff80\uff932008) time series of 250\uffc2\uffa0m expedited moderate resolution imaging spectroradiometer Normalized Difference Vegetation Index data, geophysical and biophysical data, weather and climate data, and EP models. We hypothesize that areas with fairly consistent high grassland productivity (i.e., high grassland site potential) in fair to good range condition (i.e., persistent ecosystem overperformance or normal performance, indicating a lack of severe ecological disturbance) are potentially suitable for cellulosic feedstock crop development. Unproductive (i.e., low grassland site potential) or degraded grasslands (i.e., persistent ecosystem underperformance with poor range condition) are not appropriate for cellulosic feedstock development. Grassland pixels with high or moderate ecosystem site potential and with more than 7\uffc2\uffa0years ecosystem normal performance or overperformance during 2000\uffe2\uff80\uff932008 are identified as possible regions for future cellulosic feedstock crop development (ca. 68\uffc2\uffa0000\uffc2\uffa0km2 within the GPRB, mostly in the eastern areas). Long\uffe2\uff80\uff90term climate conditions, elevation, soil organic carbon, and yearly seasonal precipitation and temperature are important performance variables to determine the suitable areas in this study. The final map delineating the suitable areas within the GPRB provides a new monitoring and modeling approach that can contribute to decision support tools to help land managers and decision makers make optimal land use decisions regarding cellulosic feedstock crop development and sustainability.

", "keywords": ["2. Zero hunger", "satellite remote sensing", "550", "land management", "04 agricultural and veterinary sciences", "15. Life on land", "ecosystem performance models", "cellulosic feedstock crops", "6. Clean water", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Greater Platte River Basin", "cellulosic biofuel", "weather data", "eMODIS NDVI"]}, "links": [{"href": "https://doi.org/10.1111/j.1757-1707.2011.01113.x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/GCB%20Bioenergy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/j.1757-1707.2011.01113.x", "name": "item", "description": "10.1111/j.1757-1707.2011.01113.x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/j.1757-1707.2011.01113.x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2011-07-21T00:00:00Z"}}, {"id": "10.1175/bams-d-23-0005.1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:19:16Z", "type": "Journal Article", "created": "2023-08-23", "title": "Observing Mineral Dust in Northern Africa, the Middle East, and Europe: Current Capabilities and Challenges ahead for the Development of Dust Services", "description": "Abstract

Mineral dust produced by wind erosion of arid and semiarid surfaces is a major component of atmospheric aerosol that affects climate, weather, ecosystems, and socioeconomic sectors such as human health, transportation, solar energy, and air quality. Understanding these effects and ultimately improving the resilience of affected countries requires a reliable, dense, and diverse set of dust observations, fundamental for the development and the provision of skillful dust-forecast-tailored products. The last decade has seen a notable improvement of dust observational capabilities in terms of considered parameters, geographical coverage, and delivery times, as well as of tailored products of interest to both the scientific community and the various end-users. Given this progress, here we review the current state of observational capabilities, including in situ, ground-based, and satellite remote sensing observations in northern Africa, the Middle East, and Europe for the provision of dust information considering the needs of various users. We also critically discuss observational gaps and related unresolved questions while providing suggestions for overcoming the current limitations. Our review aims to be a milestone for discussing dust observational gaps at a global level to address the needs of users, from research communities to nonscientific stakeholders.

Agriculture is the backbone and the main sector of the industry for many countries in the world. Assessing crop yields is key to optimising on-field decisions and defining sustainable agricultural strategies. Remote sensing applications have greatly enhanced our ability to monitor and manage farming operation. The main objective of this research was to evaluate machine learning system for within-field soya yield prediction trained on Sentinel-2 multispectral images and soil parameters. Multispectral images used in the study came from ESA\u2019s Sentinel-2 satellites. A total of 3 cloud-free Sentinel-2 multispectral images per year from specific periods of vegetation were used to obtain the time-series necessary for crop yield prediction. Yield monitor data were collected in three crop seasons (2018, 2019 and 2020) from a number of farms located in Upper Austria. The ground-truth database consisted of information about the location of the fields and crop yield monitor data on 411 ha of farmland. A novel method, namely the Polygon-Pixel Interpolation, for optimal fitting yield monitor data with satellite images is introduced. Several machine learning algorithms, such as Multiple Linear Regression, Support Vector Machine, eXtreme Gradient Boosting, Stochastic Gradient Descent and Random Forest, were compared for their performance in soya yield prediction. Among the tested machine learning algorithms, Stochastic Gradient Descent regression model performed better than the others, with a mean absolute error of 4.36 kg/pixel (0.436 t/ha) and a correlation coefficient of 0.83%.

", "keywords": ["2. Zero hunger", "precision agriculture", "stochastic gradient descent (SGD)", "polygon-pixel intersection (PPI)", "Science", "Q", "710", "high performance computing (HPC)", "04 agricultural and veterinary sciences", "15. Life on land", "630", "620", "remote sensing", "precision agriculture; remote sensing; polygon-pixel intersection (PPI); stochastic gradient descent (SGD); high performance computing (HPC)", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/9/2256/pdf"}, {"href": "https://doi.org/10.3390/rs14092256"}, {"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/rs14092256", "name": "item", "description": "10.3390/rs14092256", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs14092256"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-07T00:00:00Z"}}, {"id": "10.3390/rs71114708", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:03Z", "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": "

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.

", "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/app12126068", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:20:49Z", "type": "Journal Article", "created": "2022-06-16", "title": "Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions", "description": "

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.

", "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/rs10111720", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:00Z", "type": "Journal Article", "created": "2018-10-31", "title": "Towards Estimating Land Evaporation at Field Scales Using GLEAM", "description": "

The evaporation of water from land into the atmosphere is a key component of the hydrological cycle. Accurate estimates of this flux are essential for proper water management and irrigation scheduling. However, continuous and qualitative information on land evaporation is currently not available at the required spatio-temporal scales for agricultural applications and regional-scale water management. Here, we apply the Global Land Evaporation Amsterdam Model (GLEAM) at 100 m spatial resolution and daily time steps to provide estimates of land evaporation over The Netherlands, Flanders, and western Germany for the period 2013\uffe2\uff80\uff932017. By making extensive use of microwave-based geophysical observations, we are able to provide data under all weather conditions. The soil moisture estimates from GLEAM at high resolution compare well with in situ measurements of surface soil moisture, resulting in a median temporal correlation coefficient of 0.76 across 29 sites. Estimates of terrestrial evaporation are also evaluated using in situ eddy-covariance measurements from five sites, and compared to estimates from the coarse-scale GLEAM v3.2b, land evaporation from the Satellite Application Facility on Land Surface Analysis (LSA-SAF), and reference grass evaporation based on Makkink\uffe2\uff80\uff99s equation. All datasets compare similarly with in situ measurements and differences in the temporal statistics are small, with correlation coefficients against in situ data ranging from 0.65 to 0.95, depending on the site. Evaporation estimates from GLEAM-HR are typically bounded by the high values of the Makkink evaporation and the low values from LSA-SAF. While GLEAM-HR and LSA-SAF show the highest spatial detail, their geographical patterns diverge strongly due to differences in model assumptions, model parameterizations, and forcing data. The separate consideration of rainfall interception loss by tall vegetation in GLEAM-HR is a key cause of this divergence: while LSA-SAF reports maximum annual evaporation volumes in the Green Heart of The Netherlands, an area dominated by shrubs and grasses, GLEAM-HR shows its maximum in the national parks of the Veluwe and Heuvelrug, both densely-forested regions where rainfall interception loss is a dominant process. The pioneering dataset presented here is unique in that it provides observational-based estimates at high resolution under all weather conditions, and represents a viable alternative to traditional visible and infrared models to retrieve evaporation at field scales.

", "keywords": ["microwave remote sensing", "EVAPOTRANSPIRATION", "WACMOS-ET PROJECT", "Science", "FLUXNET", "Q", "LSA-SAF", "15. Life on land", "01 natural sciences", "6. Clean water", "MODEL", "CARBON", "VARIABILITY", "terrestrial evaporation", "root-zone soil moisture", "13. Climate action", "Earth and Environmental Sciences", "SURFACE EVAPORATION", "GLOBAL DATABASE", "WATER", "SOIL-MOISTURE RETRIEVALS", "terrestrial evaporation; root-zone soil moisture; microwave remote sensing; GLEAM; LSA-SAF", "GLEAM", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/10/11/1720/pdf"}, {"href": "https://doi.org/10.3390/rs10111720"}, {"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/rs10111720", "name": "item", "description": "10.3390/rs10111720", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs10111720"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-10-31T00:00:00Z"}}, {"id": "10.3390/rs11111350", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:01Z", "type": "Journal Article", "created": "2019-06-06", "title": "Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts", "description": "

Chlorophyll a concentration (Chla) is a well-proven proxy of biocrust development, photosynthetic organisms\uffe2\uff80\uff99 status, and recovery monitoring after environmental disturbances. However, laboratory methods for the analysis of chlorophyll require destructive sampling and are expensive and time consuming. Indirect estimation of chlorophyll a by means of soil surface reflectance analysis has been demonstrated to be an accurate, cheap, and quick alternative for chlorophyll retrieval information, especially in plants. However, its application to biocrusts has yet to be harnessed. In this study we evaluated the potential of soil surface reflectance measurements for non-destructive Chla quantification over a range of biocrust types and soils. Our results revealed that from the different spectral transformation methods and techniques, the first derivative of the reflectance and the continuum removal were the most accurate for Chla retrieval. Normalized difference values in the red-edge region and common broadband indexes (e.g., normalized difference vegetation index (NDVI)) were also sensitive to changes in Chla. However, such approaches should be carefully adapted to each specific biocrust type. On the other hand, the combination of spectral measurements with non-linear random forest (RF) models provided very good fits (R2 > 0.94) with a mean root mean square error (RMSE) of about 6.5 \uffc2\uffb5g/g soil, and alleviated the need for a specific calibration for each crust type, opening a wide range of opportunities to advance our knowledge of biocrust responses to ongoing global change and degradation processes from anthropogenic disturbance.

", "keywords": ["chlorophyll quantification", "remote sensing", "hyperspectral", "13. Climate action", "Science", "Q", "Biocrusts; biological soil crust; chlorophyll quantification; hyperspectral; random forest; remote sensing", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "random forest", "Biocrusts", "biological soil crust"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/11/11/1350/pdf"}, {"href": "https://www.mdpi.com/2072-4292/11/11/1350/pdf"}, {"href": "https://doi.org/10.3390/rs11111350"}, {"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/rs11111350", "name": "item", "description": "10.3390/rs11111350", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs11111350"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-05T00:00:00Z"}}, {"id": "10.3390/rs13163101", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:02Z", "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": "

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.

", "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": "10.4995/cigeo2021.2021.12729", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:21Z", "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": "

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.

", "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": "10.3389/frwa.2022.981745", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:20:45Z", "type": "Journal Article", "created": "2022-09-16", "title": "Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication", "description": "

The beginning of the 21st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems.

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.

", "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/app12157545", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:20:49Z", "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": "

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.

", "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/rs11080913", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:00Z", "type": "Journal Article", "created": "2019-04-15", "title": "Multispectral Contrast of Archaeological Features: A Quantitative Evaluation", "description": "

This study provides an evaluation of spectral responses of hollow ways in Upper Mesopotamia. Hollow ways were used for the transportation of animals, carts, and other moving agents for centuries. The aim is to show how the success of spectral indices varies in describing topologically simple features even in a seemingly homogeneous geographic unit. The variation is further highlighted under the changing precipitation regime. The methodology begins with an exploration of the relationship between the date of a multispectral scene and the visibility of hollow ways. The next step is to evaluate the impact of rainfall levels on numerous indices and to quantify spectral contrast. The contrast between a hollow way and its background is evaluated with Welch\uffe2\uff80\uff99s t-test and the association between precipitation regime and spectral responses of hollow ways are investigated with Correspondence Analysis and Fisher\uffe2\uff80\uff99s test. Results highlight an intrinsic relationship between the precipitation regime and the ways in which archaeological features reflects and/or emits electromagnetic energy. Next, the categorization of spectral indices based on different rainfall levels can be used as a guidance in future studies. Finally, the study suggests contrast becomes an even more fruitful concept as one moves from the spatial domain to the spectral domain.

", "keywords": ["Random Forests", "Lidar", "satellite remote sensing", "Science", "Q", "0211 other engineering and technologies", "Effectiveness of data fusion", "06 humanities and the arts", "02 engineering and technology", "Data fusion", "910", "15. Life on land", "archaeology of roads", "precipitation regime", "Imaging spectroscopy", "Precipitation regime", "spectral contrast", "Hollow ways", "Natura 2000 habitat", "13. Climate action", "Satellite remote sensing", "Upper Mesopotamia", "0601 history and archaeology", "Spectral contrast", "hollow ways"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/11/8/913/pdf"}, {"href": "https://iris.cnr.it/bitstream/20.500.14243/390208/1/prod_402195-doc_199283.pdf"}, {"href": "http://dro.dur.ac.uk/27994/1/27994.pdf"}, {"href": "http://dro.dur.ac.uk/27994/2/27994.pdf"}, {"href": "https://www.mdpi.com/2072-4292/11/8/913/pdf"}, {"href": "https://doi.org/10.3390/rs11080913"}, {"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/rs11080913", "name": "item", "description": "10.3390/rs11080913", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs11080913"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-15T00:00:00Z"}}, {"id": "10.3390/rs11091106", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:01Z", "type": "Journal Article", "created": "2019-05-09", "title": "Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion", "description": "

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

", "keywords": ["Landsat 8", "2. Zero hunger", "soil erosion", "550", "Science", "Q", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "01 natural sciences", "630", "field spectroscopy", "6. Clean water", "soil erosion; remote sensing; Sentinel-2; Landsat 8; ANN; RUSLE; field spectroscopy; OLSR; GWR", "remote sensing", "Field spectroscopy", "OLSR", "13. Climate action", "Soil erosion", "0401 agriculture", " forestry", " and fisheries", "RUSLE", "Sentinel-2", "ANN", "GWR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/11/9/1106/pdf"}, {"href": "https://doi.org/10.3390/rs11091106"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs11091106", "name": "item", "description": "10.3390/rs11091106", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs11091106"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-09T00:00:00Z"}}, {"id": "10.3390/rs12244118", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:01Z", "type": "Journal Article", "created": "2020-12-17", "title": "Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China", "description": "

Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil; however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model.

", "keywords": ["2. Zero hunger", "soil salinity; remote sensing; machine learning; predictive mapping", "soil salinity", "remote sensing", "machine learning", "13. Climate action", "Science", "Q", "0401 agriculture", " forestry", " and fisheries", "predictive mapping", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/24/4118/pdf"}, {"href": "https://doi.org/10.3390/rs12244118"}, {"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/rs12244118", "name": "item", "description": "10.3390/rs12244118", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12244118"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-16T00:00:00Z"}}, {"id": "10.3390/rs13020305", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:01Z", "type": "Journal Article", "created": "2021-01-20", "title": "Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China", "description": "

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

", "keywords": ["2. Zero hunger", "soil salinization; Sentinel-2 MSI; remote sensing; machine learning; arid area", "Science", "soil salinization", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "Sentinel-2 MSI", "6. Clean water", "remote sensing", "machine learning", "arid area", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/2/305/pdf"}, {"href": "https://doi.org/10.3390/rs13020305"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13020305", "name": "item", "description": "10.3390/rs13020305", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13020305"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-17T00:00:00Z"}}, {"id": "10.3390/rs14092106", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:02Z", "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": "

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.

", "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/s22051851", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:04Z", "type": "Journal Article", "created": "2022-02-28", "title": "Embedded Temporal Convolutional Networks for Essential Climate Variables Forecasting", "description": "

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.

", "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/w15091739", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:08Z", "type": "Journal Article", "created": "2023-05-01", "title": "Developing an Open-Source IoT Platform for Optimal Irrigation Scheduling and Decision-Making: Implementation at Olive Grove Parcels", "description": "

Climate change has reduced the availability of good quality water for agriculture, while favoring the proliferation of harmful insects, especially in Mediterranean areas. Deploying IoT-based systems can help optimize water-use efficiency in agriculture and address problems caused by extreme weather events. This work presents an IoT-based monitoring system for obtaining soil moisture, soil electrical conductivity, soil temperature and meteorological data useful in irrigation management and pest control. The proposed system was implemented and evaluated for olive parcels located both at coastal and inland areas of the eastern part of Crete; these areas face severe issues with water availability and saltwater intrusion (coastal region). The system includes the monitoring of soil moisture and atmospheric sensors, with the aim of providing information to farmers for decision-making and at the future implementation of an automated irrigation system, optimizing the use of water resources. Data acquisition was performed through smart sensors connected to a microcontroller. Data were received at a portal and made available on the cloud, being monitored in real-time through an open-source IoT platform. An e-mail alert was sent to the farmers when soil moisture was lower than a threshold value specific to the soil type or when climatic conditions favored the development of the olive fruit fly. One of the main advantages of the proposed decision-making system is a low-cost IoT solution, as it is based on open-source software and the hardware on edge devices consists of widespread economic modules. The reliability of the IoT-based monitoring system has been tested and could be used as a support service tool offering an efficient irrigation and pest control service.

", "keywords": ["2. Zero hunger", "0106 biological sciences", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "agricultural water management; decision support system; soil moisture; EC; smart sensor; Internet of Things; remote sensing", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/15/9/1739/pdf"}, {"href": "https://doi.org/10.3390/w15091739"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/w15091739", "name": "item", "description": "10.3390/w15091739", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/w15091739"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-04-30T00:00:00Z"}}, {"id": "10.4995/cigeo2021.2021.12694", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:21Z", "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": "

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.

", "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": "3138831713", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:59Z", "type": "Journal Article", "created": "2021-03-17", "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": "

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.

", "keywords": ["2. Zero hunger", "0106 biological sciences", "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/3138831713"}, {"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": "3138831713", "name": "item", "description": "3138831713", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3138831713"}, {"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": "10.5281/zenodo.8091638", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:36Z", "type": "Journal Article", "created": "2021-01-20", "title": "Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China", "description": "

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

", "keywords": ["2. Zero hunger", "soil salinization; Sentinel-2 MSI; remote sensing; machine learning; arid area", "Science", "soil salinization", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "Sentinel-2 MSI", "6. Clean water", "remote sensing", "machine learning", "arid area", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/2/305/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8091638"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8091638", "name": "item", "description": "10.5281/zenodo.8091638", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8091638"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-17T00:00:00Z"}}, {"id": "10.5281/zenodo.16889685", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:04Z", "type": "Dataset", "title": "Siora Open Soil Nutrient Dataset \u2013 Murcia, Spain (2000, 2010, 2020)", "description": "This dataset provides regional-scale direct estimates of soil nutrient properties across Spain\u2019s Region of Murcia at ~30 m spatial resolution. It includes three time slices (2000, 2010, and 2020) with coverage of five key soil parameters: Nitrogen (N), Phosphorus (P), Potassium (K), Organic Carbon (OC), and pH. \u00a0 Values were derived using calibrated reflectance spectroscopy applied to multispectral satellite observations. Terrain, climatic, and geological datasets were incorporated to minimize noise and strengthen the nutrient-specific signal. Each parameter is distributed as a single-band GeoTIFF (compressed into ZIP archives), with additional preview images for quick inspection. \u00a0 Reference laboratory methods for comparability include: \u00a0- Nitrogen \u2013 ISO 11261:1995 (Modified Kjeldahl) \u00a0- Phosphorus \u2013 ISO 11263:1994 (Spectrometric) \u00a0- Potassium \u2013 USDA\u2013NRCS (NH\u2084OAc extraction + AAS) \u00a0- Organic Carbon \u2013 ISO 10694:1995 (Dry combustion) \u00a0- pH \u2013 ISO 10390:2005 (Glass electrode) \u00a0 The Murcia release forms part of a broader open dataset series curated by Siora, covering multiple countries and regions. Intended applications include monitoring of nutrient dynamics, supporting precision agriculture workflows, environmental research, and as training data for geospatial or ML models.", "keywords": ["soil", " Murcia", " Spain", " nitrogen", " phosphorus", " potassium", " organic carbon", " pH", " geotiff", " 30 m resolution", " agriculture", " remote sensing", " spectroscopy"], "contacts": [{"organization": "Siora.ai", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16889685"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16889685", "name": "item", "description": "10.5281/zenodo.16889685", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16889685"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-08-17T00:00:00Z"}}, {"id": "10.5281/zenodo.16889567", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:04Z", "type": "Dataset", "title": "Siora Open Soil Nutrient Dataset \u2013 Ukraine (2000, 2010, 2020)", "description": "This dataset contains direct satellite-derived estimates of soil nutrient properties for Ukraine at ~250 m resolution, covering the years 2000, 2010, and 2020. Variables include Nitrogen (N), Phosphorus (P), Potassium (K), Organic Carbon (OC), and pH. \u00a0 The data are derived from multispectral reflectance spectroscopy, corrected with topographic, climatic, and geological auxiliary datasets to isolate nutrient signatures. Each variable is provided as a single-band GeoTIFF (zipped), with accompanying PNG heatmaps, thumbnails, README, metadata JSON, and CC-BY 4.0 license. \u00a0 Comparable laboratory references: \u00a0- pH \u2013 ISO 10390:2005 (Glass electrode) \u00a0- Nitrogen \u2013 ISO 11261:1995 (Modified Kjeldahl) \u00a0- Phosphorus \u2013 ISO 11263:1994 (Spectrometric determination) \u00a0- Potassium \u2013 USDA\u2013NRCS (Atomic absorption after NH\u2084OAc extraction) \u00a0- Organic Carbon \u2013 ISO 10694:1995 (Dry combustion) \u00a0 Suggested uses: nutrient trend analysis, soil health monitoring, precision agriculture benchmarking, model training and validation.", "keywords": ["soil", " nitrogen", " phosphorus", " potassium", " organic carbon", " pH", " Ukraine", " agriculture", " remote sensing", " geotiff", " spectroscopy", " open data"], "contacts": [{"organization": "Siora, Radev, Velin,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16889567"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16889567", "name": "item", "description": "10.5281/zenodo.16889567", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16889567"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-08-17T00:00:00Z"}}, {"id": "10.5281/zenodo.17618045", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:07Z", "type": "Dataset", "title": "Khuzestan Agricultural Land Use Change Dataset (2018\u20132023)", "description": "This dataset contains 116 agricultural soil and land-use sample points across Khuzestan Province (2018\u20132023). Data include field-measured organic carbon (OC), land-use classes, NDVI/NDWI indices, and spatial coordinates in WGS84. The dataset was prepared to support deep-learning modeling (ConvLSTM) for agricultural land-use change prediction. All files are provided in CSV, GeoJSON, and Shapefile formats to ensure reproducibility of the study.", "keywords": ["Khuzestan", " land use change", " ConvLSTM", " soil data", " remote sensing", " GIS", " Iran"], "contacts": [{"organization": "Makiani, M.", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17618045"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17618045", "name": "item", "description": "10.5281/zenodo.17618045", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17618045"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-09-01T00:00:00Z"}}, {"id": "10.5281/zenodo.5597232", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:18Z", "type": "Report", "title": "Integration of proximal sensor data with satellite images through signal processing on graph", "description": "Understanding the causation of vegetative components variation using sensing technology is quite promising in the agriculture domain. Different types of sensor platforms have been rapidly developing over the last decade with the aim to provide instantaneous and worthful information to the grower. Regarding plant growth conditions evaluation, remote and proximal sensing are the most common techniques that provide information on nutrient deficiency, biotic stress such as pests and diseases as well as abiotic stresses, allowing Precision Agriculture. Differences in working principles of both sensing platforms provide different output data for mapping in terms of spatial resolution and measurement noise. For a proper fusion of information coming from remote and proximal sensors for the evaluation of the crop condition, an inevitable step is the reduction of present noise in the measurements and data alignment. In this study, we address the problem of integration of two types of measurements coming from optical satellite Sentinel 2A and multiband optical sensing device Plant-O-Meter (POM) for remote and proximal sensing of the crop respectively. Presenting both measurements as signals on graphs, we utilize two procedures on the graph: filtration and clusterization in order to achieve noise removal and registration of data with different spatial resolutions. This result indicates that properly preprocessed POM measurements exhibit strong potential for accurately assessment of plant canopy condition.", "keywords": ["2. Zero hunger", "Proximal sensing", " remote sensing", " interpolation", " data filtration", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5597232"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5597232", "name": "item", "description": "10.5281/zenodo.5597232", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5597232"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-26T00:00:00Z"}}, {"id": "10.5281/zenodo.5597233", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:18Z", "type": "Report", "title": "Integration of proximal sensor data with satellite images through signal processing on graph", "description": "Understanding the causation of vegetative components variation using sensing technology is quite promising in the agriculture domain. Different types of sensor platforms have been rapidly developing over the last decade with the aim to provide instantaneous and worthful information to the grower. Regarding plant growth conditions evaluation, remote and proximal sensing are the most common techniques that provide information on nutrient deficiency, biotic stress such as pests and diseases as well as abiotic stresses, allowing Precision Agriculture. Differences in working principles of both sensing platforms provide different output data for mapping in terms of spatial resolution and measurement noise. For a proper fusion of information coming from remote and proximal sensors for the evaluation of the crop condition, an inevitable step is the reduction of present noise in the measurements and data alignment. In this study, we address the problem of integration of two types of measurements coming from optical satellite Sentinel 2A and multiband optical sensing device Plant-O-Meter (POM) for remote and proximal sensing of the crop respectively. Presenting both measurements as signals on graphs, we utilize two procedures on the graph: filtration and clusterization in order to achieve noise removal and registration of data with different spatial resolutions. This result indicates that properly preprocessed POM measurements exhibit strong potential for accurately assessment of plant canopy condition.", "keywords": ["2. Zero hunger", "Proximal sensing", " remote sensing", " interpolation", " data filtration", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5597233"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5597233", "name": "item", "description": "10.5281/zenodo.5597233", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5597233"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-26T00:00:00Z"}}, {"id": "10.5281/zenodo.8090398", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:35Z", "type": "Journal Article", "created": "2020-12-16", "title": "Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China", "description": "

Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil; however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model.

", "keywords": ["2. Zero hunger", "soil salinity; remote sensing; machine learning; predictive mapping", "soil salinity", "remote sensing", "machine learning", "13. Climate action", "Science", "Q", "0401 agriculture", " forestry", " and fisheries", "predictive mapping", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/24/4118/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8090398"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8090398", "name": "item", "description": "10.5281/zenodo.8090398", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8090398"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-16T00:00:00Z"}}, {"id": "10.5281/zenodo.8092629", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:36Z", "type": "Journal Article", "created": "2022-06-15", "title": "Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions", "description": "

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.

", "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/10.5281/zenodo.8092629"}, {"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": "10.5281/zenodo.8092629", "name": "item", "description": "10.5281/zenodo.8092629", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8092629"}, {"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": "2944731604", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:43Z", "type": "Journal Article", "created": "2019-05-09", "title": "Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion", "description": "

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

", "keywords": ["Landsat 8", "2. Zero hunger", "soil erosion", "550", "Science", "Q", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "01 natural sciences", "630", "field spectroscopy", "6. Clean water", "soil erosion; remote sensing; Sentinel-2; Landsat 8; ANN; RUSLE; field spectroscopy; OLSR; GWR", "remote sensing", "Field spectroscopy", "OLSR", "13. Climate action", "Soil erosion", "0401 agriculture", " forestry", " and fisheries", "RUSLE", "Sentinel-2", "ANN", "GWR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/11/9/1106/pdf"}, {"href": "https://doi.org/2944731604"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2944731604", "name": "item", "description": "2944731604", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2944731604"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-09T00:00:00Z"}}, {"id": "10835/7551", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:24:43Z", "type": "Journal Article", "created": "2019-06-06", "title": "Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts", "description": "

Chlorophyll a concentration (Chla) is a well-proven proxy of biocrust development, photosynthetic organisms\u2019 status, and recovery monitoring after environmental disturbances. However, laboratory methods for the analysis of chlorophyll require destructive sampling and are expensive and time consuming. Indirect estimation of chlorophyll a by means of soil surface reflectance analysis has been demonstrated to be an accurate, cheap, and quick alternative for chlorophyll retrieval information, especially in plants. However, its application to biocrusts has yet to be harnessed. In this study we evaluated the potential of soil surface reflectance measurements for non-destructive Chla quantification over a range of biocrust types and soils. Our results revealed that from the different spectral transformation methods and techniques, the first derivative of the reflectance and the continuum removal were the most accurate for Chla retrieval. Normalized difference values in the red-edge region and common broadband indexes (e.g., normalized difference vegetation index (NDVI)) were also sensitive to changes in Chla. However, such approaches should be carefully adapted to each specific biocrust type. On the other hand, the combination of spectral measurements with non-linear random forest (RF) models provided very good fits (R2 > 0.94) with a mean root mean square error (RMSE) of about 6.5 \u00b5g/g soil, and alleviated the need for a specific calibration for each crust type, opening a wide range of opportunities to advance our knowledge of biocrust responses to ongoing global change and degradation processes from anthropogenic disturbance.

", "keywords": ["chlorophyll quantification", "remote sensing", "hyperspectral", "13. Climate action", "Science", "Q", "Biocrusts; biological soil crust; chlorophyll quantification; hyperspectral; random forest; remote sensing", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "random forest", "Biocrusts", "biological soil crust"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/11/11/1350/pdf"}, {"href": "https://www.mdpi.com/2072-4292/11/11/1350/pdf"}, {"href": "https://doi.org/10835/7551"}, {"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": "10835/7551", "name": "item", "description": "10835/7551", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10835/7551"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-05T00:00:00Z"}}, {"id": "10261/395293", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:24:35Z", "type": "Journal Article", "created": "2025-02-05", "title": "Influence of Soil Texture on the Estimation of Soil Organic Carbon From Sentinel\u20102 Temporal Mosaics at\u00a034\u00a0European Sites", "description": "ABSTRACT

Multispectral imaging satellites such as Sentinel\uffe2\uff80\uff902 are considered a possible tool to assist in the mapping of soil organic carbon (SOC) using images of bare soil. However, the reported results are variable. The measured reflectance of the soil surface is not only related to SOC but also to several other environmental and edaphic factors. Soil texture is one such factor that strongly affects soil reflectance. Depending on the spatial correlation with SOC, the influence of soil texture may improve or hinder the estimation of SOC from spectral data. This study aimed to investigate these influences using local models at 34 sites in different pedo\uffe2\uff80\uff90climatic zones across 10 European countries. The study sites were individual agricultural fields or a few fields in close proximity. For each site, local models to predict SOC and the clay particle size fraction were developed using the Sentinel\uffe2\uff80\uff902 temporal mosaics of bare soil images. Overall, predicting SOC and clay was difficult, and prediction performances with a ratio of performance to deviation (RPD) >\uffe2\uff80\uff891.5 were observed at 8 and 12 of the 34 sites for SOC and clay, respectively. A general relationship between SOC prediction performance and the correlation of SOC and clay in soil was evident but explained only a small part of the large variability we observed in SOC prediction performance across the sites. Adding information on soil texture as additional predictors improved SOC prediction on average, but the additional benefit varied strongly between the sites. The average relative importance of the different Sentinel\uffe2\uff80\uff902 bands in the SOC and clay models indicated that spectral information in the red and far\uffe2\uff80\uff90red regions of the visible spectrum was more important for SOC prediction than for clay prediction. The opposite was true for the region around 2200\uffe2\uff80\uff89nm, which was more important in the clay models.Mineral dust produced by wind erosion of arid and semiarid surfaces is a major component of atmospheric aerosol that affects climate, weather, ecosystems, and socioeconomic sectors such as human health, transportation, solar energy, and air quality. Understanding these effects and ultimately improving the resilience of affected countries requires a reliable, dense, and diverse set of dust observations, fundamental for the development and the provision of skillful dust-forecast-tailored products. The last decade has seen a notable improvement of dust observational capabilities in terms of considered parameters, geographical coverage, and delivery times, as well as of tailored products of interest to both the scientific community and the various end-users. Given this progress, here we review the current state of observational capabilities, including in situ, ground-based, and satellite remote sensing observations in northern Africa, the Middle East, and Europe for the provision of dust information considering the needs of various users. We also critically discuss observational gaps and related unresolved questions while providing suggestions for overcoming the current limitations. Our review aims to be a milestone for discussing dust observational gaps at a global level to address the needs of users, from research communities to nonscientific stakeholders.

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.

", "keywords": ["Technology", "microplastics", "330", "remote sensing indice", "QH301-705.5", "QC1-999", "sustainable plasticulture", "0211 other engineering and technologies", "Plastic greenhouse", "02 engineering and technology", "remote sensing indices", "digital Atla", "01 natural sciences", "630", "Agro\u2010plastic", "12. Responsible consumption", "RPGI", "11. Sustainability", "0202 electrical engineering", " electronic engineering", " information engineering", "Biology (General)", "QD1-999", "agro-plastics", "0105 earth and related environmental sciences", "plastic footprint", "2. Zero hunger", "soil pollution", "mulching film", "T", "Physics", "plastic greenhouse; mulching film; microplastics; soil pollution; agricultural plastic coefficient; sustainable plasticulture", "04 agricultural and veterinary sciences", "15. Life on land", "Engineering (General). Civil engineering (General)", "plastic greenhouse", "6. Clean water", "Chemistry", "agricultural plastic coefficient", "agricultural plastic surface", "13. Climate action", "agro-plastics; digital Atlas; agricultural plastic surface; remote sensing indices; RPGI; plastic footprint", "0401 agriculture", " forestry", " and fisheries", "agro\u2010plastic", "TA1-2040", "microplastic", "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/11586/416192"}, {"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": "11586/416192", "name": "item", "description": "11586/416192", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11586/416192"}, {"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": "1854/LU-01JKX2FJKXN38WB8P6CAQC7AEH", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:01Z", "type": "Journal Article", "created": "2022-09-16", "title": "Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication", "description": "

The beginning of the 21st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems.The beginning of the 21st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems.

", "keywords": ["microwave remote sensing", "targeted observations", "vegetation", "snow", "soil moisture", "data assimilation", "land surface modeling"], "contacts": [{"organization": "De Lannoy, Gabri\u00eblle J.M. (author), Bechtold, Michel (author), Albergel, Cl\u00e9ment (author), Brocca, Luca (author), Calvet, Jean Christophe (author), Carrassi, Alberto (author), Crow, Wade T. (author), de Rosnay, Patricia (author), Steele-Dunne, S.C. (author),", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/25d43a6391aa3b144884152e00849bf4"}, {"rel": "self", "type": "application/geo+json", "title": "25d43a6391aa3b144884152e00849bf4", "name": "item", "description": "25d43a6391aa3b144884152e00849bf4", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/25d43a6391aa3b144884152e00849bf4"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "3112145214", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:57Z", "type": "Journal Article", "created": "2020-12-17", "title": "Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China", "description": "

Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil; however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model.

", "keywords": ["2. Zero hunger", "soil salinity; remote sensing; machine learning; predictive mapping", "soil salinity", "remote sensing", "machine learning", "13. Climate action", "Science", "Q", "0401 agriculture", " forestry", " and fisheries", "predictive mapping", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/24/4118/pdf"}, {"href": "https://doi.org/3112145214"}, {"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": "3112145214", "name": "item", "description": "3112145214", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3112145214"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-16T00:00:00Z"}}, {"id": "3122338430", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:58Z", "type": "Journal Article", "created": "2021-01-20", "title": "Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China", "description": "

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

", "keywords": ["2. Zero hunger", "soil salinization; Sentinel-2 MSI; remote sensing; machine learning; arid area", "Science", "soil salinization", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "Sentinel-2 MSI", "6. Clean water", "remote sensing", "machine learning", "arid area", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/2/305/pdf"}, {"href": "https://doi.org/3122338430"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3122338430", "name": "item", "description": "3122338430", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3122338430"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-17T00:00:00Z"}}, {"id": "2954192991", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:43Z", "type": "Journal Article", "created": "2019-06-06", "title": "Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts", "description": "

Chlorophyll a concentration (Chla) is a well-proven proxy of biocrust development, photosynthetic organisms\u2019 status, and recovery monitoring after environmental disturbances. However, laboratory methods for the analysis of chlorophyll require destructive sampling and are expensive and time consuming. Indirect estimation of chlorophyll a by means of soil surface reflectance analysis has been demonstrated to be an accurate, cheap, and quick alternative for chlorophyll retrieval information, especially in plants. However, its application to biocrusts has yet to be harnessed. In this study we evaluated the potential of soil surface reflectance measurements for non-destructive Chla quantification over a range of biocrust types and soils. Our results revealed that from the different spectral transformation methods and techniques, the first derivative of the reflectance and the continuum removal were the most accurate for Chla retrieval. Normalized difference values in the red-edge region and common broadband indexes (e.g., normalized difference vegetation index (NDVI)) were also sensitive to changes in Chla. However, such approaches should be carefully adapted to each specific biocrust type. On the other hand, the combination of spectral measurements with non-linear random forest (RF) models provided very good fits (R2 > 0.94) with a mean root mean square error (RMSE) of about 6.5 \u00b5g/g soil, and alleviated the need for a specific calibration for each crust type, opening a wide range of opportunities to advance our knowledge of biocrust responses to ongoing global change and degradation processes from anthropogenic disturbance.

", "keywords": ["chlorophyll quantification", "remote sensing", "hyperspectral", "13. Climate action", "Science", "Q", "Biocrusts; biological soil crust; chlorophyll quantification; hyperspectral; random forest; remote sensing", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "random forest", "Biocrusts", "biological soil crust"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/11/11/1350/pdf"}, {"href": "https://www.mdpi.com/2072-4292/11/11/1350/pdf"}, {"href": "https://doi.org/2954192991"}, {"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": "2954192991", "name": "item", "description": "2954192991", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2954192991"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-05T00:00:00Z"}}, {"id": "40175409", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-25T16:26:23Z", "type": "Journal Article", "created": "2025-04-04", "title": "Plasticulture detection at the country scale by combining multispectral and SAR satellite data", "description": "Abstract

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