{"type": "FeatureCollection", "features": [{"id": "10.1016/j.jag.2022.103101", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:17:04Z", "type": "Journal Article", "created": "2022-11-10", "title": "Forest foliage fuel load estimation from multi-sensor spatiotemporal features", "description": "Foliage fuel is the most flammable component in crown fires. Spatiotemporal dynamics of foliage fuel load (FFL) are important for fire managers to assess fire risk. Here, we integrated optical data from the Landsat 8 Operational Land Imager (OLI) with synthetic aperture radar (SAR) data from Sentinel-1 to estimate FFL. We first reconstructed seamless time series from the Landsat 8 and Sentinel-1 imagery by accounting for unequal time intervals between image observations and outliers. We then extracted temporal features that are proxies of the intra- and inter-annual dynamics from these time series. In addition, we derived spatial features from the imagery that quantify spatial context and therefore used varying window sizes. The random forest regression was implemented to assess the importance of the spatiotemporal features, reduce errors, and derive robust FFL estimates. The satellite estimates were validated against 96 field measurements from Pinus yunnanensis forests in the Liangshan Yi Autonomous Prefecture, Sichuan Province, China. Both the spatiotemporal features of SAR and optical data importantly contributed to FFL estimation. When only optical data was used, the model achieved a R2 of 0.75 (relative Root Mean Squared Error (rRMSE)\u00a0=\u00a025.3\u00a0%), while when only SAR data was used the R2 was 0.76 (rRMSE\u00a0=\u00a025.6\u00a0%). However, when optical and SAR data were combined, the R2 increased to 0.81 (rRMSE\u00a0=\u00a023.2\u00a0%). We also found that temporal features were more important predictors of FFL than features that captured spatial context. We demonstrated our FFL mapping method by a case study in the Chinese Sichuan Province, in relation to the occurrence of a fire. Our method needs additional validation over different tree species and forest types, yet has potential for mapping forest fuel loads and fire risk.", "keywords": ["Landsat 8", "Physical geography", "04 agricultural and veterinary sciences", "15. Life on land", "Fire risk", "01 natural sciences", "GB3-5030", "Spatiotemporal features", "Environmental sciences", "Forest foliage fuel load", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "GE1-350", "SDG 14 - Life Below Water", "Random forest", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.jag.2022.103101"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Applied%20Earth%20Observation%20and%20Geoinformation", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jag.2022.103101", "name": "item", "description": "10.1016/j.jag.2022.103101", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jag.2022.103101"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-01T00:00:00Z"}}, {"id": "10.3390/rs11091106", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:50Z", "type": "Journal Article", "created": "2019-05-09", "title": "Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region.</p></article>", "keywords": ["Landsat 8", "2. Zero hunger", "soil erosion", "550", "Science", "Q", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "01 natural sciences", "630", "field spectroscopy", "6. Clean water", "soil erosion; remote sensing; Sentinel-2; Landsat 8; ANN; RUSLE; field spectroscopy; OLSR; GWR", "remote sensing", "Field spectroscopy", "OLSR", "13. Climate action", "Soil erosion", "0401 agriculture", " forestry", " and fisheries", "RUSLE", "Sentinel-2", "ANN", "GWR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/11/9/1106/pdf"}, {"href": "https://doi.org/10.3390/rs11091106"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs11091106", "name": "item", "description": "10.3390/rs11091106", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs11091106"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-09T00:00:00Z"}}, {"id": "10.3390/rs14030714", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:52Z", "type": "Journal Article", "created": "2022-02-07", "title": "Evaluation of Agricultural Bare Soil Properties Retrieval from Landsat 8, Sentinel-2 and PRISMA Satellite Data", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The PRISMA satellite is equipped with an advanced hyperspectral Earth observation technology capable of improving the accuracy of quantitative estimation of bio-geophysical variables in various Earth Science Applications and in particular for soil science. The purpose of this research was to evaluate the ability of the PRISMA hyperspectral imager to estimate topsoil properties (i.e., organic carbon, clay, sand, silt), in comparison with current satellite multispectral sensors. To investigate this expectation, a test was carried out using topsoil data collected in Italy following two approaches. Firstly, PRISMA, Sentinel-2 and Landsat 8 spectral simulated datasets were obtained from the spectral resampling of a laboratory soil library. Subsequently, bare soil reflectance data were obtained from two experimental areas in Italy, using real satellites images, at dates close to each other. The estimation models of soil properties were calibrated employing both Partial Least Square Regression and Cubist Regression algorithms. The results of the study revealed that the best accuracies in retrieving topsoil properties were obtained by PRISMA data, using both laboratory and real datasets. Indeed, the resampled spectra of the hyperspectral imager provided the best Ratio of Performance to Inter-Quartile distance (RPIQ) for clay (4.87), sand (3.80), and organic carbon (2.59) estimation, for the spectral soil library datasets. For the bare soil reflectance obtained from real satellite imagery, a higher level of prediction accuracy was obtained from PRISMA data, with RPIQ \u00b1 SE values of 2.32 \u00b1 0.07 for clay, 3.85 \u00b1 0.19 for silt, and 3.51 \u00b1 0.16 for soil organic carbon. The results for the PRISMA hyperspectral satellite imagery with the Cubist Regression provided the best performance in the prediction of silt, sand, clay and SOC. The same variables were better estimated using PLSR models in the case of the resampled hyperspectral data. The statistical accuracy in the retrieval of SOC from real and resampled PRISMA data revealed the potential of the actual hyperspectral satellite. The results supported the expected good ability of the PRISMA imager to estimate topsoil properties.</p></article>", "keywords": ["Landsat 8", "Sentinel\u20102", "Multispectral", "multispectral", "Science", "hyperspectral; multispectral; PRISMA; soil properties; bare soil; SOC; soil texture; Sentinel-2; Landsat 8; PLSR; Cubist", "Q", "Bare soil", "Cubist", "PRISMA", "04 agricultural and veterinary sciences", "15. Life on land", "hyperspectral", "Hyperspectral", "PLSR", "bare soil", "soil properties", "Soil texture", "Bare soil; Cubist; Hyperspectral; Landsat 8; Multispectral; PLSR; PRISMA; Sentinel\u20102; SOC; Soil properties; Soil texture", "0401 agriculture", " forestry", " and fisheries", "SOC", "Soil properties", "Sentinel-2"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/3/714/pdf"}, {"href": "https://iris.cnr.it/bitstream/20.500.14243/413305/1/prod_473291-doc_192827_compressed.pdf"}, {"href": "https://www.iris.unina.it/bitstream/11588/948571/1/Evaluation%20of%20Agricultural%20Bare%20Soil%20Properties%20Retrieval%20from%20Landsat%208%2c%20Sentinel-2%20and%20PRISMA%20Satellite%20Data%20Enhanced%20Reader.pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/3/714/pdf"}, {"href": "https://doi.org/10.3390/rs14030714"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs14030714", "name": "item", "description": "10.3390/rs14030714", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs14030714"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-02T00:00:00Z"}}, {"id": "10.3390/rs71114708", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:21:52Z", "type": "Journal Article", "created": "2015-11-05", "title": "Estimation of Evapotranspiration and Crop Coefficients of Tendone Vineyards Using Multi-Sensor Remote Sensing Data in a Mediterranean Environment", "description": "<p>The sustainable management of water resources plays a key role in Mediterranean viticulture, characterized by scarcity and competition of available water. This study focuses on estimating the evapotranspiration and crop coefficients of table grapes vineyards trained on overhead \uffe2\uff80\uff9ctendone\uffe2\uff80\uff9d systems in the Apulia region (Italy). Maximum vineyard transpiration was estimated by adopting the \uffe2\uff80\uff9cdirect\uffe2\uff80\uff9d methodology for ETp proposed by the Food and Agriculture Organization in Irrigation and Drainage Paper No. 56, with crop parameters estimated from Landsat 8 and RapidEye satellite data in combination with ground-based meteorological data. The modeling results of two growing seasons (2013 and 2014) indicated that canopy growth, seasonal and 10-day sums evapotranspiration values were strictly related to thermal requirements and rainfall events. The estimated values of mean seasonal daily evapotranspiration ranged between 4.2 and 4.1 mm\uffc2\uffb7d\uffe2\uff88\uff921, while midseason estimated values of crop coefficients ranged from 0.88 to 0.93 in 2013,  and 1.02 to 1.04 in 2014, respectively. The experimental evapotranspiration values calculated represent the maximum value in absence of stress, so the resulting crop coefficients should be used with some caution. It is concluded that the retrieval of crop parameters and evapotranspiration derived from remotely-sensed data could be helpful for downscaling to the field the local weather conditions and agronomic practices and thus may be the basis for supporting grape growers and irrigation managers.</p>", "keywords": ["Landsat 8", "2. Zero hunger", "0106 biological sciences", "Evapotranspiration", "leaf area index", "Science", "Q", "evapotranspiration", "table grapes", "Remote sensing", "15. Life on land", "Vineyards", "01 natural sciences", "6. Clean water", "evapotranspiration; crop coefficient; leaf area index; Landsat 8; RapidEye; remote sensing; vineyards; table grapes", "Crop coefficient; Evapotranspiration; Landsat 8; Leaf area index; RapidEye; Remote sensing; Table grapes; Vineyards; Earth and Planetary Sciences (all)", "remote sensing", "vineyards", "Table grapes", "Crop coefficient", "Leaf area index", "RapidEye", "Earth and Planetary Sciences (all)", "crop coefficient"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/7/11/14708/pdf"}, {"href": "https://www.iris.unina.it/bitstream/11588/637935/1/remotesensing2015-07-14708.pdf"}, {"href": "https://doi.org/10.3390/rs71114708"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs71114708", "name": "item", "description": "10.3390/rs71114708", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs71114708"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-11-05T00:00:00Z"}}, {"id": "10.3390/rs9070684", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:52Z", "type": "Journal Article", "created": "2017-07-04", "title": "Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment", "description": "<p>Global climate change and increasing urbanization worldwide intensify the need for a better understanding of human heat stress dynamics in urban systems. During heat waves, which are expected to increase in number and intensity, the development of urban cool islands could be a lifesaver for many elderly and vulnerable people. The use of remote sensing data offers the unique possibility to study these dynamics with spatially distributed large datasets during all seasons of the year and including day and night-time analysis. For the city of Basel 32 high-quality Landsat 8 (L8) scenes are available since 2013, enabling comprehensive statistical analysis. Therefore, land surface temperature (LST) is calculated using L8 thermal infrared (TIR) imagery (stray light corrected) applying improved emissivity and atmospheric corrections. The data are combined with a land use/land cover (LULC) map and evaluated using administrative residential units. The observed dependence of LST on LULC is analyzed using a thermal unmixing approach based on a multiple linear regression (MLR) model, which allows for quantifying the gradual influence of different LULC types on the LST precisely. Seasonal variations due to different solar irradiance and vegetation cover indicate a higher dependence of LST on the LULC during the warmer summer months and an increasing influence of the topography and albedo during the colder seasons. Furthermore, the MLR analysis allows creating predicted LST images, which can be used to fill data gaps like in SLC-off Landsat 7 ETM+ data.</p>", "keywords": ["multiple linear regression", "Landsat 8", "land use/land cover", "Science", "atmospheric corrections", "Q", "0211 other engineering and technologies", "land surface temperature", "02 engineering and technology", "15. Life on land", "01 natural sciences", "LST analysis", "13. Climate action", "11. Sustainability", "land surface temperature; thermal infrared data; LST analysis; atmospheric corrections; land use/land cover; multiple linear regression; urban; Landsat 8", "thermal infrared data", "urban", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/7/684/pdf"}, {"href": "https://www.mdpi.com/2072-4292/9/7/684/pdf"}, {"href": "https://doi.org/10.3390/rs9070684"}, {"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/rs9070684", "name": "item", "description": "10.3390/rs9070684", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs9070684"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-07-04T00:00:00Z"}}, {"id": "10.3390/agronomy11040652", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:37Z", "type": "Journal Article", "created": "2021-03-29", "title": "Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013\u20132018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018\u20132019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41\u201371) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.</p></article>", "keywords": ["Landsat 8", "2. Zero hunger", "SAVI", "NDVI", "S", "13. Climate action", "wheat", "yield forecasting", "Agriculture", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://doi.org/10.3390/agronomy11040652"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agronomy11040652", "name": "item", "description": "10.3390/agronomy11040652", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agronomy11040652"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-29T00:00:00Z"}}, {"id": "10.3390/agronomy11050946", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:38Z", "type": "Journal Article", "created": "2021-05-11", "title": "Estimating Farm Wheat Yields from NDVI and Meteorological Data", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (Triticum aestivum) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R2 = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment.</p></article>", "keywords": ["yield estimation", "PREDICTION", "NDVI", "Triticum aestivum", "0703 Crop and Pasture Production", "3002 Agriculture", " land and farm management", "3004 Crop and pasture production", "Belgium", "0502 Environmental Science and Management", "<i>Triticum aestivum</i>", "2. Zero hunger", "Science & Technology", "S", "Plant Sciences", "Agriculture", "weather impact", "04 agricultural and veterinary sciences", "WINTER-WHEAT", "15. Life on land", "Agronomy", "winter wheat", "MODEL", "RESOLUTION", "SENTINEL-2", "0401 agriculture", " forestry", " and fisheries", "LANDSAT 8", "Life Sciences & Biomedicine"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/5/946/pdf"}, {"href": "https://doi.org/10.3390/agronomy11050946"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agronomy11050946", "name": "item", "description": "10.3390/agronomy11050946", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agronomy11050946"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-11T00:00:00Z"}}, {"id": "2944731604", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:27:00Z", "type": "Journal Article", "created": "2019-05-09", "title": "Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region.</p></article>", "keywords": ["Landsat 8", "2. Zero hunger", "soil erosion", "550", "Science", "Q", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "01 natural sciences", "630", "field spectroscopy", "6. Clean water", "soil erosion; remote sensing; Sentinel-2; Landsat 8; ANN; RUSLE; field spectroscopy; OLSR; GWR", "remote sensing", "Field spectroscopy", "OLSR", "13. Climate action", "Soil erosion", "0401 agriculture", " forestry", " and fisheries", "RUSLE", "Sentinel-2", "ANN", "GWR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/11/9/1106/pdf"}, {"href": "https://doi.org/2944731604"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2944731604", "name": "item", "description": "2944731604", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2944731604"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-09T00:00:00Z"}}, {"id": "10.5281/zenodo.16895104", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:24:07Z", "type": "Journal Article", "created": "2021-03-29", "title": "Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013\u20132018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018\u20132019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41\u201371) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.</p></article>", "keywords": ["Landsat 8", "2. Zero hunger", "SAVI", "NDVI", "S", "13. Climate action", "wheat", "yield forecasting", "Agriculture", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://doi.org/10.5281/zenodo.16895104"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16895104", "name": "item", "description": "10.5281/zenodo.16895104", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16895104"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-29T00:00:00Z"}}, {"id": "1871.1/505fa0c0-6587-48f4-a8b1-4f1ad19d6bb8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:26:15Z", "type": "Journal Article", "created": "2022-11-10", "title": "Forest foliage fuel load estimation from multi-sensor spatiotemporal features", "description": "Foliage fuel is the most flammable component in crown fires. Spatiotemporal dynamics of foliage fuel load (FFL) are important for fire managers to assess fire risk. Here, we integrated optical data from the Landsat 8 Operational Land Imager (OLI) with synthetic aperture radar (SAR) data from Sentinel-1 to estimate FFL. We first reconstructed seamless time series from the Landsat 8 and Sentinel-1 imagery by accounting for unequal time intervals between image observations and outliers. We then extracted temporal features that are proxies of the intra- and inter-annual dynamics from these time series. In addition, we derived spatial features from the imagery that quantify spatial context and therefore used varying window sizes. The random forest regression was implemented to assess the importance of the spatiotemporal features, reduce errors, and derive robust FFL estimates. The satellite estimates were validated against 96 field measurements from Pinus yunnanensis forests in the Liangshan Yi Autonomous Prefecture, Sichuan Province, China. Both the spatiotemporal features of SAR and optical data importantly contributed to FFL estimation. When only optical data was used, the model achieved a R2 of 0.75 (relative Root Mean Squared Error (rRMSE)\u00a0=\u00a025.3\u00a0%), while when only SAR data was used the R2 was 0.76 (rRMSE\u00a0=\u00a025.6\u00a0%). However, when optical and SAR data were combined, the R2 increased to 0.81 (rRMSE\u00a0=\u00a023.2\u00a0%). We also found that temporal features were more important predictors of FFL than features that captured spatial context. We demonstrated our FFL mapping method by a case study in the Chinese Sichuan Province, in relation to the occurrence of a fire. Our method needs additional validation over different tree species and forest types, yet has potential for mapping forest fuel loads and fire risk.", "keywords": ["Landsat 8", "Physical geography", "04 agricultural and veterinary sciences", "15. Life on land", "Fire risk", "01 natural sciences", "GB3-5030", "Spatiotemporal features", "Environmental sciences", "Forest foliage fuel load", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "GE1-350", "SDG 14 - Life Below Water", "Random forest", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/1871.1/505fa0c0-6587-48f4-a8b1-4f1ad19d6bb8"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Applied%20Earth%20Observation%20and%20Geoinformation", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/505fa0c0-6587-48f4-a8b1-4f1ad19d6bb8", "name": "item", "description": "1871.1/505fa0c0-6587-48f4-a8b1-4f1ad19d6bb8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/505fa0c0-6587-48f4-a8b1-4f1ad19d6bb8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-01T00:00:00Z"}}, {"id": "20.500.14243/413305", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:26:33Z", "type": "Journal Article", "created": "2022-02-06", "title": "Evaluation of Agricultural Bare Soil Properties Retrieval from Landsat 8, Sentinel-2 and PRISMA Satellite Data", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The PRISMA satellite is equipped with an advanced hyperspectral Earth observation technology capable of improving the accuracy of quantitative estimation of bio-geophysical variables in various Earth Science Applications and in particular for soil science. The purpose of this research was to evaluate the ability of the PRISMA hyperspectral imager to estimate topsoil properties (i.e., organic carbon, clay, sand, silt), in comparison with current satellite multispectral sensors. To investigate this expectation, a test was carried out using topsoil data collected in Italy following two approaches. Firstly, PRISMA, Sentinel-2 and Landsat 8 spectral simulated datasets were obtained from the spectral resampling of a laboratory soil library. Subsequently, bare soil reflectance data were obtained from two experimental areas in Italy, using real satellites images, at dates close to each other. The estimation models of soil properties were calibrated employing both Partial Least Square Regression and Cubist Regression algorithms. The results of the study revealed that the best accuracies in retrieving topsoil properties were obtained by PRISMA data, using both laboratory and real datasets. Indeed, the resampled spectra of the hyperspectral imager provided the best Ratio of Performance to Inter-Quartile distance (RPIQ) for clay (4.87), sand (3.80), and organic carbon (2.59) estimation, for the spectral soil library datasets. For the bare soil reflectance obtained from real satellite imagery, a higher level of prediction accuracy was obtained from PRISMA data, with RPIQ \u00b1 SE values of 2.32 \u00b1 0.07 for clay, 3.85 \u00b1 0.19 for silt, and 3.51 \u00b1 0.16 for soil organic carbon. The results for the PRISMA hyperspectral satellite imagery with the Cubist Regression provided the best performance in the prediction of silt, sand, clay and SOC. The same variables were better estimated using PLSR models in the case of the resampled hyperspectral data. The statistical accuracy in the retrieval of SOC from real and resampled PRISMA data revealed the potential of the actual hyperspectral satellite. The results supported the expected good ability of the PRISMA imager to estimate topsoil properties.</p></article>", "keywords": ["Landsat 8", "Sentinel\u20102", "Multispectral", "multispectral", "Science", "hyperspectral; multispectral; PRISMA; soil properties; bare soil; SOC; soil texture; Sentinel-2; Landsat 8; PLSR; Cubist", "Q", "Bare soil", "Cubist", "PRISMA", "04 agricultural and veterinary sciences", "15. Life on land", "hyperspectral", "Hyperspectral", "PLSR", "bare soil", "soil properties", "Soil texture", "Bare soil; Cubist; Hyperspectral; Landsat 8; Multispectral; PLSR; PRISMA; Sentinel\u20102; SOC; Soil properties; Soil texture", "0401 agriculture", " forestry", " and fisheries", "SOC", "Soil properties", "Sentinel-2"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/3/714/pdf"}, {"href": "https://iris.cnr.it/bitstream/20.500.14243/413305/1/prod_473291-doc_192827_compressed.pdf"}, {"href": "https://www.iris.unina.it/bitstream/11588/948571/1/Evaluation%20of%20Agricultural%20Bare%20Soil%20Properties%20Retrieval%20from%20Landsat%208%2c%20Sentinel-2%20and%20PRISMA%20Satellite%20Data%20Enhanced%20Reader.pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/3/714/pdf"}, {"href": "https://doi.org/20.500.14243/413305"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.14243/413305", "name": "item", "description": "20.500.14243/413305", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.14243/413305"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-02T00:00:00Z"}}, {"id": "3146201181", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:27:20Z", "type": "Journal Article", "created": "2021-03-29", "title": "Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013\u20132018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018\u20132019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41\u201371) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.</p></article>", "keywords": ["Landsat 8", "2. Zero hunger", "SAVI", "NDVI", "S", "13. Climate action", "wheat", "yield forecasting", "Agriculture", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://doi.org/3146201181"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3146201181", "name": "item", "description": "3146201181", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3146201181"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-29T00:00:00Z"}}, {"id": "3161294357", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:27:21Z", "type": "Journal Article", "created": "2021-05-11", "title": "Estimating Farm Wheat Yields from NDVI and Meteorological Data", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (Triticum aestivum) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R2 = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment.</p></article>", "keywords": ["yield estimation", "PREDICTION", "NDVI", "Triticum aestivum", "0703 Crop and Pasture Production", "3002 Agriculture", " land and farm management", "3004 Crop and pasture production", "Belgium", "0502 Environmental Science and Management", "<i>Triticum aestivum</i>", "2. Zero hunger", "Science & Technology", "S", "Plant Sciences", "Agriculture", "weather impact", "04 agricultural and veterinary sciences", "WINTER-WHEAT", "15. Life on land", "Agronomy", "winter wheat", "MODEL", "RESOLUTION", "SENTINEL-2", "0401 agriculture", " forestry", " and fisheries", "LANDSAT 8", "Life Sciences & Biomedicine"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/5/946/pdf"}, {"href": "https://doi.org/3161294357"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3161294357", "name": "item", "description": "3161294357", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3161294357"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-11T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Landsat+8&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Landsat+8&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Landsat+8&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Landsat+8&offset=13", "hreflang": "en-US"}], "numberMatched": 13, "numberReturned": 13, "distributedFeatures": [], "timeStamp": "2026-06-24T09:29:33.106927Z"}