{"type": "FeatureCollection", "features": [{"id": "10.1016/j.scitotenv.2021.149346", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:17:21Z", "type": "Journal Article", "created": "2021-07-31", "title": "Characterization of the main land processes occurring in Europe (2000-2018) through a MODIS NDVI seasonal parameter-based procedure", "description": "The identification and recognition of the land processes are of vital importance for a proper management of the ecosystem functions and services. However, on-ground land uses/land covers (LULC) characterization is a time-consuming task, often limited to small land areas, which can be solved using remote sensing technologies. The objective of this work is to investigate how the different MODIS NDVI seasonal parameters responded to the main land processes observed in Europe in the 2000-2018 period; characterizing their temporal trend; and evaluating which one reflected better each specific land process. NDVI time-series were evaluated using TIMESAT software, which extracted eight seasonality parameters: amplitude, base value, length of season, maximum value, left and right derivative values and small and large integrated values. These parameters were correlated with the LULC changes derived from COoRdination of INformation on the Environment Land Cover (CLC) for assessing which parameter better characterized each land process. The temporal evolution of the maximum seasonal NDVI was the parameter that better characterized the occurrence of most of the land processes evaluated (afforestation, agriculturalization, degradation, land abandonment, land restoration, urbanization; R2 from 0.67-0.97). Large integrated value also presented significant relationships but they were restricted to two of the three evaluated periods. On the contrary, land processes involving CLC categories with similar NDVI patterns were not well captured with the proposed methodology. These results evidenced that this methodology could be combined with other classification methods for improving LULC identification accuracy or for identifying LULC processes in locations where no LULC maps are available. Such information can be used by policy-makers to draw LULC management actions associated with sustainable development goals. This is especially relevant for areas where food security is at stake and where terrestrial ecosystems are threatened by severe biodiversity loss.", "keywords": ["Land cover", "Urbanization", "CORINE", "Biodiversity", "15. Life on land", "01 natural sciences", "Europe", "Normalized difference vegetation index", "13. Climate action", "Land use", "11. Sustainability", "Seasons", "TIMESAT", "Ecosystem", "Environmental Monitoring", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.iris.unict.it/bitstream/20.500.11769/511362/1/Ramirez-Cuesta%20et%20al%202021.pdf"}, {"href": "https://doi.org/10.1016/j.scitotenv.2021.149346"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.scitotenv.2021.149346", "name": "item", "description": "10.1016/j.scitotenv.2021.149346", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.scitotenv.2021.149346"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-01T00:00:00Z"}}, {"id": "10.1016/j.scitotenv.2021.152880", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:17:21Z", "type": "Journal Article", "created": "2022-01-06", "title": "Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China", "description": "Open AccessLe d\u00e9veloppement d'un syst\u00e8me pr\u00e9cis de pr\u00e9diction du rendement des cultures \u00e0 grande \u00e9chelle est d'une importance primordiale pour la gestion des ressources agricoles et la s\u00e9curit\u00e9 alimentaire mondiale. L'observation de la Terre fournit une source unique d'informations pour surveiller les cultures \u00e0 partir d'une diversit\u00e9 de gammes spectrales. Cependant, l'utilisation int\u00e9gr\u00e9e de ces donn\u00e9es et de leurs valeurs dans la pr\u00e9diction du rendement des cultures est encore peu \u00e9tudi\u00e9e. Ici, nous avons propos\u00e9 la combinaison de donn\u00e9es environnementales (climat, sol, g\u00e9ographie et topographie) avec de multiples donn\u00e9es satellitaires (indices de v\u00e9g\u00e9tation optiques, fluorescence induite par le soleil (SIF), temp\u00e9rature de surface du sol (LST) et profondeur optique de la v\u00e9g\u00e9tation micro-ondes (VOD)) dans le cadre pour estimer le rendement des cultures de ma\u00efs, de riz et de soja dans le nord-est de la Chine, et leur valeur unique et leur influence relative sur la pr\u00e9diction du rendement ont \u00e9t\u00e9 \u00e9valu\u00e9es. Deux m\u00e9thodes de r\u00e9gression lin\u00e9aire, trois m\u00e9thodes d'apprentissage automatique (ML) et un mod\u00e8le d'ensemble ML ont \u00e9t\u00e9 adopt\u00e9s pour construire des mod\u00e8les de pr\u00e9diction de rendement. Les r\u00e9sultats ont montr\u00e9 que les m\u00e9thodes individuelles de ML surpassaient les m\u00e9thodes de r\u00e9gression lin\u00e9aire, le mod\u00e8le d'ensemble de ML a encore am\u00e9lior\u00e9 les mod\u00e8les de ML uniques. De plus, les mod\u00e8les avec plus d'intrants ont obtenu de meilleures performances, la combinaison de donn\u00e9es satellitaires avec des donn\u00e9es environnementales, qui expliquaient respectivement 72\u00a0%, 69\u00a0% et 57\u00a0% de la variabilit\u00e9 du rendement du ma\u00efs, du riz et du soja, a d\u00e9montr\u00e9 des performances de pr\u00e9diction du rendement sup\u00e9rieures \u00e0 celles des intrants individuels. Alors que les donn\u00e9es satellitaires ont contribu\u00e9 \u00e0 la pr\u00e9diction du rendement des cultures principalement au d\u00e9but de la pointe de la saison de croissance, les donn\u00e9es climatiques ont fourni des informations suppl\u00e9mentaires principalement \u00e0 la pointe de la fin de la saison. Nous avons \u00e9galement constat\u00e9 que l'utilisation combin\u00e9e de l'IVE, du LST et du SIF a am\u00e9lior\u00e9 la pr\u00e9cision du mod\u00e8le par rapport au mod\u00e8le d'IVE de r\u00e9f\u00e9rence. Cependant, les indices de v\u00e9g\u00e9tation bas\u00e9s sur l'optique partageaient des informations similaires et ne fournissaient pas beaucoup d'informations suppl\u00e9mentaires au-del\u00e0 de l'IVE. Les pr\u00e9visions de rendement en cours de saison ont montr\u00e9 que les rendements des cultures peuvent \u00eatre pr\u00e9vus de mani\u00e8re satisfaisante deux \u00e0 trois mois avant la r\u00e9colte. La g\u00e9ographie, la topographie, la VOD, l'IVE, les param\u00e8tres hydrauliques du sol et les param\u00e8tres nutritifs sont plus importants pour la pr\u00e9diction du rendement des cultures.", "keywords": ["Atmospheric sciences", "Climate", "Multi-source satellite data", "Normalized Difference Vegetation Index", "Engineering", "Pathology", "Climate change", "Urban Heat Islands and Mitigation Strategies", "Linear regression", "2. Zero hunger", "Global and Planetary Change", "Vegetation Monitoring", "Ecology", "Geography", "Statistics", "Agriculture", "Geology", "Remote Sensing in Vegetation Monitoring and Phenology", "04 agricultural and veterinary sciences", "Remote sensing", "Aerospace engineering", "Archaeology", "Physical Sciences", "Metallurgy", "Medicine", "Seasons", "Global Vegetation Models", "Biomass Estimation", "Regression analysis", "Vegetation (pathology)", "Crops", " Agricultural", "Environmental Engineering", "Environmental data", "Yield (engineering)", "Zea mays", "Environmental science", "Machine learning", "FOS: Mathematics", "Crop yield", "Biology", "Global Forest Drought Response and Climate Change", "FOS: Environmental engineering", "Predictive modelling", "Food security", "FOS: Earth and related environmental sciences", "15. Life on land", "Agronomy", "Materials science", "Yield prediction", "Satellite", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Growing season", "0401 agriculture", " forestry", " and fisheries", "Mathematics"], "contacts": [{"organization": "Zhenwang Li, Lei Ding, Donghui Xu,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.scitotenv.2021.152880"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.scitotenv.2021.152880", "name": "item", "description": "10.1016/j.scitotenv.2021.152880", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.scitotenv.2021.152880"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-01T00:00:00Z"}}, {"id": "10.15302/j-fase-2014028", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:20:25Z", "type": "Journal Article", "created": "2014-10-15", "title": "Estimating The Effect Of Urease Inhibitor On Rice Yield Based On Ndvi At Key Growth Stages", "description": "The effect of the urease inhibitor, N-(n-butyl) thiophosphoric triamide (NBPT) at a range of application rates on rice production was examined in a field experiment at Jinxian County, Jiangxi Province, China. The normalized difference vegetation index (NDVI) was measured at key growth stages in both early and late rice. The results showed that the grain yield increased significantly when urea was applied with NBPT, with the highest yield observed at 1.00% NBPT (wt/wt). NDVI differed with the growth stage of rice; it remained steady from the heading to the filling stage. Rice yield could be predicted from the NDVI taken at key rice growing stages, with R<sup>2</sup> ranging from 0.34 to 0.69 in early rice and 0.49 to 0.70 in late rice. The validation test showed that RMSE (t\u00b7hm<sup>-2</sup>) values were 0.77 and 0.87 in early and late rice, respectively. Therefore, it was feasible to estimate rice yield for different amounts of urease inhibitor using NDVI.", "keywords": ["0106 biological sciences", "2. Zero hunger", "normalized difference vegetation index (NDVI)|N-(n-butyl) thiophosphoric triamide (NBPT)|rice|grain yield", "Agriculture (General)", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "01 natural sciences", "S1-972"], "contacts": [{"organization": "Kailou Liu, Yazhen Li, Huiwen Hu,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.15302/j-fase-2014028"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20of%20Agricultural%20Science%20and%20Engineering", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.15302/j-fase-2014028", "name": "item", "description": "10.15302/j-fase-2014028", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15302/j-fase-2014028"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2014-01-01T00:00:00Z"}}, {"id": "10.1594/pangaea.963212", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:20:36Z", "type": "Dataset", "title": "Stream water chemistry and landscape characteristics in Zackenberg Valley, NE Greenland summer 2021", "description": "The data contains water chemistry and spectral catchment NDVI for 14 streams in Zackenberg Valley in Northeast Greenland, sampled summer 2021 from 10th July to 15th September. We collected water samples for measuring water chemistry, and we determined landscape parameters using GIS based tools. The data was collected at three sampling periods in summer 2021 in the Zackenberg Research Station (74\u00b028'N, 20\u00b034'W). The area has a polar tundra climate with mean annual air temperature of -9.1 \u00b0C. Water chemistry (i.e. dissolved and particulate nitrogen, phosphorus, carbon; dissolved iron and silicate) and catchment characteristics (i.e. catchment area, altitude, slope, aspect, NDVI, snow cover) was measured for each of the 14 stream sites. Water chemistry samples were collected and analyzed using standard methods, and landscape characteristics were determined using GIS resources. The data was collected in order to study relationships between landscape characteristics and stream water chemistry. The water samples were collected by a team of two people, and the detailed methods are given below.", "keywords": ["inorganic", "median", "Nitrate Nitrogen", "Nitrogen", " inorganic", " dissolved/Nitrogen", " total dissolved ratio", "Nitrate", "Normalized Difference Vegetation Index", "Latitude of event", "Inductively Coupled Plasma Mass Spectrometry ICP MS", "Arctic", "Temperature", " water", "WTW", "Total organic carbon analyzer TOC VCPH TNM 1", "Total organic carbon analyzer (TOC-VCPH/TNM-1)", " Shimadzu", "Calculated", "dissolved ratio", "Nitrate/Nitrogen", " inorganic", " dissolved ratio", "total dissolved ratio", "Multiple investigations", "Temperature", "Nitrogen", " total dissolved", "Month", "dissolved", "specific", "streams", "6. Clean water", "Nitrogen", " inorganic", " dissolved", "Chemistry", "Inductively Coupled Plasma - Mass Spectrometry (ICP-MS)", " PerkinElmer Instruments", " Optima 2000 DV", "Sum cations", "Natural Sciences", "Ammonium", "Potassium Silicon ratio", "Calcium Magnesium ratio", "Conductivity Meter", " WTW", " ProfiLine Cond 3110", "Longitude of event", "Silicon", "Lachat QuickChem 8500 flow injection autoanalyser", "Nitrogen", "organic", "water chemistry", "Iron", "Calcium/Magnesium ratio", "water", "Site", "Nitrate/Ammonium ratio", "Aspect", "Normalized Differenced Vegetation Index", " median", "Ammonium Nitrogen", "Normalized Differenced Vegetation Index", "Catchment area", "Slope", "PerkinElmer Instruments", "ProfiLine Cond 3110", "Shimadzu", "Date/Time of event", "Conductivity Meter", "Nitrate Ammonium ratio", "total dissolved", "Conductivity", "Event label", "Date Time of event", "Nitrogen", " inorganic", " dissolved/Nitrogen", " organic", " dissolved ratio", "15. Life on land", "Carbon", " organic", " dissolved", "dissolved Nitrogen", "Elevation of event", "Carbon", "rivers", "Snow coverage", "Greening", "Potassium/Silicon ratio", "Optima 2000 DV", "Nitrogen", " organic", " dissolved", "13. Climate action", "Discharge", "Conductivity", " specific", "Ammonium/Nitrogen", " inorganic", " dissolved ratio"], "contacts": [{"organization": "Riis, Tenna, Tank, Jennifer, Holmboe, Cecilie Marie Hartvig, Gim\u00e9nez-Grau, Pau, Mastepanov, Mikhail, Catalan, Nuria, Stott, David, Hansen, Birgitte, Kristiansen, S\u00f8ren M, Pastor, Ada,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1594/pangaea.963212"}, {"rel": "self", "type": "application/geo+json", "title": "10.1594/pangaea.963212", "name": "item", "description": "10.1594/pangaea.963212", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1594/pangaea.963212"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.3390/agronomy10101524", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:21:43Z", "type": "Journal Article", "created": "2020-10-07", "title": "A simple light-use-efficiency model to estimate wheat yield in the semi-arid areas.", "description": "<p>In this study, a simple model, based on a light-use-efficiency model, was developed in order to estimate growth and yield of the irrigated winter wheat under semi-arid conditions. The originality of the proposed method consists in (1) the modifying of the expression of the conversion coefficient (\uffce\uffb5conv) by integrating an appropriate stress threshold (ksconv) for triggering irrigation, (2) the substitution of the product of the two maximum coefficients of interception (\uffce\uffb5imax) and conversion (\uffce\uffb5conv_max) by a single parameter \uffce\uffb5max, (3) the modeling of \uffce\uffb5max as a function of the Cumulative Growing Degree Days (CGDD) since sowing date, and (4) the dynamic expression of the harvest index (HI) as a function of the CGDD and the final harvest index (HI0) depending on the maximum value of the Normalized Difference Vegetation Index (NDVI). The calibration and validation of the proposed model were performed based on the observations of wheat dry matter (DM) and grain yield (GY) which were collected on the R3 irrigated district of the Haouz plain (center of Morocco), during three agricultural seasons. Further, the outputs of the simple model were also evaluated against the AquaCrop model estimates. The model calibration allowed the parameterization of \uffce\uffb5max in four periods according to the wheat phenological stages. By contrast, a linear evolution was sufficient to represent the relationship between HI and CGDD. For the model validation, the obtained results showed a good agreement between the estimated and observed values with a Root Mean Square Error (RMSE) of about 1.07 and 0.57 t/ha for DM and GY, respectively. These correspond to a relative RMSE of about 19% for DM and 20% for GY. Likewise, although of its simplicity, the accuracy of the proposed model seems to be comparable to that of the AquaCrop model. For GY, R2, and RMSE values were respectively 0.71 and 0.44 t/ha for the developed approach and 0.88 and 0.37 t/ha for AquaCrop. Thus, the proposed simple light-use-efficiency model can be considered as a useful tool to correctly reproduce DM and GY values.</p>", "keywords": ["days", "[SDE] Environmental Sciences", "0106 biological sciences", "2. Zero hunger", "cumulative growing degree", "simple model", "S", "grain yield", "normalized difference vegetation index", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "630", "wheat", "[SDE]Environmental Sciences", "dry matter", "0401 agriculture", " forestry", " and fisheries", "cumulative growing degree days"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/10/10/1524/pdf"}, {"href": "https://www.mdpi.com/2073-4395/10/10/1524/pdf"}, {"href": "https://doi.org/10.3390/agronomy10101524"}, {"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/agronomy10101524", "name": "item", "description": "10.3390/agronomy10101524", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agronomy10101524"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-07T00:00:00Z"}}, {"id": "10.3390/rs8020156", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:22:00Z", "type": "Journal Article", "created": "2016-02-19", "title": "Impacts of Re-Vegetation on Surface Soil Moisture over the Chinese Loess Plateau Based on Remote Sensing Datasets", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>A large-scale re-vegetation supported by the Grain for Green Project (GGP) has greatly changed local eco-hydrological systems, with an impact on soil moisture conditions for the Chinese Loess Plateau. It is important to know how, exactly, re-vegetation influences soil moisture conditions, which not only crucially constrain growth and distribution of vegetation, and hence, further re-vegetation, but also determine the degree of soil desiccation and, thus, erosion risk in the region. In this study, three eco-environmental factors, which are Soil Water Index (SWI), the Normalized Difference Vegetation Index (NDVI), and precipitation, were used to investigate the response of soil moisture in the one-meter layer of top soil to the re-vegetation during the GGP. SWI was estimated based on the backscatter coefficient produced by the European Remote Sensing Satellite (ERS-1/2) and Meteorological Operational satellite program (MetOp), while NDVI was derived from SPOT imageries. Two separate periods, which are 1998\u20132000 and 2008\u20132010, were selected to examine the spatiotemporal pattern of the chosen eco-environmental factors. It has been shown that the amount of precipitation in 1998\u20132000 was close to that of 2008\u20132010 (the difference being 13.10 mm). From 1998\u20132000 to 2008\u20132010, the average annual NDVI increased for 80.99%, while the SWI decreased for 72.64% of the area on the Loess Plateau. The average NDVI over the Loess Plateau increased rapidly by 17.76% after the 10-year GGP project. However, the average SWI decreased by 4.37% for two-thirds of the area. More specifically, 57.65% of the area on the Loess Plateau experienced an increased NDVI and decreased SWI, 23.34% of the area had an increased NDVI and SWI. NDVI and SWI decreased simultaneously for 14.99% of the area, and the decreased NDVI and increased SWI occurred at the same time for 4.02% of the area. These results indicate that re-vegetation, human activities, and climate change have impacts on soil moisture. However, re-vegetation, which consumes a large quantity of soil water, may be the major factor for soil moisture change in most areas of the Loess Plateau. It is, therefore, suggested that Soil Moisture Content (SMC) should be kept in mind when carrying out re-vegetation in China\u2019s arid and semi-arid regions.</p></article>", "keywords": ["2. Zero hunger", "China", "Science", "Q", "Soil Water Index (SWI)", "precipitation", "15. Life on land", "01 natural sciences", "6. Clean water", "remote sensing", "the Loess Plateau", "13. Climate action", "11. Sustainability", "Normalized Difference Vegetation Index (NDVI)", "Grain for Green Project (GGP)", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Qiao Jiao, Rui Li, Fei Wang, Xingmin Mu, Pengfei Li, Chunchun An,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2072-4292/8/2/156/pdf"}, {"href": "https://doi.org/10.3390/rs8020156"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs8020156", "name": "item", "description": "10.3390/rs8020156", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs8020156"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-02-19T00:00:00Z"}}, {"id": "10.3390/iecag2021-10021", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:21:50Z", "type": "Journal Article", "created": "2022-02-10", "title": "Remote Sensing (NDVI) and Apparent Soil Electrical Conductivity (ECap) to Delineate Different Zones in a Vineyard", "description": "Open AccessPresented at the 1st International Electronic Conference on Agronomy, 3\u201317 May 2021", "keywords": ["2. Zero hunger", "normalized difference vegetation index", "soil sampling", "0401 agriculture", " forestry", " and fisheries", "precision fertilization", "04 agricultural and veterinary sciences", "vineyard", "15. Life on land", "apparent soil electrical conductivity"]}, "links": [{"href": "https://www.mdpi.com/2673-9976/3/1/42/pdf"}, {"href": "https://doi.org/10.3390/iecag2021-10021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%201st%20International%20Electronic%20Conference%20on%20Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/iecag2021-10021", "name": "item", "description": "10.3390/iecag2021-10021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/iecag2021-10021"}, {"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": "10.5194/essd-13-3707-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:22:47Z", "type": "Journal Article", "created": "2021-01-07", "title": "C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)", "description": "<p>Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in the semi-arid areas where the water resources are limited. Radar observations, available at high resolution and revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared to the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gathers soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/two weeks including above-ground fresh and dry biomasses, vegetation water content based on destructive measurements, cover fraction, leaf area index and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric effects-corrected products is also provided. This database, which is the first of its kind made available in open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation parameters retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely-accessible via the DOI:  https://doi.org/10.23708/8D6WQC  (Ouaadi et al., 2020a).                         </p>", "keywords": ["550", "Arid", "Soil Moisture", "0211 other engineering and technologies", "FOS: Mechanical engineering", "02 engineering and technology", "Digital Soil Mapping Techniques", "Normalized Difference Vegetation Index", "630", "Agricultural and Biological Sciences", "Engineering", "Pathology", "GE1-350", "2. Zero hunger", "QE1-996.5", "Vegetation Monitoring", "Water content", "Ecology", "Geography", "Statistics", "Life Sciences", "Hydrology (agriculture)", "Geology", "Remote Sensing in Vegetation Monitoring and Phenology", "04 agricultural and veterinary sciences", "Remote sensing", "Soil Erosion and Agricultural Sustainability", "6. Clean water", "Satellite Observations", "Archaeology", "Physical Sciences", "Leaf area index", "Telecommunications", "Medicine", "Vegetation (pathology)", "Environmental Engineering", "Data set", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Aerospace Engineering", "Soil Science", "Environmental science", "Digital Soil Mapping", "[SDU] Sciences of the Universe [physics]", "Global Soil Information", "FOS: Mathematics", "Biology", "Radar", "Synthetic Aperture Radar Interferometry", "Canopy", "FOS: Environmental engineering", "Soil Properties", "Paleontology", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Agronomy", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "0401 agriculture", " forestry", " and fisheries", "Mathematics"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/3707/2021/essd-13-3707-2021.pdf"}, {"href": "https://doi.org/10.5194/essd-13-3707-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-13-3707-2021", "name": "item", "description": "10.5194/essd-13-3707-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-13-3707-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-07T00:00:00Z"}}, {"id": "10447/672283", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:25:57Z", "type": "Report", "title": "Characterization of the main land processes occurring in Europe (2000-2018) through a MODIS NDVI seasonal parameter-based procedure", "keywords": ["Land cover", "TIMESAT", " Normalized difference vegetation index", " CORINE", " Land use", " Land cover", "Normalized difference vegetation index", "Settore AGRI-04/A - Idraulica agraria e sistemazioni idraulico-forestali", "Land use", "CORINE", "TIMESAT", "Settore AGRI-03/A - Arboricoltura generale e coltivazioni arboree"], "contacts": [{"organization": "Minacapilli, M., Motisi, A., Consoli, S.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10447/672283"}, {"rel": "self", "type": "application/geo+json", "title": "10447/672283", "name": "item", "description": "10447/672283", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10447/672283"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-10T00:00:00Z"}}, {"id": "20.500.11769/511362", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:26:36Z", "type": "Journal Article", "created": "2021-07-30", "title": "Characterization of the main land processes occurring in Europe (2000-2018) through a MODIS NDVI seasonal parameter-based procedure", "description": "The identification and recognition of the land processes are of vital importance for a proper management of the ecosystem functions and services. However, on-ground land uses/land covers (LULC) characterization is a time-consuming task, often limited to small land areas, which can be solved using remote sensing technologies. The objective of this work is to investigate how the different MODIS NDVI seasonal parameters responded to the main land processes observed in Europe in the 2000-2018 period; characterizing their temporal trend; and evaluating which one reflected better each specific land process. NDVI time-series were evaluated using TIMESAT software, which extracted eight seasonality parameters: amplitude, base value, length of season, maximum value, left and right derivative values and small and large integrated values. These parameters were correlated with the LULC changes derived from COoRdination of INformation on the Environment Land Cover (CLC) for assessing which parameter better characterized each land process. The temporal evolution of the maximum seasonal NDVI was the parameter that better characterized the occurrence of most of the land processes evaluated (afforestation, agriculturalization, degradation, land abandonment, land restoration, urbanization; R2 from 0.67-0.97). Large integrated value also presented significant relationships but they were restricted to two of the three evaluated periods. On the contrary, land processes involving CLC categories with similar NDVI patterns were not well captured with the proposed methodology. These results evidenced that this methodology could be combined with other classification methods for improving LULC identification accuracy or for identifying LULC processes in locations where no LULC maps are available. Such information can be used by policy-makers to draw LULC management actions associated with sustainable development goals. This is especially relevant for areas where food security is at stake and where terrestrial ecosystems are threatened by severe biodiversity loss.", "keywords": ["Land cover", "Urbanization", "CORINE", "Biodiversity", "15. Life on land", "01 natural sciences", "Europe", "Normalized difference vegetation index", "13. Climate action", "Land use", "11. Sustainability", "Seasons", "TIMESAT", "Ecosystem", "Environmental Monitoring", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.iris.unict.it/bitstream/20.500.11769/511362/1/Ramirez-Cuesta%20et%20al%202021.pdf"}, {"href": "https://doi.org/20.500.11769/511362"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11769/511362", "name": "item", "description": "20.500.11769/511362", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11769/511362"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-01T00:00:00Z"}}, {"id": "2283805478", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:26:55Z", "type": "Journal Article", "created": "2016-02-19", "title": "Impacts of Re-Vegetation on Surface Soil Moisture over the Chinese Loess Plateau Based on Remote Sensing Datasets", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>A large-scale re-vegetation supported by the Grain for Green Project (GGP) has greatly changed local eco-hydrological systems, with an impact on soil moisture conditions for the Chinese Loess Plateau. It is important to know how, exactly, re-vegetation influences soil moisture conditions, which not only crucially constrain growth and distribution of vegetation, and hence, further re-vegetation, but also determine the degree of soil desiccation and, thus, erosion risk in the region. In this study, three eco-environmental factors, which are Soil Water Index (SWI), the Normalized Difference Vegetation Index (NDVI), and precipitation, were used to investigate the response of soil moisture in the one-meter layer of top soil to the re-vegetation during the GGP. SWI was estimated based on the backscatter coefficient produced by the European Remote Sensing Satellite (ERS-1/2) and Meteorological Operational satellite program (MetOp), while NDVI was derived from SPOT imageries. Two separate periods, which are 1998\u20132000 and 2008\u20132010, were selected to examine the spatiotemporal pattern of the chosen eco-environmental factors. It has been shown that the amount of precipitation in 1998\u20132000 was close to that of 2008\u20132010 (the difference being 13.10 mm). From 1998\u20132000 to 2008\u20132010, the average annual NDVI increased for 80.99%, while the SWI decreased for 72.64% of the area on the Loess Plateau. The average NDVI over the Loess Plateau increased rapidly by 17.76% after the 10-year GGP project. However, the average SWI decreased by 4.37% for two-thirds of the area. More specifically, 57.65% of the area on the Loess Plateau experienced an increased NDVI and decreased SWI, 23.34% of the area had an increased NDVI and SWI. NDVI and SWI decreased simultaneously for 14.99% of the area, and the decreased NDVI and increased SWI occurred at the same time for 4.02% of the area. These results indicate that re-vegetation, human activities, and climate change have impacts on soil moisture. However, re-vegetation, which consumes a large quantity of soil water, may be the major factor for soil moisture change in most areas of the Loess Plateau. It is, therefore, suggested that Soil Moisture Content (SMC) should be kept in mind when carrying out re-vegetation in China\u2019s arid and semi-arid regions.</p></article>", "keywords": ["2. Zero hunger", "China", "Science", "Q", "Soil Water Index (SWI)", "precipitation", "15. Life on land", "01 natural sciences", "6. Clean water", "remote sensing", "the Loess Plateau", "13. Climate action", "11. Sustainability", "Normalized Difference Vegetation Index (NDVI)", "Grain for Green Project (GGP)", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Qiao Jiao, Rui Li, Fei Wang, Xingmin Mu, Pengfei Li, Chunchun An,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2072-4292/8/2/156/pdf"}, {"href": "https://doi.org/2283805478"}, {"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": "2283805478", "name": "item", "description": "2283805478", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2283805478"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-02-19T00:00:00Z"}}, {"id": "34998760", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:27:45Z", "type": "Journal Article", "created": "2022-01-06", "title": "Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China", "description": "Open AccessLe d\u00e9veloppement d'un syst\u00e8me pr\u00e9cis de pr\u00e9diction du rendement des cultures \u00e0 grande \u00e9chelle est d'une importance primordiale pour la gestion des ressources agricoles et la s\u00e9curit\u00e9 alimentaire mondiale. L'observation de la Terre fournit une source unique d'informations pour surveiller les cultures \u00e0 partir d'une diversit\u00e9 de gammes spectrales. Cependant, l'utilisation int\u00e9gr\u00e9e de ces donn\u00e9es et de leurs valeurs dans la pr\u00e9diction du rendement des cultures est encore peu \u00e9tudi\u00e9e. Ici, nous avons propos\u00e9 la combinaison de donn\u00e9es environnementales (climat, sol, g\u00e9ographie et topographie) avec de multiples donn\u00e9es satellitaires (indices de v\u00e9g\u00e9tation optiques, fluorescence induite par le soleil (SIF), temp\u00e9rature de surface du sol (LST) et profondeur optique de la v\u00e9g\u00e9tation micro-ondes (VOD)) dans le cadre pour estimer le rendement des cultures de ma\u00efs, de riz et de soja dans le nord-est de la Chine, et leur valeur unique et leur influence relative sur la pr\u00e9diction du rendement ont \u00e9t\u00e9 \u00e9valu\u00e9es. Deux m\u00e9thodes de r\u00e9gression lin\u00e9aire, trois m\u00e9thodes d'apprentissage automatique (ML) et un mod\u00e8le d'ensemble ML ont \u00e9t\u00e9 adopt\u00e9s pour construire des mod\u00e8les de pr\u00e9diction de rendement. Les r\u00e9sultats ont montr\u00e9 que les m\u00e9thodes individuelles de ML surpassaient les m\u00e9thodes de r\u00e9gression lin\u00e9aire, le mod\u00e8le d'ensemble de ML a encore am\u00e9lior\u00e9 les mod\u00e8les de ML uniques. De plus, les mod\u00e8les avec plus d'intrants ont obtenu de meilleures performances, la combinaison de donn\u00e9es satellitaires avec des donn\u00e9es environnementales, qui expliquaient respectivement 72\u00a0%, 69\u00a0% et 57\u00a0% de la variabilit\u00e9 du rendement du ma\u00efs, du riz et du soja, a d\u00e9montr\u00e9 des performances de pr\u00e9diction du rendement sup\u00e9rieures \u00e0 celles des intrants individuels. Alors que les donn\u00e9es satellitaires ont contribu\u00e9 \u00e0 la pr\u00e9diction du rendement des cultures principalement au d\u00e9but de la pointe de la saison de croissance, les donn\u00e9es climatiques ont fourni des informations suppl\u00e9mentaires principalement \u00e0 la pointe de la fin de la saison. Nous avons \u00e9galement constat\u00e9 que l'utilisation combin\u00e9e de l'IVE, du LST et du SIF a am\u00e9lior\u00e9 la pr\u00e9cision du mod\u00e8le par rapport au mod\u00e8le d'IVE de r\u00e9f\u00e9rence. Cependant, les indices de v\u00e9g\u00e9tation bas\u00e9s sur l'optique partageaient des informations similaires et ne fournissaient pas beaucoup d'informations suppl\u00e9mentaires au-del\u00e0 de l'IVE. Les pr\u00e9visions de rendement en cours de saison ont montr\u00e9 que les rendements des cultures peuvent \u00eatre pr\u00e9vus de mani\u00e8re satisfaisante deux \u00e0 trois mois avant la r\u00e9colte. La g\u00e9ographie, la topographie, la VOD, l'IVE, les param\u00e8tres hydrauliques du sol et les param\u00e8tres nutritifs sont plus importants pour la pr\u00e9diction du rendement des cultures.", "keywords": ["Atmospheric sciences", "Climate", "Multi-source satellite data", "Normalized Difference Vegetation Index", "Engineering", "Pathology", "Climate change", "Urban Heat Islands and Mitigation Strategies", "Linear regression", "2. Zero hunger", "Global and Planetary Change", "Vegetation Monitoring", "Ecology", "Geography", "Statistics", "Agriculture", "Geology", "Remote Sensing in Vegetation Monitoring and Phenology", "04 agricultural and veterinary sciences", "Remote sensing", "Aerospace engineering", "Archaeology", "Physical Sciences", "Metallurgy", "Medicine", "Seasons", "Global Vegetation Models", "Biomass Estimation", "Regression analysis", "Vegetation (pathology)", "Crops", " Agricultural", "Environmental Engineering", "Environmental data", "Yield (engineering)", "Zea mays", "Environmental science", "Machine learning", "FOS: Mathematics", "Crop yield", "Biology", "Global Forest Drought Response and Climate Change", "FOS: Environmental engineering", "Predictive modelling", "Food security", "FOS: Earth and related environmental sciences", "15. Life on land", "Agronomy", "Materials science", "Yield prediction", "Satellite", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Growing season", "0401 agriculture", " forestry", " and fisheries", "Mathematics"], "contacts": [{"organization": "Zhenwang Li, Lei Ding, Donghui Xu,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/34998760"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "34998760", "name": "item", "description": "34998760", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/34998760"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-01T00:00:00Z"}}, {"id": "c6f96b4e-e77e-41a3-90fa-98fc01e91f64", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[7.39, 51.82], [7.39, 53.22], [14.43, 53.22], [14.43, 51.82], [7.39, 51.82]]]}, "properties": {"license": "CC BY", "rights": "Restrictions applied to assure the protection of privacy or intellectual property, and any special restrictions or limitations or warnings on using the resource or metadata. Reports, articles, papers, scientific and non - scientific works of any form, including tables, maps, or any other kind of output, in printed or electronic form, based in whole or in part on the data supplied, must contain an acknowledgement of the form: \"Data reused from the BonaRes Data Centre www.bonares.de. This data were created as part of the ZALF Datenerfassung's research activities.\" Although every care has been taken in preparing and testing the data, the ZALF Datenerfassung and the BonaRes Data Centre cannot guarantee that the data are correct; neither does the ZALF Datenerfassung and the BonaRes Data Centre accept any liability whatsoever for any error, missing data or omission in the data, or for any loss or damage arising from its use. The ZALF Datenerfassung and BonaRes Data Centre will not be responsible for any direct or indirect use which might be made of the data.", "updated": "2025-10-06", "type": "Service", "created": "2025-10-01", "language": "eng", "title": "Web Map Service of the dataset 'Maize Phenometrics'", "description": "This Web Map Service includes spatial information used by datasets 'Maize Phenometrics'", "keywords": ["infoMapAccessService", "maize", "phenology", "normalized difference vegetation index"], "contacts": [{"name": "Leibniz Centre for Agricultural Landscape Research", "organization": "ZALF", "position": "Research Platform 'Data Analysis & Simulation' - Workgroup Research Data Management", "roles": ["publisher"], "phones": [{"value": "+49 33432 82 300"}], "emails": [{"value": "dataservice@zalf.de"}], "addresses": [{"deliveryPoint": ["Eberswalder Strasse 84"], "city": "M\u00fcncheberg", "administrativeArea": "Brandenburg", "postalCode": "15374", "country": "Germany"}], "links": [{"href": 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