{"type": "FeatureCollection", "features": [{"id": "10.1016/j.agee.2022.108124", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:15:05Z", "type": "Journal Article", "created": "2022-08-18", "title": "Assessing almond response to irrigation and soil management practices using vegetation indexes time-series and plant water status measurements", "description": "Open AccessThis research was funded in the frame of the projects PRECIRIEGO RTC-2017\u20136365-2 financed by Agencia Estatal de Investigaci\u00f3n with European Regional Development Fund co-funds; and the European Union H2020 project SHUI GA 773903. The research was supported also by the CajaMar Caja Rural Contract \u201cEfficient use of water resources under climate change scenarios\u201d. I. Buesa and J.M. Ram\u00edrez-Cuesta acknowledge the postdoctoral financial support received from Juan de la Cierva Spanish Postdoctoral Program (FJC2019\u2013042122-I and IJC2020\u2013043601-I, respectively). Authors acknowledge David Hortelano and Jos\u00e9 Luis Ru\u00edz Garc\u00eda for the help provided in the field measurements acquisition. This work represents a contribution to CSIC Thematic Interdisciplinary Platform PTI TELEDETECT.", "keywords": ["0106 biological sciences", "Soil management", "Almonds", "F06 Irrigation", "01 natural sciences", "12. Responsible consumption", "Vegetation index", "Sentinel 2", "Remote sensing sustainable agriculture", "P33 Soil chemistry and physics", "F40 Plant ecology", "2. Zero hunger", "precision agriculture", "Precision agriculture", "Sustainable agriculture", "Water use efficiency", "Vegetation cover", "F07 Soil cultivation", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "Tree canopy", "F60 Plant physiology and biochemistry", "6. Clean water", "Water management", "P30 Soil science and management", "P10 Water resources and management", "0401 agriculture", " forestry", " and fisheries", "Remote sensing", " sustainable agriculture", "Sentinel-2"]}, "links": [{"href": "https://www.iris.unict.it/bitstream/20.500.11769/552491/2/Agriculture%2c%20ecosystems%20and%20environment%202022.pdf"}, {"href": "https://doi.org/10.1016/j.agee.2022.108124"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agee.2022.108124", "name": "item", "description": "10.1016/j.agee.2022.108124", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agee.2022.108124"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-01T00:00:00Z"}}, {"id": "10.1016/j.scitotenv.2021.149346", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:16:02Z", "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": "2026-09-20T16:16:02Z", "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": "10.1029/2020wr028752", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:16:42Z", "type": "Journal Article", "created": "2021-10-17", "title": "Are Remote Sensing Evapotranspiration Models Reliable Across South American Ecoregions", "description": "Abstract

Many remote sensing\uffe2\uff80\uff90based evapotranspiration (RSBET) algorithms have been proposed in the past decades and evaluated using flux tower data, mainly over North America and Europe. Model evaluation across South America has been done locally or using only a single algorithm at a time. Here, we provide the first evaluation of multiple RSBET models, at a daily scale, across a wide variety of biomes, climate zones, and land uses in South America. We used meteorological data from 25 flux towers to force four RSBET models: Priestley\uffe2\uff80\uff93Taylor Jet Propulsion Laboratory (PT\uffe2\uff80\uff90JPL), Global Land Evaporation Amsterdam Model (GLEAM), Penman\uffe2\uff80\uff93Monteith Mu model (PM\uffe2\uff80\uff90MOD), and Penman\uffe2\uff80\uff93Monteith Nagler model (PM\uffe2\uff80\uff90VI). was predicted satisfactorily by all four models, with correlations consistently higher () for GLEAM and PT\uffe2\uff80\uff90JPL, and PM\uffe2\uff80\uff90MOD and PM\uffe2\uff80\uff90VI presenting overall better responses in terms of percent bias (%). As for PM\uffe2\uff80\uff90VI, this outcome is expected, given that the model requires calibration with local data. Model skill seems to be unrelated to land\uffe2\uff80\uff90use but instead presented some dependency on biome and climate, with the models producing the best results for wet to moderately wet environments. Our findings show the suitability of individual models for a number of combinations of land cover types, biomes, and climates. At the same time, no model outperformed the others for all conditions, which emphasizes the need for adapting individual algorithms to take into account intrinsic characteristics of climates and ecosystems in South America.

", "keywords": ["ATMOSPHERE WATER FLUX", "550", "VEGETATION INDEX", "Penman-Monteith", "RIPARIAN EVAPOTRANSPIRATION", "0207 environmental engineering", "02 engineering and technology", "SURFACE-TEMPERATURE", "01 natural sciences", "transpiration", "SEMIARID ENVIRONMENT", "CARBON-DIOXIDE", "ENERGY-BALANCE CLOSURE", "https://purl.org/becyt/ford/1.5", "https://purl.org/becyt/ford/1", "Water Science and Technology", "0105 earth and related environmental sciences", "RAINFALL INTERCEPTION", "PRIESTLEY-TAYLOR", "WACMOS-ET PROJECT", "TRANSPIRATION", "15. Life on land", "EDDY COVARIANCE MEASUREMENTS", "name=Water and Environmental Engineering", "MODIS", "13. Climate action", "Earth and Environmental Sciences", "Priestley-Taylor", "PENMAN-MONTEITH", "/dk/atira/pure/core/keywords/water_and_environmental_engineering"]}, "links": [{"href": "https://centaur.reading.ac.uk/101236/1/agujournaltemplateDinizetal.pdf"}, {"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR028752"}, {"href": "https://doi.org/10.1029/2020wr028752"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water%20Resources%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1029/2020wr028752", "name": "item", "description": "10.1029/2020wr028752", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1029/2020wr028752"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-01T00:00:00Z"}}, {"id": "10.3390/rs13040727", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:11Z", "type": "Journal Article", "created": "2021-02-17", "title": "On the Utility of High-Resolution Soil Moisture Data for Better Constraining Thermal-Based Energy Balance over Three Semi-Arid Agricultural Areas", "description": "

Over semi-arid agricultural areas, the surface energy balance and its components are largely dependent on the soil water availability. In such conditions, the land surface temperature (LST) retrieved from the thermal bands has been commonly used to represent the high spatial variability of the surface evaporative fraction and associated fluxes. In contrast, however, the soil moisture (SM) retrieved from microwave data has rarely been used thus far due to the unavailability of high-resolution (field scale) SM products until recent times. Soil evaporation is controlled by the surface SM. Moreover, the surface SM dynamics is temporally related to root zone SM, which provides information about the water status of plants. The aim of this work was to assess the gain in terms of flux estimates when integrating microwave-derived SM data in a thermal-based energy balance model at the field scale. In this study, SM products were derived from three different methodologies: the first approach inverts SM, labeled hereafter as \uffe2\uff80\uff98SMO20\uffe2\uff80\uff99, from the backscattering coefficient and the interferometric coherence derived from Sentinel-1 products in the water cloud model (WCM); the second approach inverts SM from Sentinel-1 and Sentinel-2 data based on machine learning algorithms trained on a synthetic dataset simulated by the WCM noted \uffe2\uff80\uff98SME16\uffe2\uff80\uff99; and the third approach disaggregates the soil moisture active and passive SM at 100 m resolution using Landsat optical/thermal data \uffe2\uff80\uff98SMO19\uffe2\uff80\uff99. These SM products, combined with the Landsat based vegetation index and LST, are integrated simultaneously within an energy balance model (TSEB-SM) to predict the latent (LE) and sensible (H) heat fluxes over two irrigated and rainfed wheat crop sites located in the Haouz Plain in the center of Morocco. H and LE were measured over each site using an eddy covariance system and their values were used to evaluate the potential of TSEB-SM against the classical two source energy balance (TSEB) model solely based on optical/thermal data. Globally, TSEB systematically overestimates LE (mean bias of 100 W/m2) and underestimates H (mean bias of \uffe2\uff88\uff92110 W/m2), while TSEB-SM significantly reduces those biases, regardless of the SM product used as input. This is linked to the parameterization of the Priestley Taylor coefficient, which is set to \uffce\uffb1PT = 1.26 by default in TSEB and adjusted across the season in TSEB-SM. The best performance of TSEB-SM was obtained over the irrigated field using the three retrieved SM products with a mean R2 of 0.72 and 0.92, and a mean RMSE of 31 and 36 W/m2 for LE and H, respectively. This opens up perspectives for applying the TSEB-SM model over extended irrigated agricultural areas to better predict the crop water needs at the field scale.

", "keywords": ["2. Zero hunger", "550", "Science", "Q", "0208 environmental biotechnology", "0207 environmental engineering", "TSEB-SM", "land surface temperature", "02 engineering and technology", "15. Life on land", "surface soil moisture", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "winter wheat", "13. Climate action", "semi-arid region", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "TSEB", "environment", "vegetation index"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/4/727/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/4/727/pdf"}, {"href": "https://doi.org/10.3390/rs13040727"}, {"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/rs13040727", "name": "item", "description": "10.3390/rs13040727", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13040727"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-17T00:00:00Z"}}, {"id": "10.3390/iecag2021-10021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:05Z", "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": "10.15302/j-fase-2014028", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:18:10Z", "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 R2 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-2) 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": "10.3390/rs13040716", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:11Z", "type": "Journal Article", "created": "2021-02-16", "title": "Application of Remote Sensing Techniques to Discriminate the Effect of Different Soil Management Treatments over Rainfed Vineyards in Chianti Terroir", "description": "

The work aimed to discriminate among different soil management treatments in terms of beneficial effects by high-resolution thermal and spectral vegetation imagery using an unmanned aerial vehicle and open-source GIS software. Five soil management treatments were applied in two organic vineyards (cv. Sangiovese) from Chianti Classico terroir (Tuscany, Italy) during two experimental years. The treatments tested consisted of conventional tillage, spontaneous vegetation, pigeon bean (Vicia faba var. minor Beck) incorporated in spring, mixture of barley (Hordeum vulgare L.) and clover (Trifolium squarrosum L.) incorporated or left as dead mulch in late spring. The images acquired remotely were analyzed through map-algebra and map-statistics in QGIS and correlated with field ecophysiological measurements. The surface temperature, crop water stress index (CWSI) and normalized difference vegetation index (NDVI) of each vine row under treatments were compared based on frequency distribution functions and statistics descriptors of position. The spectral vegetation and thermal-based indices were significantly correlated with the respective leaf area index (R2 = 0.89) and stem water potential measurements (R2 = 0.59), and thus are an expression of the crop vigor and water status. The gravel and active limestone soil components determined the spatial variability of vine biophysical (e.g., canopy vigor) and physiological characteristics (e.g., vine chlorophyll content) in both farms. The vine canopy surface temperature, and CWSI were lower on the spontaneous and pigeon bean treatments in both farms, thus evidencing less physiological stress on the vine rows derived from the cover crop residual effect. In conclusion, the proposed methodology showed the capacity to discriminate across soil management practices and map the spatial variability within vineyards. The methodology could serve as a simple and non-invasive tool for precision soil management in rainfed vineyards to guide producers on using the most efficient and profitable practice.

", "keywords": ["cover crops; crop water stress index (CWSI); spectral vegetation index; sustainable agriculture", "2. Zero hunger", "0106 biological sciences", "Cover crops; Crop water stress index (CWSI); Spectral vegetation index; Sustainable agriculture", "Science", "Q", "crop water stress index (CWSI)", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water", "cover crops; cropwater stress index (CWSI); spectral vegetation index; sustainable agriculture", "spectral vegetation index", "sustainable agriculture", "0401 agriculture", " forestry", " and fisheries", "cover crops"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/4/716/pdf"}, {"href": "https://arpi.unipi.it/bitstream/11568/1112167/1/Puig%20Sirera%20et%20al_2021.pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/4/716/pdf"}, {"href": "https://doi.org/10.3390/rs13040716"}, {"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/rs13040716", "name": "item", "description": "10.3390/rs13040716", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13040716"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-16T00:00:00Z"}}, {"id": "10.1594/pangaea.963212", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:18:17Z", "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/rs12193228", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:10Z", "type": "Journal Article", "created": "2020-10-05", "title": "Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery", "description": "

The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R2) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production.

", "keywords": ["2. Zero hunger", "object-oriented segmentation method", "optimal index method", "rice", "Science", "Q", "rice; growth indicators; multi-stage vegetation index; unmanned aerial vehicle; optimal index method; object-oriented segmentation method; estimation accuracy", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "multi-stage vegetation index", "15. Life on land", "estimation accuracy", "growth indicators", "13. Climate action", "unmanned aerial vehicle", "0401 agriculture", " forestry", " and fisheries"], "contacts": [{"organization": "Zhengchao Qiu, Haitao Xiang, Fei Ma, Changwen Du,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://doi.org/10.3390/rs12193228"}, {"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/rs12193228", "name": "item", "description": "10.3390/rs12193228", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12193228"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-03T00:00:00Z"}}, {"id": "10.3390/rs8020156", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:12Z", "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": "

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.

", "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/agronomy10101524", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:00Z", "type": "Journal Article", "created": "2020-10-07", "title": "A simple light-use-efficiency model to estimate wheat yield in the semi-arid areas.", "description": "

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.

", "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/plants13091212", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:09Z", "type": "Journal Article", "created": "2024-04-29", "title": "Precision Estimation of Crop Coefficient for Maize Cultivation Using High-Resolution Satellite Imagery to Enhance Evapotranspiration Assessment in Agriculture", "description": "

The estimation of crop evapotranspiration (ETc) is crucial for irrigation water management, especially in arid regions. This can be particularly relevant in the Po Valley (Italy), where arable lands suffer from drought damages on an annual basis, causing drastic crop yield losses. This study presents a novel approach for vegetation-based estimation of crop evapotranspiration (ETc) for maize. Three years of high-resolution multispectral satellite (Sentinel-2)-based Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Leaf Area Index (LAI) time series data were used to derive crop coefficients of maize in nine plots at the Acqua Campus experimental farm of Irrigation Consortium for the Emilia Romagna Canal (CER), Italy. Since certain vegetation indices (VIs) (such as NDVI) have an exponential nature compared to the other indices, both linear and power regression models were evaluated to estimate the crop coefficient (Kc). In the context of linear regression, the correlations between Food and Agriculture Organization (FAO)-based Kc and NDWI, NDRE, NDVI, and LAI-based Kc were 0.833, 0.870, 0.886, and 0.771, respectively. Strong correlation values in the case of power regression (NDWI: 0.876, NDRE: 0.872, NDVI: 0.888, LAI: 0.746) indicated an alternative approach to provide crop coefficients for the vegetation period. The VI-based ETc values were calculated using reference evapotranspiration (ET0) and VI-based Kc. The weather station data of CER were used to calculate ET0 based on Penman-Monteith estimation. Out of the Vis, NDWI and NDVI-based ETc performed the best both in the cases of linear (NDWI RMSE: 0.43 \u00b1 0.12; NDVI RMSE: 0.43 \u00b1 0.095) and power (NDWI RMSE: 0.44 \u00b1 0.116; NDVI RMSE: 0.44 \u00b1 0.103) approaches. The findings affirm the efficacy of the developed methodology in accurately assessing the evapotranspiration rate. Consequently, it offers a more refined temporal estimation of water requirements for maize cultivation in the region.

", "keywords": ["2. Zero hunger", "Botany", "04 agricultural and veterinary sciences", "vegetation index-based Kc", "15. Life on land", "01 natural sciences", "Article", "6. Clean water", "maize water demand", "QK1-989", "vegetation index-based crop evapotranspiration", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.mdpi.com/2223-7747/13/9/1212/pdf"}, {"href": "https://doi.org/10.3390/plants13091212"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Plants", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/plants13091212", "name": "item", "description": "10.3390/plants13091212", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/plants13091212"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-27T00:00:00Z"}}, {"id": "10.3390/rs12121917", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:10Z", "type": "Journal Article", "created": "2020-06-15", "title": "Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements", "description": "

Yield is one of the primary concerns for any farmer since it is a key to economic prosperity. Yield productivity zones\uffe2\uff80\uff94that is to say, areas with the same yield level within fields over the long-term\uffe2\uff80\uff94are a form of derived (predicted) data from periodic remote sensing, in this study according to the Enhanced Vegetation Index (EVI). The delineation of yield productivity zones can (a) increase economic prosperity and (b) reduce the environmental burden by employing site-specific crop management practices which implement advanced geospatial technologies that respect soil heterogeneity. This paper presents yield productivity zone identification and computing based on Sentinel-2A/B and Landsat 8 multispectral satellite data and also quantifies the success rate of yield prediction in comparison to the measured yield data. Yield data on spring barley, winter wheat, corn, and oilseed rape were measured with a spatial resolution of up to several meters directly by a CASE IH harvester in the field. The yield data were available from three plots in three years on the Rost\uffc4\uff9bnice Farm in the Czech Republic, with an overall acreage of 176 hectares. The presented yield productivity zones concept was found to be credible for the prediction of yield, including its geospatial variations.

", "keywords": ["2. Zero hunger", "yield productivity zones", "precision agriculture", "Science", "Q", "Enhanced Vegetation Index", "04 agricultural and veterinary sciences", "yield productivity zones; yield measurements; satellite images; precision agriculture; Enhanced Vegetation Index", "15. Life on land", "01 natural sciences", "yield measurements", "0401 agriculture", " forestry", " and fisheries", "satellite images", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/12/1917/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/12/1917/pdf"}, {"href": "https://doi.org/10.3390/rs12121917"}, {"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/rs12121917", "name": "item", "description": "10.3390/rs12121917", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12121917"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-13T00:00:00Z"}}, {"id": "10.5061/dryad.266m0", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:25Z", "type": "Dataset", "title": "Data from: Multi-decadal time series of remotely sensed vegetation improves prediction of soil carbon in a subtropical grassland", "description": "unspecifiedSoil carbon sequestration in agroecosystems could play a key role in climate change mitigation but will require accurate predictions of soil organic carbon (SOC) stocks over spatial scales relevant to land management. Spatial variation in underlying drivers of SOC, such as plant productivity and soil mineralogy, complicates these predictions. Recent advances in the availability of remotely sensed data make it practical to generate multidecadal time series of vegetation indices with high spatial resolution and coverage. However, the utility of such data largely is unknown, only having been tested with shorter (e.g., 1-2 year) data summaries. Across a 2000 ha subtropical grassland, we found that a long time series (28 years) of a vegetation index (Enhanced Vegetation Index; EVI) derived from the Landsat 5 satellite significantly enhanced prediction of spatially varying SOC pools, while a short summary (2 years) was an ineffective predictor. EVI was the best predictor for surface SOC (0-5 cm depth) and total measured SOC stocks (0-15 cm). The optimum models for SOC in the upper soil layer combined EVI records with elevation and calcium concentration, while deeper SOC was more strongly associated with calcium availability. We demonstrate how data from the open access Landsat archive can predict SOC stocks, a key ecosystem metric, and illustrate the rich variety of analytical approaches that can be applied to long time series of remotely sensed greenness. Overall, our results showed that SOC pools were closely coupled to EVI in this ecosystem, demonstrating that maintenance of higher average green leaf area is correlated with higher SOC. The strong associations of vegetation greenness and calcium concentration with SOC suggest that the ability to sequester additional SOC likely will rely on strategic management of pasture vegetation and soil fertility.", "keywords": ["2. Zero hunger", "13. Climate action", "vegetation greenness", "grazing lands", "Paspalum notatum", "subtropical pasture", "15. Life on land", "Soil carbon", "enhanced vegetation index"], "contacts": [{"organization": "Wilson, Chris H., Caughlin, T. Trevor, Rifai, Sami W., Boughton, Elizabeth H., Mack, Michelle C., Flory, S. Luke,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.266m0"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.266m0", "name": "item", "description": "10.5061/dryad.266m0", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.266m0"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-04-03T00:00:00Z"}}, {"id": "10.5194/essd-13-3707-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:41Z", "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": "

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).

", "keywords": ["550", "Arid", "Soil Moisture", "0211 other engineering and technologies", "FOS: Mechanical engineering", "02 engineering and technology", "Digital Soil Mapping Techniques", "Normalized Difference Vegetation Index", "630", "Agricultural and Biological Sciences", "Engineering", "Pathology", "GE1-350", "2. Zero hunger", "QE1-996.5", "Vegetation Monitoring", "Water content", "Ecology", "Geography", "Statistics", "Life Sciences", "Hydrology (agriculture)", "Geology", "Remote Sensing in Vegetation Monitoring and Phenology", "04 agricultural and veterinary sciences", "Remote sensing", "Soil Erosion and Agricultural Sustainability", "6. Clean water", "Satellite Observations", "Archaeology", "Physical Sciences", "Leaf area index", "Telecommunications", "Medicine", "Vegetation (pathology)", "Environmental Engineering", "Data set", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Aerospace Engineering", "Soil Science", "Environmental science", "Digital Soil Mapping", "[SDU] Sciences of the Universe [physics]", "Global Soil Information", "FOS: Mathematics", "Biology", "Radar", "Synthetic Aperture Radar Interferometry", "Canopy", "FOS: Environmental engineering", "Soil Properties", "Paleontology", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Agronomy", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "0401 agriculture", " forestry", " and fisheries", "Mathematics"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/3707/2021/essd-13-3707-2021.pdf"}, {"href": "https://doi.org/10.5194/essd-13-3707-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-13-3707-2021", "name": "item", "description": "10.5194/essd-13-3707-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-13-3707-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-07T00:00:00Z"}}, {"id": "10.5281/zenodo.8085976", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:21:01Z", "type": "Journal Article", "created": "2020-10-05", "title": "Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery", "description": "

The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R2) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production.

", "keywords": ["2. Zero hunger", "object-oriented segmentation method", "optimal index method", "rice", "Science", "Q", "rice; growth indicators; multi-stage vegetation index; unmanned aerial vehicle; optimal index method; object-oriented segmentation method; estimation accuracy", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "multi-stage vegetation index", "15. Life on land", "growth indicators", "13. Climate action", "unmanned aerial vehicle", "0401 agriculture", " forestry", " and fisheries"], "contacts": [{"organization": "Zhengchao Qiu, Haitao Xiang, Fei Ma, Changwen Du,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8085976"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8085976", "name": "item", "description": "10.5281/zenodo.8085976", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8085976"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-03T00:00:00Z"}}, {"id": "10.5281/zenodo.8090556", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:21:01Z", "type": "Journal Article", "created": "2020-06-15", "title": "Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements", "description": "

Yield is one of the primary concerns for any farmer since it is a key to economic prosperity. Yield productivity zones\u2014that is to say, areas with the same yield level within fields over the long-term\u2014are a form of derived (predicted) data from periodic remote sensing, in this study according to the Enhanced Vegetation Index (EVI). The delineation of yield productivity zones can (a) increase economic prosperity and (b) reduce the environmental burden by employing site-specific crop management practices which implement advanced geospatial technologies that respect soil heterogeneity. This paper presents yield productivity zone identification and computing based on Sentinel-2A/B and Landsat 8 multispectral satellite data and also quantifies the success rate of yield prediction in comparison to the measured yield data. Yield data on spring barley, winter wheat, corn, and oilseed rape were measured with a spatial resolution of up to several meters directly by a CASE IH harvester in the field. The yield data were available from three plots in three years on the Rost\u011bnice Farm in the Czech Republic, with an overall acreage of 176 hectares. The presented yield productivity zones concept was found to be credible for the prediction of yield, including its geospatial variations.

", "keywords": ["2. Zero hunger", "yield productivity zones", "precision agriculture", "Science", "Q", "Enhanced Vegetation Index", "04 agricultural and veterinary sciences", "yield productivity zones; yield measurements; satellite images; precision agriculture; Enhanced Vegetation Index", "15. Life on land", "01 natural sciences", "yield measurements", "0401 agriculture", " forestry", " and fisheries", "satellite images", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/12/1917/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/12/1917/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8090556"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8090556", "name": "item", "description": "10.5281/zenodo.8090556", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8090556"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-13T00:00:00Z"}}, {"id": "3092600768", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:22:45Z", "type": "Journal Article", "created": "2020-10-05", "title": "Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery", "description": "

The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R2) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production.

", "keywords": ["2. Zero hunger", "object-oriented segmentation method", "optimal index method", "rice", "Science", "Q", "rice; growth indicators; multi-stage vegetation index; unmanned aerial vehicle; optimal index method; object-oriented segmentation method; estimation accuracy", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "multi-stage vegetation index", "15. Life on land", "estimation accuracy", "growth indicators", "13. Climate action", "unmanned aerial vehicle", "0401 agriculture", " forestry", " and fisheries"], "contacts": [{"organization": "Zhengchao Qiu, Haitao Xiang, Fei Ma, Changwen Du,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://doi.org/3092600768"}, {"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": "3092600768", "name": "item", "description": "3092600768", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3092600768"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-03T00:00:00Z"}}, {"id": "10447/672283", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:21:47Z", "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": "2026-09-20T16:22:12Z", "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": "20.500.11769/552491", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:22:12Z", "type": "Journal Article", "created": "2022-08-18", "title": "Assessing almond response to irrigation and soil management practices using vegetation indexes time-series and plant water status measurements", "description": "Current water scarcity scenario has led to the implementation of sustainable agricultural practices intended to improve water use efficiency. The present work evaluates during three agricultural campaigns (2018-2020) the response of a young almond orchard to two management practices in terms by combining remote sensing indexes (Normalized Difference Vegetation Index, NDVI; and Soil Adjusted Vegetation Indexes, SAVI) and physiological/ morphological measurement (stem water potential, \u03a8stem; trunk perimeter and canopy diameter). The management practices included (I) sustained deficit irrigation and (II) soil management. Severe deficit irrigation resulted in lower vegetation indexes (VI) values, \u03a8stem and tree dimensions (13 %, 23 % and 14 % lower, respectively) than those obtained for full irrigation strategy; whereas moderate deficit irrigation did not affect any of the parameters analysed. The presence of vegetation cover in the inter-row resulted in a VIs increase (19-42 %) and in lower tree dimensions (reductions of 7-8 % for trunk perimeter and 0.34-0.37 m for canopy diameter) when compared to bare soil treatment, but did not have any influence on \u03a8stem. The present study proves the suitability of remote sensing and physiological measurements for assessing almond response to the different management practices.", "keywords": ["0106 biological sciences", "Soil management", "Almonds", "F06 Irrigation", "01 natural sciences", "12. Responsible consumption", "Vegetation index", "Sentinel 2", "Remote sensing sustainable agriculture", "P33 Soil chemistry and physics", "F40 Plant ecology", "2. Zero hunger", "precision agriculture", "Precision agriculture", "Sustainable agriculture", "Water use efficiency", "Vegetation cover", "F07 Soil cultivation", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "Tree canopy", "F60 Plant physiology and biochemistry", "6. Clean water", "Water management", "P30 Soil science and management", "P10 Water resources and management", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2"]}, "links": [{"href": "https://www.iris.unict.it/bitstream/20.500.11769/552491/2/Agriculture%2c%20ecosystems%20and%20environment%202022.pdf"}, {"href": "https://doi.org/20.500.11769/552491"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11769/552491", "name": "item", "description": "20.500.11769/552491", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11769/552491"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-01T00:00:00Z"}}, {"id": "2283805478", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:22:25Z", "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": "

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.

", "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": "3035570271", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:22:43Z", "type": "Journal Article", "created": "2020-06-15", "title": "Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements", "description": "

Yield is one of the primary concerns for any farmer since it is a key to economic prosperity. Yield productivity zones\u2014that is to say, areas with the same yield level within fields over the long-term\u2014are a form of derived (predicted) data from periodic remote sensing, in this study according to the Enhanced Vegetation Index (EVI). The delineation of yield productivity zones can (a) increase economic prosperity and (b) reduce the environmental burden by employing site-specific crop management practices which implement advanced geospatial technologies that respect soil heterogeneity. This paper presents yield productivity zone identification and computing based on Sentinel-2A/B and Landsat 8 multispectral satellite data and also quantifies the success rate of yield prediction in comparison to the measured yield data. Yield data on spring barley, winter wheat, corn, and oilseed rape were measured with a spatial resolution of up to several meters directly by a CASE IH harvester in the field. The yield data were available from three plots in three years on the Rost\u011bnice Farm in the Czech Republic, with an overall acreage of 176 hectares. The presented yield productivity zones concept was found to be credible for the prediction of yield, including its geospatial variations.

", "keywords": ["2. Zero hunger", "yield productivity zones", "precision agriculture", "Science", "Q", "Enhanced Vegetation Index", "04 agricultural and veterinary sciences", "yield productivity zones; yield measurements; satellite images; precision agriculture; Enhanced Vegetation Index", "15. Life on land", "01 natural sciences", "yield measurements", "0401 agriculture", " forestry", " and fisheries", "satellite images", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/12/1917/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/12/1917/pdf"}, {"href": "https://doi.org/3035570271"}, {"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": "3035570271", "name": "item", "description": "3035570271", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3035570271"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-13T00:00:00Z"}}, {"id": "34998760", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:22:58Z", "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": "e6a7ef25-f346-405b-abd2-fddec29c99d3", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[5.81, 47.26], [5.81, 54.76], [15.77, 54.76], [15.77, 47.26], [5.81, 47.26]]]}, "properties": {"themes": [{"concepts": [{"id": "farming"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "vegetation index"}, {"id": "crop monitoring"}, {"id": "spectral analysis"}, {"id": "appropriate technology"}], "scheme": "AGROVOC Multilingual agricultural thesaurus"}, {"concepts": [{"id": "opendata"}, {"id": "low cost"}, {"id": "spectral sensor"}, {"id": "open source device"}, {"id": "net ecosystem carbon balance"}, {"id": "water use efficiency"}], "scheme": "Individual"}, {"concepts": [{"id": "Bodennutzung"}], "scheme": "GEMET - INSPIRE themes, version 1.0"}, {"concepts": [{"id": "Germany"}, {"id": "Brandenburg"}, {"id": "M\u00e4rkisch Oderland"}, {"id": "Philippines"}, {"id": "Benin"}], "scheme": "individual"}], "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": "2024-03-01", "type": "Dataset", "created": "2024-02-15", "language": "eng", "title": "Low-cost ground-based NDVI sensor device for manual and automated crop monitoring - Field application automatic", "description": "NDVI measurements from field testing in Germany of automatic standalone low-cost sensor device\n\nGeneral description see mother table: (https://doi.org/10.4228/zalf-db3a-6q09); Related datasets are listed in the metadata element 'Related Identifier'.\nDataset version 1.0", "formats": [{"name": "CSV"}], "keywords": ["vegetation index", "crop monitoring", "spectral analysis", "appropriate technology", "opendata", "low cost", "spectral sensor", "open source device", "net ecosystem carbon balance", "water use efficiency", "Bodennutzung", "Germany", "Brandenburg", "M\u00e4rkisch Oderland", "Philippines", "Benin"], "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": {"url": null, "protocol": null, "protocol_url": "", "name": "https://ror.org/01ygyzs83", "name_url": "", "description": "ROR", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Reena Macagga", "organization": "Leibniz Centre for Agricultural Landscape Research", "position": null, "roles": ["author"], "phones": [{"value": null}], "emails": [{"value": "reena.macagga@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": null, "protocol": null, "protocol_url": "", "name": "0000-0003-3407-5177", "name_url": "", "description": "ORCID", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Mathias Hoffmann", "organization": "Leibniz Centre for Agricultural Landscape Research", "position": null, "roles": ["author"], "phones": [{"value": null}], "emails": [{"value": "mathias.hoffmann@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": null, "protocol": null, "protocol_url": "", "name": "0000-0002-2776-1403", "name_url": "", "description": "ORCID", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Geoffroy Sossa", "organization": "University of Sciences, Techniques and Technologies of Bamako (USTTB)", "position": null, "roles": ["author"], "phones": [{"value": null}], "emails": [{"value": "sogeof1992@gmail.com"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Yvonne Ayaribil", "organization": "Leibniz Centre for Agricultural Landscape Research", "position": null, "roles": ["author"], "phones": [{"value": null}], "emails": [{"value": "Yvonne.Ayaribil@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Rinan Bayot", "organization": "University of the Philippines Los Ba\u00f1os", "position": null, "roles": ["author"], "phones": [{"value": null}], "emails": [{"value": "rmbayot1@up.edu.ph"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Pearl Sanchez", "organization": "University of the Philippines Los Ba\u00f1os", "position": null, "roles": ["author"], "phones": [{"value": null}], "emails": [{"value": "pbsanchez1@up.edu.ph"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": null, "protocol": null, "protocol_url": "", "name": "0000-0001-6253-5690", "name_url": "", "description": "ORCID", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "J\u00fcrgen Augustin", "organization": "Leibniz Centre for Agricultural Landscape Research", "position": null, "roles": ["author"], "phones": [{"value": null}], "emails": [{"value": "jaug@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": null, "protocol": null, "protocol_url": "", "name": "0000-0002-8560-2814", "name_url": "", "description": "ORCID", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Sonoko Dorothea Bellingrath-Kimura", "organization": "Leibniz Centre for Agricultural Landscape Research", "position": null, "roles": ["author"], "phones": [{"value": null}], "emails": [{"value": "Sonoko.Bellingrath-Kimura@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": null, "protocol": null, "protocol_url": "", "name": "0000-0001-7392-7796", "name_url": "", "description": "ORCID", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Mathias Hoffmann", "organization": "Leibniz Centre for Agricultural Landscape Research", "position": null, "roles": ["projectLeader"], "phones": [{"value": null}], "emails": [{"value": "mathias.hoffmann@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": null, "protocol": null, "protocol_url": "", "name": "0000-0002-2776-1403", "name_url": "", "description": "ORCID", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Shukrona Giyasidinova", "organization": "Leibniz Centre for Agricultural Landscape Research", "position": null, "roles": ["dataCollector"], "phones": [{"value": null}], "emails": [{"value": "reena.macagga@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Natalija Obradovi\u0107", "organization": "Leibniz Centre for Agricultural Landscape Research", "position": null, "roles": ["dataCollector"], "phones": [{"value": null}], "emails": [{"value": "reena.macagga@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Mario Bel\u00edo Miranda", "organization": "Leibniz Centre for Agricultural Landscape Research", "position": null, "roles": ["dataCollector"], "phones": [{"value": null}], "emails": [{"value": "reena.macagga@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Isabel Zentgraf", "organization": "Leibniz Centre for Agricultural Landscape Research", "position": null, "roles": ["dataCollector"], "phones": [{"value": null}], "emails": [{"value": "reena.macagga@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Michael Alcantara", "organization": "Leibniz Centre for Agricultural Landscape Research", "position": null, "roles": ["dataCollector"], "phones": [{"value": null}], "emails": [{"value": "reena.macagga@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"organization": "University of Sciences, Techniques and Technologies of Bamako (USTTB);Leibniz Centre for Agricultural Landscape Research;University of the Philippines Los Ba\u00f1os", "roles": ["contributor"]}], "title_alternate": "Data collection: Part 5/7, table: Field application automatic"}, "links": [{"href": "https://maps.bonares.de/mapapps/resources/apps/bonares/index.html?lang=en&mid=e6a7ef25-f346-405b-abd2-fddec29c99d3", "rel": "information"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2f520316-f6b8-44f4-a622-c13e7c80c722", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "e6a7ef25-f346-405b-abd2-fddec29c99d3", "name": "item", "description": "e6a7ef25-f346-405b-abd2-fddec29c99d3", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/e6a7ef25-f346-405b-abd2-fddec29c99d3"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-01T00:00:00Z"}}, {"id": "PMC11085199", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:24:19Z", "type": "Journal Article", "created": "2024-04-29", "title": "Precision Estimation of Crop Coefficient for Maize Cultivation Using High-Resolution Satellite Imagery to Enhance Evapotranspiration Assessment in Agriculture", "description": "

The estimation of crop evapotranspiration (ETc) is crucial for irrigation water management, especially in arid regions. This can be particularly relevant in the Po Valley (Italy), where arable lands suffer from drought damages on an annual basis, causing drastic crop yield losses. This study presents a novel approach for vegetation-based estimation of crop evapotranspiration (ETc) for maize. Three years of high-resolution multispectral satellite (Sentinel-2)-based Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Leaf Area Index (LAI) time series data were used to derive crop coefficients of maize in nine plots at the Acqua Campus experimental farm of Irrigation Consortium for the Emilia Romagna Canal (CER), Italy. Since certain vegetation indices (VIs) (such as NDVI) have an exponential nature compared to the other indices, both linear and power regression models were evaluated to estimate the crop coefficient (Kc). In the context of linear regression, the correlations between Food and Agriculture Organization (FAO)-based Kc and NDWI, NDRE, NDVI, and LAI-based Kc were 0.833, 0.870, 0.886, and 0.771, respectively. Strong correlation values in the case of power regression (NDWI: 0.876, NDRE: 0.872, NDVI: 0.888, LAI: 0.746) indicated an alternative approach to provide crop coefficients for the vegetation period. The VI-based ETc values were calculated using reference evapotranspiration (ET0) and VI-based Kc. The weather station data of CER were used to calculate ET0 based on Penman-Monteith estimation. Out of the Vis, NDWI and NDVI-based ETc performed the best both in the cases of linear (NDWI RMSE: 0.43 \u00b1 0.12; NDVI RMSE: 0.43 \u00b1 0.095) and power (NDWI RMSE: 0.44 \u00b1 0.116; NDVI RMSE: 0.44 \u00b1 0.103) approaches. The findings affirm the efficacy of the developed methodology in accurately assessing the evapotranspiration rate. Consequently, it offers a more refined temporal estimation of water requirements for maize cultivation in the region.

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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": "2024-03-01", "type": "Dataset", "created": "2024-02-15", "language": "eng", "title": "Low-cost ground-based NDVI sensor device for manual and automated crop monitoring", "description": "Ground-based NDVI sensors are vital for an accurate, localized crop condition and growth assessment, but their high cost and labor-intensive nature limit accessibility. Here, we present a low-cost NDVI sensor device, priced under 250 Euros, which can be utilized both manually as a handheld tool and automatically as a standalone device to address this issue. We calibrated and field validated the low-cost NDVI sensor against a commercial sensor for ground-based NDVI measurements. Our calibration revealed a slight underestimation at higher NDVI values measured from the low-cost sensor, which we adjusted using a correction function. Field validation proved the accuracy of low-cost sensor device, with a high correlation (r\u00b2 = 0.99; y = 1.04x + 0.003) and good agreement with the reference device, after applying then derived correction function. Further field trial application of the low-cost sensor as a handheld device (manual mode) in Benin and the Philippines for rice and pineapple cultivation, respectively, demonstrated the reliability of the device to monitor crop development and biomass accumulation. An additional field testing in an oat field site in Germany showed that the developed low-cost NDVI sensor, functioning as a standalone automatic device, is robust, reliable and capable to monitor crop development during senescence, through higher temporal resolution NDVI measurements. 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