{"type": "FeatureCollection", "features": [{"id": "10.3390/agronomy11040652", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:20:40Z", "type": "Journal Article", "created": "2021-03-29", "title": "Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013\u20132018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018\u20132019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41\u201371) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.</p></article>", "keywords": ["Landsat 8", "2. Zero hunger", "SAVI", "NDVI", "S", "13. Climate action", "wheat", "yield forecasting", "Agriculture", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://doi.org/10.3390/agronomy11040652"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agronomy11040652", "name": "item", "description": "10.3390/agronomy11040652", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agronomy11040652"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-29T00:00:00Z"}}, {"id": "3129983189", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:26:10Z", "type": "Journal Article", "created": "2021-02-23", "title": "Using NDVI for the assessment of canopy cover in agricultural crops within modelling research", "description": "Open AccessPeer reviewed", "keywords": ["2. Zero hunger", "0106 biological sciences", "Meta-analysis", "Canopy cover", "NDVI", "Crop modelling", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences"]}, "links": [{"href": "https://doi.org/3129983189"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Computers%20and%20Electronics%20in%20Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3129983189", "name": "item", "description": "3129983189", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3129983189"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-01T00:00:00Z"}}, {"id": "10.5194/gmd-10-1945-2017", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:21:48Z", "type": "Journal Article", "created": "2017-05-17", "title": "A non-linear Granger-causality framework to investigate climate\u2013vegetation dynamics", "description": "<p>Abstract. Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These come in the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to further unravel the influence of climate on vegetation dynamics. However, as advocated in this article, commonly used statistical methods are often too simplistic to represent complex climate\uffe2\uff80\uff93vegetation relationships due to linearity assumptions. Therefore, as an extension of linear Granger-causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods, and predictive modelling by means of random forests. Experimental results on global data sets indicate that, with this framework, it is possible to detect non-linear patterns that are much less visible with traditional Granger-causality methods. In addition, we discuss extensive experimental results that highlight the importance of considering non-linear aspects of climate\uffe2\uff80\uff93vegetation dynamics.                     </p>", "keywords": ["QE1-996.5", "0207 environmental engineering", "TIME-SERIES", "Geology", "02 engineering and technology", "15. Life on land", "SOIL-MOISTURE", "SAMPLE TESTS", "SURFACE-TEMPERATURE", "01 natural sciences", "RANDOM FORESTS", "CARBON-DIOXIDE", "NDVI DATA", "13. Climate action", "Earth and Environmental Sciences", "PRECIPITATION", "GLOBAL TERRESTRIAL ECOSYSTEMS", "SDG 13 - Climate Action", "SATELLITE", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://gmd.copernicus.org/articles/10/1945/2017/gmd-10-1945-2017.pdf"}, {"href": "https://doi.org/10.5194/gmd-10-1945-2017"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/gmd-10-1945-2017", "name": "item", "description": "10.5194/gmd-10-1945-2017", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/gmd-10-1945-2017"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-05-17T00:00:00Z"}}, {"id": "10.1016/j.agwat.2017.08.007", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:15:20Z", "type": "Journal Article", "created": "2017-08-10", "title": "Performance of the two-source energy budget (TSEB) model for the monitoring of evapotranspiration over irrigated annual crops in North Africa", "description": "Abstract   The main objective of this study was to evaluate the performance and the domain of validity of the two-source energy balance model (TSEB) for the monitoring of actual evapotranspiration ( ET a  ) as a first step towards its use for irrigation planning. Secondary objectives were to analyze the ability of TSEB model to detect water stress and to evaluate evapotranspiration partition between evaporation (E) and transpiration (T) over irrigated annual crops. Within this context, TSEB was compared to the calibrated FAO-56 dual approach, taken as a reference tool for the monitoring of crop water consumption. TSEB computes  ET a   as the residual of a double component energy balance driven by the radiative surface temperature ( T s  ) used as a proxy of crop hydric conditions; the FAO-56 dual crop coefficient approach uses the Normalized Difference Vegetation Index (NDVI) as a proxy of Basal Crop Coefficient ( K cb  ) and assesses the hydric status directly by solving a two layer soil water budget. Both approaches were evaluated over four plots of wheat and sugar beet located in the Haouz plain (Marrakech, Morocco) that were instrumented with eddy covariance systems during the 2012 and 2013 growing seasons. Series of ASTER images were acquired during the first agricultural season. Both models offered fair performances compared to  ET a   observations with Root Mean Square Error (RMSE) lower than 1\u00a0mm\u00a0day \u22121  apart from the FAO-56 dual approach on the sugar beet plot because of uncertain irrigation inputs. This highlights a major weakness of this model when water inputs are uncertain; a very likely case at the plot scale. By contrast, the TSEB model offered smoother performances in all cases. The potentialities of both approaches to predict a water stress index based on the departure from potential evapotranspiration ( ET  c ) was evaluated: although the FAO-56 dual was better suited to detect high water stresses, the TSEB model was able to detect moderate stresses without a need to prescribe water inputs. Finally, the partition of  ET a   between soil evaporation and plant transpiration was estimated indirectly by confrontation between simulated soil evaporation and surface (0\u20135\u00a0cm) soil moisture acquired spatially with Theta Probe sensors and taken as a proxy of soil evaporation. TSEB evaporation was well correlated to surface soil moisture (r\u00a0=\u00a00.82) for low Leaf Area Index (LAI) values ( 2 \u00a0m \u22122 ). In addition, TSEB predicted partition compared well to snapshot measurements based on the stable isotope method. This in-depth comparison of two simple tools to monitor  ET a   leads us to the conclusion that the TSEB model can reasonably be used to map  ET a   on large scale and possibly for the decision-making process of irrigation scheduling.", "keywords": ["FAO-56", "2. Zero hunger", "550", "Evapotranspiration", "NDVI", "Water stress", "0207 environmental engineering", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "6. Clean water", "Surface temperature", "0401 agriculture", " forestry", " and fisheries", "TSEB"]}, "links": [{"href": "https://doi.org/10.1016/j.agwat.2017.08.007"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20Water%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agwat.2017.08.007", "name": "item", "description": "10.1016/j.agwat.2017.08.007", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agwat.2017.08.007"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-01T00:00:00Z"}}, {"id": "10.1016/j.compag.2021.106038", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:15:38Z", "type": "Journal Article", "created": "2021-02-23", "title": "Using NDVI for the assessment of canopy cover in agricultural crops within modelling research", "description": "The fraction of green canopy cover (CC) is an important feature commonly used to characterize crop growth and for calibration of crop and hydrological models. It is well accepted that there is a relation between CC and      NDVI through linear or quadratic models, however a straight-forward empirical approach, to derive CC from NDVI observations, is still lacking. In this study, we conducted a meta-analysis of the NDVI-CC relationships with data      collected from 19 different studies (N=1397). Generic models are proposed here for 13 different agricultural crops, and the associated degree of uncertainty, together with the magnitude of error were quantified for each model (RMSE around 6-18% of CC).We observed that correlations are adequate for the majority of crops as R2 values were above 75% for most cases, and coefficient estimates were significant for most of the linear and quadratic models. Extrapolation to conditions different than those found in the studies may require local validation, as obtained regressions are affected by non-sampling errors or sources of systematic error that need further investigation. In a case study with wheat, we tested the use of NDVI as a proxy to estimate CC and to calibrate the AquaCrop model. Simulation outcomes were validated with field data collected from three growing seasons and confirmed that the NDVI-CC relationship was useful for modelling research. We highlight that the overall applicability of these relationships to modelling is promising as the RMSE are in line with acceptable levels published in several sensitivity analyses. Funding from the European Commission under project SHui \u2013 Grant agreement ID 773903. Peer reviewed", "keywords": ["0106 biological sciences", "2. Zero hunger", "Meta-analysis", "Canopy cover", "NDVI", "Crop modelling", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.compag.2021.106038"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Computers%20and%20Electronics%20in%20Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.compag.2021.106038", "name": "item", "description": "10.1016/j.compag.2021.106038", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.compag.2021.106038"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-01T00:00:00Z"}}, {"id": "10.1016/j.fcr.2010.08.012", "type": "Feature", "geometry": null, "properties": {"license": "Closed Access", "updated": "2026-07-26T16:15:53Z", "type": "Journal Article", "created": "2010-09-29", "title": "The Effect Of Tillage, Crop Rotation And Residue Management On Maize And Wheat Growth And Development Evaluated With An Optical Sensor", "description": "Abstract   Crop growth and development as well as yield are the result of the efficiency of the chosen agricultural management system within the boundaries of the agro-ecological environment. End-of-season yield results do not permit the evaluation of within-season management interactions with the production environment and do not allow for full understanding of the management practice applied. Crop growth and development were measured during the 2004, 2006 and 2008 crop cycles with an optical handheld NDVI sensor for all plots of the different management treatments of a long-term (since 1991) sustainability trial in the highlands of Mexico. Cropping systems varying in (1) tillage (conventional vs. zero tillage); (2) residue management (retention vs. removal); (3) rotation (monocropping vs. a maize [ Zea mays  L.]/wheat [ Triticum aestivum  L.] rotation) were compared. The NDVI-handheld sensor was evaluated as a tool to monitor crop growth and development and was found to be an excellent tool for this purpose. There was a strong relation between NDVI and biomass accumulation of maize and wheat. The measurement with the handheld sensor was non-destructive and fast so that a representative plot area could be measured easily and time-efficiently. Zero tillage induced different crop growth dynamics over time compared to conventional tillage. Zero tillage with residue retention is characterized by a slower initial crop growth, compensated for by an increased growth in the later stages, positively influencing final grain yield. Also crop rotation influenced early crop growth, with lower NDVI values for crops sown after wheat than crops after maize. Zero tillage with residue removal had low NDVI values throughout the growing season. Zero tillage with retention of crop residues results in time efficient use of resources, as opposed to conventional tillage, regardless of residue management, and zero tillage with residue removal. The results indicated that different tillage, rotation and residue management practices influence crop growth and development. It is important to monitor and understand crop growth under different management systems to select the right varieties and adjust timing and practice of input supply (fertilizer, irrigation etc.) in a holistic way in each cropping system.", "keywords": ["2. Zero hunger", "Conservation agriculture", "Ecoagriculture", "Residue management", "Triticum aestivum", "04 agricultural and veterinary sciences", "15. Life on land", "Crop rotations", "Semiarid zones", "Zero tillage", "Soil conservation", "Crop growth", "Zea mays l.", "0401 agriculture", " forestry", " and fisheries", "Field Scale", "Rainfed agriculture", "Ndvi hand-held sensor"]}, "links": [{"href": "https://doi.org/10.1016/j.fcr.2010.08.012"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Field%20Crops%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.fcr.2010.08.012", "name": "item", "description": "10.1016/j.fcr.2010.08.012", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.fcr.2010.08.012"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2011-01-01T00:00:00Z"}}, {"id": "10.1088/1748-9326/aa7145", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:18:10Z", "type": "Journal Article", "created": "2017-05-05", "title": "Vegetation anomalies caused by antecedent precipitation in most of the world", "description": "Quantifying environmental controls on vegetation is critical to predict the net effect of climate change on global ecosystems and the subsequent feedback on climate. Following a non-linear Granger causality framework based on a random forest predictive model, we exploit the current wealth of multi-decadal satellite data records to uncover the main drivers of monthly vegetation variability at the global scale. Results indicate that water availability is the most dominant factor driving vegetation globally: about 61% of the vegetated surface was primarily water-limited during 1981\u20132010. This included semiarid climates but also transitional ecoregions. Intra-annually, temperature controls Northern Hemisphere deciduous forests during the growing season, while antecedent precipitation largely dominates vegetation dynamics during the senescence period. The uncovered dependency of global vegetation on water availability is substantially larger than previously reported. This is owed to the ability of the framework to (1) disentangle the co-linearities between radiation/temperature and precipitation, and (2) quantify non-linear impacts of climate on vegetation. Our results reveal a prolonged effect of precipitation anomalies in dry regions: due to the long memory of soil moisture and the cumulative, non-linear, response of vegetation, water-limited regions show sensitivity to the values of precipitation occurring three months earlier. Meanwhile, the impacts of temperature and radiation anomalies are more immediate and dissipate shortly, pointing to a higher resilience of vegetation to these anomalies. Despite being infrequent by definition, hydro-climatic extremes are responsible for up to 10% of the vegetation variability during the 1981\u20132010 period in certain areas, particularly in water-limited ecosystems. Our approach is a first step towards a quantitative comparison of the resistance and resilience signature of different ecosystems, and can be used to benchmark Earth system models in their representations of past vegetation sensitivity to changes in climate.", "keywords": ["Science", "QC1-999", "water", "TROPICAL FORESTS", "0207 environmental engineering", "02 engineering and technology", "SOIL-MOISTURE", "Environmental technology. Sanitary engineering", "01 natural sciences", "stress", "water stress", "global vegetation", "AMAZON", "FORESTS", "CLIMATE EXTREMES", "hydro-climatic extremes", "ecosystem resilience", "DRY-SEASON", "GE1-350", "TEMPERATURE", "SATELLITE", "TD1-1066", "0105 earth and related environmental sciences", "Physics", "Q", "Biology and Life Sciences", "15. Life on land", "6. Clean water", "Environmental sciences", "NDVI DATA", "13. Climate action", "Earth and Environmental Sciences", "GROWING-SEASON", "Granger causality", "CARBON-CYCLE"]}, "links": [{"href": "https://doi.org/10.1088/1748-9326/aa7145"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Research%20Letters", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1088/1748-9326/aa7145", "name": "item", "description": "10.1088/1748-9326/aa7145", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1088/1748-9326/aa7145"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-07-01T00:00:00Z"}}, {"id": "10.1111/j.1757-1707.2011.01113.x", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:18:54Z", "type": "Journal Article", "created": "2011-07-21", "title": "Identifying Grasslands Suitable For Cellulosic Feedstock Crops In The Greater Platte River Basin: Dynamic Modeling Of Ecosystem Performance With 250 M Emodis", "description": "Abstract<p>This study dynamically monitors ecosystem performance (EP) to identify grasslands potentially suitable for cellulosic feedstock crops (e.g., switchgrass) within the Greater Platte River Basin (GPRB). We computed grassland site potential and EP anomalies using 9\uffe2\uff80\uff90year (2000\uffe2\uff80\uff932008) time series of 250\uffc2\uffa0m expedited moderate resolution imaging spectroradiometer Normalized Difference Vegetation Index data, geophysical and biophysical data, weather and climate data, and EP models. We hypothesize that areas with fairly consistent high grassland productivity (i.e., high grassland site potential) in fair to good range condition (i.e., persistent ecosystem overperformance or normal performance, indicating a lack of severe ecological disturbance) are potentially suitable for cellulosic feedstock crop development. Unproductive (i.e., low grassland site potential) or degraded grasslands (i.e., persistent ecosystem underperformance with poor range condition) are not appropriate for cellulosic feedstock development. Grassland pixels with high or moderate ecosystem site potential and with more than 7\uffc2\uffa0years ecosystem normal performance or overperformance during 2000\uffe2\uff80\uff932008 are identified as possible regions for future cellulosic feedstock crop development (ca. 68\uffc2\uffa0000\uffc2\uffa0km2 within the GPRB, mostly in the eastern areas). Long\uffe2\uff80\uff90term climate conditions, elevation, soil organic carbon, and yearly seasonal precipitation and temperature are important performance variables to determine the suitable areas in this study. The final map delineating the suitable areas within the GPRB provides a new monitoring and modeling approach that can contribute to decision support tools to help land managers and decision makers make optimal land use decisions regarding cellulosic feedstock crop development and sustainability.</p>", "keywords": ["2. Zero hunger", "satellite remote sensing", "550", "land management", "04 agricultural and veterinary sciences", "15. Life on land", "ecosystem performance models", "cellulosic feedstock crops", "6. Clean water", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Greater Platte River Basin", "cellulosic biofuel", "weather data", "eMODIS NDVI"]}, "links": [{"href": "https://doi.org/10.1111/j.1757-1707.2011.01113.x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/GCB%20Bioenergy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/j.1757-1707.2011.01113.x", "name": "item", "description": "10.1111/j.1757-1707.2011.01113.x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/j.1757-1707.2011.01113.x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2011-07-21T00:00:00Z"}}, {"id": "10.15302/j-fase-2014028", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:19:28Z", "type": "Journal Article", "created": "2014-10-15", "title": "Estimating The Effect Of Urease Inhibitor On Rice Yield Based On Ndvi At Key Growth Stages", "description": "The effect of the urease inhibitor, N-(n-butyl) thiophosphoric triamide (NBPT) at a range of application rates on rice production was examined in a field experiment at Jinxian County, Jiangxi Province, China. The normalized difference vegetation index (NDVI) was measured at key growth stages in both early and late rice. The results showed that the grain yield increased significantly when urea was applied with NBPT, with the highest yield observed at 1.00% NBPT (wt/wt). NDVI differed with the growth stage of rice; it remained steady from the heading to the filling stage. Rice yield could be predicted from the NDVI taken at key rice growing stages, with R<sup>2</sup> ranging from 0.34 to 0.69 in early rice and 0.49 to 0.70 in late rice. The validation test showed that RMSE (t\u00b7hm<sup>-2</sup>) values were 0.77 and 0.87 in early and late rice, respectively. Therefore, it was feasible to estimate rice yield for different amounts of urease inhibitor using NDVI.", "keywords": ["0106 biological sciences", "2. Zero hunger", "normalized difference vegetation index (NDVI)|N-(n-butyl) thiophosphoric triamide (NBPT)|rice|grain yield", "Agriculture (General)", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "01 natural sciences", "S1-972"], "contacts": [{"organization": "Kailou Liu, Yazhen Li, Huiwen Hu,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.15302/j-fase-2014028"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20of%20Agricultural%20Science%20and%20Engineering", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.15302/j-fase-2014028", "name": "item", "description": "10.15302/j-fase-2014028", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15302/j-fase-2014028"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2014-01-01T00:00:00Z"}}, {"id": "10.3390/rs8020156", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:20:57Z", "type": "Journal Article", "created": "2016-02-19", "title": "Impacts of Re-Vegetation on Surface Soil Moisture over the Chinese Loess Plateau Based on Remote Sensing Datasets", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>A large-scale re-vegetation supported by the Grain for Green Project (GGP) has greatly changed local eco-hydrological systems, with an impact on soil moisture conditions for the Chinese Loess Plateau. It is important to know how, exactly, re-vegetation influences soil moisture conditions, which not only crucially constrain growth and distribution of vegetation, and hence, further re-vegetation, but also determine the degree of soil desiccation and, thus, erosion risk in the region. In this study, three eco-environmental factors, which are Soil Water Index (SWI), the Normalized Difference Vegetation Index (NDVI), and precipitation, were used to investigate the response of soil moisture in the one-meter layer of top soil to the re-vegetation during the GGP. SWI was estimated based on the backscatter coefficient produced by the European Remote Sensing Satellite (ERS-1/2) and Meteorological Operational satellite program (MetOp), while NDVI was derived from SPOT imageries. Two separate periods, which are 1998\u20132000 and 2008\u20132010, were selected to examine the spatiotemporal pattern of the chosen eco-environmental factors. It has been shown that the amount of precipitation in 1998\u20132000 was close to that of 2008\u20132010 (the difference being 13.10 mm). From 1998\u20132000 to 2008\u20132010, the average annual NDVI increased for 80.99%, while the SWI decreased for 72.64% of the area on the Loess Plateau. The average NDVI over the Loess Plateau increased rapidly by 17.76% after the 10-year GGP project. However, the average SWI decreased by 4.37% for two-thirds of the area. More specifically, 57.65% of the area on the Loess Plateau experienced an increased NDVI and decreased SWI, 23.34% of the area had an increased NDVI and SWI. NDVI and SWI decreased simultaneously for 14.99% of the area, and the decreased NDVI and increased SWI occurred at the same time for 4.02% of the area. These results indicate that re-vegetation, human activities, and climate change have impacts on soil moisture. However, re-vegetation, which consumes a large quantity of soil water, may be the major factor for soil moisture change in most areas of the Loess Plateau. It is, therefore, suggested that Soil Moisture Content (SMC) should be kept in mind when carrying out re-vegetation in China\u2019s arid and semi-arid regions.</p></article>", "keywords": ["2. Zero hunger", "China", "Science", "Q", "Soil Water Index (SWI)", "precipitation", "15. Life on land", "01 natural sciences", "6. Clean water", "remote sensing", "the Loess Plateau", "13. Climate action", "11. Sustainability", "Normalized Difference Vegetation Index (NDVI)", "Grain for Green Project (GGP)", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Qiao Jiao, Rui Li, Fei Wang, Xingmin Mu, Pengfei Li, Chunchun An,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2072-4292/8/2/156/pdf"}, {"href": "https://doi.org/10.3390/rs8020156"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs8020156", "name": "item", "description": "10.3390/rs8020156", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs8020156"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-02-19T00:00:00Z"}}, {"id": "10.3390/rs14071639", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:20:57Z", "type": "Journal Article", "created": "2022-03-30", "title": "Mapping Soil Properties with Fixed Rank Kriging of Proximally Sensed Soil Data Fused with Sentinel-2 Biophysical Parameter", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Soil surveys with line-scanning platforms appear to have great advantages over the traditional methods used to collect soil information for the development of field-scale soil mapping and applications. These carry VNIR (visible and near infrared) spectrometers and have been used in recent years extensively for the assessment of soil fertility at the field scale, and the delineation of site-specific management zones (MZ). A challenging feature of VNIR applications in precision agriculture (PA) is the massiveness of the derived datasets that contain point predictions of soil properties, and the interpolation techniques involved in incorporating these data into site-specific management plans. In this study, fixed-rank kriging (FRK) geostatistical interpolation, which is a flexible, non-stationary spatial interpolation method especially suited to handling huge datasets, was applied to massive VNIR soil scanner data for the production of useful, smooth interpolated maps, appropriate for the delineation of site-specific MZ maps. Moreover, auxiliary Sentinel-2 data-based biophysical parameters NDVI (normalized difference vegetation index) and fAPAR (fraction of photosynthetically active radiation absorbed by the canopy) were included as covariates to improve the filtering performance of the interpolator and the ability to generate uniform patterns of spatial variation from which it is easier to receive a meaningful interpretation in PA applications. Results from the VNIR prediction dataset obtained from a pivot-irrigated field in Albacete, southeastern Spain, during 2019, have shown that FRK variants outperform ordinary kriging in terms of filtering capacity, by doubling the noise removal metrics while keeping the computation cost reasonably low. Such features, along with the capacity to handle a large volume of spatial information, nominate the method as ideal for PA applications with massive proximal and remote sensing datasets.</p></article>", "keywords": ["MANAGEMENT ZONES", "precision agriculture", "PREDICTION", "NDVI", "SPATIAL VARIABILITY", "Science", "MODELS", "Q", "PHYSICAL-PROPERTIES", "ONLINE", "04 agricultural and veterinary sciences", "VNIR spectrometer", "15. Life on land", "geostatistical interpolation", "VARIABLES", "DELINEATION", "geostatistical interpolation; VNIR spectrometer; NDVI; fAPAR; precision agriculture", "Earth and Environmental Sciences", "fAPAR", "QUALITY", "0401 agriculture", " forestry", " and fisheries", "precision", "DATA FUSION", "agriculture"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/7/1639/pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/7/1639/pdf"}, {"href": "https://doi.org/10.3390/rs14071639"}, {"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/rs14071639", "name": "item", "description": "10.3390/rs14071639", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs14071639"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-29T00:00:00Z"}}, {"id": "10.3390/rs9121276", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:20:58Z", "type": "Journal Article", "created": "2017-12-08", "title": "Irrigation Performance Assessment in Table Grape Using the Reflectance-Based Crop Coefficient", "description": "<p>In this paper, we present the results of our study on the operational application of the reflectance-based crop coefficient for assessing table grape irrigation requirements. The methodology was applied to provide irrigation advice and to assess the irrigation performance. The net irrigation water requirements (NIWR) simulated using the reflectance-based basal crop coefficient were provided to the farmer during the growing season and compared with the actual irrigation volumes applied. Two treatments were implemented in the field, increasing and reducing the irrigation doses by 25%, respectively, compared to the regular management. The experiment was carried out in a commercial orchard during three consecutive growing seasons in Northern Chile. The NIWR based on the model was approximately 900 mm per season for the orchard at tree maturity. The experimental results demonstrate that the regular irrigation applied covered only 76% of the NIWR for the whole season, and the analysis of monthly and weekly accumulated values indicates several periods of water shortage. The regular management system tended to underestimate the water requirements from October to January and overestimate the water requirements after harvest from February to April. The level of the deficit of water was quantified using such plant physiological parameters as stem water potential, vegetative development (coverage), and fruit productivity. The estimated NIWR was roughly covered in the treatment where the irrigation dose was increased, and the analyses of the crop production and fruit quality point to the relative advantage of this treatment. Finally, we conclude that the proposed approach allows the analysis of irrigation performance on the scale of commercial fields. These analytic capabilities are based on the well-demonstrated relationship of the crop evapotranspiration with the information provided by satellite images, and provide valuable information for irrigation management by identifying periods of water shortage and over-irrigation.</p>", "keywords": ["0106 biological sciences", "2. Zero hunger", "NDVI", "Science", "Q", "evapotranspiration", "earth observation", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water", "0401 agriculture", " forestry", " and fisheries", "crop water requirements", "plant water status", "crop coefficient", "table grape"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/12/1276/pdf"}, {"href": "https://doi.org/10.3390/rs9121276"}, {"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/rs9121276", "name": "item", "description": "10.3390/rs9121276", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs9121276"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-12-08T00:00:00Z"}}, {"id": "10.23986/afsci.148486", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:20:21Z", "type": "Journal Article", "created": "2025-05-26", "title": "Defining critical SOC/clay thresholds for soil health in boreal croplands using satellite-based NDVI proxies for productivity and resilience", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The European Union\u2019s soil strategy underscores the necessity for establishing feasible criteria to assess the soil health condition. In this study, we developed a method to define a critical threshold value for SOC/clay ratio on the basis of crop productivity and resilience. The study integrated data from national soil monitoring (NSM) of Finnish cropland soils (n=505) with satellite-based normalized difference vegetation index (NDVI) obtained from the EcoDataCube (EDC) portal. The study area was confined to the boreal environmental zone to ensure consistent pedo-climatic conditions. The results show that the interannual variation in crop productivity increases rapidly below SOC/clay ratio of 0.09 (95% confidence intervals ranging from 0.07 to 0.16), whereas the corresponding threshold for mean productivity was 0.13 (0.09\u20130.16). The observed threshold values were found applicable for both cereals and temporary ley. The SOC/clay ratio of 1:13 (=0.08), regarded as a criterion for healthy soil in the current Soil Monitoring Law proposal, based on studies by Johannes et al. (2017) and Prout et al. (2021), is lower than the mean thresholds estimated in this study but aligns close to the lower bound of the 95% confidence intervals. In this research, Finnish agricultural land served as the case study area, but the method is easily applicable to various pedo-climatic regions and potentially to different land use types.</p></article>", "keywords": ["S", "Soil Monitoring Law", " SOC/clay ratio", " cropland", " NDVI", " satellite data", " national soil monitoring", "Agriculture (General)", "Agriculture", "S1-972"], "contacts": [{"organization": "Heikkinen, Jaakko, Keskinen, Riikka, Ylivainio, Kari,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.23986/afsci.148486"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20and%20Food%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.23986/afsci.148486", "name": "item", "description": "10.23986/afsci.148486", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.23986/afsci.148486"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-26T00:00:00Z"}}, {"id": "10.2478/contagri-2024-0022", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:20:22Z", "type": "Journal Article", "created": "2024-12-12", "title": "Potential of Optical Sensors for Predicting Winter Wheat Yield Through Variable-Rate Nitrogen Application", "description": "Summary                <p>The main lever of precision agriculture is technology that provides a better understanding of the agro-ecological conditions, enables decision-making based on facts and natural laws, and facilitates precise implementation of practices based on local specificities. One of the key elements of plant production is nitrogen (N), which is traditionally applied as mineral fertilizer in large quantities. Optimizing nitrogen input is one of the priorities in precision agriculture, not only for its importance in the plant food chain but also for its environmental impact. This study investigated the potential of two optical sensors, GreenSeeker and Plant-O-Meter, in predicting nitrogen supply during the 2021-2022 growing season. The experimental material in this study included two wheat varieties, subjected to different nitrogen application rates. The objective was to estimate the potential of using NDVI (Normalized Difference Vegetation Index) measurements of wheat canopy, which are indicators of plant status, and to analyze correlations between these values and final wheat yield. GreenSeeker and Plant-O-Meter sensors, which emit light at precise wavelengths and measure plant reflectance, were used for monitoring plant status and NDVI measurements. The results showed a strong correlation between the NDVI values measured by both sensors. However, this relationship decreased during the fully ripe stage due to physiological changes in the wheat plants. The correlation between NDVI values and grain yield differed significantly between the evaluated sensors. Additional correlation analyses between NDVI measurements and yield indicated differences associated with wheat varieties, indicating that the varieties responded differently to environmental conditions. This study aligns with current agricultural approaches and contributes to more efficient and environmentally friendly agricultural practices.</p", "keywords": ["0106 biological sciences", "precision agriculture", "optical sensors", "S", "wheat", "ndvi", "0401 agriculture", " forestry", " and fisheries", "Agriculture", "04 agricultural and veterinary sciences", "01 natural sciences", "nitrogen"]}, "links": [{"href": "https://doi.org/10.2478/contagri-2024-0022"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Contemporary%20Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2478/contagri-2024-0022", "name": "item", "description": "10.2478/contagri-2024-0022", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2478/contagri-2024-0022"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-12-01T00:00:00Z"}}, {"id": "10.3390/agronomy11050946", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:20:40Z", "type": "Journal Article", "created": "2021-05-11", "title": "Estimating Farm Wheat Yields from NDVI and Meteorological Data", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (Triticum aestivum) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R2 = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment.</p></article>", "keywords": ["yield estimation", "PREDICTION", "NDVI", "Triticum aestivum", "0703 Crop and Pasture Production", "3002 Agriculture", " land and farm management", "3004 Crop and pasture production", "Belgium", "0502 Environmental Science and Management", "<i>Triticum aestivum</i>", "2. Zero hunger", "Science & Technology", "S", "Plant Sciences", "Agriculture", "weather impact", "04 agricultural and veterinary sciences", "WINTER-WHEAT", "15. Life on land", "Agronomy", "winter wheat", "MODEL", "RESOLUTION", "SENTINEL-2", "0401 agriculture", " forestry", " and fisheries", "LANDSAT 8", "Life Sciences & Biomedicine"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/5/946/pdf"}, {"href": "https://doi.org/10.3390/agronomy11050946"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agronomy11050946", "name": "item", "description": "10.3390/agronomy11050946", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agronomy11050946"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-11T00:00:00Z"}}, {"id": "10.3390/land11060774", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:20:49Z", "type": "Journal Article", "created": "2022-05-25", "title": "Investigating Plant Response to Soil Characteristics and Slope Positions in a Small Catchment", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Methods enabling stakeholders to receive information on plant stress in agricultural settings in a timely manner can help mitigate a possible decrease in plant productivity. The present work aims to study the soil\u2013plant interaction using field measurements of plant reflectance, soil water content, and selected soil physical and chemical parameters. Particular emphasis was placed on sloping transects. We further compared ground- and Sentinel-2 satellite-based Normalized Vegetation Index (NDVI) time series data in different land use types. The Photochemical Reflectance Index (PRI) and NDVI were measured concurrently with calculating the fraction of absorbed photochemically active radiation (fAPAR) and leaf area index (LAI) values of three vegetation types (a grassland, three vineyard sites, and a cropland with maize). Each land use site had an upper and a lower study point of a given slope. The NDVI, fAPAR, and LAI averaged values were the lowest for the grassland (0.293, 0.197, and 0.51, respectively), which showed the highest signs of water stress. Maize had the highest NDVI values (0.653) among vegetation types. Slope position affected NDVI, PRI, and fAPAR values significantly for the grassland and cropland (p &lt; 0.05), while the soil water content (SWC) was different for all three vineyard sites (p &lt; 0.05). The strongest connections were observed between soil physical and chemical parameters and NDVI values for the vineyard samples and the selected soil parameters and PRI for the grassland. Measured and satellite-retrieved NDVI values of the different land use types were compared, and strong correlations (r = 0.761) between the methods were found. For the maize, the satellite-based NDVI values were higher, while for the grassland they were slightly lower compared to the field-based measurements. Our study indicated that incorporating Sentinel-derived NDVI can greatly improve the value of field monitoring and provides an opportunity to extend field research in more depth. The present study further highlights the close relations in the soil\u2013plant\u2013water system, and continuous monitoring can greatly help in developing site-specific climate change mitigating methods.</p></article>", "keywords": ["2. Zero hunger", "land use sites", "NDVI", "S", "Agriculture", "soil parameters", "04 agricultural and veterinary sciences", "15. Life on land", "spectral reflectance", "satellite imagery", "plant stress", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "land use sites; soil parameters; plant stress; spectral reflectance; NDVI; satellite imagery"]}, "links": [{"href": "http://www.mdpi.com/2073-445X/11/6/774/pdf"}, {"href": "https://www.mdpi.com/2073-445X/11/6/774/pdf"}, {"href": "https://doi.org/10.3390/land11060774"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/land11060774", "name": "item", "description": "10.3390/land11060774", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/land11060774"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-25T00:00:00Z"}}, {"id": "10.3390/rs13061133", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:20:56Z", "type": "Journal Article", "created": "2021-03-16", "title": "Assessing Irrigation Water Use with Remote Sensing-Based Soil Water Balance at an Irrigation Scheme Level in a Semi-Arid Region of Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This study aims to evaluate a remote sensing-based approach to allow estimation of the temporal and spatial distribution of crop evapotranspiration (ET) and irrigation water requirements over irrigated areas in semi-arid regions. The method is based on the daily step FAO-56 Soil Water Balance model combined with a time series of basal crop coefficients and the fractional vegetation cover derived from high-resolution satellite Normalized Difference Vegetation Index (NDVI) imagery. The model was first calibrated and validated at plot scale using ET measured by eddy-covariance systems over wheat fields and olive orchards representing the main crops grown in the study area of the Haouz plain (central Morocco). The results showed that the model provided good estimates of ET for wheat and olive trees with a root mean square error (RMSE) of about 0.56 and 0.54 mm/day respectively. The model was then used to compare remotely sensed estimates of irrigation requirements (RS-IWR) and irrigation water supplied (WS) at plot scale over an irrigation district in the Haouz plain through three growing seasons. The comparison indicated a large spatio-temporal variability in irrigation water demands and supplies; the median values of WS and RS-IWR were 130 (175), 117 (175) and 118 (112) mm respectively in the 2002\u20132003, 2005\u20132006 and 2008\u20132009 seasons. This could be attributed to inadequate irrigation supply and/or to farmers\u2019 socio-economic considerations and management practices. The findings demonstrate the potential for irrigation managers to use remote sensing-based models to monitor irrigation water usage for efficient and sustainable use of water resources.</p></article>", "keywords": ["0106 biological sciences", "2. Zero hunger", "FAO-56 soil water balance", "550", "[SDE.MCG]Environmental Sciences/Global Changes", "Science", "water", "Q", "evapotranspiration", "balance", "15. Life on land", "01 natural sciences", "630", "irrigation", "6. Clean water", "[SDE.MCG] Environmental Sciences/Global Changes", "remote sensing", "evapotranspiration; irrigation; water; remote sensing; FAO-56 soil water balance; NDVI time series", "FAO-56 soil water", "NDVI time series"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/6/1133/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/6/1133/pdf"}, {"href": "https://doi.org/10.3390/rs13061133"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13061133", "name": "item", "description": "10.3390/rs13061133", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13061133"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-16T00:00:00Z"}}, {"id": "10.3390/rs13091636", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:20:56Z", "type": "Journal Article", "created": "2021-04-22", "title": "In-Season Interactions between Vine Vigor, Water Status and Wine Quality in Terrain-Based Management-Zones in a \u2018Cabernet Sauvignon\u2019 Vineyard", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Wine quality is the final outcome of the interactions within a vineyard between meteorological conditions, terrain and soil properties, plant physiology and numerous viticultural decisions, all of which are commonly summarized as the terroir effect. Associations between wine quality and a single soil or topographic factor are usually weak, but little information is available on the effect of terrain (elevation, aspect and slope) as a compound micro-terroir factor. We used the topographic wetness index (TWI) as a steady-state hydrologic and integrative measure to delineate management zones (MZs) within a vineyard and to study the interactions between vine vigor, water status and grape and wine quality. The study was conducted in a commercial 2.5-ha Vitis vinifera \u2018Cabernet Sauvignon\u2019 vineyard in Israel. Based on the TWI, the vineyard was divided into three MZs located along an elongate wadi that crosses the vineyard and bears water only in the rainy winter season. MZ1 was the most distant from the wadi and had low TWI values, MZ3 was closest to the wadi and had high TWI values. Remotely sensed crop water stress index (CWSI) was measured simultaneously with canopy cover (as determined by normalized difference vegetation index; NDVI) and with field measurements of midday stem water potential (\u03a8stem) and leaf area index (LAI) on several days during the growing seasons of 2017 and 2018. Vines in MZ1 had narrow trunk diameter and low LAI and canopy cover on most measurement days compared to the other two MZs. MZ1 vines also exhibited the highest water stress (highest CWSI and lowest \u03a8stem), lowest yield and highest wine quality. MZ3 vines showed higher LAI on most measurement days, lowest water deficit stress (\u03a8stem) during phenological stage I, highest yield and lowest wine quality. Yet, in stage III, MZ3 vines exhibited a similar water deficit stress (CWSI and \u03a8stem) as MZ2, suggesting that the relatively high vigor in MZ3 vines resulted in higher water deficit stress than expected towards the end of the season, possibly because of high water consumption over the course of the season. TWI and its classification into three MZs served as a reliable predictor for most of the attributes in the vineyard and for their dynamics within the season, and, thus, can be used as a key factor in delineation of MZs for irrigation. Yet, in-season remotely sensed monitoring is required to follow the vine dynamics to improve precision irrigation decisions.</p></article>", "keywords": ["in-field spatial variability", "2. Zero hunger", "0106 biological sciences", "In-field spatial variability", "CWSI", "Topographic wetness index", "NDVI", "Science", "UAV", "Q", "15. Life on land", "01 natural sciences", "6. Clean water", "topographic wetness index", "Vitis vinifera", "Irrigation management zones", "irrigation management zones", "UAV; CWSI; NDVI; irrigation management zones; topographic wetness index; in-field spatial variability; <i>Vitis vinifera</i>"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/9/1636/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/9/1636/pdf"}, {"href": "https://doi.org/10.3390/rs13091636"}, {"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/rs13091636", "name": "item", "description": "10.3390/rs13091636", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13091636"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-22T00:00:00Z"}}, {"id": "10.3390/s17091966", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:20:58Z", "type": "Journal Article", "created": "2017-08-28", "title": "Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The recent deployment of ESA\u2019s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015\u2013November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.</p></article>", "keywords": ["[SDE] Environmental Sciences", "NDVI", "Chemical technology", "HUMIDITE DU SOL", "soil moisture; SAR; Sentinel-1; NDVI; Sentinel-2; change detection", "0211 other engineering and technologies", "soil water content", "TP1-1185", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Article", "remote sensing", "Sentinel-1", "cartography", "soil moisture", "Sentinel-2", "TELEDETECTION", "change detection", "CARTOGRAPHIE", "SAR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/17/9/1966/pdf"}, {"href": "https://doi.org/10.3390/s17091966"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/s17091966", "name": "item", "description": "10.3390/s17091966", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s17091966"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-08-26T00:00:00Z"}}, {"id": "10.3390/su10010270", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:20:59Z", "type": "Journal Article", "created": "2018-01-22", "title": "Responses of Vegetation Cover to Environmental Change in Large Cities of China", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Vegetation cover is crucial for the sustainability of urban ecosystems; however, this cover has been undergoing substantial changes in cities. Based on climate data, city statistical data, nighttime light data and the Normalized Difference Vegetation Index (NDVI) dataset, we investigate the spatiotemporal variations of climate factors, urban lands and vegetation cover in 71 large cities of China during 1998\u20132012, and explore their correlations. A regression model between growing-season NDVI (G-NDVI) and urban land proportion (PU) is built to quantify the impact of urbanization on vegetation cover change. The results indicate that the spatiotemporal variations of temperature, precipitation, PU and G-NDVI are greatly different among the 71 cities which experienced rapid urbanization. The spatial difference of G-NDVI is closely related to diverse climate conditions, while the inter-annual variations of G-NDVI are less sensitive to climate changes. In addition, there is a negative correlation between G-NDVI trend and PU change, indicating vegetation cover in cities have been negatively impacted by urbanization. For most of the inland cities, the urbanization impacts on vegetation cover in urban areas are more severe than in suburban areas. But the opposite occurs in 17 cities mainly located in the coastal areas which have been undergoing the most rapid urbanization. Overall, the impacts of urbanization on G-NDVI change are estimated to be \u22120.026 per decade in urban areas and \u22120.015 per decade in suburban areas during 1998\u20132012. The long-term developments of cities would persist and continue to impact on the environmental change and sustainability. We use a 15-year window here as a case study, which implies the millennia of human effects on the natural biotas and warns us to manage landscapes and preserve ecological environments properly.</p></article>", "keywords": ["13. Climate action", "11. Sustainability", "vegetation cover; urbanization; climate change; NDVI; cities; China", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2071-1050/10/1/270/pdf"}, {"href": "https://www.mdpi.com/2071-1050/10/1/270/pdf"}, {"href": "https://doi.org/10.3390/su10010270"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/su10010270", "name": "item", "description": "10.3390/su10010270", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/su10010270"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-20T00:00:00Z"}}, {"id": "10.3390/w14081188", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:21:03Z", "type": "Journal Article", "created": "2022-04-10", "title": "Estimating Yield from NDVI, Weather Data, and Soil Water Depletion for Sugar Beet and Potato in Northern Belgium", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Crop-yield models based on vegetation indices such as the normalized difference vegetation index (NDVI) have been developed to monitor crop yield at higher spatial and temporal resolutions compared to agricultural statistical data. We evaluated the model performance of NDVI-based random forest models for sugar beet and potato farm yields in northern Belgium during 2016\u20132018. We also evaluated whether weather variables and root-zone soil water depletion during the growing season improved the model performance. The NDVI integral did not explain early and late potato yield variability and only partly explained sugar-beet yield variability. The NDVI series of early and late potato crops were not sensitive enough to yield affecting weather and soil water conditions. We found that water-saturated conditions early in the growing season and elevated temperatures late in the growing season explained a large part of the sugar-beet and late-potato yield variability. The NDVI integral in combination with monthly precipitation, maximum temperature, and root-zone soil water depletion during the growing season explained farm-scale sugar beet (R2 = 0.84, MSE = 48.8) and late potato (R2 = 0.56, MSE = 57.3) yield variability well from 2016 to 2018 in northern Belgium.</p></article>", "keywords": ["AquaCrop-OSPy", "STRESS", "root-zone soil water depletion; AquaCrop-OSPy; sugar beet; potato; crop yield; NDVI; Belgium; weather impact; random forest", "NDVI", "Environmental Sciences & Ecology", "root-zone soil water depletion", "01 natural sciences", "Belgium", "INDEX", "0105 earth and related environmental sciences", "2. Zero hunger", "Science & Technology", "PRODUCTIVITY", "CROP", "sugar beet", "weather impact", "04 agricultural and veterinary sciences", "crop yield", "WINTER-WHEAT", "15. Life on land", "MODEL", "Physical Sciences", "Water Resources", "potato", "0401 agriculture", " forestry", " and fisheries", "Life Sciences & Biomedicine", "Environmental Sciences", "random forest"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/14/8/1188/pdf"}, {"href": "https://www.mdpi.com/2073-4441/14/8/1188/pdf"}, {"href": "https://doi.org/10.3390/w14081188"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/w14081188", "name": "item", "description": "10.3390/w14081188", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/w14081188"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-08T00:00:00Z"}}, {"id": "1854/LU-8751352", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:25:10Z", "type": "Journal Article", "created": "2022-03-29", "title": "Mapping Soil Properties with Fixed Rank Kriging of Proximally Sensed Soil Data Fused with Sentinel-2 Biophysical Parameter", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Soil surveys with line-scanning platforms appear to have great advantages over the traditional methods used to collect soil information for the development of field-scale soil mapping and applications. These carry VNIR (visible and near infrared) spectrometers and have been used in recent years extensively for the assessment of soil fertility at the field scale, and the delineation of site-specific management zones (MZ). A challenging feature of VNIR applications in precision agriculture (PA) is the massiveness of the derived datasets that contain point predictions of soil properties, and the interpolation techniques involved in incorporating these data into site-specific management plans. In this study, fixed-rank kriging (FRK) geostatistical interpolation, which is a flexible, non-stationary spatial interpolation method especially suited to handling huge datasets, was applied to massive VNIR soil scanner data for the production of useful, smooth interpolated maps, appropriate for the delineation of site-specific MZ maps. Moreover, auxiliary Sentinel-2 data-based biophysical parameters NDVI (normalized difference vegetation index) and fAPAR (fraction of photosynthetically active radiation absorbed by the canopy) were included as covariates to improve the filtering performance of the interpolator and the ability to generate uniform patterns of spatial variation from which it is easier to receive a meaningful interpretation in PA applications. Results from the VNIR prediction dataset obtained from a pivot-irrigated field in Albacete, southeastern Spain, during 2019, have shown that FRK variants outperform ordinary kriging in terms of filtering capacity, by doubling the noise removal metrics while keeping the computation cost reasonably low. Such features, along with the capacity to handle a large volume of spatial information, nominate the method as ideal for PA applications with massive proximal and remote sensing datasets.</p></article>", "keywords": ["Technology", "MANAGEMENT ZONES", "PREDICTION", "NDVI", "SPATIAL VARIABILITY", "Science", "MODELS", "PHYSICAL-PROPERTIES", "ONLINE", "Environmental Sciences & Ecology", "VNIR spectrometer", "geostatistical interpolation", "VARIABLES", "0203 Classical Physics", "Remote Sensing", "geostatistical interpolation; VNIR spectrometer; NDVI; fAPAR; precision agriculture", "0909 Geomatic Engineering", "QUALITY", "DATA FUSION", "Geosciences", " Multidisciplinary", "Imaging Science & Photographic Technology", "agriculture", "Science & Technology", "precision agriculture", "Q", "Geology", "04 agricultural and veterinary sciences", "15. Life on land", "DELINEATION", "Earth and Environmental Sciences", "Physical Sciences", "fAPAR", "0401 agriculture", " forestry", " and fisheries", "precision", "4013 Geomatic engineering", "0406 Physical Geography and Environmental Geoscience", "Life Sciences & Biomedicine", "3701 Atmospheric sciences", "Environmental Sciences", "3709 Physical geography and environmental geoscience"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/7/1639/pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/7/1639/pdf"}, {"href": "https://doi.org/1854/LU-8751352"}, {"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": "1854/LU-8751352", "name": "item", "description": "1854/LU-8751352", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-8751352"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-29T00:00:00Z"}}, {"id": "2747196278", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:25:47Z", "type": "Journal Article", "created": "2017-08-28", "title": "Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The recent deployment of ESA\u2019s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015\u2013November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.</p></article>", "keywords": ["[SDE] Environmental Sciences", "NDVI", "Chemical technology", "HUMIDITE DU SOL", "soil moisture; SAR; Sentinel-1; NDVI; Sentinel-2; change detection", "0211 other engineering and technologies", "soil water content", "TP1-1185", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Article", "remote sensing", "Sentinel-1", "cartography", "soil moisture", "Sentinel-2", "TELEDETECTION", "change detection", "CARTOGRAPHIE", "SAR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/17/9/1966/pdf"}, {"href": "https://doi.org/2747196278"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2747196278", "name": "item", "description": "2747196278", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2747196278"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-08-26T00:00:00Z"}}, {"id": "3161294357", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:26:13Z", "type": "Journal Article", "created": "2021-05-11", "title": "Estimating Farm Wheat Yields from NDVI and Meteorological Data", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (Triticum aestivum) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R2 = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment.</p></article>", "keywords": ["yield estimation", "PREDICTION", "NDVI", "Triticum aestivum", "0703 Crop and Pasture Production", "3002 Agriculture", " land and farm management", "3004 Crop and pasture production", "Belgium", "0502 Environmental Science and Management", "<i>Triticum aestivum</i>", "2. Zero hunger", "Science & Technology", "S", "Plant Sciences", "Agriculture", "weather impact", "04 agricultural and veterinary sciences", "WINTER-WHEAT", "15. Life on land", "Agronomy", "winter wheat", "MODEL", "RESOLUTION", "SENTINEL-2", "0401 agriculture", " forestry", " and fisheries", "LANDSAT 8", "Life Sciences & Biomedicine"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/5/946/pdf"}, {"href": "https://doi.org/3161294357"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3161294357", "name": "item", "description": "3161294357", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3161294357"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-11T00:00:00Z"}}, {"id": "10.5281/zenodo.16895104", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:23:10Z", "type": "Journal Article", "created": "2021-03-29", "title": "Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013\u20132018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018\u20132019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41\u201371) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.</p></article>", "keywords": ["Landsat 8", "2. Zero hunger", "SAVI", "NDVI", "S", "13. Climate action", "wheat", "yield forecasting", "Agriculture", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://doi.org/10.5281/zenodo.16895104"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16895104", "name": "item", "description": "10.5281/zenodo.16895104", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16895104"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-29T00:00:00Z"}}, {"id": "10261/253216", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:24:36Z", "type": "Journal Article", "created": "2021-04-22", "title": "In-Season Interactions between Vine Vigor, Water Status and Wine Quality in Terrain-Based Management-Zones in a \u2018Cabernet Sauvignon\u2019 Vineyard", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Wine quality is the final outcome of the interactions within a vineyard between meteorological conditions, terrain and soil properties, plant physiology and numerous viticultural decisions, all of which are commonly summarized as the terroir effect. Associations between wine quality and a single soil or topographic factor are usually weak, but little information is available on the effect of terrain (elevation, aspect and slope) as a compound micro-terroir factor. We used the topographic wetness index (TWI) as a steady-state hydrologic and integrative measure to delineate management zones (MZs) within a vineyard and to study the interactions between vine vigor, water status and grape and wine quality. The study was conducted in a commercial 2.5-ha Vitis vinifera \u2018Cabernet Sauvignon\u2019 vineyard in Israel. Based on the TWI, the vineyard was divided into three MZs located along an elongate wadi that crosses the vineyard and bears water only in the rainy winter season. MZ1 was the most distant from the wadi and had low TWI values, MZ3 was closest to the wadi and had high TWI values. Remotely sensed crop water stress index (CWSI) was measured simultaneously with canopy cover (as determined by normalized difference vegetation index; NDVI) and with field measurements of midday stem water potential (\u03a8stem) and leaf area index (LAI) on several days during the growing seasons of 2017 and 2018. Vines in MZ1 had narrow trunk diameter and low LAI and canopy cover on most measurement days compared to the other two MZs. MZ1 vines also exhibited the highest water stress (highest CWSI and lowest \u03a8stem), lowest yield and highest wine quality. MZ3 vines showed higher LAI on most measurement days, lowest water deficit stress (\u03a8stem) during phenological stage I, highest yield and lowest wine quality. Yet, in stage III, MZ3 vines exhibited a similar water deficit stress (CWSI and \u03a8stem) as MZ2, suggesting that the relatively high vigor in MZ3 vines resulted in higher water deficit stress than expected towards the end of the season, possibly because of high water consumption over the course of the season. TWI and its classification into three MZs served as a reliable predictor for most of the attributes in the vineyard and for their dynamics within the season, and, thus, can be used as a key factor in delineation of MZs for irrigation. Yet, in-season remotely sensed monitoring is required to follow the vine dynamics to improve precision irrigation decisions.</p></article>", "keywords": ["in-field spatial variability", "2. Zero hunger", "0106 biological sciences", "In-field spatial variability", "CWSI", "Topographic wetness index", "NDVI", "Science", "UAV", "Q", "15. Life on land", "01 natural sciences", "6. Clean water", "topographic wetness index", "Vitis vinifera", "Irrigation management zones", "irrigation management zones", "UAV; CWSI; NDVI; irrigation management zones; topographic wetness index; in-field spatial variability; <i>Vitis vinifera</i>"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/9/1636/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/9/1636/pdf"}, {"href": "https://doi.org/10261/253216"}, {"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": "10261/253216", "name": "item", "description": "10261/253216", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/253216"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-22T00:00:00Z"}}, {"id": "2787641788", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:25:48Z", "type": "Journal Article", "created": "2018-01-22", "title": "Responses of Vegetation Cover to Environmental Change in Large Cities of China", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Vegetation cover is crucial for the sustainability of urban ecosystems; however, this cover has been undergoing substantial changes in cities. Based on climate data, city statistical data, nighttime light data and the Normalized Difference Vegetation Index (NDVI) dataset, we investigate the spatiotemporal variations of climate factors, urban lands and vegetation cover in 71 large cities of China during 1998\u20132012, and explore their correlations. A regression model between growing-season NDVI (G-NDVI) and urban land proportion (PU) is built to quantify the impact of urbanization on vegetation cover change. The results indicate that the spatiotemporal variations of temperature, precipitation, PU and G-NDVI are greatly different among the 71 cities which experienced rapid urbanization. The spatial difference of G-NDVI is closely related to diverse climate conditions, while the inter-annual variations of G-NDVI are less sensitive to climate changes. In addition, there is a negative correlation between G-NDVI trend and PU change, indicating vegetation cover in cities have been negatively impacted by urbanization. For most of the inland cities, the urbanization impacts on vegetation cover in urban areas are more severe than in suburban areas. But the opposite occurs in 17 cities mainly located in the coastal areas which have been undergoing the most rapid urbanization. Overall, the impacts of urbanization on G-NDVI change are estimated to be \u22120.026 per decade in urban areas and \u22120.015 per decade in suburban areas during 1998\u20132012. The long-term developments of cities would persist and continue to impact on the environmental change and sustainability. We use a 15-year window here as a case study, which implies the millennia of human effects on the natural biotas and warns us to manage landscapes and preserve ecological environments properly.</p></article>", "keywords": ["13. Climate action", "11. Sustainability", "vegetation cover; urbanization; climate change; NDVI; cities; China", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2071-1050/10/1/270/pdf"}, {"href": "https://www.mdpi.com/2071-1050/10/1/270/pdf"}, {"href": "https://doi.org/2787641788"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2787641788", "name": "item", "description": "2787641788", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2787641788"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-20T00:00:00Z"}}, {"id": "10396/24059", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:24:44Z", "type": "Report", "title": "Spatial crop-water variations in rainfed wheat systems: From simulation modelling to site-specific management", "description": "Open AccessEn campos en pendiente, los cultivos de secano experimentan diferentes grados de estr\u00e9s h\u00eddrico causados por variaciones espaciales de la humedad en el suelo, y los rendimientos var\u00edan espacialmente dentro del mismo campo. Esta variabilidad supone una oportunidad para la agricultura de precisi\u00f3n a trav\u00e9s del manejo espacialmente variable. Sin embargo, si bien se han logrado avances significativos en los aspectos de la ingenier\u00eda de la variaci\u00f3n espacial, como el aumento de la resoluci\u00f3n espacial de los sistemas de datos y la automatizaci\u00f3n, se ha avanzado mucho menos en relaci\u00f3n a la simulaci\u00f3n de las respuestas de los cultivos a las variaciones espaciales de la humedad y los flujos h\u00eddricos. La mayor\u00eda de los estudios sobre las brechas de rendimiento de secano ignoran la variabilidad dentro de la parcela. Sin embargo, el uso de modelos de simulaci\u00f3n de cultivos como medida de apoyo a los sistemas de gesti\u00f3n espacialmente variable, requiere que los enfoques de modelaci\u00f3n espacial del agua sean capaces de representar y simular con precisi\u00f3n la variaci\u00f3n dentro del campo de los factores relacionados con el agua disponible y la respuesta de los cultivos. Esta tesis doctoral representa una nueva contribuci\u00f3n a la agronom\u00eda de los sistemas agr\u00edcolas de secano, con \u00e9nfasis en el papel que juegan los flujos de agua en zonas de topograf\u00eda ondulada en la determinaci\u00f3n de las variaciones espaciales del rendimiento del trigo. La tesis se ha desarrollado en cap\u00edtulos que se complementan siguiendo un enfoque integrador. La presente tesis doctoral revis\u00f3 algunos de los modelos hidrol\u00f3gicos y de cultivo m\u00e1s ampliamente adoptados y explor\u00f3 nuevas oportunidades para simular variaciones espaciales del agua a nivel de campo mediante la incorporaci\u00f3n del flujo lateral de escorrent\u00eda superficial y sub-superficial en las zonas de menor elevaci\u00f3n del campo. Desde este punto de vista, se evaluaron las variaciones espaciales de las brechas de rendimiento en trigo de secano, en C\u00f3rdoba, Espa\u00f1a, que son causadas por flujos laterales de los puntos altos a los bajos. Desde una perspectiva agron\u00f3mica, las entradas laterales del agua contribuyen a las variaciones de rendimiento en los sistemas de producci\u00f3n de trigo de secano como el que se ha estudiado en el \u00e1mbito de esta tesis. La contribuci\u00f3n neta de estos flujos a las variaciones espaciales de los rendimientos potenciales de secano se mostr\u00f3 relevante pero altamente irregular entre diferentes a\u00f1os. A pesar de la variabilidad interanual, t\u00edpica de las condiciones mediterr\u00e1neas, la existencia de dichos flujos hizo que los rendimientos de trigo simulados variaran un +16% desde las \u00e1reas m\u00e1s elevadas de un campo hacia abajo. El rendimiento medio observado oscil\u00f3 entre 1.3 y 5.4 Mg de rendimiento de grano (GY) ha\u22121. Las respuestas de rendimiento neto al flujo lateral, cuenca abajo, fueron en promedio 383 kg de rendimiento de grano (GY) ha\u22121, y la productividad marginal de agua de LIF alcanz\u00f3 24.6 (\u00b113.2) kg GY ha\u22121 mm\u22121 en a\u00f1os de m\u00e1xima capacidad de respuesta. Dichos a\u00f1os de m\u00e1xima capacidad de respuesta se asociaron con bajas precipitaciones durante las etapas vegetativas del cultivo en combinaci\u00f3n con flujos laterales en las etapas posteriores a la floraci\u00f3n. En condiciones de campo, estas diferencias solo fueron visibles en uno de los dos a\u00f1os experimentales. Las implicaciones econ\u00f3micas asociadas con m\u00faltiples escenarios de tasa de aplicaci\u00f3n variable de nitr\u00f3geno se exploraron a trav\u00e9s de un caso de estudio y se propusieron varias recomendaciones. Tanto el tama\u00f1o de la finca (el \u00e1rea sembrada anual) como la estructura topogr\u00e1fica afectaron la din\u00e1mica de los rendimientos de la inversi\u00f3n. Bajo las condiciones actuales de pol\u00edtica agr\u00edcola, y de precios, la adopci\u00f3n de la tasa de aplicaci\u00f3n variable tendr\u00eda una ventaja econ\u00f3mica en fincas similares a la del caso de estudio con un \u00e1rea sembrada anual superior a 567 ha a\u00f1o\u22121. Sin embargo, las tendencias actuales en los precios de la energ\u00eda, los costes de transporte y los impactos tanto en los precios de los cereales como en los costes de los fertilizantes mejoran la viabilidad de la adopci\u00f3n de esta tecnolog\u00eda para una poblaci\u00f3n m\u00e1s amplia de tipos de fincas. La rentabilidad de la adopci\u00f3n de aplicaci\u00f3n variable de nitr\u00f3geno mejora bajo dichos escenarios y, en ausencia de apoyos adicionales, el \u00e1rea m\u00ednima para la adopci\u00f3n de aplicaci\u00f3n variable disminuye hasta un rango de 68-177 ha a\u00f1o\u22121 de \u00e1rea de siembra. La combinaci\u00f3n de aumentos de precios con la introducci\u00f3n de un subsidio adicional asociado al \u00e1rea de cultivo podr\u00eda reducir sustancialmente el umbral de adopci\u00f3n hasta 46 ha a\u00f1o\u22121, lo que hace que la tecnolog\u00eda sea econ\u00f3micamente viable para una poblaci\u00f3n mucho m\u00e1s amplia de agricultores.", "keywords": ["Agricultural crops", "Water management", "Artificial Neural Network", "Precision agriculture", "Crop modelling", "NDVI", "Spatial modelling", "Machine learning", "Water balance"], "contacts": [{"organization": "Roquette Tenreiro, Tom\u00e1s", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10396/24059"}, {"rel": "self", "type": "application/geo+json", "title": "10396/24059", "name": "item", "description": "10396/24059", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10396/24059"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "2283805478", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:25:39Z", "type": "Journal Article", "created": "2016-02-19", "title": "Impacts of Re-Vegetation on Surface Soil Moisture over the Chinese Loess Plateau Based on Remote Sensing Datasets", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>A large-scale re-vegetation supported by the Grain for Green Project (GGP) has greatly changed local eco-hydrological systems, with an impact on soil moisture conditions for the Chinese Loess Plateau. It is important to know how, exactly, re-vegetation influences soil moisture conditions, which not only crucially constrain growth and distribution of vegetation, and hence, further re-vegetation, but also determine the degree of soil desiccation and, thus, erosion risk in the region. In this study, three eco-environmental factors, which are Soil Water Index (SWI), the Normalized Difference Vegetation Index (NDVI), and precipitation, were used to investigate the response of soil moisture in the one-meter layer of top soil to the re-vegetation during the GGP. SWI was estimated based on the backscatter coefficient produced by the European Remote Sensing Satellite (ERS-1/2) and Meteorological Operational satellite program (MetOp), while NDVI was derived from SPOT imageries. Two separate periods, which are 1998\u20132000 and 2008\u20132010, were selected to examine the spatiotemporal pattern of the chosen eco-environmental factors. It has been shown that the amount of precipitation in 1998\u20132000 was close to that of 2008\u20132010 (the difference being 13.10 mm). From 1998\u20132000 to 2008\u20132010, the average annual NDVI increased for 80.99%, while the SWI decreased for 72.64% of the area on the Loess Plateau. The average NDVI over the Loess Plateau increased rapidly by 17.76% after the 10-year GGP project. However, the average SWI decreased by 4.37% for two-thirds of the area. More specifically, 57.65% of the area on the Loess Plateau experienced an increased NDVI and decreased SWI, 23.34% of the area had an increased NDVI and SWI. NDVI and SWI decreased simultaneously for 14.99% of the area, and the decreased NDVI and increased SWI occurred at the same time for 4.02% of the area. These results indicate that re-vegetation, human activities, and climate change have impacts on soil moisture. However, re-vegetation, which consumes a large quantity of soil water, may be the major factor for soil moisture change in most areas of the Loess Plateau. It is, therefore, suggested that Soil Moisture Content (SMC) should be kept in mind when carrying out re-vegetation in China\u2019s arid and semi-arid regions.</p></article>", "keywords": ["2. Zero hunger", "China", "Science", "Q", "Soil Water Index (SWI)", "precipitation", "15. Life on land", "01 natural sciences", "6. Clean water", "remote sensing", "the Loess Plateau", "13. Climate action", "11. Sustainability", "Normalized Difference Vegetation Index (NDVI)", "Grain for Green Project (GGP)", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Qiao Jiao, Rui Li, Fei Wang, Xingmin Mu, Pengfei Li, Chunchun An,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2072-4292/8/2/156/pdf"}, {"href": "https://doi.org/2283805478"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2283805478", "name": "item", "description": "2283805478", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2283805478"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-02-19T00:00:00Z"}}, {"id": "2744657337", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:25:46Z", "type": "Journal Article", "created": "2017-08-10", "title": "Performance of the two-source energy budget (TSEB) model for the monitoring of evapotranspiration over irrigated annual crops in North Africa", "description": "Abstract   The main objective of this study was to evaluate the performance and the domain of validity of the two-source energy balance model (TSEB) for the monitoring of actual evapotranspiration ( ET a  ) as a first step towards its use for irrigation planning. Secondary objectives were to analyze the ability of TSEB model to detect water stress and to evaluate evapotranspiration partition between evaporation (E) and transpiration (T) over irrigated annual crops. Within this context, TSEB was compared to the calibrated FAO-56 dual approach, taken as a reference tool for the monitoring of crop water consumption. TSEB computes  ET a   as the residual of a double component energy balance driven by the radiative surface temperature ( T s  ) used as a proxy of crop hydric conditions; the FAO-56 dual crop coefficient approach uses the Normalized Difference Vegetation Index (NDVI) as a proxy of Basal Crop Coefficient ( K cb  ) and assesses the hydric status directly by solving a two layer soil water budget. Both approaches were evaluated over four plots of wheat and sugar beet located in the Haouz plain (Marrakech, Morocco) that were instrumented with eddy covariance systems during the 2012 and 2013 growing seasons. Series of ASTER images were acquired during the first agricultural season. Both models offered fair performances compared to  ET a   observations with Root Mean Square Error (RMSE) lower than 1\u00a0mm\u00a0day \u22121  apart from the FAO-56 dual approach on the sugar beet plot because of uncertain irrigation inputs. This highlights a major weakness of this model when water inputs are uncertain; a very likely case at the plot scale. By contrast, the TSEB model offered smoother performances in all cases. The potentialities of both approaches to predict a water stress index based on the departure from potential evapotranspiration ( ET  c ) was evaluated: although the FAO-56 dual was better suited to detect high water stresses, the TSEB model was able to detect moderate stresses without a need to prescribe water inputs. Finally, the partition of  ET a   between soil evaporation and plant transpiration was estimated indirectly by confrontation between simulated soil evaporation and surface (0\u20135\u00a0cm) soil moisture acquired spatially with Theta Probe sensors and taken as a proxy of soil evaporation. TSEB evaporation was well correlated to surface soil moisture (r\u00a0=\u00a00.82) for low Leaf Area Index (LAI) values ( 2 \u00a0m \u22122 ). In addition, TSEB predicted partition compared well to snapshot measurements based on the stable isotope method. This in-depth comparison of two simple tools to monitor  ET a   leads us to the conclusion that the TSEB model can reasonably be used to map  ET a   on large scale and possibly for the decision-making process of irrigation scheduling.", "keywords": ["FAO-56", "2. Zero hunger", "550", "Evapotranspiration", "NDVI", "Water stress", "0207 environmental engineering", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "6. Clean water", "Surface temperature", "0401 agriculture", " forestry", " and fisheries", "TSEB"]}, "links": [{"href": "https://doi.org/2744657337"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20Water%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2744657337", "name": "item", "description": "2744657337", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2744657337"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-01T00:00:00Z"}}, {"id": "3138831713", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:26:11Z", "type": "Journal Article", "created": "2021-03-17", "title": "Assessing Irrigation Water Use with Remote Sensing-Based Soil Water Balance at an Irrigation Scheme Level in a Semi-Arid Region of Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This study aims to evaluate a remote sensing-based approach to allow estimation of the temporal and spatial distribution of crop evapotranspiration (ET) and irrigation water requirements over irrigated areas in semi-arid regions. The method is based on the daily step FAO-56 Soil Water Balance model combined with a time series of basal crop coefficients and the fractional vegetation cover derived from high-resolution satellite Normalized Difference Vegetation Index (NDVI) imagery. The model was first calibrated and validated at plot scale using ET measured by eddy-covariance systems over wheat fields and olive orchards representing the main crops grown in the study area of the Haouz plain (central Morocco). The results showed that the model provided good estimates of ET for wheat and olive trees with a root mean square error (RMSE) of about 0.56 and 0.54 mm/day respectively. The model was then used to compare remotely sensed estimates of irrigation requirements (RS-IWR) and irrigation water supplied (WS) at plot scale over an irrigation district in the Haouz plain through three growing seasons. The comparison indicated a large spatio-temporal variability in irrigation water demands and supplies; the median values of WS and RS-IWR were 130 (175), 117 (175) and 118 (112) mm respectively in the 2002\u20132003, 2005\u20132006 and 2008\u20132009 seasons. This could be attributed to inadequate irrigation supply and/or to farmers\u2019 socio-economic considerations and management practices. The findings demonstrate the potential for irrigation managers to use remote sensing-based models to monitor irrigation water usage for efficient and sustainable use of water resources.</p></article>", "keywords": ["2. Zero hunger", "0106 biological sciences", "FAO-56 soil water balance", "550", "[SDE.MCG]Environmental Sciences/Global Changes", "Science", "water", "Q", "evapotranspiration", "balance", "15. Life on land", "01 natural sciences", "630", "irrigation", "6. Clean water", "[SDE.MCG] Environmental Sciences/Global Changes", "remote sensing", "evapotranspiration; irrigation; water; remote sensing; FAO-56 soil water balance; NDVI time series", "FAO-56 soil water", "NDVI time series"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/6/1133/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/6/1133/pdf"}, {"href": "https://doi.org/3138831713"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3138831713", "name": "item", "description": "3138831713", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3138831713"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-16T00:00:00Z"}}, {"id": "3146201181", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:26:12Z", "type": "Journal Article", "created": "2021-03-29", "title": "Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013\u20132018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018\u20132019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41\u201371) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.</p></article>", "keywords": ["Landsat 8", "2. Zero hunger", "SAVI", "NDVI", "S", "13. Climate action", "wheat", "yield forecasting", "Agriculture", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://doi.org/3146201181"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3146201181", "name": "item", "description": "3146201181", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3146201181"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-29T00:00:00Z"}}, {"id": "3164629963", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:26:13Z", "type": "Journal Article", "created": "2021-04-23", "title": "In-Season Interactions between Vine Vigor, Water Status and Wine Quality in Terrain-Based Management-Zones in a \u2018Cabernet Sauvignon\u2019 Vineyard", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Wine quality is the final outcome of the interactions within a vineyard between meteorological conditions, terrain and soil properties, plant physiology and numerous viticultural decisions, all of which are commonly summarized as the terroir effect. Associations between wine quality and a single soil or topographic factor are usually weak, but little information is available on the effect of terrain (elevation, aspect and slope) as a compound micro-terroir factor. We used the topographic wetness index (TWI) as a steady-state hydrologic and integrative measure to delineate management zones (MZs) within a vineyard and to study the interactions between vine vigor, water status and grape and wine quality. The study was conducted in a commercial 2.5-ha Vitis vinifera \u2018Cabernet Sauvignon\u2019 vineyard in Israel. Based on the TWI, the vineyard was divided into three MZs located along an elongate wadi that crosses the vineyard and bears water only in the rainy winter season. MZ1 was the most distant from the wadi and had low TWI values, MZ3 was closest to the wadi and had high TWI values. Remotely sensed crop water stress index (CWSI) was measured simultaneously with canopy cover (as determined by normalized difference vegetation index; NDVI) and with field measurements of midday stem water potential (\u03a8stem) and leaf area index (LAI) on several days during the growing seasons of 2017 and 2018. Vines in MZ1 had narrow trunk diameter and low LAI and canopy cover on most measurement days compared to the other two MZs. MZ1 vines also exhibited the highest water stress (highest CWSI and lowest \u03a8stem), lowest yield and highest wine quality. MZ3 vines showed higher LAI on most measurement days, lowest water deficit stress (\u03a8stem) during phenological stage I, highest yield and lowest wine quality. Yet, in stage III, MZ3 vines exhibited a similar water deficit stress (CWSI and \u03a8stem) as MZ2, suggesting that the relatively high vigor in MZ3 vines resulted in higher water deficit stress than expected towards the end of the season, possibly because of high water consumption over the course of the season. TWI and its classification into three MZs served as a reliable predictor for most of the attributes in the vineyard and for their dynamics within the season, and, thus, can be used as a key factor in delineation of MZs for irrigation. Yet, in-season remotely sensed monitoring is required to follow the vine dynamics to improve precision irrigation decisions.</p></article>", "keywords": ["in-field spatial variability", "2. Zero hunger", "0106 biological sciences", "In-field spatial variability", "CWSI", "Topographic wetness index", "NDVI", "Science", "UAV", "Q", "15. Life on land", "01 natural sciences", "6. Clean water", "topographic wetness index", "Vitis vinifera", "Irrigation management zones", "irrigation management zones", "UAV; CWSI; NDVI; irrigation management zones; topographic wetness index; in-field spatial variability; <i>Vitis vinifera</i>"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/9/1636/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/9/1636/pdf"}, {"href": "https://doi.org/3164629963"}, {"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": "3164629963", "name": "item", "description": "3164629963", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3164629963"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-22T00:00:00Z"}}, {"id": "energidataservice_102_ReCoverageMunicipality", "type": "Feature", "geometry": null, "properties": {"license": "https://publications.europa.eu/resource/authority/licence/CC_BY_4_0", "updated": "2025-02-11T07:19:17", "type": "Dataset", "title": "RE Coverage Municipality", "description": "RE (renewable energy) coverage of consumption per municipality per hour. Combined with [Declaration, Transmission Emissions](https://www.energidataservice.dk/tso-electricity/DeclarationTransmissionEmission) it delivers the emissions per kWh per municipality per hour. The total emissions per kWh in a municipality are Emission Factors *consumption* (1-REshare), where emission factor are provided by [Declaration, Transmission Emissions](https://www.energidataservice.dk/tso-electricity/DeclarationTransmissionEmission).", "formats": [{"name": "CSV"}], "keywords": ["co2", "data-om-luftkvalitet", "deklaration", "dk", "el", "eldeklaration", "elektricitet", "elforbrug", "elforsyning", "elforsyningsstatistik", "emissioner", "energi", "energidata", "energistatistik", "forbrug-af-el", "forbrug-af-elektricitet", "forbrugsd\u00e6kning", "havvind", "landvind", "luftkvalitet", "luftkvalitetsdata", "milj\u00f8", "milj\u00f8deklaration", "sol", "solceller", "solenergi", "vedvarende-energi", "vind", "vindenergi", "vindm\u00f8ller"], "contacts": [{"organization": "Energinet", "roles": ["creator"]}, {"organization": "https://www.energinet.dk/id/organization/energinet", "roles": ["publisher"]}]}, "links": [{"href": "http://data.europa.eu/88u/dataset/energidataservice_102_recoveragemunicipality~~1"}, {"href": "https://api.energidataservice.dk/dataset/ReCoverageMunicipality"}, {"href": "https://www.energidataservice.dk/tso-electricity/ReCoverageMunicipality"}, {"rel": "self", "type": "application/geo+json", "title": "energidataservice_102_ReCoverageMunicipality", "name": "item", "description": "energidataservice_102_ReCoverageMunicipality", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/energidataservice_102_ReCoverageMunicipality"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "energidataservice_110_GridCapacityMapDSO", "type": "Feature", "geometry": null, "properties": {"license": "https://publications.europa.eu/resource/authority/licence/CC_BY_4_0", "updated": "2024-07-10T10:01:26", "type": "Dataset", "title": "Grid Capacity Map DSO", "description": "The dataset is the quarterly dataset that describes the grid capacity at the DSO level, capacity at the general level, and for wind and solar production units. Data is related to\u00a0[www.kapacitetskort.dk](https://storymaps.arcgis.com/stories/eb5b387e376f49b8996d5e7c47fbdd37).", "formats": [{"name": "CSV"}], "keywords": ["dk", "el", "elektricitet", "elforsyning", "elforsyningsnet", "elforsyningsnettet", "elforsyningsstatistik", "elnettet", "eltransmissionsnet", "eltransmissionsnettet", "energi", "energidata", "energistatistik", "forbindelser", "havvind", "import-og-eksport-af-el", "import-og-eksport-af-elektricitet", "landvind", "sol", "solceller", "solenergi", "transmissionsnet", "transmissionsnettet", "udveksling", "vedvarende-energi", "vind", "vindenergi", "vindm\u00f8ller"], "contacts": [{"organization": "Energinet", "roles": ["creator"]}, {"organization": "https://www.energinet.dk/id/organization/energinet", "roles": ["publisher"]}]}, "links": [{"href": "http://data.europa.eu/88u/dataset/energidataservice_110_gridcapacitymapdso~~1"}, {"href": "https://api.energidataservice.dk/dataset/GridCapacityMapDSO"}, {"href": "https://www.energidataservice.dk/tso-electricity/GridCapacityMapDSO"}, {"rel": "self", "type": "application/geo+json", "title": "energidataservice_110_GridCapacityMapDSO", "name": "item", "description": "energidataservice_110_GridCapacityMapDSO", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/energidataservice_110_GridCapacityMapDSO"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "energidataservice_39_Forecasts_5Min", "type": "Feature", "geometry": null, "properties": {"license": "https://publications.europa.eu/resource/authority/licence/CC_BY_4_0", "updated": "2024-11-01T07:24:31", "type": "Dataset", "title": "Forecast Wind and Solar Power, 5 min", "description": "Forecast of Wind and Solar power with a resolution for 5 minutes. This forecast is base for the one hour resolution edition: [Forecast Wind and Solar power, hour resolution](https://www.energidataservice.dk/tso-electricity/Forecasts_Hour).  **Caution:** Due to a technical issue data is missing in the period April 12th-16th 2024 and will not be available. Solar power forecasts are not updated between 6pm and 6am. Therefore, no 5 hour forecast exist for solar power between 11pm and 11am.  1 Hour and 5 Hour Forecasts are released up to 15 minutes before the start of the forecast and might be updated until 1 minute before the forecast starts. Within this time frame up to 3 updates can take place.  Please note that the definition of offshore relates to the substation, not the wind generators: * In system operation, the term \"Offshore\" relates to the location of the substation. * In energy statistics, onshore and offshore relate to the the location of the wind generator. * Comparison of forecasts and actual generation is only relevant for the total wind generation.", "formats": [{"name": "CSV"}], "keywords": ["dk", "el", "elektricitet", "elforsyning", "elforsyningsstatistik", "energi", "energidata", "energistatistik", "havvind", "landvind", "sol", "solceller", "solenergi", "vedvarende-energi", "vind", "vindenergi", "vindm\u00f8ller"], "contacts": [{"organization": "Energinet", "roles": ["creator"]}, {"organization": "https://www.energinet.dk/id/organization/energinet", "roles": ["publisher"]}]}, "links": [{"href": "http://data.europa.eu/88u/dataset/energidataservice_39_forecasts_5min~~1"}, {"href": "https://api.energidataservice.dk/dataset/Forecasts_5Min"}, {"href": "https://www.energidataservice.dk/tso-electricity/Forecasts_5Min"}, {"rel": "self", "type": "application/geo+json", "title": "energidataservice_39_Forecasts_5Min", "name": "item", "description": "energidataservice_39_Forecasts_5Min", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/energidataservice_39_Forecasts_5Min"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "energidataservice_111_GridCapacityMapTSO", "type": "Feature", "geometry": null, "properties": {"license": "https://publications.europa.eu/resource/authority/licence/CC_BY_4_0", "updated": "2024-09-10T07:18:15", "type": "Dataset", "title": "Grid Capacity Map TSO", "description": "The dataset is the monthly dataset that describes the grid capacity at the TSO level, capacity at the general level, and for wind and solar production units. Data is related to\u00a0[www.kapacitetskort.dk](https://storymaps.arcgis.com/stories/eb5b387e376f49b8996d5e7c47fbdd37).", "formats": [{"name": "CSV"}], "keywords": ["dk", "el", "elektricitet", "elforsyning", "elforsyningsnet", "elforsyningsnettet", "elforsyningsstatistik", "elnettet", "eltransmissionsnet", "eltransmissionsnettet", "energi", "energidata", "energistatistik", "forbindelser", "havvind", "import-og-eksport-af-el", "import-og-eksport-af-elektricitet", "landvind", "sol", "solceller", "solenergi", "transmissionsnet", "transmissionsnettet", "udveksling", "vedvarende-energi", "vind", "vindenergi", "vindm\u00f8ller"], "contacts": [{"organization": "Energinet", "roles": ["creator"]}, {"organization": "https://www.energinet.dk/id/organization/energinet", "roles": ["publisher"]}]}, "links": [{"href": "http://data.europa.eu/88u/dataset/energidataservice_111_gridcapacitymaptso~~1"}, {"href": "https://api.energidataservice.dk/dataset/GridCapacityMapTSO"}, {"href": "https://www.energidataservice.dk/tso-electricity/GridCapacityMapTSO"}, {"rel": "self", "type": "application/geo+json", "title": "energidataservice_111_GridCapacityMapTSO", "name": "item", "description": "energidataservice_111_GridCapacityMapTSO", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/energidataservice_111_GridCapacityMapTSO"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "energidataservice_11_CommunityProduction", "type": "Feature", "geometry": null, "properties": {"license": "https://publications.europa.eu/resource/authority/licence/CC_BY_4_0", "updated": "2024-07-19T11:26:51", "type": "Dataset", "title": "Production per Municipality", "description": "This dataset contains monthly electricity production data in MWh for each municipality, categorized by production from Wind Power, Solar Power, Decentral- and Central Power Plants. One month per municipality (number 101 - 860, whereas 999 is not stated).  [See data visualized](https://energinet.dk/data-om-energi/udvikling-i-elproduktion/).", "formats": [{"name": "CSV"}], "keywords": ["dk", "el", "elektricitet", "elforsyning", "elforsyningsstatistik", "elproduktion", "energi", "energidata", "energikilder", "energistatistik", "havvind", "landvind", "produktionsform", "sol", "solceller", "solenergi", "vedvarende-energi", "vind", "vindenergi", "vindm\u00f8ller"], "contacts": [{"organization": "Energinet", "roles": ["creator"]}, {"organization": "https://www.energinet.dk/id/organization/energinet", "roles": ["publisher"]}]}, "links": [{"href": "http://data.europa.eu/88u/dataset/energidataservice_11_communityproduction~~1"}, {"href": "https://api.energidataservice.dk/dataset/CommunityProduction"}, {"href": "https://www.energidataservice.dk/tso-electricity/CommunityProduction"}, {"rel": "self", "type": "application/geo+json", "title": "energidataservice_11_CommunityProduction", "name": "item", "description": "energidataservice_11_CommunityProduction", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/energidataservice_11_CommunityProduction"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "energidataservice_27_ElectricityBalanceNonv", "type": "Feature", "geometry": null, "properties": {"license": "https://publications.europa.eu/resource/authority/licence/CC_BY_4_0", "updated": "2025-04-23T14:04:15", "type": "Dataset", "title": "Electricity Balance Non-Validated (to be Discontinued, please read description)", "description": "Data represents the overall balance of consumption, production, import and export of electricity in an area. Production is divided into main production types.  **Caution:** All values in the dataset are in 15-minute resolution since 2025-04-23 12.45 CET instead of hour resolution as required by ENTSO-E. Between 2025-04-07 23.45 CET and 2025-04-23 12.45 CET values are missing sporadically in connection with the transition to 15-minute resolution. The missing data will not be added to the dataset, why data in the period must be used with caution.  Note that data is expressed in MWh/h, or average load, not MWh.   This dataset will not be restored to hour resolution, as the dataflow is completely integrated to the ENTSO-E data transfer. Instead, Energinet is working on a new dataset package, that will replace this dataset. Updates will be published on this page and on [NEWS](https://www.energidataservice.dk/news). NOTE: Data are based on upscaled real-time power measurements from the SCADA system. More precisely, the values are averages of power measurements every 4th second. Errors will occur, and will generally not be corrected. For statistical purposes, see [Production and Consumption - Settlement](https://www.energidataservice.dk/tso-electricity/ProductionConsumptionSettlement).  Gross consumption = sum of production + sum of exchange to connected areas. A positive exchange is import of electricity, while a negative is export.   The total production is the sum of all production types.", "formats": [{"name": "CSV"}], "keywords": ["co2", "data-om-luftkvalitet", "deklaration", "dk", "el", "eldeklaration", "elektricitet", "elforbrug", "elforsyning", "elforsyningsstatistik", "elproduktion", "emissioner", "energi", "energidata", "energikilder", "energistatistik", "forbrug-af-el", "forbrug-af-elektricitet", "forbrugsd\u00e6kning", "havvind", "landvind", "luftkvalitet", "luftkvalitetsdata", "milj\u00f8", "milj\u00f8deklaration", "produktionsform", "sol", "solceller", "solenergi", "vedvarende-energi", "vind", "vindenergi", "vindm\u00f8ller"], "contacts": [{"organization": "Energinet", "roles": ["creator"]}, {"organization": "https://www.energinet.dk/id/organization/energinet", "roles": ["publisher"]}]}, "links": [{"href": "http://data.europa.eu/88u/dataset/energidataservice_27_electricitybalancenonv~~1"}, {"href": "https://api.energidataservice.dk/dataset/ElectricityBalanceNonv"}, {"href": "https://www.energidataservice.dk/tso-electricity/ElectricityBalanceNonv"}, {"rel": "self", "type": "application/geo+json", "title": "energidataservice_27_ElectricityBalanceNonv", "name": "item", "description": "energidataservice_27_ElectricityBalanceNonv", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/energidataservice_27_ElectricityBalanceNonv"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "energidataservice_28_ElectricityProdex5MinRealtime", "type": "Feature", "geometry": null, "properties": {"license": "https://publications.europa.eu/resource/authority/licence/CC_BY_4_0", "updated": "2025-06-19T10:31:30", "type": "Dataset", "title": "Electricity Production and Exchange 5 min Realtime", "description": "The electricity production and exchange in MW in 5 minutes intervals and updated every 5th minute. NOTE: Data are based on upscaled real-time power measurements from the SCADA system. Errors will occur, and will generally not be corrected. For statistical purposes, see [Production and Consumption - Settlement](https://www.energidataservice.dk/tso-electricity/ProductionConsumptionSettlement).  The total load, including loses, is calculated as the sum of production from power plants, solar and wind power plus the exchange to Germany, Sweden, Norway, and The Netherlands. The exchange between Bornholm and Price area SE4 is included in the exchange DK2 to Sweden, and should not be taken into account.", "formats": [{"name": "CSV"}], "keywords": ["dk", "el", "elektricitet", "elforsyning", "elforsyningsnet", "elforsyningsnettet", "elforsyningsstatistik", "elleverand\u00f8rer", "elmarked", "elmarkedet", "elnettet", "elpriser", "elproduktion", "eltransmissionsnet", "eltransmissionsnettet", "energi", "energidata", "energikilder", "energistatistik", "forbindelser", "havvind", "import-og-eksport-af-el", "import-og-eksport-af-elektricitet", "landvind", "produktionsform", "sol", "solceller", "solenergi", "transmissionsnet", "transmissionsnettet", "udveksling", "vedvarende-energi", "vind", "vindenergi", "vindm\u00f8ller"], "contacts": [{"organization": "Energinet", "roles": ["creator"]}, {"organization": "https://www.energinet.dk/id/organization/energinet", "roles": ["publisher"]}]}, "links": [{"href": "http://data.europa.eu/88u/dataset/energidataservice_28_electricityprodex5minrealtime~~1"}, {"href": "https://api.energidataservice.dk/dataset/ElectricityProdex5MinRealtime"}, {"href": "https://www.energidataservice.dk/tso-electricity/ElectricityProdex5MinRealtime"}, {"rel": "self", "type": "application/geo+json", "title": "energidataservice_28_ElectricityProdex5MinRealtime", "name": "item", "description": "energidataservice_28_ElectricityProdex5MinRealtime", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/energidataservice_28_ElectricityProdex5MinRealtime"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "energidataservice_40_Forecasts_Hour", "type": "Feature", "geometry": null, "properties": {"license": "https://publications.europa.eu/resource/authority/licence/CC_BY_4_0", "updated": "2025-04-10T07:58:06", "type": "Dataset", "title": "Forecast Wind and Solar Power, Hour Resolution", "description": "Forecast of wind and solar power with a resolution of one hour. Solar power forecasts are not updated between 6pm and 6am. Therefore, no 5 Hour forecast exists for Solar power between 11pm and 11am.  1 Hour and 5 Hour forecasts are released up to 15 minutes before the start of the forecast and might be updated until 1 minute before the forecast starts. Within this time frame up to 3 updates can take place.   The hourly forecast values are calculated from the values in [Forecast Wind and Solar power, 5min](https://www.energidataservice.dk/tso-electricity/Forecasts_5Min).  The following illustrates how the value from 14:00 to 15:00 is calculated: ((Value at 14:00) / 2 + (All values from 14:05 to 14:55) + (Value at 15:00) / 2) / 12.  Due to a technical issue data is missing in the period April 12th-16th 2024 and will not be available.", "formats": [{"name": "CSV"}], "keywords": ["dk", "el", "elektricitet", "elforsyning", "elforsyningsstatistik", "energi", "energidata", "energistatistik", "havvind", "landvind", "sol", "solceller", "solenergi", "vedvarende-energi", "vind", "vindenergi", "vindm\u00f8ller"], "contacts": [{"organization": "Energinet", "roles": ["creator"]}, {"organization": "https://www.energinet.dk/id/organization/energinet", "roles": ["publisher"]}]}, "links": [{"href": "http://data.europa.eu/88u/dataset/energidataservice_40_forecasts_hour~~1"}, {"href": "https://api.energidataservice.dk/dataset/Forecasts_Hour"}, {"href": "https://www.energidataservice.dk/tso-electricity/Forecasts_Hour"}, {"rel": "self", "type": "application/geo+json", "title": "energidataservice_40_Forecasts_Hour", "name": "item", "description": "energidataservice_40_Forecasts_Hour", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/energidataservice_40_Forecasts_Hour"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "energidataservice_55_PowerSystemRightNow", "type": "Feature", "geometry": null, "properties": {"license": "https://publications.europa.eu/resource/authority/licence/CC_BY_4_0", "updated": "2025-02-27T12:46:01", "type": "Dataset", "title": "Power System Right Now", "description": "Production, flow, and balance on interconnectors right now in one-minute resolution.  **Caution:** Data in the following columns will be unavailable for a period of time: Imbalance DK1, Imbalance DK2, mFRR Activated DK1, mFRR Activated DK2. We are working on reestablishing the dataflow as soon as possible. We are sorry for the inconvenience it may cause.  **Pilot phase: Realtime publication of imbalances** 6 new columns have been added to the dataset as part of a pilot phase: Realtime publication of imbalances. * Imbalance DK1/DK2: The imbalance the system would experience without TSO regulations [MW]. * aFRR activated DK1/DK2: The current value of aFRR activations [MW]. * mFRR activated DK1/DK2: The current value of mFRR activations [MW].  **Q&A:** Energinet is experiencing significant interest in the pilot and has received many questions. Answers to these questions are compiled on [Q&A: Realtime publication of imbalances](https://www.energidataservice.dk/QA-realtime-publication-of-imbalances).  Users of the dataset are advised that the real time dataset may contain errors. It is important to stress that this is a pilot phase aimed at confirming whether real time publication of imbalance volumes has a positive impact on system balancing. NOTE: Data are based on upscaled realtime power measurements from the SCADA system. Errors will occur, and will generally not be corrected. For statistical purposes, see [Production and Consumption - Settlement](https://www.energidataservice.dk/tso-electricity/ProductionConsumptionSettlement).", "formats": [{"name": "CSV"}], "keywords": ["dk", "el", "elektricitet", "elforbrug", "elforsyning", "elforsyningsstatistik", "elproduktion", "energi", "energidata", "energikilder", "energistatistik", "forbrug-af-el", "forbrug-af-elektricitet", "havvind", "landvind", "produktionsform", "sol", "solceller", "solenergi", "vedvarende-energi", "vind", "vindenergi", "vindm\u00f8ller"], "contacts": [{"organization": "Energinet", "roles": ["creator"]}, {"organization": "https://www.energinet.dk/id/organization/energinet", "roles": ["publisher"]}]}, "links": [{"href": "http://data.europa.eu/88u/dataset/energidataservice_55_powersystemrightnow~~1"}, {"href": "https://api.energidataservice.dk/dataset/PowerSystemRightNow"}, {"href": "https://www.energidataservice.dk/tso-electricity/PowerSystemRightNow"}, {"rel": "self", "type": "application/geo+json", "title": "energidataservice_55_PowerSystemRightNow", "name": "item", "description": "energidataservice_55_PowerSystemRightNow", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/energidataservice_55_PowerSystemRightNow"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "energidataservice_57_ProductionConsumptionSettlement", "type": "Feature", "geometry": null, "properties": {"license": "https://publications.europa.eu/resource/authority/licence/CC_BY_4_0", "updated": "2025-02-25T10:27:47", "type": "Dataset", "title": "Production and Consumption - Settlement", "description": "Production and consumption based on settlement data from Datahub. Data from 2005, but please note additional info in comments.  Data is updated with a 9-15 day delay.  Measurement data may be updated up to 2 years after the specific date, however data is normally 99% correct after 15 days, and will gradually be improved with the last update after 3 months. After 3 months, data will normally be 99,9% correct.  Data from 2005, but not in all rows: * Commercial power separated from local power from 2021. * Solar power divided into the four columns from 2020. Before 2020 all solar power is placed in SolarPowerSelfConMWh. * Transmission grid loss from 1/10 2008. Zero is actually a null. * GridLossDistributionMWh and GridLossInterconnectorsMWh from 1/1 2021. * In some cases, zero should actually be a null.", "formats": [{"name": "CSV"}], "keywords": ["co2", "data-om-luftkvalitet", "deklaration", "dk", "el", "eldeklaration", "elektricitet", "elforbrug", "elforsyning", "elforsyningsstatistik", "elproduktion", "emissioner", "energi", "energidata", "energikilder", "energistatistik", "forbrug-af-el", "forbrug-af-elektricitet", "forbrugsd\u00e6kning", "havvind", "landvind", "luftkvalitet", "luftkvalitetsdata", "milj\u00f8", "milj\u00f8deklaration", "produktionsform", "sol", "solceller", "solenergi", "vedvarende-energi", "vind", "vindenergi", "vindm\u00f8ller"], "contacts": [{"organization": "Energinet", "roles": ["creator"]}, {"organization": "https://www.energinet.dk/id/organization/energinet", "roles": ["publisher"]}]}, "links": [{"href": "http://data.europa.eu/88u/dataset/energidataservice_57_productionconsumptionsettlement~~1"}, {"href": "https://api.energidataservice.dk/dataset/ProductionConsumptionSettlement"}, {"href": "https://www.energidataservice.dk/tso-electricity/ProductionConsumptionSettlement"}, {"rel": "self", "type": "application/geo+json", "title": "energidataservice_57_ProductionConsumptionSettlement", "name": "item", "description": "energidataservice_57_ProductionConsumptionSettlement", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/energidataservice_57_ProductionConsumptionSettlement"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "PMC5621168", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:28:20Z", "type": "Journal Article", "created": "2017-08-28", "title": "Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The recent deployment of ESA\u2019s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015\u2013November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.</p></article>", "keywords": ["[SDE] Environmental Sciences", "NDVI", "Chemical technology", "HUMIDITE DU SOL", "soil moisture; SAR; Sentinel-1; NDVI; Sentinel-2; change detection", "0211 other engineering and technologies", "soil water content", "TP1-1185", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Article", "remote sensing", "Sentinel-1", "cartography", "soil moisture", "Sentinel-2", "TELEDETECTION", "change detection", "CARTOGRAPHIE", "SAR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/17/9/1966/pdf"}, {"href": "https://doi.org/PMC5621168"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC5621168", "name": "item", "description": "PMC5621168", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC5621168"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-08-26T00:00:00Z"}}, {"id": "energidataservice_58_ProductionMunicipalityHour", "type": "Feature", "geometry": null, "properties": {"license": "https://publications.europa.eu/resource/authority/licence/CC_BY_4_0", "updated": "2024-02-29T13:10:23", "type": "Dataset", "title": "Production per Municipality per Hour", "description": "Contains the electricity production per municipality per hour in MWh, divided by production from Wind Power, Solar Power, Thermal (Decentral and Central Power Plants). Note, hydropower is included in Wind power.  One month per municipality (number 101 - 860, whereas 999 is not stated).", "formats": [{"name": "CSV"}], "keywords": ["dk", "el", "elektricitet", "elforsyning", "elforsyningsstatistik", "elproduktion", "energi", "energidata", "energikilder", "energistatistik", "havvind", "landvind", "produktionsform", "sol", "solceller", "solenergi", "vedvarende-energi", "vind", "vindenergi", "vindm\u00f8ller"], "contacts": [{"organization": "Energinet", "roles": ["creator"]}, {"organization": "https://www.energinet.dk/id/organization/energinet", "roles": ["publisher"]}]}, "links": [{"href": "http://data.europa.eu/88u/dataset/energidataservice_58_productionmunicipalityhour~~1"}, {"href": "https://api.energidataservice.dk/dataset/ProductionMunicipalityHour"}, {"href": "https://www.energidataservice.dk/tso-electricity/ProductionMunicipalityHour"}, {"rel": "self", "type": "application/geo+json", "title": "energidataservice_58_ProductionMunicipalityHour", "name": "item", "description": "energidataservice_58_ProductionMunicipalityHour", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/energidataservice_58_ProductionMunicipalityHour"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "energidataservice_7_CapacityPerMunicipality", "type": "Feature", "geometry": null, "properties": {"license": "https://publications.europa.eu/resource/authority/licence/CC_BY_4_0", "updated": "2025-04-25T12:46:54", "type": "Dataset", "title": "Capacity per Municipality", "description": "Capacity and number of production units per municipality and type. The dataset is updated during the first week of the following month.  **Please note:** the capacities are calculated at midnight on the first day of the month.  A version, where thermal power plants are divided into main fuels, is under development.", "formats": [{"name": "CSV"}], "keywords": ["dk", "el", "elektricitet", "elforsyning", "elforsyningsstatistik", "elproduktion", "energi", "energidata", "energikilder", "energistatistik", "havvind", "landvind", "produktionsform", "sol", "solceller", "solenergi", "vedvarende-energi", "vind", "vindenergi", "vindm\u00f8ller"], "contacts": [{"organization": "Energinet", "roles": ["creator"]}, {"organization": "https://www.energinet.dk/id/organization/energinet", "roles": ["publisher"]}]}, "links": [{"href": "http://data.europa.eu/88u/dataset/energidataservice_7_capacitypermunicipality~~1"}, {"href": "https://api.energidataservice.dk/dataset/CapacityPerMunicipality"}, {"href": "https://www.energidataservice.dk/tso-electricity/CapacityPerMunicipality"}, {"rel": "self", "type": "application/geo+json", "title": "energidataservice_7_CapacityPerMunicipality", "name": "item", "description": "energidataservice_7_CapacityPerMunicipality", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/energidataservice_7_CapacityPerMunicipality"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "oai:helvia.uco.es:10396/24059", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:33:01Z", "type": "Report", "title": "Spatial crop-water variations in rainfed wheat systems: From simulation modelling to site-specific management", "description": "Open AccessEn campos en pendiente, los cultivos de secano experimentan diferentes grados de estr\u00e9s h\u00eddrico causados por variaciones espaciales de la humedad en el suelo, y los rendimientos var\u00edan espacialmente dentro del mismo campo. Esta variabilidad supone una oportunidad para la agricultura de precisi\u00f3n a trav\u00e9s del manejo espacialmente variable. Sin embargo, si bien se han logrado avances significativos en los aspectos de la ingenier\u00eda de la variaci\u00f3n espacial, como el aumento de la resoluci\u00f3n espacial de los sistemas de datos y la automatizaci\u00f3n, se ha avanzado mucho menos en relaci\u00f3n a la simulaci\u00f3n de las respuestas de los cultivos a las variaciones espaciales de la humedad y los flujos h\u00eddricos. La mayor\u00eda de los estudios sobre las brechas de rendimiento de secano ignoran la variabilidad dentro de la parcela. Sin embargo, el uso de modelos de simulaci\u00f3n de cultivos como medida de apoyo a los sistemas de gesti\u00f3n espacialmente variable, requiere que los enfoques de modelaci\u00f3n espacial del agua sean capaces de representar y simular con precisi\u00f3n la variaci\u00f3n dentro del campo de los factores relacionados con el agua disponible y la respuesta de los cultivos. Esta tesis doctoral representa una nueva contribuci\u00f3n a la agronom\u00eda de los sistemas agr\u00edcolas de secano, con \u00e9nfasis en el papel que juegan los flujos de agua en zonas de topograf\u00eda ondulada en la determinaci\u00f3n de las variaciones espaciales del rendimiento del trigo. La tesis se ha desarrollado en cap\u00edtulos que se complementan siguiendo un enfoque integrador. La presente tesis doctoral revis\u00f3 algunos de los modelos hidrol\u00f3gicos y de cultivo m\u00e1s ampliamente adoptados y explor\u00f3 nuevas oportunidades para simular variaciones espaciales del agua a nivel de campo mediante la incorporaci\u00f3n del flujo lateral de escorrent\u00eda superficial y sub-superficial en las zonas de menor elevaci\u00f3n del campo. Desde este punto de vista, se evaluaron las variaciones espaciales de las brechas de rendimiento en trigo de secano, en C\u00f3rdoba, Espa\u00f1a, que son causadas por flujos laterales de los puntos altos a los bajos. Desde una perspectiva agron\u00f3mica, las entradas laterales del agua contribuyen a las variaciones de rendimiento en los sistemas de producci\u00f3n de trigo de secano como el que se ha estudiado en el \u00e1mbito de esta tesis. La contribuci\u00f3n neta de estos flujos a las variaciones espaciales de los rendimientos potenciales de secano se mostr\u00f3 relevante pero altamente irregular entre diferentes a\u00f1os. A pesar de la variabilidad interanual, t\u00edpica de las condiciones mediterr\u00e1neas, la existencia de dichos flujos hizo que los rendimientos de trigo simulados variaran un +16% desde las \u00e1reas m\u00e1s elevadas de un campo hacia abajo. El rendimiento medio observado oscil\u00f3 entre 1.3 y 5.4 Mg de rendimiento de grano (GY) ha\u22121. Las respuestas de rendimiento neto al flujo lateral, cuenca abajo, fueron en promedio 383 kg de rendimiento de grano (GY) ha\u22121, y la productividad marginal de agua de LIF alcanz\u00f3 24.6 (\u00b113.2) kg GY ha\u22121 mm\u22121 en a\u00f1os de m\u00e1xima capacidad de respuesta. Dichos a\u00f1os de m\u00e1xima capacidad de respuesta se asociaron con bajas precipitaciones durante las etapas vegetativas del cultivo en combinaci\u00f3n con flujos laterales en las etapas posteriores a la floraci\u00f3n. En condiciones de campo, estas diferencias solo fueron visibles en uno de los dos a\u00f1os experimentales. Las implicaciones econ\u00f3micas asociadas con m\u00faltiples escenarios de tasa de aplicaci\u00f3n variable de nitr\u00f3geno se exploraron a trav\u00e9s de un caso de estudio y se propusieron varias recomendaciones. Tanto el tama\u00f1o de la finca (el \u00e1rea sembrada anual) como la estructura topogr\u00e1fica afectaron la din\u00e1mica de los rendimientos de la inversi\u00f3n. Bajo las condiciones actuales de pol\u00edtica agr\u00edcola, y de precios, la adopci\u00f3n de la tasa de aplicaci\u00f3n variable tendr\u00eda una ventaja econ\u00f3mica en fincas similares a la del caso de estudio con un \u00e1rea sembrada anual superior a 567 ha a\u00f1o\u22121. Sin embargo, las tendencias actuales en los precios de la energ\u00eda, los costes de transporte y los impactos tanto en los precios de los cereales como en los costes de los fertilizantes mejoran la viabilidad de la adopci\u00f3n de esta tecnolog\u00eda para una poblaci\u00f3n m\u00e1s amplia de tipos de fincas. La rentabilidad de la adopci\u00f3n de aplicaci\u00f3n variable de nitr\u00f3geno mejora bajo dichos escenarios y, en ausencia de apoyos adicionales, el \u00e1rea m\u00ednima para la adopci\u00f3n de aplicaci\u00f3n variable disminuye hasta un rango de 68-177 ha a\u00f1o\u22121 de \u00e1rea de siembra. La combinaci\u00f3n de aumentos de precios con la introducci\u00f3n de un subsidio adicional asociado al \u00e1rea de cultivo podr\u00eda reducir sustancialmente el umbral de adopci\u00f3n hasta 46 ha a\u00f1o\u22121, lo que hace que la tecnolog\u00eda sea econ\u00f3micamente viable para una poblaci\u00f3n mucho m\u00e1s amplia de agricultores.", "keywords": ["Agricultural crops", "Water management", "Artificial Neural Network", "Precision agriculture", "Crop modelling", "NDVI", "Spatial modelling", "Machine learning", "Water balance"], "contacts": [{"organization": "Roquette Tenreiro, Tom\u00e1s", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/oai:helvia.uco.es:10396/24059"}, {"rel": "self", "type": "application/geo+json", "title": "oai:helvia.uco.es:10396/24059", "name": "item", "description": "oai:helvia.uco.es:10396/24059", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/oai:helvia.uco.es:10396/24059"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=NDVI&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=NDVI&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=NDVI&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=NDVI&offset=47", "hreflang": "en-US"}], "numberMatched": 47, "numberReturned": 47, "distributedFeatures": [], "timeStamp": "2026-07-26T17:55:59.319689Z"}