{"type": "FeatureCollection", "features": [{"id": "10.1016/j.agwat.2018.06.014", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:15:20Z", "type": "Journal Article", "created": "2018-06-18", "title": "Estimating the water budget components of irrigated crops: Combining the FAO-56 dual crop coefficient with surface temperature and vegetation index data", "description": "Abstract   The FAO-56 dual crop coefficient (FAO-2Kc) model has been extensively used at the field scale to estimate the crop water requirements by means of the simulated evapotranspiration (ET) and its two components evaporation (E) and transpiration (T). Given that the main limitation of FAO-2Kc for operational irrigation management over large areas is the unavailability (over most irrigated areas) of irrigation data, this study investigates the feasibility 1) to constrain the FAO-2Kc ET from LST and VI data, 2) to retrieve irrigation amounts and dates from LST and VI data and 3) to estimate the root-zone soil moisture (RZSM) at the daily scale. In practice, the vegetation and soil temperatures retrieved from LST/VI data are used to estimate the FAO-2Kc vegetation stress coefficient (Ks) and soil evaporation reduction coefficient (Kr), respectively. The modeling and remote sensing combined approach is tested over a wheat crop field in central Morocco, and results are evaluated in terms of ET, irrigation and RZSM estimates. ET is estimated with a RMSE of 0.68\u202fmm day-1 compared to 0.84\u202fmm day-1 for the standard (without using LST data) FAO-2Kc based on tabulated values for the parameters. The total irrigation depth (67\u202fmm) is correctly estimated and is very close to the actual effective irrigation (69.8\u202fmm) applied by the farmer. Daily RZSM is estimated with an R2 value of 0.68 (0.42) and a RMSE value of 0.034 (0.061) m3 m-3 by forcing FAO-2Kc using the retrieved irrigation (from LST-derived estimates and precipitation only). Since spaceborne LST data are currently not available at both high-spatial and high-temporal resolution, a sensitivity analysis is finally undertaken to assess the potential and applicability of the proposed methodology to temporally-sparse thermal data.", "keywords": ["FAO-56", "0106 biological sciences", "2. Zero hunger", "550", "Evapotranspiration", "[SDE.IE]Environmental Sciences/Environmental Engineering", "Root-zone soil moisture", "[SDV.SA.STA] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture", "Root-Zone Soil Moisture", "Surface Temperature", "[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation", "01 natural sciences", "6. Clean water", "Surface temperature", "[SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture", "[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation", "[SDE.IE] Environmental Sciences/Environmental Engineering", "Irrigation", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.agwat.2018.06.014"}, {"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.2018.06.014", "name": "item", "description": "10.1016/j.agwat.2018.06.014", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agwat.2018.06.014"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-09-01T00:00:00Z"}}, {"id": "10.1016/j.rse.2019.111627", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:16:30Z", "type": "Journal Article", "created": "2020-01-10", "title": "Irrigation retrieval from Landsat optical/thermal data integrated into a crop water balance model: A case study over winter wheat fields in a semi-arid region", "description": "Abstract   Monitoring irrigation is essential for an efficient management of water resources in arid and semi-arid regions. We propose to estimate the timing and the amount of irrigation throughout the agricultural season using optical and thermal Landsat-7/8 data. The approach is implemented in four steps: i) partitioning the Landsat land surface temperature (LST) to derive the crop water stress coefficient (Ks), ii) estimating the daily root zone soil moisture (RZSM) from the integration of Landsat-derived Ks into a crop water balance model, iii) retrieving irrigation at the Landsat pixel scale and iv) aggregating pixel-scale irrigation estimates at the crop field scale. The new irrigation retrieval method is tested over three agricultural areas during four seasons and is evaluated over five winter wheat fields under different irrigation techniques (drip, flood and no-irrigation). The model is very accurate for the seasonal accumulated amounts (R ~ 0.95 and RMSE ~ 44\u00a0mm). However, lower agreements with observed irrigations are obtained at the daily scale. To assess the performance of the irrigation retrieval method over a range of time periods, the daily predicted and observed irrigations are cumulated from 1 to 90\u00a0days. Generally, acceptable errors (R\u00a0=\u00a00.52 and RMSE\u00a0=\u00a027\u00a0mm) are obtained for irrigations cumulated over 15\u00a0days and the performance gradually improves by increasing the accumulation period, depicting a strong link to the frequency of Landsat overpasses (16\u00a0days or 8\u00a0days by combining Landsat-7 and -8). Despite the uncertainties in retrieved irrigations at daily to weekly scales, the daily RZSM and evapotranspiration simulated from the retrieved daily irrigations are estimated accurately and are very close to those estimated from actual irrigations. This research demonstrates the utility of high spatial resolution optical and thermal data for estimating irrigation and consequently for better closing the water budget over agricultural areas. We also show that significant improvements can be expected at daily to weekly time scales by reducing the revisit time of high-spatial resolution thermal data, as included in the TRISHNA future mission requirements.", "keywords": ["[SDE] Environmental Sciences", "2. Zero hunger", "550", "Evapotranspiration", "0208 environmental biotechnology", "Root-zone soil moisture", "0207 environmental engineering", "FAO-56 model", "02 engineering and technology", "15. Life on land", "630", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "[SDE]Environmental Sciences", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment", "Irrigation", "Landsat", "Land surface temperature"], "contacts": [{"organization": "Olivera-Guerra, Luis Enrique, Merlin, Olivier, Er-Raki, Salah,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.rse.2019.111627"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing%20of%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.rse.2019.111627", "name": "item", "description": "10.1016/j.rse.2019.111627", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.rse.2019.111627"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-01T00:00:00Z"}}, {"id": "10.3390/rs10111720", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:20:55Z", "type": "Journal Article", "created": "2018-10-31", "title": "Towards Estimating Land Evaporation at Field Scales Using GLEAM", "description": "<p>The evaporation of water from land into the atmosphere is a key component of the hydrological cycle. Accurate estimates of this flux are essential for proper water management and irrigation scheduling. However, continuous and qualitative information on land evaporation is currently not available at the required spatio-temporal scales for agricultural applications and regional-scale water management. Here, we apply the Global Land Evaporation Amsterdam Model (GLEAM) at 100 m spatial resolution and daily time steps to provide estimates of land evaporation over The Netherlands, Flanders, and western Germany for the period 2013\uffe2\uff80\uff932017. By making extensive use of microwave-based geophysical observations, we are able to provide data under all weather conditions. The soil moisture estimates from GLEAM at high resolution compare well with in situ measurements of surface soil moisture, resulting in a median temporal correlation coefficient of 0.76 across 29 sites. Estimates of terrestrial evaporation are also evaluated using in situ eddy-covariance measurements from five sites, and compared to estimates from the coarse-scale GLEAM v3.2b, land evaporation from the Satellite Application Facility on Land Surface Analysis (LSA-SAF), and reference grass evaporation based on Makkink\uffe2\uff80\uff99s equation. All datasets compare similarly with in situ measurements and differences in the temporal statistics are small, with correlation coefficients against in situ data ranging from 0.65 to 0.95, depending on the site. Evaporation estimates from GLEAM-HR are typically bounded by the high values of the Makkink evaporation and the low values from LSA-SAF. While GLEAM-HR and LSA-SAF show the highest spatial detail, their geographical patterns diverge strongly due to differences in model assumptions, model parameterizations, and forcing data. The separate consideration of rainfall interception loss by tall vegetation in GLEAM-HR is a key cause of this divergence: while LSA-SAF reports maximum annual evaporation volumes in the Green Heart of The Netherlands, an area dominated by shrubs and grasses, GLEAM-HR shows its maximum in the national parks of the Veluwe and Heuvelrug, both densely-forested regions where rainfall interception loss is a dominant process. The pioneering dataset presented here is unique in that it provides observational-based estimates at high resolution under all weather conditions, and represents a viable alternative to traditional visible and infrared models to retrieve evaporation at field scales.</p>", "keywords": ["microwave remote sensing", "EVAPOTRANSPIRATION", "WACMOS-ET PROJECT", "Science", "FLUXNET", "Q", "LSA-SAF", "15. Life on land", "01 natural sciences", "6. Clean water", "MODEL", "CARBON", "VARIABILITY", "terrestrial evaporation", "root-zone soil moisture", "13. Climate action", "Earth and Environmental Sciences", "SURFACE EVAPORATION", "GLOBAL DATABASE", "WATER", "SOIL-MOISTURE RETRIEVALS", "terrestrial evaporation; root-zone soil moisture; microwave remote sensing; GLEAM; LSA-SAF", "GLEAM", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/10/11/1720/pdf"}, {"href": "https://doi.org/10.3390/rs10111720"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs10111720", "name": "item", "description": "10.3390/rs10111720", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs10111720"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-10-31T00:00:00Z"}}, {"id": "2809041101", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:25:49Z", "type": "Journal Article", "created": "2018-06-18", "title": "Estimating the water budget components of irrigated crops: Combining the FAO-56 dual crop coefficient with surface temperature and vegetation index data", "description": "Abstract   The FAO-56 dual crop coefficient (FAO-2Kc) model has been extensively used at the field scale to estimate the crop water requirements by means of the simulated evapotranspiration (ET) and its two components evaporation (E) and transpiration (T). Given that the main limitation of FAO-2Kc for operational irrigation management over large areas is the unavailability (over most irrigated areas) of irrigation data, this study investigates the feasibility 1) to constrain the FAO-2Kc ET from LST and VI data, 2) to retrieve irrigation amounts and dates from LST and VI data and 3) to estimate the root-zone soil moisture (RZSM) at the daily scale. In practice, the vegetation and soil temperatures retrieved from LST/VI data are used to estimate the FAO-2Kc vegetation stress coefficient (Ks) and soil evaporation reduction coefficient (Kr), respectively. The modeling and remote sensing combined approach is tested over a wheat crop field in central Morocco, and results are evaluated in terms of ET, irrigation and RZSM estimates. ET is estimated with a RMSE of 0.68\u202fmm day-1 compared to 0.84\u202fmm day-1 for the standard (without using LST data) FAO-2Kc based on tabulated values for the parameters. The total irrigation depth (67\u202fmm) is correctly estimated and is very close to the actual effective irrigation (69.8\u202fmm) applied by the farmer. Daily RZSM is estimated with an R2 value of 0.68 (0.42) and a RMSE value of 0.034 (0.061) m3 m-3 by forcing FAO-2Kc using the retrieved irrigation (from LST-derived estimates and precipitation only). Since spaceborne LST data are currently not available at both high-spatial and high-temporal resolution, a sensitivity analysis is finally undertaken to assess the potential and applicability of the proposed methodology to temporally-sparse thermal data.", "keywords": ["FAO-56", "0106 biological sciences", "2. Zero hunger", "550", "Evapotranspiration", "[SDE.IE]Environmental Sciences/Environmental Engineering", "Root-zone soil moisture", "[SDV.SA.STA] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture", "Root-Zone Soil Moisture", "Surface Temperature", "[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation", "01 natural sciences", "6. Clean water", "Surface temperature", "[SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture", "[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation", "[SDE.IE] Environmental Sciences/Environmental Engineering", "Irrigation", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/2809041101"}, {"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": "2809041101", "name": "item", "description": "2809041101", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2809041101"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-09-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Root-Zone+Soil+Moisture&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=Root-Zone+Soil+Moisture&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=Root-Zone+Soil+Moisture&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Root-Zone+Soil+Moisture&offset=4", "hreflang": "en-US"}], "numberMatched": 4, "numberReturned": 4, "distributedFeatures": [], "timeStamp": "2026-07-27T16:06:24.516807Z"}