{"type": "FeatureCollection", "features": [{"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": "10.17660/actahortic.2022.1335.46", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:19:42Z", "type": "Journal Article", "created": "2022-04-06", "title": "Mapping deep percolation using remote sensing over an irrigated area in the Haouz plain (Marrakech, Morocco)", "description": "This study aims to estimate the spatial deep percolation (DP) by combining remote sensing data and SAMIR (SAtellite Monitoring of IRrigation) tool. In this study, DP was derived as the residual component of water balance in the root zone. The Indirect computation of water balance requires climate data (reference evapotranspiration (ET0) and rainfall), land cover, crop coefficient derived from normalized difference vegetation index (NDVI), and hydrodynamic soil parameters like soil moisture at field capacity and the wilting point. The main water balance component is evapotranspiration. It is spatialized based on the FAO-56 approach and the relationship between crop coefficient and NDVI. This approach was tested over an irrigated area in the Haouz plain during the agricultural period (2011-2012). The results showed that DP followed water supply fluctuations (sum of rainfall and irrigation provided by the manager, ORMVAH). High DP values are observed during heavy rainfall in March (around 36, 27, and 20 mm) for sugar beet, wheat, and olive trees, respectively. However, from April to June, the vegetation cover was exposed to high water stress for the rest of the season mainly due to the mismatch of water supply.", "keywords": ["remote sensing", "SAMIR", "water balance", "550", "0208 environmental biotechnology", "0207 environmental engineering", "FAO-56 model", "02 engineering and technology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "deep percolation", "environment", "630", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment"]}, "links": [{"href": "https://doi.org/10.17660/actahortic.2022.1335.46"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Acta%20Horticulturae", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.17660/actahortic.2022.1335.46", "name": "item", "description": "10.17660/actahortic.2022.1335.46", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.17660/actahortic.2022.1335.46"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FAO-56+model&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=FAO-56+model&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=FAO-56+model&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FAO-56+model&offset=2", "hreflang": "en-US"}], "numberMatched": 2, "numberReturned": 2, "distributedFeatures": [], "timeStamp": "2026-07-27T10:00:16.784762Z"}