Over semi-arid agricultural areas, the surface energy balance and its components are largely dependent on the soil water availability. In such conditions, the land surface temperature (LST) retrieved from the thermal bands has been commonly used to represent the high spatial variability of the surface evaporative fraction and associated fluxes. In contrast, however, the soil moisture (SM) retrieved from microwave data has rarely been used thus far due to the unavailability of high-resolution (field scale) SM products until recent times. Soil evaporation is controlled by the surface SM. Moreover, the surface SM dynamics is temporally related to root zone SM, which provides information about the water status of plants. The aim of this work was to assess the gain in terms of flux estimates when integrating microwave-derived SM data in a thermal-based energy balance model at the field scale. In this study, SM products were derived from three different methodologies: the first approach inverts SM, labeled hereafter as \uffe2\uff80\uff98SMO20\uffe2\uff80\uff99, from the backscattering coefficient and the interferometric coherence derived from Sentinel-1 products in the water cloud model (WCM); the second approach inverts SM from Sentinel-1 and Sentinel-2 data based on machine learning algorithms trained on a synthetic dataset simulated by the WCM noted \uffe2\uff80\uff98SME16\uffe2\uff80\uff99; and the third approach disaggregates the soil moisture active and passive SM at 100 m resolution using Landsat optical/thermal data \uffe2\uff80\uff98SMO19\uffe2\uff80\uff99. These SM products, combined with the Landsat based vegetation index and LST, are integrated simultaneously within an energy balance model (TSEB-SM) to predict the latent (LE) and sensible (H) heat fluxes over two irrigated and rainfed wheat crop sites located in the Haouz Plain in the center of Morocco. H and LE were measured over each site using an eddy covariance system and their values were used to evaluate the potential of TSEB-SM against the classical two source energy balance (TSEB) model solely based on optical/thermal data. Globally, TSEB systematically overestimates LE (mean bias of 100 W/m2) and underestimates H (mean bias of \uffe2\uff88\uff92110 W/m2), while TSEB-SM significantly reduces those biases, regardless of the SM product used as input. This is linked to the parameterization of the Priestley Taylor coefficient, which is set to \uffce\uffb1PT = 1.26 by default in TSEB and adjusted across the season in TSEB-SM. The best performance of TSEB-SM was obtained over the irrigated field using the three retrieved SM products with a mean R2 of 0.72 and 0.92, and a mean RMSE of 31 and 36 W/m2 for LE and H, respectively. This opens up perspectives for applying the TSEB-SM model over extended irrigated agricultural areas to better predict the crop water needs at the field scale.
", "keywords": ["2. Zero hunger", "550", "Science", "Q", "0208 environmental biotechnology", "0207 environmental engineering", "TSEB-SM", "land surface temperature", "02 engineering and technology", "15. Life on land", "surface soil moisture", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "winter wheat", "13. Climate action", "semi-arid region", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "TSEB", "environment", "vegetation index"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/4/727/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/4/727/pdf"}, {"href": "https://doi.org/10.3390/rs13040727"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13040727", "name": "item", "description": "10.3390/rs13040727", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13040727"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-17T00:00:00Z"}}, {"id": "10.3390/rs13142667", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:08Z", "type": "Journal Article", "created": "2021-07-07", "title": "Irrigation amounts and timing retrieval through data assimilation of surface soil moisture into the FAO-56 approach in the South Mediterranean region", "description": "Agricultural water use represents more than 70% of the world\uffe2\uff80\uff99s freshwater through irrigation water inputs that are poorly known at the field scale. Irrigation monitoring is thus an important issue for optimizing water use in particular with regards to the water scarcity that the semi-arid regions are already facing. In this context, the aim of this study is to develop and evaluate a new approach to predict seasonal to daily irrigation timing and amounts at the field scale. The method is based on surface soil moisture (SSM) data assimilated into a simple land surface (FAO-56) model through a particle filter technique based on an ensemble of irrigation scenarios. The approach is implemented in three steps. First, synthetic experiments are designed to assess the impact of the frequency of observation, the errors on SSM and the a priori constraints on the irrigation scenarios for different irrigation techniques (flooding and drip). In a second step, the method is evaluated using in situ SSM measurements with different revisit times (3, 6 and 12 days) to mimic the available SSM product derived from remote sensing observation. Finally, SSM estimates from Sentinel-1 are used. Data are collected on different wheat fields grown in Morocco, for both flood and drip irrigation techniques in addition to rainfed fields used for an indirect evaluation of the method performance. Using in situ data, accurate results are obtained. With an observation every 6 days to mimic the Sentinel-1 revisit time, the seasonal amounts are retrieved with R > 0.98, RMSE < 32 mm and bias < 2.5 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation as more than 70% of the detected irrigation events have a time difference from actual irrigation events shorter than 4 days. Over the drip irrigated fields, the statistical metrics are R = 0.74, RMSE = 24.8 mm and bias = 2.3 mm for irrigation amounts cumulated over 15 days. When using SSM products derived from Sentinel-1 data, the statistical metrics on 15-day cumulated amounts slightly dropped to R = 0.64, RMSE = 28.7 mm and bias = 1.9 mm. The metrics on the seasonal amount retrievals are close to assimilating in situ observations with R = 0.99, RMSE = 33.5 mm and bias = \uffe2\uff88\uff9218.8 mm. Finally, among four rainfed seasons, only one false event was detected. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas.
", "keywords": ["550", "Science", "particle filters", "0207 environmental engineering", "02 engineering and technology", "01 natural sciences", "irrigation timing and amounts", "Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture irrigation timing and amounts", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "semi-arid Mediterranean region", "data assimilation", "0105 earth and related environmental sciences", "FAO-56", "2. Zero hunger", "Q", "15. Life on land", "surface soil moisture", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "winter wheat", "irrigation timing and amounts; surface soil moisture; data assimilation; particle filters; FAO-56; Sentinel-1; semi-arid Mediterranean region; winter wheat", "13. Climate action", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "ZONE MEDITERRANEENNE", "Sentinel-1", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/14/2667/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/14/2667/pdf"}, {"href": "https://doi.org/10.3390/rs13142667"}, {"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/rs13142667", "name": "item", "description": "10.3390/rs13142667", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13142667"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-07T00:00:00Z"}}, {"id": "10.3390/rs12101621", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:07Z", "type": "Journal Article", "created": "2020-05-20", "title": "Potential for the Detection of Irrigation Events on Maize Plots Using Sentinel-1 Soil Moisture Products", "description": "Although the real timing and flow rates used for crop irrigation are controlled at the scale of individual plots by the irrigator, they are not generally known by the farm upper management. This information is nevertheless essential, not only to compute the water balance of irrigated plots and to schedule irrigation, but also for the management of water resources at regional scales. The aim of the present study was to detect irrigation timing using time series of surface soil moisture (SSM) derived from Sentinel-1 radar observations. The method consisted of assessing the direction of change of surface soil moisture (SSM) between observations and a water balance model, and to use thresholds to be calibrated. The performance of the approach was assessed on the F-score quantifying the accuracy of the irrigation event detections and ranging from 0 (none of the irrigation timing is correct) to 100 (perfect irrigation detection). The study focused on five irrigated and one rainfed plot of maize in South-West France, where the approach was tested using in situ measurements and surface soil moisture (SSM) maps derived from Sentinel-1 radar data. The use of in situ data showed that (1) irrigation timing was detected with a good accuracy (F-score in the range (80\uffe2\uff80\uff9383) for all plots) and (2) the optimal revisit time between two SSM observations was 2\uffe2\uff80\uff934 days. The higher uncertainties of microwave SSM products, especially when the crop is well developed (normalized difference of vegetation index (NDVI) > 0.7), degraded the score (F-score = 69), but various possibilities of improvement were discussed. This paper opens perspectives for the irrigation detection at the plot scale over large areas and thus for the improvement of irrigation water management.
", "keywords": ["[SDE] Environmental Sciences", "FAO-56", "2. Zero hunger", "550", "Science", "Q", "sprinkler; corn; France; irrigation timing; FAO-56; surface soil moisture; SAR", "15. Life on land", "surface soil moisture", "630", "6. Clean water", "surface soil", "corn", "moisture", "irrigation timing", "[SDE]Environmental Sciences", "[SDE.ES] Environmental Sciences/Environment and Society", "sprinkler", "France", "[SDE.ES]Environmental Sciences/Environment and Society", "SAR"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/10/1621/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/10/1621/pdf"}, {"href": "https://doi.org/10.3390/rs12101621"}, {"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/rs12101621", "name": "item", "description": "10.3390/rs12101621", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12101621"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-05-19T00:00:00Z"}}, {"id": "10.3390/rs14010167", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-25T16:21:08Z", "type": "Journal Article", "created": "2022-01-10", "title": "Disaggregation of SMAP Soil Moisture at 20 m Resolution: Validation and Sub-Field Scale Analysis", "description": "This paper introduces a modified version of the DisPATCh (Disaggregation based on Physical And Theoretical scale Change) algorithm to disaggregate an SMAP surface soil moisture (SSM) product at a 20 m spatial resolution, through the use of sharpened Sentinel-3 land surface temperature (LST) data. Using sharpened LST as a high resolution proxy of SSM is a novel approach that needs to be validated and can be employed in a variety of applications that currently lack in a product with a similar high spatio-temporal resolution. The proposed high resolution SSM product was validated against available in situ data for two different fields, and it was also compared with two coarser DisPATCh products produced, disaggregating SMAP through the use of an LST at 1 km from Sentinel-3 and MODIS. From the correlation between in situ data and disaggregated SSM products, a general improvement was found in terms of Pearson\uffe2\uff80\uff99s correlation coefficient (R) for the proposed high resolution product with respect to the two products at 1 km. For the first field analyzed, R was equal to 0.47 when considering the 20 m product, an improvement compared to the 0.28 and 0.39 for the 1 km products. The improvement was especially noticeable during the summer season, in which it was only possible to successfully capture field-specific irrigation practices at the 20 m resolution. For the second field, R was 0.31 for the 20 m product, also an improvement compared to the 0.21 and 0.23 for the 1 km product. Additionally, the new product was able to depict SSM spatial variability at a sub-field scale and a validation analysis is also proposed at this scale. The main advantage of the proposed product is its very high spatio-temporal resolution, which opens up new opportunities to apply remotely sensed SSM data in disciplines that require fine spatial scales, such as agriculture and water management.
", "keywords": ["validation", "550", "[SDE.IE]Environmental Sciences/Environmental Engineering", "Science", "Q", "SMAP", "04 agricultural and veterinary sciences", "surface soil moisture", "333", "6. Clean water", "631", "surface soil moisture; disaggregation; DISPATCH; SMAP; validation", "DISPATCH", "disaggregation", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "0401 agriculture", " forestry", " and fisheries", "[SDE.IE] Environmental Sciences/Environmental Engineering", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/1/167/pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/1/167/pdf"}, {"href": "https://doi.org/10.3390/rs14010167"}, {"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/rs14010167", "name": "item", "description": "10.3390/rs14010167", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs14010167"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-31T00:00:00Z"}}, {"id": "10754/627861", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:24:53Z", "type": "Journal Article", "created": "2018-04-24", "title": "Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil", "description": "Radar data have been used to retrieve and monitor the surface soil moisture (SM) changes in various conditions. However, the calibration of radar models whether empirically or physically-based, is still subject to large uncertainties especially at high-spatial resolution. To help calibrate radar-based retrieval approaches to supervising SM at high resolution, this paper presents an innovative synergistic method combining Sentinel-1 (S1) microwave and Landsat-7/8 (L7/8) thermal data. First, the S1 backscatter coefficient was normalized by its maximum and minimum values obtained during 2015\u20132016 agriculture season. Second, the normalized S1 backscatter coefficient was calibrated from reference points provided by a thermal-derived SM proxy named soil evaporative efficiency (SEE, defined as the ratio of actual to potential soil evaporation). SEE was estimated as the radiometric soil temperature normalized by its minimum and maximum values reached in a water-saturated and dry soil, respectively. We estimated both soil temperature endmembers by using a soil energy balance model forced by available meteorological forcing. The proposed approach was evaluated against in situ SM measurements collected over three bare soil fields in a semi-arid region in Morocco and we compared it against a classical approach based on radar data only. The two polarizations VV (vertical transmit and receive) and VH (vertical transmit and horizontal receive) of the S1 data available over the area are tested to analyse the sensitivity of radar signal to SM at high incidence angles (39\u00b0\u201343\u00b0). We found that the VV polarization was better correlated to SM than the VH polarization with a determination coefficient of 0.47 and 0.28, respectively. By combining S1 (VV) and L7/8 data, we reduced the root mean square difference between satellite and in situ SM to 0.03\u202fm3\u202fm\u22123, which is far smaller than 0.16\u202fm3\u202fm\u22123 when using S1 (VV) only.", "keywords": ["550", "[SDE.IE]Environmental Sciences/Environmental Engineering", "Sentinel-1 (A/B)", "near surface soil moisture", "Bare soil", "0211 other engineering and technologies", "Sentinel-1 (AB)", "02 engineering and technology", "15. Life on land", "Landsat-78", "01 natural sciences", "Energy balance modelling", "Near surface soil moisture", "Landsat-7/8", "bare soil", "13. Climate action", "energy balance modelling", "soil evaporation", "[SDE.IE] Environmental Sciences/Environmental Engineering", "Soil evaporation", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://hal.archives-ouvertes.fr/hal-01912888/file/Amazirh%20et%20al_2018%20%281%29.pdf"}, {"href": "https://doi.org/10754/627861"}, {"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": "10754/627861", "name": "item", "description": "10754/627861", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10754/627861"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-06-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Surface+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=Surface+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=Surface+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=Surface+soil+moisture&offset=7", "hreflang": "en-US"}], "numberMatched": 7, "numberReturned": 7, "distributedFeatures": [], "timeStamp": "2026-07-26T01:48:31.606982Z"}