Evapotranspiration (ET) plays a crucial role in the water cycle, influencing hydrology, agriculture, and climate. Capturing its spatial and temporal distribution at broad scales is vital for effective water resource management. This study validates five ET estimation products, using direct measurements from Eddy covariance towers across different crops in Morocco's Haouz plain. The aim is to ascertain each product's performance at the field scale, including their usage over irrigated land. Findings indicate variable performances of these products depending on the crop type and irrigation scheme. Given the variance across spatial and temporal resolutions, and differing irrigation schemes, no single product uniformly excelled, showing the necessity of selecting a suitable product based on specific needs.

Growing agricultural demands for the global population are unlocking the path to developing innovative solutions for efficient water management. Herein, an intelligent variable rate irrigation system (fuzzy-VRI) is proposed for decision-making to achieve optimized irrigation in various delimited zones. The proposed system automatically creates irrigation maps for a center pivot irrigation system for a variable rate application of water. Primary inputs are satellite imagery on remotely sensed soil moisture (SSM), soil-adjusted vegetation index (SAVI), canopy temperature (CT), and nitrogen content (NI). The system relates these inputs to set reference values for the rotation speed controllers and individual openings of each central pivot sprinkler valve. The results showed that the system can detect and characterize the spatial variability of the crop and further, the fuzzy logic solved the uncertainties of an irrigation system and defined a control model for high-precision irrigation. The proposed approach is validated through the comparison between the recommended irrigation and actual irrigation at two field sites, and the results showed that the developed approach gives an accurate estimation of irrigation with a reduction in the volume of irrigated water of up to 27% in some cases. Future research should implement the fuzzy-VRI real-time during field trials in order to quantify its effect on irrigation use, yield, and water use efficiency. Note to Practitioners—This work is motivated by the objective of managing irrigation more efficiently. It will be a site-specific irrigation management tool and we proposed a theoretical framework that aims an artificial intelligence approach to automatically create optimal control maps for a center pivot irrigation system. At the heart of this system will be the fuzzy logic, which will define the reference values for the rotation speed controllers and the individual opening of each center pivot sprinkler valve. Currently, there is a lack of these types of systems which ends up generating an increase in demand for more intelligent, automated, and accurate systems. The proposed system will be based on decision-making - whether to apply more or less water - and will use remote sensing data, therefore, the innovative irrigation system will efficiently describe the spatial variability of the crop. The results indicate that edaphoclimatic variables, when well combined with fuzzy logic, can resolve uncertainties and nonlinearities of an irrigation system and define a control model for high precision irrigation. However, it will not always be possible to reduce water consumption, but this technology has many uses to increase farm profitability.

International audience

Food and Agriculture Organization (FAO) dual crop coefficient (FAO-2Kc) is one of the widely used formulation to estimate soil evaporation (E) due to its operationality and simplicity. The FAO-2Kc method could explicitly distinguish the contribution of E and plant transpiration, separately. However, systematic and random uncertainty in E observations still exist. In this vein, this paper attempts to improve FAO-2Kc evapotranspiration estimates through assimilating SMAP-based disaggregated soil moisture (SM) into FAO-2Kc E component via the soil evaporation coefficient. Sequential data assimilation methods (Kalman filter) was used for this purpose, where E is strongly linked to SM especially under arid atmospheric conditions where energy is not the limited factor. The proposed approach is applied over a semi-arid site in central Morocco. Results revealed that the assimilation approach provides better results in term of evapotranspiration by decreasing the root mean square error from 0.98 mm/day to 0.75 mm/day compared to the standard FAO-2Kc.

A precise estimate of surface evapotranspiration (ET) is fundamental in water science for determining the crop water needs and for optimizing water management practices and irrigation regimes. FAO-56 dual crop coefficient (FAO-2Kc) based on a water balance model allows the partitioning between bare soil evaporation (E) and plant transpiration (Tr). However, its performance for estimating the water use efficiency is limited by uncertainties in the modeled evaporation/transpiration partitioning [1], [2] due to its simplicity. This paper aims to improve the accuracy of the ET components, through assimilating remotely sensed data. Remotely sensed soil moisture (SM) and land surface temperature (LST) are simultaneously assimilated into FAO-2Kc. SM was used to improve the E component while LST to update the plant Tr element. SM and LST data were derived from SMAP and Landsat 7/8 remotely sensed observations during the 2015–2016 wheat season, respectively. The standard FAO-2Kc yields an error of 0.98 mm/day with a bias of 0.47 mm/day. Assimilating combined SM and LST into FAO-2Kc leads to an improvement of the ET prediction with an error of 0.73 mm/day compared to eddy correlation measurements.

Surface soil moisture (SM) is an essential component for crop water stress detection and irrigation management. It controls soil evaporation and plant transpiration. SM dynamics is temporally related to root zone soil moisture which is the primary measure of the plant's water status. The aim of this work is to assess the robustness of high-resolution SM product derived from remote sensing on the energy balance based latent and sensible heat fluxes. Radar SM products retrieved from Sentinel-1 data only combined with Landsat Normalized Difference Vegetation index and land surface temperature are used together to feed the energy balance model TSEB-SM to estimate turbulent fluxes. The model estimates have been evaluated against the Eddy-covariance measurements over an irrigated wheat field situated in the Haouz plain in the center of Morocco. The results are very encouraging, with few observed anomalies meanly linked to the retrieved Priestley Taylor coefficient that is affected by SM.

C-band radar observations are well known to have great potentials for the monitoring of crop hydric conditions. Recent studies suggested that the observed difference of backscattering coefficient ($\sigma_{o}$) between ascending and descending pass over tropical forest could be related to the physiological functioning of the trees. Likewise, the water movement within annual crops could lead to a daily cycle of both$\sigma_{o}$and temporal coherence ($\rho$). The objective of this paper is to present the preliminary results of an experiment carried out on a winter wheat field in Morocco that was instrumented with six C-band antennas during 2020 growing season. The preliminary results showed strong daily cycles of$\rho$and$\sigma_{o}$that are analyzed in relation to wind speed, surface soil moisture and evapotranspiration. This work open insights for the monitoring of the crop hydric status using C-band radar data acquired by Sentinel-1 and by potential future radar geostationary missions.

For sustainable irrigation water management as well as ecosystem health monitoring, it is important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data is thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data.

The disaggregation based on physical and theoretical scale change (DISPATCH) algorithm was developed to improve the spatial resolution of Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) using 1 km resolution Moderate resolution Imaging Spectroradiometer (MODIS) data. The main objective of this paper is firstly, to adapt the DISPATCH algorithm to the 100 m resolution Landsat data and secondly, to determine an optimal intermediate spatial resolution (ISR) between the original (40 km) SMOS resolution and the targeted 100 m resolution. It is found that the ISR (set to 1 km, 3 km and 5 km) impacts the accuracy in the sequentially downscaled 100 m resolution SM depending on both the SM heterogeneity present within the spatial extent considered, and the gap between the low to high resolution ratio.

The soil evaporation is under atmospheric conditions non-limited in energy-strongly linked to the near-surface soil moisture sensed by microwave radiometers. This has been the rationale for developing the DisPATCh (Disaggregation based on Physical And Theoretical scale Change) method, which relies on thermal-derived evaporation to improve the spatial resolution of SMOS (Soil Moisture and Ocean Salinity) like data. In practice, the disaggregation scheme estimates the 0-5 cm soil moisture at 1 km resolution by combining 40 km SMOS soil moisture, 1 km resolution MODIS (MODerate resolution Imaging Spectroradiometer) data, and a multi-scale soil evaporation model. This paper provides an overview of 1) the current status and main assumptions of DisPATCh, 2) the DisPATCh-based processor implemented in the Centre Aval de Traitement des Données SMOS (CATDS), and 3) related ongoing research including advanced modeling of soil evaporation and the prospect of coupling thermal- and radar-based soil moisture downscaling approaches.

The SUD-MED project aims at monitoring water resources over Mediterranean regions. As part of the project, this paper presents a method we developed for estimating cereal water requirement. The method consists in driving the simple model developed by the FAO with remotely-sensed data. It was tested on an little area cultivated with wheat in the semi-arid Marrakech plain (Morocco). We use a time series of high spatial resolution images acquired by SPOT-4/HRVIR during the 2001/2002 agricultural season. The method outlines the spatio-temporal patterns of crop cycles. The associated maps of phenological variables and seasonal evapotranspiration appear consistent with regional rainfall and irrigation features. Perspectives of improvement are finally discussed.

Remote sensing and crop models have proved to be useful to monitor vegetation and estimate above ground biomass. In this study, NDVI from VEGETATION, MODIS, and Landsat reflectance data were compared with field measurements. The phenology was inferred and identified the main stages. LAI obtained from reflectance was used with the STICS model to give estimates of grain yield within about 5% of field measurements. No clear relationship was established between the sowing date and yield. Temperature seems to be the most important driver of wheat phenology.