Estimating evapotranspiration (ET) beyond the local or point scale is essential for many water-related studies. By exploiting the relationship between surface biophysical parameters and thermal emission, continuous ET at such larger spatial scales can be obtained. In this study, we applied the EVASPA tool, which provides an ensemble of ET estimates, over southern France. This was done using MODIS data, including Land Surface Temperature, NDVI, and albedo, resulting in 243 ET estimates. Initial evaluations using in-situ flux data yielded reasonable results even when a simple average was used, with a broad absolute and performance range between the member estimates being observed. Additionally, our uncertainty analyses indicated that ensemble-based contextual modelling can provide sufficient spread for enhanced flux simulations. As EVASPA is intended for operational use, this work aims to guide the establishment of an optimal weighting criteria for the members to improve ET estimates.

The European ECOSTRESS Hub (EEH) funded by European Space Agency targets at generating land surface temperature (LST), evapotranspiration (ET) and gross primary productivity (GPP) from the high-resolution ECOSTRESS observations. In Phase 1 (2020-2022), EEH LST obtained using the split-window and temperature and emissivity separation algorithms achieved good accuracy with an overall RMSE around 2 K. Evaluation of three ET estimates using different models, namely the Surface Energy Balance System (SEBS), Two Source Energy Balance (TSEB) parametric models, and the non-parametric Surface Temperature Initiated Closure (STIC) model, indicated that STIC ET had the highest accuracy (RMSE of ~70 W m-2). In Phase 2 (2023-2026), the surface energy balance will be coupled with photosynthesis through canopy-stomatal conductance. Overall, EEH is promising to advance the science of terrestrial ecosystem processes and facilitate the preparation for the future high-resolution thermal missions.

Inversion of the surface-energy-budget (SEB) for evapotranspiration using directional temperatures is susceptible to thermal-radiation-directionality. Here, we apply the SPARSE/SPARSE4 schemes for directionality analyses using ‘synthetic references’ from SCOPE: experiments from sparsely-vegetated/water-stressed to fully-vegetated/sufficiently-watered canopies. The SCOPE simulations were then used to invert/evaluate the aforementioned SEB methods. Since, oblique-versus-nadir-based retrievals should ideally be equivalent, analyses to check the angular retrieval consistency were performed. Within sparsely/densely vegetated surfaces, where anisotropy is usually low due to uniformity, little oblique-nadir retrieval inconsistencies were observed. Relatively larger inconsistencies were however observed at intermediate LAIs. With regard to other characteristics, the incoming-radiation (particularly around noon) appeared to be the main driver of the observed inconsistencies, with wind speed and vegetation water status also contributing to some of the mismatches. Influences from the soil water status were mostly in sparsely-vegetated surface experiments. SPARSE4, which discriminates illuminated from shaded elements, was nonetheless observed to reduce the retrieval inconsistencies in most of the scenarios (especially with increasing view and canopy-cover).

Increasing need for fresh water resources, particularly in irrigation, makes it imperative to develop tools to monitor and improve water use efficiency at field, regional and national levels. Satellite observations are highly suitable for this task and for this reason FAO, the custodian agency of Sustainable Development Goals Indicators 6.4.1 and 6.4.2, developed the WaPOR portal through which it distributes satellite-based (Terra and Aqua, PROBA-V and Landsat) evapotranspiration (ET) maps. The aim of this study is to evaluate the suitability of using Copernicus data (Sentinel-2 and Sentinel-3 observations and ERA5 meteorological model) to produce high-resolution, national-scale ET maps during the evolution of WaPOR portal. Results indicate that Copernicus-based maps show generally similar ET patterns to WaPOR maps across climatic and land-use gradients while providing more accurate and detailed field-level estimates compared to MODIS-based WaPOR maps.

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.

Identifying the irrigated areas is essential for waters managers who are in charge of distributing this resource over a large scale. The monitoring of water soil content and irrigation is a powerful tool for water resource management. The potential of Sentinel-1 (S1) and Sentinel-2 (S2) data for estimating the soil moisture and irrigation is studied over covered surfaces. An inversion algorithm of the Water Cloud Model (WCM) was developed after calibrating and validating the model over the Kairouan plain, a semi-arid region in Tunisia. The aim is to restitute soil moisture over the whole region. The developed algorithm used a synergy between S1, radar data in VV polarization, and NDVI derived from S2 optical data at high spatial resolution. The results showed good accuracy between retrieved and measured soil moisture with a Root Mean Square Error (RMSE) lower than 6 vol.%. Then, the resulting soil moisture maps were used for irrigation mapping. The process used a combination of Support Vector Machine (SVM) and Decision Tree classifications to distinguish between irrigated and non-irrigated agricultural fields. Results from the annual irrigation map show that the overall accuracy on the classification is about 77%.

The monitoring of the water cycle at the Earth surface which tightly interacts with the climate change processes as well as a number of practical applications (agriculture, soil and water quality assessment, irrigation and water resource management, etc…) requires surface temperature measurements at local scale. Such is the goal of the Indian-French high spatio-temporal TRISHNA mission (Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment). The scientific objectives of the mission and research work conducted to consolidate the mission specifications are presented. Progress in modelling of surface fluxes is then discussed. The main specifications of the mission such as the revisit, the spatial resolution, the overpass time, the spectral bands and the orbit are analyzed and justified. The resulting baseline of the mission is given.

Evapotranspiration (ET) can be mapped using thermal infrared and spectral reflectance data. Various ET models have been developed but there was no competitive evaluation of them over a large range of situations. Ensemble model averaging is a tool that can be used for deriving ET from multi-model simulations. In this study, we used bayesian model averaging, which consists in weighting each model according to their performances when deriving the ensemble average. It was applied to the monitoring of ET over a saltmarsh scrub area in South France from MODIS data. ET monitoring was improved ( RMSE=0.57 mm d-1) when using a weighted averaging procedure as compared to the performances of a simple average or to the performances of each individual model.

The CoSMOS (Campaign for validating the Operation of the Soil Moisture and Ocean Salinity mission) campaign was conducted during November of 2005 in the Goulburn River Catchment, in SE Australia. The main objective of CoSMOS was to obtain a series of L-band measurements from the air in order to validate the L-band emission model that will be used by the SMOS (Soil Moisture and Ocean Salinity) ground segment processor. In addition, the campaign was designed to investigate open questions including the sun-glint effect over land, the application of polarimetric measurements over land, and to clarify the importance of dew and interception for soil moisture retrievals. This paper summarises the campaign activities, and presents progress on the analysis of the CoSMOS data set.

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.

The SUD-MED project aims at monitoring water resources over Mediterranean semi-arid regions. As part of the project, this paper presents a method we developed for the monitoring of cereal evapotranspiration and water supply. The method is based on the STICS crop model. The model is first calibrated and evaluated at field scale from an experiment that has taken place in the Yaqui Valley (Mexico). The performance of the model was found satisfactory on this semi-arid area. The calibration procedure is then applied using eight images acquired by the SPOT-5/HRVIR sensor during the 2001/2002 agricultural season. The test site is located in the Haouz plain surrounding Marrakech (Morocco). The time series of satellite data allows us to furnish some key-parameters of the STICS model. The map of seasonal evapotranspiration appears consistent with the drought observed during the 2001/2002 agricultural season.

This paper presents a new approach to aggregate spatially distributed land surface heat fluxes and landscape characteristics, adopting a probabilistic point of view and using remote sensing data. This method is tested in the Marrakech plain (Morocco), with different remote sensing sensors, and different probabilistic approaches, in order to reach the best evaluation of large scale heat fluxes and characteristics probability distributions.

This study is part of the SUDMED project from IRD (Research Institute for Development), CESBIO, University of Marrakech and Moroccan administrations in charge of agriculture, forestry and water management. The objective of this project is the hydro-ecological modeling of hydrological resources on the Marrakech region. In this context, it is important to characterize the snow cover and the melting dynamics as it is the main water source for the plain. Evaluate the snow cover using satellite images at a short temporal scale is a first step to estimate water quantity available during the melting season, in spring. The objective of this study is to calculate the snow cover area and to follow its evolution during the winter season using both high and low satellite imagery. For this purpose, several snow cover indices (SCI) calculated from low resolution images SPOT/VEGETATION (pixel size of 1 km/sup 2/) have been compared with the snow cover percentage (SCP) calculated derived from high spatial resolution sensors (in particular LandSat-TM). Particular attention was given to the classification procedure and the co-registration problem. Then, pixel-based regressions between SCP and SCI have been calibrated for several dates (when both VEGETATION and TM images were available) with the objective of finding the most robust relationship. The previous relation is applied to a time series of 30 SPOT/VEGETATION images acquired from December 1998 to May 1999. The dynamics of snow cover is compared with rainfalls and flow chronicles observed on three mountainous watersheds. The main interest of this study is to show that low resolution precision can be easily improved using high resolution imagery to obtain reliable quantitative information on snow cover. This last information is an important input of snowmelt runoff models.

This paper presents a methodology to interpret and then assimilate the SMOS surface soil moisture data into a modelling framework. This methodology is designed for multiscale applications in hydrology.

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.