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Thermal-based two-source energy balance modeling is essential to estimate the land evapotranspiration (ET) in a wide range of spatial and temporal scales. However, the use of thermal-derived land surface temperature (LST) is not sufficient to simultaneously constrain both soil and vegetation flux components. Therefore, assumptions (about either soil or vegetation fluxes) are commonly required. To avoid such assumptions, an energy balance model, TSEB-SM, was recently developed by in order to consider the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc) normally used. While TSEB-SM has been successfully tested using in situ measurements, this paper represents its first evaluation in real life using 1 km resolution satellite data, comprised of MODIS (MODerate resolution Imaging Spectroradiometer) for LST and fc data and 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations. The approach is applied during a 4-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco. The field used was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural seasons, while it remained unploughed (as bare soil) during the 2015–2016 (B1) agricultural season. The classical TSEB model, which is driven only by LST and fc data, significantly overestimates latent heat fluxes (LE) and underestimates sensible heat fluxes (H) for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W m -2 for LE and -104 , -71 , -128 and -181 W m -2 for H , for S1, S2, S3 and B1, respectively. Meanwhile, when using TSEB-SM (SM and LST combined data), these errors are significantly reduced, resulting in mean bias values estimated as 39, 4, 7 and 62 W m -2 for LE and -10 , 24, 7, and -59 W m -2 for H , for S1, S2, S3 and B1, respectively. Consequently, this finding confirms again the robustness of the TSEB-SM in estimating latent/sensible heat fluxes at a large scale by using readily available satellite data. In addition, the TSEB-SM approach has the original feature to allow for calibration of its main parameters (soil resistance and Priestley–Taylor coefficient) from satellite data uniquely, without relying either on in situ measurements or on a priori parameter values. [ABSTRACT FROM AUTHOR]

Due to their image-based nature, "contextual" approaches are very attractive to estimate evapotranspiration (ET) from remotely-sensed land surface temperature (LST) data. Their application is however limited to highly heterogeneous areas where the soil and vegetation temperature endmembers (Tends) can be observed at the thermal sensor resolution. This paper aims to develop a simple theoretical approach to estimate Tends independently from LST images. Soil Tends are simulated by a soil energy balance model forced by meteorological data. Vegetation Tends are obtained from soil Tends and air temperature. Model-derived soil Tends are first evaluated with in situ measurements made over an irrigated area in Morocco. The root mean square difference (RMSD) between modeled and ground-based soil Tends is estimated as 2.4 °C. Model-derived soil Tends are next compared with the soil Tends retrieved from 90-m resolution ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data collected over two irrigated areas in Mexico and Spain. Such a comparison reveals a strong consistency between model-derived and high-resolution image-based soil Tends. A recent contextual ET model (SEB-1S) is then applied to 90-m resolution and to 1-km resolution (aggregated) ASTER data using the model-derived or image-based Tends as the input. The RMSD between 90-m resolution SEB-1S and in situ ET is estimated as 65 and 82 W⋅m-2, and the RMSD between 1-km resolution SEB-1S and aggregated SEB-1S ET is estimated as 78 and 56 W⋅m-2 for the image-based and model-derived Tends, respectively. In light of the above results, Tends should be estimated a priori when contextual models are applied to low resolution images. Moreover, the consistency over highly heterogeneous areas between model-derived and high-resolution image-based Tends provides a meaningful basis for developing mixed modeling observational approaches. [ABSTRACT FROM AUTHOR]

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 'SMO20', 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 'SME16'; and the third approach disaggregates the soil moisture active and passive SM at 100 m resolution using Landsat optical/thermal data 'SMO19'. 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 −110 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 αPT = 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. [ABSTRACT FROM AUTHOR]

In Morocco, cereal production shows a high interannual variability due to uncertain rainfall and recurrent drought periods that may occur at key phenological stages. In view of the importance of this resource to the country's economy, it is important to better characterize the impact of drought strength and duration on yields in order to better anticipate production at the seasonal scale. While meteorological drought is usually derived from precipitation anomalies, the monitoring of agronomical drought may be more complex because of data availability issues. In this study, drought is assessed through, on one hand, classical indices derived from remote sensing data (Vegetation Condition Index -VCI-, Temperature Condition Index -TCI- and Vegetation Health Index -VHI-) and on the other hand, the outputs of a land data assimilation system (LDAS). NDVI and land surface temperature MODIS products are used to compute VCI, TCI and VHI. The LDAS has been developed by the CNRM/Meteo-France and is composed of the ISBA-A-gs land surface model forced by ERA-5 re-analysis constrained by LAI product and a surface soil moisture derived from scatterometer data. LAI, Transpiration and soil moisture at 20 and 40 cm depth are used. The study period is 2001-2017. The first results obtained on the Settat agricultural province which is the most productive province of cereal in Morocco exhibit a strong correlation between cereal production obtained from agricultural statistics and VCI and VHI during February to March (R=0.8-0.9) corresponding to the critical flowering and grain-filling stages. By contrast, the correlation with TCI was found to be significant during the tillering stage (December-January). TCI is a normalized temperature index computed from the observed extreme values of the time series. It thus represents the anomaly due to air temperature condition. A modified temperature index hereafter called Temperature Hydric Conditions Index (THCI) for which extreme temperature values are computed spatially over each agricultural province at the daily time step is proposed. In order to discard temperature change due to elevation conditions, pixel are filtered using the SRTM MNT. A higher correlation (R=0.87) was observed between THCI and cereal production during the grain maturity stage in April. Finally, outputs from the LDAS appeared very promising as strong correlation were obtained with LAI assimilated in March (R=0.92), with transpiration from February until March (R=0.85-0.9) and for soil moisture at 20 and 40 cm deep at the beginning of crop season (December R=0.86) during emergence. Our current work is focused on the development of empirical models for early forecasting cereal production at the provincial and national scales based on linear and non linear (support vector machine regression) approaches. [ABSTRACT FROM AUTHOR]

A precise estimate of ET is fundamental for determining the crop water needs and subsequently for optimizing water management practices and irrigation regimes. Soil moisture and land surface temperature are the essential components of the hydrological cycle, especially, for controlling soil evaporation, plant transpiration and partition rainwater into infiltration and runoff. In semi-arid regions, a knowledge of the root zone soil moisture represents an important stake for monitoring water resources, because it contributes at a time in detecting the periods of water stress and anticipating the water need of the cultures. For that purpose, the microwave-derived near-surface soil moisture and thermal-derived land surface temperature are integrated simultaneously within a new energy balance model. In practice, both information types are used to retrieve the main parameters of soil evaporation via the soil resistance (rss) and plant transpiration via the Priestly Taylor coefficient (αPT). A two-source energy balance model named TSEB-SM is first derived from the TSEB formalism by explicitly representing soil evaporation using the empirical parameters (a_rss, b_rss) of the relationship between r_ss and soil surface moisture. For the fraction cover fc =<0.5, the model calibration consists of inverting r_ss at Terra and Aqua overpass times. When fc>0.5, the calibration consists in estimating αPT at the daily scale. The procedure is applied over a rainfed wheat field in the Tensift basin, central Morocco. The field was seeded during three wheat seasons (September 2014-Juin 2015 (S1), September 2016-Juin 2017 (S2), and October 2017-May 2018 (S3)), while it had remained under bare soil conditions during the 2015-2016 (B1) agricultural season. The mean retrieved values of soil resistance calculated for the entire study period using satellite data are (7.32, 4.58), which are relatively close to those estimated in Sellers et al. (1992) (8.2, 4.3). The calibrated daily αPT for S1 and S2 ranges between 0 and 1.38, and is mainly dependent on the rainfall distribution along the agricultural season. Moreover, the retrieved αPT shows similar dynamics when using in-situ and satellite data, with a relative error of about 11 and 19 % for S1 and S2 respectively. The calibrated daily αPT for S3 remains at a mostly constant value (~0.7) throughout the study period, and a significant relative difference of about 34% is obtained when comparing results using satellite and in-situ data. For all four seasons TSEB tends to significantly overestimate latent heat fluxes. The overall mean bias is 119, 181, 94 and 128 W/m2 for S1, B1, S2 and S3 respectively, when using satellite data. The errors are much reduced when using TSEB-SM witha mean bias of 39, 62, 4 and 7 W/m2 for S1, B1, S2 and S3 respectively. The analysis of the retrieved (arss, brss) and αPT variabilities using satellite data indicate the robustness of the approach, which combines microwave and optical/thermal data to retrieve a water stress indicator at the daily time scale. [ABSTRACT FROM AUTHOR]