Abstract
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.
Highlights
Mapping evapotranspiration (ET) is of paramount importance in water resources management [1] as it is the most important component that controls the energy and water balance [2]
The DisPATCh algorithm is based on the contextual methodology, which relies on determining the wet and dry temperatures within each SMAP pixel, and the soil evaporative efficiency (SEE) used for downscaling soil moisture (SM)
Over rainfed areas, SM is relatively uniform over large areas as it mostly depends on large scale rainfall events, which can affect the temperature endmembers calculation and the SMO19 for the Sidi Rahal site, as reported by Ojha et al (2019)
Summary
Mapping evapotranspiration (ET) is of paramount importance in water resources management [1] as it is the most important component that controls the energy and water balance [2]. A spatialized estimation of ET can help manage the overall water resources in a sustainable manner. Remote sensing is the only viable technique that can provide spatially explicit ET using energy balance methods and has shown great potential for characterizing land surfaces through land use, vegetation coverage, water stress, etc. The normalized difference vegetation index (NDVI) is one of the most commonly used vegetation index to feed ET models [3]. It is computed from red and near infrared bands and can be considered as a useful indicator to characterize vegetation status, and ET rates [4]. NDVI is classically used to derive the vegetation fraction cover (fc), which helps constrain the partition between soil evaporation and plant transpiration [5]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
