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Open AccessArticle

On the Utility of High-Resolution Soil Moisture Data for Better Constraining Thermal-Based Energy Balance over Three Semi-Arid Agricultural Areas

1
CRSA, Mohammed VI Polytechnic University UM6P, 43150 Ben Guerir, Morocco
2
CESBIO (Centre d’Études Spatiales de la BIOsphère), University of Toulouse, CNES/CNRS/INRAE/IRD/UPS, 18 Avenue Edouard Belin, 31401 Toulouse, France
3
ProcEDE, Department of Applied Physics, Faculty of Sciences and technologies, Cadi Ayyad University, 40000 Marrakech, Morocco
4
LMFE, Department of Physics, Faculty of Sciences Semlalia, Cadi Ayyad University, 40000 Marrakech, Morocco
5
MISCOM, National School of Applied Sciences, Cadi Ayyad University, 46000 Safi, Morocco
*
Author to whom correspondence should be addressed.
Academic Editor: Patricia de Rosnay
Remote Sens. 2021, 13(4), 727; https://doi.org/10.3390/rs13040727
Received: 11 December 2020 / Revised: 29 January 2021 / Accepted: 29 January 2021 / Published: 17 February 2021
(This article belongs to the Special Issue Remote Sensing in Irrigated Crop Water Stress Assessment)
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. View Full-Text
Keywords: TSEB; TSEB-SM; surface soil moisture; land surface temperature; vegetation index; winter wheat; semi-arid region TSEB; TSEB-SM; surface soil moisture; land surface temperature; vegetation index; winter wheat; semi-arid region
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MDPI and ACS Style

Ait Hssaine, B.; Chehbouni, A.; Er-Raki, S.; Khabba, S.; Ezzahar, J.; Ouaadi, N.; Ojha, N.; Rivalland, V.; Merlin, O. On the Utility of High-Resolution Soil Moisture Data for Better Constraining Thermal-Based Energy Balance over Three Semi-Arid Agricultural Areas. Remote Sens. 2021, 13, 727. https://doi.org/10.3390/rs13040727

AMA Style

Ait Hssaine B, Chehbouni A, Er-Raki S, Khabba S, Ezzahar J, Ouaadi N, Ojha N, Rivalland V, Merlin O. On the Utility of High-Resolution Soil Moisture Data for Better Constraining Thermal-Based Energy Balance over Three Semi-Arid Agricultural Areas. Remote Sensing. 2021; 13(4):727. https://doi.org/10.3390/rs13040727

Chicago/Turabian Style

Ait Hssaine, Bouchra; Chehbouni, Abdelghani; Er-Raki, Salah; Khabba, Said; Ezzahar, Jamal; Ouaadi, Nadia; Ojha, Nitu; Rivalland, Vincent; Merlin, Olivier. 2021. "On the Utility of High-Resolution Soil Moisture Data for Better Constraining Thermal-Based Energy Balance over Three Semi-Arid Agricultural Areas" Remote Sens. 13, no. 4: 727. https://doi.org/10.3390/rs13040727

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