Publications

Hssaine, BA; Merlin, O; Ezzahar, J; Ojha, N; Er-Raki, S; Khabba, S (2020). An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data. HYDROLOGY AND EARTH SYSTEM SCIENCES, 24(4), 1781-1803.

Abstract
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 Ait Hssaine et al. (2018b) in order to consider the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (f(c)) 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 f(c) 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 f(c) 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.

DOI:
10.5194/hess-24-1781-2020

ISSN:
1027-5606