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

Bouchra Ait Hssaine,Nitu Ojha,Salah Er-Raki,Olivier Merlin,Said Khabba,Jamal Ezzahar

doi:10.5194/hess-24-1781-2020

Abstract

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 (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.

Highlights

Evapotranspiration (ET) is a crucial water flux for drought monitoring (Bhattarai et al, 2019; Gerhards et al, 2019; Mallick et al, 2014, 2016; Mallick et al, 2018), water resource management (Madugundu et al, 2017; Tasumi, 2019) and climate simulation (Littell et al, 2016; Molden et al, 2010) in the semi-arid ecosystems
Numerous models based on land surface temperature (LST) data have been developed, such as (i) residual balance methods that consider ET to be the residual term of the energy balance, like TSEB and SEBS, (ii) contextual methods that estimate ET as the potential ET times the evaporative efficiency (Moran et al, 1994) or as the available energy times the evaporative fraction (Merlin et al, 2013; Roerink et al, 2000) and (iii) other categories of models that integrate LST into a water balance model (Olivera-Guerra et al, 2018) or into the Penman–Monteith energy balance (PMEB) equation to directly estimate ET (Amazirh et al, 2017; Mallick et al, 2015, 2018)
The model is based on the original TSEB formalism, meaning that the energy balance for vegetation is the same as in TSEB using the PT formula, the soil evaporation is estimated as a function of soil moisture (SM) using a soil resistance developed by Sellers et al (1992)

Summary

Introduction

Evapotranspiration (ET) is a crucial water flux for drought monitoring (Bhattarai et al, 2019; Gerhards et al, 2019; Mallick et al, 2014, 2016; Mallick et al, 2018), water resource management (Madugundu et al, 2017; Tasumi, 2019) and climate simulation (Littell et al, 2016; Molden et al, 2010) in the semi-arid ecosystems. The spatial modeling has become a dominant means of estimating ET fluxes over regional and continental areas (Anderson et al, 2007; Fisher et al, 2017). In this context, numerous models based on land surface temperature (LST) data have been developed, such as (i) residual balance methods that consider ET to be the residual term of the energy balance, like TSEB (two-source energy balance, Norman et al, 1995) and SEBS (surface energy balance system, Su, 2002), (ii) contextual methods that estimate ET as the potential ET times the evaporative efficiency (Moran et al, 1994) or as the available energy times the evaporative fraction (Merlin et al, 2013; Roerink et al, 2000) and (iii) other categories of models that integrate LST into a water balance model (Olivera-Guerra et al, 2018) or into the Penman–Monteith energy balance (PMEB) equation to directly estimate ET (Amazirh et al, 2017; Mallick et al, 2015, 2018)

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Conclusion