Modelling high-resolution Evapotranspiration in fragmented croplands from the constellation of Sentinels

Chintala, Syam and Harmya, T S and Kambhammettu, B.V.N.P. and et al, . (2022) Modelling high-resolution Evapotranspiration in fragmented croplands from the constellation of Sentinels. Remote Sensing Applications: Society and Environment, 26. ISSN 2352-9385

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Abstract

The Sentinels for Evapotranspiration (Sen-ET) developed by the European Space Agency (ESA) provides a synoptic representation of actual ET (ETa) and other land-surface energy fluxes at high spatial (20 m) and temporal (daily) resolutions by the fusion of Sentinel-2 and Sentinel-3 datasets. However, the non-availability of optical sensor enabled Sentinel-2 images during the periods of high cloud cover such as monsoon limits the perennial use of Sen-ET. Here, we propose an alternate method to estimate ETa fluxes using locally observed weather data and the polarimetric parameters derived from Sentinel-1 with an application to fragmented, heterogeneous croplands. At first, the Sen-ET modelling approach with cloud-free datasets is validated using eddy covariance (EC) flux datasets (R2 = 0.74). Sen-ET derived ETa fluxes were used to develop crop coefficient (Kc) curves by considering reference ET (ET0) obtained from the weather data (Monteith, 1965b). Functional relations were then established between Sentinel-1 derived polarimetric parameters and Kc considering cloud-free datasets, and further translated to the periods of high cloud cover. We observed a strong dependency of VH on Kc for sugarcane and vegetables, and radar vegetation index (RVI) on Kc for rice (Rveg2=0.55,Rs.c.2=0.61,Rrice.2=0.51). The performance of Sentinel-1 derived ETa estimates were further validated with EC fluxes and found to be high during non-monsoon period (R2 = 0.74, RMSE = 1.25) than in monsoon period (R2 = 0.63, RMSE = 0.82). Our results indicate the suitability of Sentinel-1 in estimating ETa fluxes during the periods of high cloud cover for effective irrigation management. © 2022 Elsevier B.V.

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