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Evapotranspiration (ET) plays a vital role in surface energy balance and water management. Paddy rice is one of the most water-intensive crops and a staple food worldwide; thus, paddy field ET estimation is an impactful task. In this paper, an improved Surface Energy Balance Algorithm for Land (SEBALR) model is proposed to estimate the paddy field ET and is validated in different climate zones; it considers the advection effect and its applicable condition at the pixel scale and requires no ground-measured data. SEBALR contains two core modules: rice growth period detection and ET retrieval. SEBALR accurately monitored the planting and harvest dates, with an average error of less than 9 days, except for a paddy field located in the US, which had adopted dry seeding. The comparison between the estimation results and eddy covariance data shows that SEBALR can provide a more precise ET estimation than the original Surface Energy Balance Algorithm for Land, yielding a root mean squared error (RMSE) of 1.02 mm·d-1, mean relative error (MRE) of 22.97% and Pearson’s correlation (R2) of 0.790. SEBALR successfully monitored paddy field ET from 2001 to 2019 in Nanchang City (NC), China. Yearly ET did not show an apparent change in trend from 2001 to 2019 in NC. SEBALR can be used to monitor the paddy rice growth period and estimate paddy field ET at a regional scale with limited data, providing helpful information for agricultural practices and water management.
Wei et al. (Fri,) studied this question.