Radiation estimation and crop growth trajectory reconstruction by novel algorithms improve MOD16 evapotranspiration predictability for global multi-site paddy rice ecosystems

Reliable estimations in evapotranspiration (ET) of paddy rice ecosystems by satellite products are critical because of their important roles in regional hydrological processes and climate change. However, the NASA MODIS ET products (MOD16A2) and its derivatives do not have good correlations with all global paddy rice ET observations. In this research, MOD16 model sensitivity analyses and parameter optimization strategies were conducted in order to solve the problem. Results suggested that underestimation of daily net radiation (R-n) in overcast conditions and less satisfactory reconstruction of field-scale leaf area index (LAI) growth trajectory from the start date of field flooding and transplanting (FFTD) to the end of growing seasons by MODIS coarse vegetation index were identified as two major causes. A Light and Temperature-Driven Growth model and a Phenology-based LAI temporal Smoothing method fusion algorithm (LTDG_PhenoS) and an improved R-n estimation method were introducted and evaluated in paddy rice fields in South Korea, Japan, China, Philippines, India, Spain, Italy, and the USA from 2002 to 2019. The LTDG_PhenoS algorithm considers Landsat and MODIS EVI observations and meteorological data as input variables and 30-m LAI daily time series as outcomes. Introducing the global cloudy index algorithm resulted in improved estimations of daily R(n )under all-sky conditions, with a significant decrease of root mean square error (RMSE) from 1.87 to 1.11 MJ m(-2) day(-1). The LTDG_PhenoS algorithm well reconstructed crop LAI growth dynamics from the FFTD to the end of rice growing seasons, with a substantial decline of RMSE from 1.49 to 0.27 m(2)/m(-2). The FFTD estimations by the LTDG_PhenoS algorithm had an R-2 of 0.97 and a small RMSE of less than 12-days. Daily ET rates estimated by novel algorithms had a substantial decline in RMSE from 2.88 to 0.90 mm day (-1).

Automated summary

Reliable estimations of evapotranspiration in paddy rice ecosystems are crucial for regional hydrological processes and climate change. However, current satellite products do not have good correlations with global paddy rice ET observations. This research conducted sensitivity analyses and optimization strategies to address this issue. The introduction of a new algorithm, LTDG_PhenoS, improved the estimation accuracy by considering multiple factors and resulted in significant decreases in RMSE.