Reconstruction of remotely sensed daily evapotranspiration data in cloudy-sky conditions

Abstract:

The unavailability of thermal infrared satellite observations in cloudy conditions has limited the spatial distribution and temporal coverage of remotely sensed evapotranspiration (ET) data. As a result, a number of approaches have been developed to reconstruct remotely sensed daily ET data in cloudy-sky conditions. Despite the wide application of these approaches, no work has been conducted to compare their performance over a wide variety of climatic and vegetative conditions. In this study, three commonly used ET reconstruction approaches, namely reference ET interpolation (EToF), variational data assimilation (VDA), and land surface temperature reconstruction (LSTR), are used to obtain daily ET data under cloudy conditions.The abovementioned three approaches are applied to the Heihe River Basin (HRB) in the northwestern China during the growing season in 2015. The HRB covers an area of approximately 1,432,000 km² and contains a wide variety of land covers such as alpine meadow, cropland, riparian forest and desert. The ET calculated from the large rapture scintillometer measurements in combined with flux towers observations over the grassland, cropland, and riparian forest are used to evaluate performance of the abovementioned methods. The results show that the EToF approaches underestimate ET in cloudy days at the grassland and riparian sites because of the negatively biased ET/ETo in clear-sky days. The VDA and LSTR approaches system overestimates ET, which are mainly attributed to the over-predicted evaporative fraction values in VDA and under- reconstructed LST in cloudy days, respectively. The results also indicate that the root mean square error (RMSE) of reconstructed ET values are lowest with value about 1.4 mm/day and highest with value greater than 2.0 mm/day as the numbers of continuous cloudy days are 2 and 3, respectively. These outcomes suggest that synergistic use of space-borne microwave and thermal infrared LST observations into remote sensing-based ET methods can improve the reconstruction of ET data under cloudy conditions.