Modeling Evapotranspiration at Larger Temporal Scales: Effects of Temporal Aggregation and Data Gaps

Abstract

Evapotranspiration (ET) at weekly and monthly time scales is often needed for various applications. When using remote sensing (RS)-based models, this can be achieved either by averaging all the required input variables to the intended time scale and simulating ET using models (input aggregation), or by estimating daily ET from the models and averaging to weekly or monthly ET (output aggregation). It is not clear if both these aggregation approaches yield the same outcome when using RS-based models for the estimation of ET. Another issue in obtaining ET at longer time scales is the lack of enough satellite observations to estimate ET with reasonable accuracy. This study aimed to compare the input and output aggregation approaches to obtain ET at weekly and monthly time scales using three RS ET models, namely, Priestley–Taylor Jet Propulsion Lab (PT-JPL), Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE), and Surface Temperature Initiated Closure (STIC) models. The study was conducted using in situ data over six sites of different agro-climatic conditions in India, Tunisia, and France. The results indicate that the input aggregation provided relatively better results for monthly and weekly ET values than the output aggregation, having a lower RMSE (1–40%). Further, it was found that at least seven to eight satellite observations per month are required to obtain reliable ET estimate when using RS-based models.