Testing a simple Random Forest approach to predict surface evapotranspiration from remote sensing data

BARRAZA, VERÓNICA; DOUNA, VANESA; GRINGS, FRANCISCO

Montevideo

Encuentro; KHIPU Latin American Meeting In Artificial Intelligence; 2019

Facultad de Ingeniería, Universidad de la República de Uruguay

Evapotranspiration (ET), which is the sum of the water evaporated and transpired from the land surface to the atmosphere, is crucial to ecosystems as it affects the soil, the vegetation, the atmosphere and mediates their interaction. Modelling and quantifying it accurately is critical for sustainable agriculture, forest conservation, and natural resource management. Although ET cannot be remotely sensed directly, remote sensing provides continuous data on surface and biophysical variables, and thus it has been an invaluable tool for estimating ET. In this work, we have evaluated the potential of a Random Forest regressor to predict daily evapotranspiration in three sites in Northern Australia from daily in-situ meteorological data,and satellite data on leaf area index and land surface temperature.We have obtained satisfactory performances with RMSE errors around 1mm/day (rRMSE around 0.3), which are comparable to those obtained in previous works by different methods. Sensitivity to variations in the training sample and the importance of the input variables have been analyzed. Our promising results and the simplicity of the method reinforce the relevance of deeply exploring this approach in other ecosystems at different temporal and spatial scales, aiming to develop a versatile and operative ET product.