On the added value of sequential deep learning for the upscaling of evapotranspiration - B. Kraft; B. Kraft; B. Kraft; J. A. Nelson; S. Walther; F. Gans; U. Weber; G. Duveiller; M. Reichstein; W. Zhang; M. Rußwurm; D. Tuia; et al. (Biogeosciences)
Estimating ecosystem–atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary methodology that is independent from process-based and semi-empirical approaches. However, a systematic evaluation of the skill and robustness of different ML approaches is an active field of research that requires more investigation. Concretely, deep learning approaches in the time domain have not been explored systematically for this task.