Enhanced Estimation of Reference Evapotranspiration using Hybrid Deep Learning Models and Remote Sensing Variables

Abstract

Effective water resources management and irrigation scheduling for agricultural sector highly depend on the precise estimation of reference evapotranspiration, ET0 . Both long short-term memory (LSTM) and gated recurrent unit (GRU) showed their equivalent capability in estimating ET0 and achieved the highest R2 and lowest prediction errors. Hybrid deep learning models, CNN-LSTM and CNN-GRU managed to improve the accuracy of the prediction. Incorporation of surface reflectance bands and auxiliary variables enhanced the performance of the models. This study provides valuable insights into deep learning algorithms and further confirms the potential of remote sensing variables as an alternative data source for ET0 estimation.