A Novel Hybrid Deep Learning Framework for Evaluating Field Evapotranspiration Considering the Impact of Soil Salinity

Abstract

Accurate evaluation of evapotranspiration (ET) is crucial for efficient agricultural water management. Data-driven models exhibit strong predictive ET capabilities, yet significant limitations like naive extrapolation hamper wider generalization. In this perspective, we explore a novel hybrid deep learning (DL) framework to integrate domain knowledge and demonstrate its potential for evaluating ET under the influence of soil salinity. Specifically, we integrated physical constraints from process models (Penman-Monteith or Shuttleworth-Wallace) and salinity-induced stomatal stress mechanisms into the DL algorithm, and evaluated its performance by comparing four diverse scenarios. Results demonstrate that hybrid DL framework offers a promising alternative for ET estimation, achieving comparable accuracy to pure DL during training and validation. Nonetheless, due to the limited available measurements, data-driven model may not adequately capture plant responses to salt stress, leading to significant prediction biases observed during independent testing. Encouragingly, the hybrid DL model (DL-SS) integrating Shuttleworth-Wallace and salinity-induced stomatal stress mechanisms demonstrated enhanced interpretability, generalizability, and extrapolation capabilities. During testing, DL-SS consistently showed optimal performance, yielding root mean square error (RMSE) values of 37.4 W m⁻² for sunflower and 39.2 W m⁻² for maize. Compared to traditional Jarvis-type approaches (JPM and JSW) and pure DL model during testing, DL-SS achieved substantial reductions in RMSE values: 51%, 33%, and 43% for sunflower, and 45%, 31%, and 35% for maize, respectively. These findings highlight the importance of integrating prior scientific knowledge into data-driven models to enhance extrapolation capability of ET modeling, especially in salinized regions where conventional models may struggle.