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

作者：Yao Rong, Weishu Wang, Peijin Wu, Pu Wang, Chenglong Zhang, Chaozi Wang, Zailin Huo · 发表于：Water Resources Research · 年份：2024 · DOI：10.1029/2023wr036809 · 被引用次数：17 · 研究领域：Plant Water Relations and Carbon Dynamics、Soil and Unsaturated Flow、Irrigation Practices and Water Management

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 a...