Towards a Mechanism-Informed Intelligent Framework for Identification of Compound Drought-Heat Extremes in Croplands

Compound drought and heat extremes (CDHEs) exert impacts that exceed the sum of their individual components. With global warming amplifying the associated risks, CDHEs have become a critical threat to agricultural production. Thus, identifying and monitoring CDHEs in cropland systems is key for food security. As CDHEs formation and evolution are shaped by climatic factors, hydrological cycles, and ecosystem feedbacks, their fine- and large-scale identification in agricultural areas poses substantial challenges.

Our study reviews existing methods for identifying CDHEs, including combined threshold approaches, comprehensive index methods, traditional machine learning techniques, and improved mechanistic modeling. We summarize the current limitations of these methods as follows: (1) Combined threshold and comprehensive index methods often focus on a single aspect of CDHEs, failing to systematically describe the complex processes of compound events. (2) While traditional machine learning methods attempt to integrate characteristics of the hazard-bearing body, disaster-causing factors, and hazard-inducing environment to establish complex nonlinear relationships between multiple elements and compound event indices, their "black-box" nature lacks mechanistic interpretability. Furthermore, these methods rely heavily on large volumes of high-quality samples to achieve satisfactory accuracy. (3) Improved mechanistic models, typically based on classical agricultural process models such as APSIM and AquaCrop, introduce CDHE impact modules to address the oversimplification of these effects in original models. Nevertheless, these mechanistic models require extensive input parameters, and their calibration processes depend on substantial amounts of measured data. Additionally, the computational resources needed for simulations are considerable, making the cost of analyzing CDHEs over large farmland areas under various future climate scenarios prohibitive for individual researchers.

To address these challenges, this study highlights the potential of physics-informed neural network models for identifying compound events and proposes future research directions regarding mechanistic constraints, neural network architecture design, and experimental plans: (1) Farmland CDHEs are essentially phenomena of water and heat imbalance within the soil-crop-atmosphere (SCA) system. Utilizing the Richards equation and the Penman-Monteith formula can characterize this process by constraining the water and heat environmental factors at the two key interfaces: root-soil and leaf-atmosphere. (2) Solar-induced chlorophyll fluorescence (SIF), a byproduct of vegetation photosynthesis closely related to GPP, responds rapidly to physiological damage caused by stress. Utilizing multi-band SIF data can provide a detailed depiction of crop physiological responses to stress from the perspective of the hazard-affected body. (3) Automated design of model architectures incorporating mechanistic information for farmland compound events can be achieved through distillation learning. (4) Future work should integrate ground-based water and heat control experiments with site-specific hyperspectral SIF observation data. Through continuous combinatorial experimental design, this approach can lead to the development of accurate and efficient physics-informed neural networks. Coupled with large-scale satellite and reanalysis data products, this framework aims to enable the large-area identification of farmland CDHEs under future climate scenarios.