Modeling of Drought-Induced Crop Yield Loss Based on Solar-Induced Chlorophyll Fluorescence by Machine Learning Methods

Against the accelerating backdrop of global warming, drought-induced crop yield loss not only causes direct economic losses but may also disrupt the dynamic balance of food production and consumption, ultimately threatening global food security. In order to quantify drought-induced crop yield loss for safeguarding national food security, this study developed a model for evaluating drought-induced yield reduction in winter wheat by integrating solar-induced chlorophyll fluorescence (SIF), vegetation indices (VIs), and meteorological data. The results demonstrated that the following: (1) SIF could effectively capture interannual fluctuations in winter wheat yield and serve as a reliable quantitative indicator of yield variation. (2) Utilizing vegetation data such as SIF and the near-infrared reflectance of vegetation (NIRv), the developed models could directly quantify drought-induced yield losses in winter wheat based on normalized anomalies of vegetation and meteorological variables, without the need for additional auxiliary data or complex computations. Among all variable combinations tested, SIF demonstrated superior performance, yielding the most accurate predictions. (3) Both random forest (RF) and extreme gradient boosting (XGBoost) algorithms had similar performance in evaluating drought-induced yield loss. The results highlighted the advantages of combining the normalized anomaly of multiple sources of data as inputs in stress-induced crop yield loss evaluation, which was helpful for quick monitoring and early warning of the crop yield loss in the major grain production region.