Enhanced Early Detection of Cadmium Stress in Rice: Introducing a Novel Spectral Index Based on an Enhanced GAMI-Net Model

Soil cadmium contamination poses a significant threat to global food security and human health, making the timely and accurate diagnosis of cadmium stress in rice crucial for effective pollution control and agricultural management. However, during the early growth stages of rice, particularly the tillering stage, the spectral response to cadmium stress is subtle, rendering traditional remote sensing methods inadequate. This study aims to develop an efficient early diagnosis index, the Cadmium Early Stress Index (CESI), for rapid and accurate detection of cadmium stress in rice at a regional scale. By integrating field surveys with Sentinel-2 satellite data, the study extracts multi-angle spectral features and employs an enhanced Generalized Additive Model Neural Network (E-GAMI-Net) for analysis. E-GAMI-Net analysis identified key indicators for early diagnosis, including log-transformed reflectance at 941 nm (R941_log), Optimized Soil-Adjusted Vegetation Index (OSAVI), and the interaction between Red Edge Amplitude and Chlorophyll content. Based on these findings, CESI was constructed, demonstrating superior diagnostic performance (R 2 = 0.77, RMSE = 0.09 mg/kg) compared to existing methods. CESI also exhibited high stability under noise interference, with only a 5.6% reduction in R 2 under 15% noise. In regional-scale remote sensing applications, CESI successfully generated cadmium stress distribution maps, identifying previously undetected moderate stress areas. CESI’s high accuracy (R 2 = 0.6073, RMSE = 0.3021) and stability make it a promising tool for large-scale cadmium stress monitoring and precision agriculture management.