NSII: a novel soybean identification index based on Sentinel-2 imagery for accurate and efficient soybean mapping

Accurate mapping of soybean cultivation areas is crucial for agricultural monitoring, resource management, and food security. However, the spectral overlap between soybean and other crops, such as corn, poses significant challenges for remote sensing-based identification. This study proposes a novel soybean identification index (NSII), which is calculated using the second red-edge band (RE2), the first short-wave infrared band (SWIR1), and the Enhanced Vegetation Index (EVI) derived from Sentinel-2 imagery within the optimal time window identified through spectral feature analysis. NSII was implemented in 12 major soybean producing regions in the United States and China over a three-year period (2020-2022). Experimental results from 2020 to 2022 show that the average accuracy of NSII is 0.85, and the average F1 score is 0.80. Compared with the existing Soybean Mapping Composite Index (SMCI), the accuracy increased by 8 percentage points and the F1 score increased by 6 percentage points. NSII also exhibits strong stability and transferability, with consistent performance across diverse climatic and cropping conditions. This study provides a robust and efficient tool for soybean mapping, offering significant potential for precision agriculture and sustainable resource management.