Phenology Index-Based Method for Mapping Winter Wheat and Summer Maize Rotation Cropping Pattern with Sentinel-2 Imagery

As a common agricultural intensification, the winter wheat and summer maize rotation cropping pattern (wheat-maize) plays a crucial role in achieving sustainable food security in China. Reliable regional wheat-maize maps are of great importance to ensure the sustainability of agro-ecosystems. However, conventional previous studies typically depended on vegetation index time-series for detecting wheat-maize, which was challenging for rapid wheat-maize mapping. This study proposed a simpler phenology index-based method for mapping wheat-maize from multi-temporal Sentinel-2 data. To better explore the mapping performance, two indices (i.e., normalized difference vegetation index (NDVI) and two-band enhanced vegetation index (EVI2)) and two mathematical combinations (i.e., multiplication and addition) were introduced to generate four uncorrelated indices. The wheat-maize maps obtained using phenology indices were evaluated using samples and high-precision maps derived from random forest. The results showed that the resulting maps achieved high overall accuracy of above 94% and F1-score of over 0.95, as well as agreed well with random forest derived maps (overall accuracy ≥ 91%, F1-score ≥ 0.88). In addition, this study found that EVI2 was better suited for designing phenology difference-based index than NDVI; concerning combination approaches, multiplication performed better than addition in enhancing spectral differences. Our results demonstrated the advantages of index-based method in mapping wheat-maize and its potential to be applied over larger regions. We hope that this study will advance our understanding of phenology-based methods in agriculture mapping.