Development of novel indices for maize mapping in heterogeneous agricultural landscapes

Maize occupies 12% of global grain production and plays a critical role in ensuring food security. However, most maize-mapping methods rely heavily on reference labels and repeated model training, limiting their utility in data-scarce heterogeneous agricultural regions. To overcome this limitation, a minimally sample-dependent dual-index constraint framework that directly exploits canopy-level spectral signals from Sentinel-2 imagery was proposed. Maize Chlorophyll and Water Content Index (MCWCI) was developed to capture spectral absorption associated with chlorophyll and water content, whereas Near-Infrared Difference Vegetation Index (NirDVI) was designed to enhance sensitivity to Nir-response differences between maize and other crops arising from canopy structure and integrated physiological characteristics. Cross-regional and cross-temporal assessments conducted in the Loess Plateau, Iowa, and Mato Grosso demonstrated the stability and transferability of the proposed framework. Results showed that the proposed framework achieved F1-scores ranging from 82.54% to 95.43% across all regions and improved F1-scores by 3.84%–41.80% compared with existing indices, including GWCCI, NDCI, DCNI, and TCARI. In addition, the framework shows better performance in delineating field boundary characterized by small and fragmented fields, providing a practical and transferable solution for large-scale maize mapping.