Adaptive high-quality sampling for winter wheat early mapping: A novel cascade index and machine learning approach

Abstract

Precise and timely mapping of winter wheat is essential for food security. Current methods are limited by insufficient training data and a lack of long-term early mapping verification. This research proposes a framework that uses a cascade index to generate high-quality training samples for winter wheat mapping automatically. By considering the phenological characteristics of winter wheat and similar crops, the cascade index method screens and acquires these samples. Combined with a random forest model, mapping was conducted in Henan Province and the Agricultural Statistics District (ASD) 2020 area in the U.S. In Henan, early mapping from 2018 to 2022 assessed differences between model transfer and current-year samples. Results showed that using October-April imagery based on model migration achieved an overall accuracy (OA) of over 90%, while October-February imagery based on current-year samples also exceeded 90%. In some years, early mapping using only October-December data also achieved over 90% OA. These findings demonstrate the proposed model's viability for large-scale early winter wheat mapping using satellite imagery. Furthermore, this method demonstrates adaptability, mapping results achieving over 93.69% OA when transferred to the United States.