Cotton Cultivated Area Extraction Based on Multi-Feature Combination and CSSDI under Spatial Constraint

Abstract

Cotton is an important economic crop, but large-scale field extraction and estimation can be difficult, particularly in areas where cotton fields are small and discretely distributed. Moreover, cotton and soybean are cultivated together in some areas, further increasing the difficulty of cotton extraction. In this paper, an innovative method for cotton area estimation using Sentinel-2 images, land use status data (LUSD), and field survey data is proposed. Three areas in Hubei province (i.e., Jingzhou, Xiaogan, and Huanggang) were used as research sites to test the performance of the proposed extraction method. First, the Sentinel-2 images were spatially constrained using LUSD categories of irrigated land and dry land. Seven classification schemes were created based on spectral features, vegetation index (VI) features, and texture features, which were then used to generate the SVM classifier. To minimize misclassification between cotton and soybean fields, the cotton and soybean separation index (CSSDI) was introduced based on the red band and red-edge band of Sentinel-2. The configuration combining VI and spectral features yielded the best cotton extraction results, with F1 scores of 86.93%, 80.11%, and 71.58% for Jingzhou, Xiaogan, and Huanggang. When CSSDI was incorporated, the F1 score for Huanggang increased to 79.33%. An alternative approach using LUSD for non-target sample augmentation was also introduced. The method was used for Huangmei county, resulting in an F1 score of 78.69% and an area error of 7.01%. These results demonstrate the potential of the proposed method to extract cotton cultivated areas, particularly in regions with smaller and scattered plots.