Extraction of Cotton Cultivation Areas Based on Deep Learning and Sentinel-2 Image Data

Cotton is a crucial economic crop, and timely and accurate acquisition of its spatial distribution information is of great significance for yield prediction, as well as for the formulation and adjustment of agricultural policies. To accurately and efficiently extract cotton cultivation areas at a large scale, in this study, we focused on the Santun River Irrigation District in Xinjiang as the research area. Utilizing Sentinel-2 satellite imagery from 2019 to 2024, four cotton extraction models—U-Net, SegNet, DeepLabV3+, and CBAM-UNet—were constructed. The models were evaluated using metrics, including the mean intersection over union (mIoU), precision, recall, F1-score, and over accuracy (OA), to assess the models’ performances in cotton extraction. The results demonstrate that the CBAM-UNet model achieved the highest accuracy, with an mIoU, precision, recall, F1-score, and OA of 84.02%, 88.99%, 94.75%, 91.78%, and 95.56%, respectively. The absolute error of the extracted cotton areas from 2019 to 2024 ranged between 923.69 and 1445.46 hm 2 , with absolute percentage errors of less than 10%. The coefficient of determination (R 2 ) between the extracted results and statistical data was 0.9817, indicating the best fit. The findings of this study provide technical support for rapid cotton identification and extraction in large- and medium-sized irrigation districts.