Integration of Landsat and MODIS Imagery for Mapping 30-m Cotton Cultivation Areas in Xinjiang, China from 2000 to 2020

Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles. A thorough understanding of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry. Xinjiang is the primary cotton-producing region in China. However, long-term data of cotton cultivation areas with high spatial resolution are unavailable for Xinjiang, China. Therefore, this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest (RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) images, and validated the applicability and accuracy of dataset at a large spatial scale. Then, this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period. The results showed that a high classification accuracy was achieved (overall accuracy > 85%, F1 > 0.80), strongly agreeing with county-level agricultural statistical yearbook data (R² > 0.72). Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang, with a total increase of 1131.26 kha from 2000 to 2020. Notably, cotton cultivation area in southern Xinjiang expanded substantially, with that in Aksu increasing from 20.10% in 2000 to 28.17% in 2020, representing an expansion of 374.29 kha. In northern Xinjiang, the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period. In contrast, cotton cultivation in eastern Xinjiang declined, decreasing from 2.22% in 2000 to merely 0.24% in 2020. Standard deviation ellipse analysis revealed a ‘northeast-southwest’ spatial distribution, with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period. Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors, with effective irrigation area (r = 0.963, P < 0.05) and total agricultural machinery power (r = 0.823) showing significant positive correlations, whereas climatic variables exhibiting weak associations (r < 0.200). These results provide valuable scientific data for informed agricultural management, sustainable development, and policymaking.