Regional-scale precision mapping of cotton suitability using UAV and satellite data in arid environments

This study addresses the critical issues of water scarcity and soil salinization impacting cotton production in South Xinjiang, China. It introduces an innovative framework for assessing regional cotton crop suitability by integrating ground-measured soil water and salt data with UAV multispectral and Sentinel-2A satellite imagery from the 2022 cotton growing season. An optimized set of vegetation indices was identified through multicollinearity analysis and full subset selection. Six advanced machine learning methods, including Random Forest (RF), were used alongside the ratio mean method to effectively upscale soil water and salt content models from the field to the regional level. A newly developed cotton suitability index was created to categorize soil water and salt conditions, resulting in detailed suitability maps for 2022 and 2023. Key findings include: (1) Model Performance: The RF model outperformed others in predicting soil water and salt content, with R² values ranging from 0.763 to 0.846 for soil moisture and 0.703–0.843 for soil salinity. It showed greater accuracy at 0–10 cm depth than 10–20 cm depth. (2) Imagery Correlation: A significant correlation was observed between UAV and Sentinel-2A imagery (R² = 0.498–0.745). Reflectivity corrections in Sentinel-2A data notably improved RF model inversion accuracy (R² gains of 0.114–0.384). (3) Suitability Analysis: The cotton suitability index maps for 2022 and 2023 indicated that most fields in Tumushuke (TMSK) were moderately suitable for cotton growth, although some areas were unsuitable. This highlights the need for additional irrigation and targeted soil water and salt management to meet cotton requirements and reduce salinity risks. Overall, this study enhances precision agriculture techniques for arid environments and provides valuable insights for managing soil salinity, supporting sustainable cotton production in challenging climates.