Effects of Spatial Resolution on Assessing Cotton Water Stress Using Unmanned Aerial System Imagery

Accurate detection of cotton water stress is essential for improving irrigation efficiency and yield prediction. Unmanned aerial system (UAS) imagery offers an effective means for high-throughput crop monitoring, yet its performance across spatial resolutions remains insufficiently characterized. This study aimed to (1) evaluate the performance of UAS-derived Water Deficit Index (WDI) and Crop Water Stress Index (CWSI) across cotton growth stages and (2) examine how spatial resolution influences stress detection and yield prediction. Field experiments were conducted in Lubbock County, Texas, during the 2021–2022 growing seasons under three irrigation treatments (30%, 60%, and 90% ET replacement). Multispectral and thermal UAS imagery were processed to generate WDI and CWSI maps at spatial resolutions ranging from 0.1 to 4.0 m. Results showed that WDI outperformed CWSI at distinguishing water-stress levels, particularly during early growth stages. A 0.5 m resolution provided the best balance between detection accuracy and computational efficiency, whereas finer resolutions improved detection at the expense of processing time. Coarser resolutions (≥1 m) reduced accuracy due to spatial averaging and plot-mixing effects. These findings highlight the need to optimize UAS flight altitude and sensor configuration to achieve efficient, scalable, and precise cotton water-stress assessment and yield prediction.