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

Abstract

Highlights: What are the main findings? The Water Deficit Index (WDI) achieved higher accuracy than the Crop Water Stress Index (CWSI) in assessing cotton water stress and predicting yield. Spatial resolution influenced water stress detection, with finer images better capturing variability across irrigation treatments and growth stages. What are the implications of the main findings? These findings provide valuable insights into the application of high-spatial-resolution UAS imagery for assessing cotton water stress. These findings show the importance of optimizing UAS flight altitude and sensor configuration to ensure accurate, scalable, and efficient assessment of cotton water stress and yield prediction. 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.