Quantification of interactions of soil conditions, plant available water and weather conditions on crop development and production is the key for optimizing field management to achieve optimal production. The goal of this study was to quantify the effects of soil and weather conditions on cotton development and production using temporal aerial imagery data, weather and soil apparent electrical conductivity (EC_a) of the field. Soil texture, i.e., percent of sand and clay content, was calculated from EC_a to estimate three soil quality indicators, including field capacity, wilting point and total available water. A water stress coefficient K_s was calculated using soil texture and weather data. Image features of canopy size and vegetation indices (VIs) were extracted from unmanned aerial vehicle (UAV)-based multispectral images at three growth stages of cotton in 2018 and 2019. Pearson correlation (r), analysis of variance (ANOVA) and eXtreme Gradient Boosting (XGBoost) were used to quantify the relationships between crop response derived from UAV images and environments (soil texture and weather). Results showed that soil clay content in shallower layers (0–0.4 m) affected crop development in earlier growth stages (June and July) while those in deeper layers (0.4–0.7 m) affected the later-season growth stages (August and September). Soil clay content at 0.4–0.7 m had a higher impact on crop development when water inputs were not sufficient, while K_s features had a higher contribution to the prediction of crop growth when irrigation was applied and water stress was less.

量化土壤条件、植物有效水和天气条件对作物发育和生产的相互作用是优化田间管理以实现最佳生产的关键。本研究的目的是使用时间航空影像数据、天气和田间土壤表观电导率 (EC _a ) 来量化土壤和天气条件对棉花发育和生产的影响。土壤质地，即沙子和粘土含量的百分比，由EC _a计算，以估计三个土壤质量指标，包括田间持水量、萎蔫点和总有效水。A 水分胁迫系数K _s使用土壤质地和天气数据计算。从 2018 年和 2019 年棉花三个生长阶段的基于无人机 (UAV) 的多光谱图像中提取冠层大小和植被指数 (VIs) 的图像特征。皮尔逊相关 ( r )、方差分析 (ANOVA) 和极梯度Boosting (XGBoost) 用于量化来自无人机图像的作物响应与环境（土壤质地和天气）之间的关系。结果表明，较浅层（0-0.4 m）的土壤粘土含量影响早期生长阶段（6月和7月）的作物发育，而较深层（0.4-0.7 m）的土壤粘土含量影响后期生长阶段（8月和9月） . 当水分输入不足时，0.4-0.7 m 的土壤粘土含量对作物发育的影响更大，而当灌溉和水分胁迫较少时， K _s特征对预测作物生长的贡献更大。