Infrared thermal imaging cameras are effective tools to monitor the spatial distribution of canopy temperature (T_c), which is the basis of the crop water stress index (CWSI) calculation. This paper presents a new method to improve the CWSI performance in crop water stress diagnosis based on T_c measured by thermal imaging. Cumulative frequency curves of pixel T_c extracted from each thermal image were analysed. Different statistical quantiles of T_c were determined, and the average T_c over different statistics quantiles were used to calculate the CWSI separately. There were large gaps among the CWSI based on T_c over different statistical quantiles. We compared the coefficient of determination (R²) of relationships among the CWSI based on T_c over different statistical quantiles and relative leaf photosynthetic activities. The general sensitive CWSI showed the best correlations with leaf photosynthetic activities, which were calculated based on average values of the top 60%, 50%, 70%, 50% of T_c statistics at different growth stages. The ranges of the CWSI with optimal leaf water use efficiency (between turning-points for downward trends in photosynthesis and transpiration) were 0.556–0.569, 0.481–0.486, 0.571–0.641, and 0.511–0.606 at tillering, panicle initiation to booting, heading to anthesis, and milk to soft dough stages respectively. The corresponding soil moisture levels were consistent with the lower thresholds of the rice under control irrigation. Based on the spatial distribution of canopy temperatures measured by thermal imaging cameras, the general sensitive CWSI, which was calculated by removing low temperatures, had a better performance in crop water stress diagnosis.

红外热像仪是监测冠层温度（T _c）的空间分布的有效工具，冠层温度是计算作物水分胁迫指数（CWSI）的基础。本文提出了一种基于热成像测量的T _c提高作物水分胁迫诊断中CWSI性能的新方法。分析了从每个热图像提取的像素T _c的累积频率曲线。的不同的统计分位数Ť _Ç测定，平均Ť _Ç在不同的统计分位数被用来分别计算CWSI。基于T _c的CWSI之间存在较大差距在不同的统计分位数上。我们比较了基于T _c的CWSI之间在不同统计分位数和相对叶片光合作用活性之间的关系的确定系数（R ²）。一般敏感的CWSI显示与叶片光合作用的最佳相关性，这是根据最高T _c的60％，50％，70％，50％的平均值计算得出的不同成长阶段的统计数据。在分,、穗启动到启动，抽穗时，具有最佳叶片水分利用效率（在光合作用和蒸腾作用的下降趋势的转折点之间）的CWSI范围为0.556-0.569、0.481-0.486、0.571-0.641和0.511-0.606到花期，从牛奶到软面团阶段。相应的土壤水分含量与对照灌溉下稻的下限一致。根据通过热像仪测量的冠层温度的空间分布，通过去除低温计算出的一般敏感CWSI在作物水分胁迫诊断中具有更好的性能。