Using thermal infrared remote sensing from unmanned aerial vehicles (UAVs) to obtain crop canopy temperature and calculate the crop water stress index (CWSI) is a promising method for monitoring field water conditions. However, such endeavors are often constrained to instantaneous scales due to the diurnal variability of thermal infrared data. To address this limitation, we developed a daily-scale CWSI suitable for UAV remote sensing, enhancing the temporal representativeness of crop water stress diagnostics. We focused on spring maize in the Hetao Irrigation District of Inner Mongolia and investigated four key growth stages. UAV thermal infrared was used to obtain multiple instantaneous statistical CWSI (CWSIs) values during the day. UAV multispectral data and the Penman–Monteith model were combined to obtain the actual evapotranspiration and daily-scale CWSI (CWSIt_day). A temporal upscaling model from instantaneous CSWI to daily-scale CWSI was established by comparing the relationships between the CWSIs and CWSIt_day at different times. Results show that compared to the fluctuations of the CWSIs values throughout the day, those of the CWSIt_day values were smaller, with values of 0.13, 0.09, 0.03, and 0.03 during the ninth leaf (V9), tasseling (VT), silking (R1), and milk (R3) stages, respectively. The CWSIt_day demonstrated a higher correlation with the measured stomatal conductance (gs) at different time periods, thereby being more stable and temporally representative. However, both indices may incorrectly interpret the decline in leaf physiological activity due to aging as water stress at the end of maize growth, leading to overestimated CWSI values. The temporal upscaling model, which was developed by combining CWSIs values observed at 12:00, 14:00, and 16:00 with the random forest regression algorithm, achieved coefficient of determination of 0.794 and root mean square error of 0.04. Hence, multiple instantaneous observations can be used effectively instead of daily-scale observations, providing key insights into the popularization and application of the CWSIt_day. Overall, this study presents a new method for obtaining continuous CWSI values with high temporal and spatial resolutions based on a UAV platform.

中文翻译：

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结合无人机遥感数据估计日尺度作物水分胁迫指数：增强诊断时间代表性


利用无人机 （UAV） 的热红外遥感获得作物冠层温度并计算作物水分胁迫指数 （CWSI） 是一种很有前途的田间水分状况监测方法。然而，由于热红外数据的昼夜变化，这种努力通常局限于瞬时尺度。为了解决这一限制，我们开发了适用于无人机遥感的日尺度 CWSI，增强了作物水分胁迫诊断的时间代表性。我们以内蒙古河套灌区的春玉米为研究对象，研究了 4 个关键生长阶段。无人机热红外用于获得白天的多个瞬时统计 CWSI （CWSIs） 值。将无人机多光谱数据和 Penman-Monteith 模型相结合，得到实际蒸散量和日尺度 CWSI （CWSIt_day）。通过比较不同时间 CWSI 和 CWSIt_day 之间的关系，建立了从瞬时 CSWI 到日尺度 CWSI 的时间放大模型。结果表明，与 CWSIs 值全天的波动相比，CWSIt_day值的波动较小，在第九叶 （V9）、抽穗 （VT）、吐丝 （R1） 和乳汁 （R3） 阶段分别为 0.13、0.09、0.03 和 0.03。CWSIt_day 与在不同时期测得的气孔导度 （gs） 具有更高的相关性，因此更稳定且具有时间代表性。然而，这两个指数都可能错误地将衰老引起的叶片生理活性下降解释为玉米生长结束时的水分胁迫，从而导致高估 CWSI 值。 通过将 12：00、14：00 和 16：00 观察到的 CWSI 值与随机森林回归算法相结合而开发的时间放大模型，实现了 0.794 的决定系数和 0.04 的均方根误差。因此，可以有效地使用多个瞬时观测值来代替每日尺度的观测值，从而为CWSIt_day的普及和应用提供关键见解。总的来说，本研究提出了一种基于无人机平台获得具有高时间和空间分辨率的连续 CWSI 值的新方法。