Assessing accuracy of crop water stress inversion of soil water content all day long

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-04-24 DOI:10.1007/s11119-024-10143-y

Bei Zhang, Jialiang Huang, Tianjin Dai, Sisi Jing, Yi Hua, Qiuyu Zhang, Hao Liu, Yuxiao Wu, Zhitao Zhang, Junying Chen

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Abstract

There is growing interest in using canopy temperature (Tc), including crop water Stress index (CWSI), for irrigation management. However, Tc varies greatly in one day, while soil water content (SWC) varies little, which may lead to different conclusions on whether irrigation is needed based on CWSI at different times. For this end, Tc of winter wheat was continuously monitored, and the data of such environmental factors as atmospheric temperature and soil water content (SWC) were simultaneously collected. CWSI was calculated based on empirical formulation and Tc and CWSI were generalized based on the normalization formulation. The correlation SWC between Tc and CWSI before and after generalization was compared and error analysis was based on SWC theoretical formula. The results showed: (1) the accuracy of SWC retrieval by Tc and CWSI increased firstly and then decreased with time during the day. The optimal time for Tc monitoring SWC was between 10:00 ~ 16:00 (R² > 0.72) and the optimal time for CWSI monitoring SWC was between 9:00 ~ 18:00 (R² > 0.69). (2) CWSI and Tc were mapped based on the relationship between crop water stress and soil water deficit and normalized canopy temperature expressions characterized the relationship between crop water stress and soil water deficit. (3) The accuracy of inversion of SWC by mapping Tc from 18:00 ~ 8:00 is increased from 0.5 ~ 0.6 to 0.7 ~ 0.8; the accuracy of soil water content inversion by mapping CWSI from 18:00 ~ 8:00 was improved from 0.2 ~ 0.4 to 0.4 ~ 0.6. (4) The theoretical expression of SWC deduced based on CWSI also proves that considering the relationship between crop water stress and soil water deficit change can effectively reduce the relative error from 30 to 5% in the morning and evening. This study contributes to the understanding of the reason why the correlation between Tc and SWC varies greatly during the day and solves the time-limited problem of thermal infrared remote sensing monitoring of crop water stress.

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评估作物水分胁迫全天土壤含水量反演的准确性

利用冠层温度（Tc）（包括作物水分胁迫指数（CWSI））进行灌溉管理的兴趣日益浓厚。然而，Tc 在一天内的变化很大，而土壤含水量（SWC）的变化却很小，这可能会导致在不同时间根据 CWSI 得出是否需要灌溉的不同结论。为此，对冬小麦的 Tc 进行了连续监测，并同时收集了大气温度和土壤含水量（SWC）等环境因素的数据。根据经验公式计算了 CWSI，并根据归一化公式对 Tc 和 CWSI 进行了归纳。比较了归一化前后 Tc 和 CWSI 之间的相关性 SWC，并根据 SWC 理论公式进行了误差分析。结果表明(1) 用 Tc 和 CWSI 进行 SWC 检索的准确率在一天中随着时间的推移先上升后下降。Tc 监测 SWC 的最佳时间为 10:00 ~ 16:00（R2 为 0.72），CWSI 监测 SWC 的最佳时间为 9:00 ~ 18:00（R2 为 0.69）。(2）根据作物水分胁迫与土壤缺水的关系绘制了 CWSI 和 Tc 图，归一化冠层温度表达式表征了作物水分胁迫与土壤缺水的关系。(3) 通过绘制 18:00 ~ 8:00 Tc 反演 SWC 的精度由 0.5 ~ 0.6 提高到 0.7 ~ 0.8；通过绘制 18:00 ~ 8:00 CWSI 反演土壤含水量的精度由 0.2 ~ 0.4 提高到 0.4 ~ 0.6。(4) 基于 CWSI 推算的 SWC 理论表达式也证明，考虑作物水分胁迫与土壤水分亏缺变化之间的关系，可有效地将早晚的相对误差从 30%减小到 5%。该研究有助于理解 Tc 与 SWC 的相关性在白天变化较大的原因，解决了作物水分胁迫热红外遥感监测的时间限制问题。

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.