Journal of Integrative Agriculture ›› 2024, Vol. 23 ›› Issue (7): 2458-2475.DOI: 10.1016/j.jia.2024.03.042

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基于无人机遥感的CWSI和Ts-Ta-VIs干旱指数在旱地作物（高粱、玉米）水分监测中的比较评估

  

  • 收稿日期:2023-12-05 接受日期:2024-02-06 出版日期:2024-07-20 发布日期:2024-07-02

Comparison of CWSI and Ts-Ta-VIs in moisture monitoring of dryland crops (sorghum and maize) based on UAV remote sensing

Hui Chen1, 2, Hongxing Chen1, 2, Song Zhang1, 2, Shengxi Chen1, 2, Fulang Cen1, 3, Quanzhi Zhao1, 3, Xiaoyun Huang1, 2, Tengbing He1, 2#, Zhenran Gao1, 2#
  

  1. 1 College of Agriculture, Guizhou University, Guiyang 550025, China
    2 Institute of New Rural Development, Guizhou University, Guiyang 550025, China
    3 Institute of Rice Industry Technology Research, College of Agricultural Sciences, Guizhou University, Guiyang 550025, China
  • Received:2023-12-05 Accepted:2024-02-06 Online:2024-07-20 Published:2024-07-02
  • About author:Hui Chen, Mobile: +86-15186961838, E-mail: gs.huichen21@ gzu.edu.cn; # Correspondence Tengbing He, E-mail: tbhe@gzu. edu.cn; Zhenran Gao, E-mail: zrgao@gzu.edu.cn
  • Supported by:
    This work was supported by the National Key Research and Development Program of China (2022YFD1901 500/2022YFD1901505), the Key Laboratory of Molecular Breeding for Grain and Oil Crops in Guizhou Province, China (Qiankehezhongyindi (2023) 008), and the Key Laboratory of Functional Agriculture of Guizhou Provincial Higher Education Institutions, China (Qianjiaoji (2023) 007). 

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摘要：

农业干旱遥感监测对现代农业精准灌溉至关重要。本研究基于无人机遥感技术，探讨了基于冠层温度的经验作物水分胁迫指数（CWSI）和由地表温度（Ts）、空气温度（Ta）及5种植被指数（VIs）构建的三维干旱指数（TDDI）在监测旱地作物水分状况方面的适用性。研究采用了三种机器学习算法（随机森林回归、支持向量回归和偏最小二乘回归）来比较干旱指数在高粱和玉米植被含水量（VMC）估测中的性能。研究的主要结果如下：(1)各干旱指数比较发现，Ts-Ta-归一化差异植被指数（TDDIn）和Ts-Ta-增强植被指数（TDDIe）较其它指数与作物水分的相关性更高，指数对不同灌溉水平的指数含水量敏感性不同，其中玉米在完全灌溉处理下与TDDIe指数相关性最高（r=−0.93）；（2）在时空特征上，TDDIn、TDDIe和CWSI在时间序列上的波动差异不大，变异系数分别为0.25、0.18和0.24，三者均能表征农田作物水分分布特征，但三维指数在降雨/灌溉后的作物水分空间分布上判断更为准确；（3）在预测单一作物的水分含量时，基于TDDIn和TDDIe的RFR模型对VMC的估计最为准确（R2>0.7），而在考虑多作物样本时，基于TDDIn的模型预测VMC的准确度最高，R2和RMSE分别为0.62%和14.26%。与CWSI比较，三维干旱指数在作物水分估算中更加有效。

Abstract:

Monitoring agricultural drought using remote sensing data is crucial for precision irrigation in modern agriculture.  Utilizing unmanned aerial vehicle (UAV) remote sensing, we explored the applicability of an empirical crop water stress index (CWSI) based on canopy temperature and three-dimensional drought indices (TDDI) constructed from surface temperature (Ts), air temperature (Ta) and five vegetation indices (VIs) for monitoring the moisture status of dryland crops.  Three machine learning algorithms (random forest regression (RFR), support vector regression, and partial least squares regression) were used to compare the performance of the drought indices for vegetation moisture content (VMC) estimation in sorghum and maize.  The main results of the study were as follows: (1) Comparative analysis of the drought indices revealed that Ts-Ta-normalized difference vegetation index (TDDIn) and Ts-Ta-enhanced vegetation index (TDDIe) were more strongly correlated with VMC compared with the other indices.  The indices exhibited varying sensitivities to VMC under different irrigation regimes; the strongest correlation observed was for the TDDIe index with maize under the fully irrigated treatment (r=−0.93). (2) Regarding spatial and temporal characteristics, the TDDIn, TDDIe and CWSI indices showed minimal differences.  Over the experimental period, with coefficients of variation were 0.25, 0.18 and 0.24, respectively. All three indices were capable of effectively characterizing the moisture distribution in dryland maize and sorghum crops, but the TDDI indices more accurately monitored the spatial distribution of crop moisture after a rainfall or irrigation event.  (3) For prediction of the moisture content of single crops, RFR models based on TDDIn and TDDIe estimated VMC most accurately (R2>0.7), and the TDDIn-based model predicted VMC with the highest accuracy when considering multiple-crop samples, with R2 and RMSE of 0.62 and 14.26%, respectively. Thus, TDDI proved more effective than the CWSI in estimating crop water content.

Key words: maize , sorghum ,  Ts-Ta-VIs ,  CWSI ,  UAV ,  machine learning ,  crop moisture monitoring