Influence of Time-Lag Effects between Winter-Wheat Canopy Temperature and Atmospheric Temperature on the Accuracy of CWSI Inversion of Photosynthetic Parameters

• Published in: Plants (Basel) Vol. 13; no. 12; p. 1702
• Main Authors: Wang, Yujin, Lu, Yule, Yang, Ning, Wang, Jiankun, Huang, Zugui, Chen, Junying, Zhang, Zhitao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.06.2024; MDPI
• Subjects: Accuracy; Algorithms; Atmospheric temperature; Canopies; Correlation analysis; Crops; Cross correlation; CWSI; Irrigation; Lattice theory; Machine learning; Methods; Parameter modification; Photosynthesis; photosynthetic rate; Physiology; Remote sensing; stomatal conductance; Time lag; time-lag effects; transpiration rate; Trends; Triticum aestivum; Water; Water stress; Wheat; Winter wheat; stomatal conductance; time-lag effects; transpiration rate; winter wheat; CWSI; photosynthetic rate
• ISSN: 2223-7747; 2223-7747
• DOI: 10.3390/plants13121702

When calculating the CWSI, previous researchers usually used canopy temperature and atmospheric temperature at the same time. However, it takes some time for the canopy temperature (Tc) to respond to atmospheric temperature (Ta), suggesting the time-lag effects between Ta and Tc. In order to investigate time-lag effects between Ta and Tc on the accuracy of the CWSI inversion of photosynthetic parameters in winter wheat, we conducted an experiment. In this study, four moisture treatments were set up: T1 (95% of field water holding capacity), T2 (80% of field water holding capacity), T3 (65% of field water holding capacity), and T4 (50% of field water holding capacity). We quantified the time-lag parameter in winter wheat using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and gray time-lag correlation analysis. Based on the time-lag parameter, we modified the CWSI theoretical and empirical models and assessed the impact of time-lag effects on the accuracy of the CWSI inversion of photosynthesis parameters. Finally, we applied several machine learning algorithms to predict the daily variation in the CWSI after time-lag correction. The results show that: (1) The time-lag parameter calculated using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and gray time-lag correlation analysis are 44-70, 32-44, 42-58, and 76-97 min, respectively. (2) The CWSI empirical model corrected by the time-lag mutual information method has the highest correlation with photosynthetic parameters. (3) GA-SVM has the highest prediction accuracy for the CWSI empirical model corrected by the time-lag mutual information method. Considering time lag effects between Ta and Tc effectively enhanced the correlation between CWSI and photosynthetic parameters, which can provide theoretical support for thermal infrared remote sensing to diagnose crop water stress conditions.