Abstract
Aims
Considering time lag effects between atmospheric temperature (Ta) and canopy temperature (Tc) may improve the accuracy of Crop Water Stress Index (CWSI) inversions of photosynthetic parameters, which is crucial for enhancing the precision in monitoring crop water stress conditions.
Methods
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 grey time-lag correlation analysis; Based on the time lag parameter, we modified CWSI theoretical and empirical model, and assessed the impact of time lag effects on the accuracy of CWSI inversion of photosynthesis parameters. Finally, we applied several machine learning algorithms to predict the daily variation of CWSI after time-lag correction.
Results
The results showed that: (1) The time lag parameter calculated using the time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and grey time-lag correlation an-alysis were 44–70, 32–44, 42–58, and 76–97 min. (2) CWSI empirical model corrected by the time-lag mutual information method had the highest correlation with photosynthetic parameters. (3) GA-SVM had the highest prediction accuracy for CWSI empirical model corrected by the time-lag mutual information method.
Conclusions
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.