Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters

Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events. Leaf water content (LWC) is an important waterlogging indicator, and hyperspectral remote sensing provides a non-destructive, real-time and reliable method to determine LWC. Thus, based on a pot experiment, winter wheat was subjected to different gradients of waterlogging stress at the jointing stage. Leaf hyperspectral data and LWC were collected every 7 days after waterlogging treatment until the winter wheat was mature. Combined with methods such as vegetation index construction, correlation analysis, regression analysis, BP neural network (BPNN), etc., we found that the effect of waterlogging stress on LWC had the characteristics of hysteresis and all waterlogging stress led to the decrease of LWC. LWC decreased faster under severe stress than under slight stress, but the effect of long-term slight stress was greater than that of short-term severe stress. The sensitive spectral bands of LWC were located in the visible (VIS, 400-780 nm) and short-wave infrared (SWIR, 1400-2500 nm) regions. The BPNN Model with the original spectrum at 648 nm, the first derivative spectrum at 500 nm, the red edge position (lambda r), the new vegetation index RVI (437, 466), NDVI (437, 466) and NDVI (747, 1 956) as independent variables was the best model for inverting the LWC of waterlogging in winter wheat (modeling set: R-2 =0.889, RMSE=0.138; validation set: R-2 =0.891, RMSE=0.518). These results have important theoretical significance and practical application value for the precise control of waterlogging stress.

Automated summary

Waterlogging is increasingly affecting food production, with leaf water content (LWC) being an important indicator of waterlogging. Hyperspectral remote sensing is a valuable method to determine LWC, as demonstrated in a study on winter wheat under different waterlogging stress levels. The study found that waterlogging stress leads to a decrease in LWC, with faster decrease under severe stress but a greater effect of long-term slight stress. The best model for determining LWC in winter wheat under waterlogging stress was found to be a BP neural network model based on specific spectral bands.