Using spectral transformation processes to estimate chlorophyll content of winter wheat under low temperature stress

Chlorophyll content is a vital index of photosynthetic capacity and crop growth status. In recent years, freeze injury had become a main meteorological disaster at jointing stage of winter wheat in the northern region of China. Although global climate had been warming since the 1980s, freeze injury had not weakened. Climate warming had led to a decline in the proportion of wheat varieties planted in winter and to a rise in the proportion of the varieties planted in spring. With increasing warm autumn years, the issue of wheat overgrowth has worsened, decreasing the ability of wheat to resist cold. Climate change has not been stable and extreme weather events have increased, implying that there was still the risk of freeze injury of wheat. Shanxi Province suffered freeze injury and the maximum area affected by freeze injury has been estimated at 0.26 million hectares. This had a severe impact on the growth and development of wheat, and ultimately affected the yield of wheat. The rapid and accurate estimation of chlorophyll content of winter wheat is meaningful in resisting the occurrence of freeze injury. However, the routine methods of measuring chlorophyll content are complex and time-consuming. Therefore, developing a rapid and non-destructive chlorophyll content diagnosis technology can be an effective way to monitor winter wheat freeze injury. Here, two varieties of winter wheat were treated under -6℃ temperature stress for 4, 8 and 12 hours at jointing stage. Moreover, canopy spectra were collected and the raw spectrum transformed with respect to 15 transformation methods and then the spectral transformation processes of chlorophyll content of winter wheat were analyzed in the PLSR model. The aim was to select the optimal spectral transformation of chlorophyll content in winter wheat under low temperature stress, and provide theoretical basis for monitoring freeze injury of winter wheat. The results showed that the chlorophyll content of two winter wheat varieties declined with increasing time of low-temperature stress. With increasing number of days after freeze, the differences between low temperature and control treatments decreased gradually. Moreover, the near-infrared region reflectance increased greatly with strengthening low-temperature stress and the visible region had no significant difference in short-term stress after 5 days. While the yellow and red bands increased, the near-infrared region decreased in differences after 10, 20 and 35 days of stress. Compared with the raw spectrum, the transformation methods under differential treatments (e.g., reciprocal, logarithm, power, and square root transformation methods) failed to improve the relevance of chlorophyll content and therefore the overall performance of model was poor. Other differential transformation processes of chlorophyll content diagnosis models had higher precision than raw spectral analysis, except for T₆(lgR)', R is the spectral reflectance. Moreover, the second-order differential for raw spectrum (T₁₅, R") had a higher accuracy (R_C²=0.930, RMSE_C=0.340; R_V²=0.753) respectively for the calibrated and validated models after comprehensive evaluation of predicted performance and complexity level of different models. It showed that the second-order differential for raw spectrum (T₁₅, R") was the most plausible transformation method of spectral reflectance for evaluating chlorophyll content of winter wheat under low temperature stress.