Study on Quantitative Inversion of Leaf Water Content of Winter Wheat Based on Discrete Wavelet Technique
ZHU Yu-chen1, 2, WANG Yan-cang3, 4, 5, LI Xiao-fang6, LIU Xing-yu3, GU Xiao-he4*, ZHAO Qi-chao3, 4, 5
1. Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, China
2. Fujian Provincial Key Laboratory of Water Cycling and Eco-Geological Processes, Xiamen 361000, China
3. North China Institute of Aerospace Engineering，Langfang 065000, China
4. Information Technology Research Center，Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
5. Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province，Langfang 065000, China
6. Langfang Normal University, Langfang 065000, China
Abstract：Due to the influence of the field environment, winter wheat canopy spectra collected in the field contain a large amount of noise unrelated to the target information, which limits the ability of hyperspectral data to estimate the information of winter wheat plants. In order to limit the influence of noise information on spectral information and explore the methods to improve the estimation ability of spectral information on the water supply of winter wheat plants, this study obtained the hyperspectral data of winter wheat and its leaf water content information through field experiments, processed and analyzed the hyperspectral data by discrete wavelet algorithm, combined with correlation analysis algorithm and partial least squares algorithm, and quantitatively analyzed the influence of five types of wavelet bases on the discrete wavelet algorithm to separate The results show that: (1) the discrete wavelet algorithm can be used to separate the available spectral information from noise, and (2) the wavelet bases can be used to separate the available spectral information from noise, to provide theoretical and methodological support for the processing and analysis of the spectral data in the field. The results show that (1) the sensitive bands are mostly distributed in D1—D5 scales, and the distribution intervals of sensitive bands are relatively consistent among wavelet bases. However, there are some differences in band positions and correlation strengths, which indicates that the choice of wavelet bases can influence the correlation strengths and band positions of high-frequency information and winter wheat leaf water content. (2) The available spectral information and noise information both show a pattern of increasing and then decreasing with the increase of decomposition scale. The interference strength of noise information on the estimation ability of high-frequency information decreases with the increase of scale, and the estimation ability of high-frequency information on the water content of winter wheat leaves decreases with the increase of scale. (3) The accuracy and stability of the model are the results of the combined effect of available spectral information and noise information, in which the estimation model constructed based on the D5 scale of Meyer wavelet basis is the optimal model with R2=0.625 and RMSE=1.562 for modeling accuracy and R2=0.767 and RMSE=1.828 for validation accuracy. Spectral processing and analysis, and provide some reference for the processing and analysis of spectral information that is heavily influenced by noise, and also provide basic support for the detection of the water content of crop leaves within regions with high annual water vapor content, such as southwest and south China, or in the north in summer.
朱玉晨，王延仓，李笑芳，刘星宇，顾晓鹤，赵起超. 基于离散小波技术定量反演冬小麦叶片含水量的研究[J]. 光谱学与光谱分析, 2023, 43(09): 2902-2909.
ZHU Yu-chen, WANG Yan-cang, LI Xiao-fang, LIU Xing-yu, GU Xiao-he, ZHAO Qi-chao. Study on Quantitative Inversion of Leaf Water Content of Winter Wheat Based on Discrete Wavelet Technique. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2902-2909.
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