**5. Conclusions**

In this study, corn canopy spectrum data were collected for three growing stages. First, an S-G filter and SNV correction were applied to the reflectance spectra. Subsequently, the dynamic migration of the canopy spectral characteristics and the chlorophyll content dynamic changes in the three growing periods of G1 to G3 were analyzed. Extraction of the chlorophyll characteristic variables was carried out. Finally, the PLSR detection model of the maize chlorophyll content was established. The conclusions were as follows.

The noise point of the spectral curve was significantly reduced. The scattering effect of the reflection spectrum was significantly reduced after the preprocessing steps of S-G filtering and SNV correction. The reflectance spectrum increased in the 325–400 and 761–970 nm regions as the growth stage advanced and the growth period shifted. The reflectance decreased in the 401–700 and 971–1075 nm regions as the growth stage advanced.

The characteristic variables selected on the basis of the local extrema of correlation coefficients were more evenly distributed compared with the maximum correlation coefficient method. This method weakened the autocorrelation and information redundancy of variables and reflected comprehensive information. The wavelet coefficient obtained by performing CWT on the reflectance spectra was used to efficiently analyze the information of chlorophyll and leaf structure substances in a deep and comprehensive way. The spectral feature extraction method based on CWT highlighted the spectral reflectance features of a specific scale, while suppressing the noncorrelated spectral features and noise of other spectral bands with high flexibility. The proposed method effectively improved the matching accuracy of spectral features.

The highest *Rv* <sup>2</sup> was for the detection model established using the CA peak WFs. The results showed that the CA peakWFs had excellent detection capability for chlorophyll content. CWT combined with the local extremum of the correlation coefficient method is a potentially accurate and efficient strategy for detecting the chlorophyll content of corn crops.

**Author Contributions:** Conceptualization, J.Z. and M.L.; methodology, J.Z.; software, D.G.; validation, L.Q., N.L.; formal analysis, J.Z.; investigation, H.S.; resources, H.S. and Y.Z.; data curation, J.Z.; writing—original draft preparation, J.Z.; writing—review and editing, H.S.; supervision, M.L.; project administration, H.S.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** The project was supported by the National Key Research and Development Program of China (Grant No. 2016YFD0200600-2016YFD0200602), the National Natural Science Foundation of China (Grant No. 31971785 and 31501219), the National Key Research and Development Program of China (Grant No. 2018YFD0300505-1), the Fundamental Research Funds for the Central Universities (Grant No. 2020TC036) and the Graduate Training Project of China Agricultural University (JG2019004 and YW2020007).

**Acknowledgments:** The authors wish to thank students in Key Laboratory of Modern Precision Agriculture System Integration Research for the help of Sample processing and chemical analysis. Also, many thanks to Dry Farming Institute of Hebei Academy of Agricultural and Forestry Sciences for providing experimental site. We would like to thank Zizheng Xing, Zhiyong Zhang, Ning Liu, Longsheng Cheng, and Song Li for their help with field data collection.

**Conflicts of Interest:** The authors declare no conflict of interest.
