{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T15:42:46Z","timestamp":1774712566678,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T00:00:00Z","timestamp":1598832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Fund of China","award":["Grant No. 31971785, 31501219, and 41801245"],"award-info":[{"award-number":["Grant No. 31971785, 31501219, and 41801245"]}]},{"name":"the Fundamental Research Funds for the Central Universities of China","award":["Grant No. 2020TC036"],"award-info":[{"award-number":["Grant No. 2020TC036"]}]},{"name":"the Graduate Training Project of China Agricultural University","award":["Grant No. JD2019004, and YW2020007"],"award-info":[{"award-number":["Grant No. JD2019004, and YW2020007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The analysis of chlorophyll concentration based on spectroscopy has great importance for monitoring the growth state and guiding the precision nitrogen management of potato crops in the field. A suitable data processing and modeling method could improve the stability and accuracy of chlorophyll analysis. To develop such a method, we collected the modelling data by conducting field experiments at the tillering, tuber-formation, tuber-bulking, and tuber-maturity stages in 2018. A chlorophyll analysis model was established using the partial least-square (PLS) algorithm based on original reflectance, standard normal variate reflectance, and wavelet features (WFs) under different decomposition scales (21\u2013210, Scales 1\u201310), which were optimized by the competitive adaptive reweighted sampling (CARS) algorithm. The performances of various models were compared. The WFs under Scale 3 had the strongest correlation with chlorophyll concentration with a correlation coefficient of \u22120.82. In the model calibration process, the optimal model was the Scale3-CARS-PLS, which was established based on the sensitive WFs under Scale 3 selected by CARS, with the largest coefficient of determination of calibration set (Rc2) of 0.93 and the smallest Rc2\u2212Rcv2 value of 0.14. In the model validation process, the Scale3-CARS-PLS model had the largest coefficient of determination of validation set (Rv2) of 0.85 and the smallest root\u2013mean\u2013square error of cross-validation (RMSEV) value of 2.77 mg\/L, demonstrating good prediction capability of chlorophyll concentration. Finally, the analysis performance of the Scale3-CARS-PLS model was measured using the testing data collected in 2020; the R2 and RMSE values were 0.69 and 3.36 mg\/L, showing excellent applicability. Therefore, the Scale3-CARS-PLS model could be used to analyze chlorophyll concentration. This study indicated the best decomposition scale of continuous wavelet transform and provided an important support method for chlorophyll analysis in the potato crops.<\/jats:p>","DOI":"10.3390\/rs12172826","type":"journal-article","created":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T11:53:49Z","timestamp":1598874829000},"page":"2826","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Analysis of Chlorophyll Concentration in Potato Crop by Coupling Continuous Wavelet Transform and Spectral Variable Optimization"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1129-9071","authenticated-orcid":false,"given":"Ning","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China"}]},{"given":"Zizheng","family":"Xing","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"}]},{"given":"Ruomei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"}]},{"given":"Lang","family":"Qiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"}]},{"given":"Minzan","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China"}]},{"given":"Gang","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China"}]},{"given":"Hong","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, N., Zhao, R., Qiao, L., Zhang, Y., Li, M., Sun, H., Xing, Z., and Wang, X. 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