{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T23:23:57Z","timestamp":1771889037414,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,18]],"date-time":"2022-09-18T00:00:00Z","timestamp":1663459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Central Public-interest Scientific Institution Basal Research Fund","award":["Y2022GH05"],"award-info":[{"award-number":["Y2022GH05"]}]},{"name":"Central Public-interest Scientific Institution Basal Research Fund","award":["1610132021024"],"award-info":[{"award-number":["1610132021024"]}]},{"name":"Central Public-interest Scientific Institution Basal Research Fund","award":["2017KCXTD015"],"award-info":[{"award-number":["2017KCXTD015"]}]},{"name":"Fundamental Research Funds for Central Non-profit Scientific Institution","award":["Y2022GH05"],"award-info":[{"award-number":["Y2022GH05"]}]},{"name":"Fundamental Research Funds for Central Non-profit Scientific Institution","award":["1610132021024"],"award-info":[{"award-number":["1610132021024"]}]},{"name":"Fundamental Research Funds for Central Non-profit Scientific Institution","award":["2017KCXTD015"],"award-info":[{"award-number":["2017KCXTD015"]}]},{"name":"Shantou University Team Building Project for Innovative and Strong University","award":["Y2022GH05"],"award-info":[{"award-number":["Y2022GH05"]}]},{"name":"Shantou University Team Building Project for Innovative and Strong University","award":["1610132021024"],"award-info":[{"award-number":["1610132021024"]}]},{"name":"Shantou University Team Building Project for Innovative and Strong University","award":["2017KCXTD015"],"award-info":[{"award-number":["2017KCXTD015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>SPAD value was measured by a portable chlorophyll instrument, which can reflect the relative chlorophyll content of vegetation well. Chlorophyll is an important organic chemical substance in plants that acquires and transmits energy during photosynthesis. The continuous spectral curve of winter wheat can be obtained rapidly in a specific band range by using hyperspectral remote sensing technology to estimate the SPAD value of winter wheat, which is of great significance to the growth monitoring and yield estimation research of winter wheat. In this study, with winter wheat as the research object, the spectral data and corresponding SPAD value in different growth stages were used as the data source, 20 kinds of data preprocessing spectra and sensitive spectral indices set the data as model input values, the partial least square regression (PLSR) model was established to estimate the SPAD value, and the model estimation results of different model input values at different growth stages were compared in detail. The results showed that the set of sensitive spectral indices selected in this study as input values can effectively improve the accuracy and stability of the PLSR model. In addition, the effects of 20 spectral data pretreatment methods on the estimation results of the SPAD value were compared and analyzed in different growth stages. It was found that the spectral data pretreated by the combination of wavelet packet denoising, first-order derivative transformation and principal component analysis can improve the accuracy and stability of PLSR model, and it is suitable for all growth stages. The results also showed that the estimation model is highly sensitive to the standard deviation of the SPAD value (STDchl) in sample sets. When the standard deviation is greater than 5.5 SPAD, the larger the STDchl is, the higher the model estimation accuracy is, and the more stable the model is. At this time, the model estimation accuracy is higher (R2V is greater than 0.5, ratio of performance to deviation is greater than 1.4), which can meet the estimation requirements of the SPAD value.<\/jats:p>","DOI":"10.3390\/rs14184660","type":"journal-article","created":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T04:49:22Z","timestamp":1663562962000},"page":"4660","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Winter Wheat SPAD Value Inversion Based on Multiple Pretreatment Methods"],"prefix":"10.3390","volume":"14","author":[{"given":"Lanzhi","family":"Shen","sequence":"first","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences\/Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081, China"},{"name":"21st Century Space Technology Application Co., Ltd., Beijing 100096, China"},{"name":"Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University, Shantou 515063, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9674-6020","authenticated-orcid":false,"given":"Maofang","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences\/Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081, China"}]},{"given":"Jingwen","family":"Yan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University, Shantou 515063, China"}]},{"given":"Qizhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences\/Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081, China"}]},{"given":"Hua","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Emergency Management, Institute of Disaster Prevention, Sanhe 065201, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,18]]},"reference":[{"key":"ref_1","first-page":"24","article-title":"Hyperspectral inversion of chlorophyll content combined with PRO-4SAIL and BP neural network","volume":"516","author":"Guo","year":"2020","journal-title":"Bull. 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