{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T08:55:23Z","timestamp":1776329723339,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52174160"],"award-info":[{"award-number":["52174160"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To non-destructively and rapidly monitor the chlorophyll content of winter wheat leaves under CO2 microleakage stress, and to establish the quantitative relationship between chlorophyll content and sensitive bands in the winter wheat growing season from 2023 to 2024, the leakage rate was set to 1 L\/min, 3 L\/min, 5 L\/min, and 0 L\/min through field experiments. The dimensional reduction was realized, fractional differential processing of a wheat canopy spectrum was carried out, a multiple linear regression (MLR) and partial least squares regression (PLSR) estimation model was constructed using a SPA selection band, and the model\u2019s accuracy was evaluated. The optimal model for hyperspectral estimation of wheat SPAD under CO2 microleakage stress was screened. The results show that the spectral curves of winter wheat leaves under CO2 microleakage stress showed a \u201cred shift\u201d of the green peak and a \u201cblue shift\u201d of the red edge. Compared with 1 L\/min and 3 L\/min, wheat leaves were more affected by CO2 at 5 L\/min. Evaluation of the accuracy of the MLR and PLSR models shows that the MLR model is better, where the MLR estimation model based on 1.1, 1.8, 0.4, and 1.7 differential SPAD is the best for leakage rates of 1 L\/min, 3 L\/min, 5 L\/min, and 0 L\/min, with validation set R2 of 0.832, 0.760, 0.928, and 0.773, which are 11.528, 14.2, 17.048, and 37.3% higher than the raw spectra, respectively. This method can be used to estimate the chlorophyll content of winter wheat leaves under CO2 trace-leakage stress and to dynamically monitor CO2 trace-leakage stress in crops.<\/jats:p>","DOI":"10.3390\/s24154776","type":"journal-article","created":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T10:46:20Z","timestamp":1721817980000},"page":"4776","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Hyperspectral Characteristics and SPAD Estimation of Wheat Leaves under CO2 Microleakage Stress"],"prefix":"10.3390","volume":"24","author":[{"given":"Liuya","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Debao","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqing","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renxu","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"142206","DOI":"10.1016\/j.scitotenv.2020.142206","article-title":"Exploring Spatial Characteristics of City-Level CO2 Emissions in China and Their Influencing Factors from Global and Local Perspectives","volume":"754","author":"Liu","year":"2021","journal-title":"Sci. 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