{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T16:04:18Z","timestamp":1781885058510,"version":"3.54.5"},"reference-count":44,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42271389"],"award-info":[{"award-number":["42271389"]}]},{"name":"National Natural Science Foundation of China","award":["52174160"],"award-info":[{"award-number":["52174160"]}]},{"name":"National Natural Science Foundation of China","award":["P23044"],"award-info":[{"award-number":["P23044"]}]},{"name":"National Natural Science Foundation of China","award":["ZD20232304"],"award-info":[{"award-number":["ZD20232304"]}]},{"name":"project of Sinopec Shengli Oilfield Company","award":["42271389"],"award-info":[{"award-number":["42271389"]}]},{"name":"project of Sinopec Shengli Oilfield Company","award":["52174160"],"award-info":[{"award-number":["52174160"]}]},{"name":"project of Sinopec Shengli Oilfield Company","award":["P23044"],"award-info":[{"award-number":["P23044"]}]},{"name":"project of Sinopec Shengli Oilfield Company","award":["ZD20232304"],"award-info":[{"award-number":["ZD20232304"]}]},{"name":"priority projects for the \u201cScience and Technology for the Development of Mongolia\u201d initiative in 2023","award":["42271389"],"award-info":[{"award-number":["42271389"]}]},{"name":"priority projects for the \u201cScience and Technology for the Development of Mongolia\u201d initiative in 2023","award":["52174160"],"award-info":[{"award-number":["52174160"]}]},{"name":"priority projects for the \u201cScience and Technology for the Development of Mongolia\u201d initiative in 2023","award":["P23044"],"award-info":[{"award-number":["P23044"]}]},{"name":"priority projects for the \u201cScience and Technology for the Development of Mongolia\u201d initiative in 2023","award":["ZD20232304"],"award-info":[{"award-number":["ZD20232304"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The leaf chlorophyll content (LCC) of winter wheat, an important food crop widely grown worldwide, is a key indicator for assessing its growth and health status in response to CO2 stress. However, the remote sensing quantitative estimation of winter wheat LCC under CO2 stress conditions also faces challenges such as an unclear spectral sensitivity range, baseline drift, overlapping spectral peaks, and complex spectral response due to CO2 stress changes. To address these challenges, this study introduced the fractional order derivative (FOD) and continuous wavelet transform (CWT) techniques into the estimation of winter wheat LCC. Combined with the raw hyperspectral data, we deeply analyzed the spectral response characteristics of winter wheat LCC under CO2 stress. We proposed a stacking model including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and adaptive boosting (AdaBoost) to filter the optimal combination from a large number of feature variables. We use a dual-band combination and vegetation index strategy to achieve the accurate estimation of LCC in winter wheat under CO2 stress. The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO2 stress. (2) The 1.2-order derivative dual-band index (RVI (R720, R522)) constructed by combining the sensitive spectral bands of the CO2 response of winter wheat leaves achieved a high-precision estimation of the LCC under CO2 stress conditions (R2 = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R2 = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO2 impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R2 = 0.906). This study not only provides a scientific basis and technical support for the accurate estimation of LCC in winter wheat under CO2 stress but also provides new ideas and methods for coping with climate change, optimizing crop-growing conditions, and improving crop yield and quality in agricultural management. The proposed method is also of great reference value for estimating physiological parameters of other crops under similar environmental stresses.<\/jats:p>","DOI":"10.3390\/rs16173341","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T04:15:01Z","timestamp":1725855301000},"page":"3341","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Hyperspectral Estimation of Chlorophyll Content in Wheat under CO2 Stress Based on Fractional Order Differentiation and Continuous Wavelet Transforms"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6694-4589","authenticated-orcid":false,"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":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debao","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"},{"name":"Inner Mongolia Research Institute of China University of Mining and Technology-Beijing, Ordos 010300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maochen","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinbao","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyi","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guidan","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weining","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.envexpbot.2012.02.007","article-title":"Potential Impact of CO2 Leakage from Carbon Capture and Storage (CCS) Systems on Growth and Yield in Spring Field Bean","volume":"80","author":"Colls","year":"2012","journal-title":"Environ. Exp. Bot."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10456","DOI":"10.1021\/acs.energyfuels.4c00715","article-title":"Comprehensive Review on Leakage Characteristics and Diffusion Laws of Carbon Dioxide Pipelines","volume":"38","author":"Shang","year":"2024","journal-title":"Energy Fuels"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1016\/j.scitotenv.2017.07.030","article-title":"Increased NO2 Emission by Inhibited Plant Growth in the CO2 Leaked Soil Environment: Simulation of CO2 Leakage from Carbon Capture and Storage (CCS) Site","volume":"607\u2013608","author":"Kim","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.scitotenv.2015.02.055","article-title":"Enhancement of Farmland Greenhouse Gas Emissions from Leakage of Stored CO2: Simulation of Leaked CO2 from CCS","volume":"518\u2013519","author":"Zhang","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.ijggc.2011.12.006","article-title":"Experimental Observation of Signature Changes in Bulk Soil Electrical Conductivity in Response to Engineered Surface CO2 Leakage","volume":"7","author":"Zhou","year":"2012","journal-title":"Int. J. Greenh. Gas Control"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3891","DOI":"10.1016\/j.egypro.2014.11.419","article-title":"An Assessment of near Surface CO2 Leakage Detection Techniques under Australian Conditions","volume":"63","author":"Feitz","year":"2014","journal-title":"Energy Procedia"},{"key":"ref_7","first-page":"0144","article-title":"Effect of Doubled Atmospheric CO2 and Nitrogen Application on Photosynthetic Rate and Chlorophyll Fluorescence Character of Winter Wheat. Acta Bot","volume":"31","author":"Wang","year":"2011","journal-title":"Boreal-Occident Sin"},{"key":"ref_8","first-page":"17","article-title":"Effect of Elevated Atmospheric CO2 Concentration on Leaf Characteristic and Evapotranspiration in Winter Wheat","volume":"14","author":"Xu","year":"2014","journal-title":"Sci. Technol. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1111\/j.1469-8137.2006.01688.x","article-title":"Enhancement of Rice Canopy Carbon Gain by Elevated CO2 is Sensitive to Growth Stage and Leaf Nitrogen Concentration","volume":"170","author":"Sakai","year":"2006","journal-title":"New Phytol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1080\/01431161.2020.1823038","article-title":"Hyperspectral Characteristics and Inversion Model Estimation of Winter Wheat under Different Elevated CO2 Concentrations","volume":"42","author":"Liu","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wu, R., Fan, Y., Zhang, L., Yuan, D., and Gao, G. (2024). Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices. Appl. Sci., 14.","DOI":"10.3390\/app14104245"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108559","DOI":"10.1016\/j.compag.2023.108559","article-title":"Comparison of Leaf Chlorophyll Content Retrieval Performance of Citrus Using FOD and CWT Methods with Field-Based Full-Spectrum Hyperspectral Reflectance Data","volume":"217","author":"Xiao","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1080\/01431169408954109","article-title":"Ratios of Leaf Reflectances in Narrow Wavebands as Indicators of Plant Stress","volume":"15","author":"Gregory","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/S0034-4257(97)00110-7","article-title":"Reflectance Wavebands and Indices for Remote Estimation of Photosynthesis and Stomatal Conductance in Pine Canopies","volume":"63","author":"Carter","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijggc.2015.01.016","article-title":"Identifying the Spectral Responses of Several Plant Species under CO2 Leakage and Waterlogging Stresses2","volume":"37","author":"Jiang","year":"2015","journal-title":"Int. J. Greenh. Gas Control"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1109\/TDEI.2022.3226164","article-title":"Pixel-Level Assessment Model of Contamination Conditions of Composite Insulators Based on Hyperspectral Imaging Technology and a Semi-Supervised Ladder Network","volume":"30","author":"Kong","year":"2023","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.rse.2018.09.020","article-title":"New Methods for Improving the Remote Sensing Estimation of Soil Organic Matter Content (SOMC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in Northwest China","volume":"218","author":"Wang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"105092","DOI":"10.1016\/j.oregeorev.2022.105092","article-title":"Estimation of Rock Copper Content Based on Fractional-Order Derivative and Visible Near-Infrared\u2013Shortwave Infrared Spectroscopy","volume":"150","author":"Jiang","year":"2022","journal-title":"Ore Geol. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"126607","DOI":"10.1016\/j.eja.2022.126607","article-title":"Accurate Modeling of Vertical Leaf Nitrogen Distribution in Summer Maize Using in Situ Leaf Spectroscopy via CWT and PLS-Based Approaches","volume":"140","author":"Li","year":"2022","journal-title":"Eur. J. Agron."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108008","DOI":"10.1016\/j.compag.2023.108008","article-title":"UAV-Borne Hyperspectral Estimation of Nitrogen Content in Tobacco Leaves Based on Ensemble Learning Methods","volume":"211","author":"Zhang","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, L., Yuan, D., Fan, Y., and Yang, R. (2024). Hyperspectral Characteristics and SPAD Estimation of Wheat Leaves under CO2 Microleakage Stress. Sensors, 24.","DOI":"10.3390\/s24154776"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6825","DOI":"10.1080\/01431161.2023.2275323","article-title":"Identificating Vegetation Stress under Natural Gas Micro-Leakage Based on Leaf Scale Temporal Hyperspectrum","volume":"44","author":"Pan","year":"2023","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105275","DOI":"10.1016\/j.compag.2020.105275","article-title":"A Possible Fractional Order Derivative and Optimized Spectral Indices for Assessing Total Nitrogen Content in Cotton","volume":"171","author":"Abulaiti","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hasan, U., Jia, K., Wang, L., Wang, C., Shen, Z., Yu, W., Sun, Y., Jiang, H., Zhang, Z., and Guo, J. (2023). Retrieval of Leaf Chlorophyll Contents (LCCs) in Litchi Based on Fractional Order Derivatives and VCPA-GA-ML Algorithms. Plants, 12.","DOI":"10.3390\/plants12030501"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, C., Li, X., Meng, X., Xiao, Z., Wu, X., Wang, X., Ren, L., Li, Y., Zhao, C., and Yang, C. (2023). Hyperspectral Estimation of Nitrogen Content in Wheat Based on Fractional Difference and Continuous Wavelet Transform. Agriculture, 13.","DOI":"10.3390\/agriculture13051017"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gao, G., Zhang, L., Wu, L., and Yuan, D. (2024). Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index. Appl. Sci., 14.","DOI":"10.3390\/app14020703"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and Photographic Infrared Linear Combinations for Monitoring Vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of Leaf-Area Index from Quality of Light on the Forest Floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_29","first-page":"3213","article-title":"Hyperspectral characteristics and chlorophyll content estimation of winter wheat under ozone stress","volume":"43","author":"Yang","year":"2023","journal-title":"Acta Ecol. Sin."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships Between Leaf Pigment Content and Spectral Reflectance Across a Wide Range of Species, Leaf Structures and Developmental Stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density","volume":"76","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"L08403","DOI":"10.1029\/2005GL022688","article-title":"Remote Estimation of Canopy Chlorophyll Content in Crops","volume":"32","author":"Gitelson","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/07038992.1996.10855178","article-title":"Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications","volume":"22","author":"Chen","year":"1996","journal-title":"Can. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1562\/0031-8655(2001)074<0038:OPANEO>2.0.CO;2","article-title":"Optical properties and nondestructive estimation of anthocyanin content in plant leaves","volume":"74","author":"Gitelson","year":"2001","journal-title":"Photochem. Photobiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1034\/j.1399-3054.1999.106119.x","article-title":"Non-Destructive Optical Detection of Pigment Changes during Leaf Senescence and Fruit Ripening","volume":"106","author":"Merzlyak","year":"1999","journal-title":"Physiol. Plant."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s11119-008-9100-2","article-title":"Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves","volume":"10","author":"Devadas","year":"2009","journal-title":"Precis. Agric."},{"key":"ref_37","unstructured":"Ya, S.M., Steven, M., and Foody, G. (2011). Remote sensing or barley stressed with CO2 and herbicide. Environ. Sci. Agric. Food Sci., 551\u2013563. Available online: https:\/\/api.semanticscholar.org\/CorpusID:131132759."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1016\/j.ijggc.2009.03.003","article-title":"Monitoring Effects of a Controlled Subsurface Carbon Dioxide Release on Vegetation Using a Hyperspectral Imager","volume":"3","author":"Keith","year":"2009","journal-title":"Int. J. Greenh. Gas Control"},{"key":"ref_39","first-page":"1882","article-title":"Research on the Spectral Feature and Identification of the Surface Vegetation Stressed by Stored CO2 Underground Leakage","volume":"32","author":"Chen","year":"2012","journal-title":"Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"112001","DOI":"10.1016\/j.ecolind.2024.112001","article-title":"Assessment of Remote-Sensed Vegetation Indices for Estimating Forest Chlorophyll Concentration","volume":"162","author":"Gao","year":"2024","journal-title":"Ecol. Indic."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1080\/01431160802339431","article-title":"The Effects of High Soil CO2 Concentrations on Leaf Reflectance of Maize Plants","volume":"30","author":"Noomen","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.geoderma.2005.07.016","article-title":"Small-Scale Spatial Variation in Soil CO2 Concentration in a Natural Carbon Dioxide Spring and Some Related Plant Responses","volume":"133","author":"Vodnik","year":"2006","journal-title":"Geoderma"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2327","DOI":"10.18306\/dlkxjz.2022.12.011","article-title":"Estimation of soil organic carbon and its uncertainty in Qinghai Province","volume":"41","author":"Zhou","year":"2022","journal-title":"Prog. Geogr."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3341\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:51:47Z","timestamp":1760111507000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3341"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,9]]},"references-count":44,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173341"],"URL":"https:\/\/doi.org\/10.3390\/rs16173341","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,9]]}}}