{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T05:12:59Z","timestamp":1783919579519,"version":"3.55.0"},"reference-count":85,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T00:00:00Z","timestamp":1697068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Plan","award":["2019YFD1000104"],"award-info":[{"award-number":["2019YFD1000104"]}]},{"name":"National Key Research and Development Plan","award":["31901963"],"award-info":[{"award-number":["31901963"]}]},{"name":"National Key Research and Development Plan","award":["31972356"],"award-info":[{"award-number":["31972356"]}]},{"name":"National Key Research and Development Plan","award":["CARS 26"],"award-info":[{"award-number":["CARS 26"]}]},{"name":"National Natural Fund Project","award":["2019YFD1000104"],"award-info":[{"award-number":["2019YFD1000104"]}]},{"name":"National Natural Fund Project","award":["31901963"],"award-info":[{"award-number":["31901963"]}]},{"name":"National Natural Fund Project","award":["31972356"],"award-info":[{"award-number":["31972356"]}]},{"name":"National Natural Fund Project","award":["CARS 26"],"award-info":[{"award-number":["CARS 26"]}]},{"name":"earmarked fund","award":["2019YFD1000104"],"award-info":[{"award-number":["2019YFD1000104"]}]},{"name":"earmarked fund","award":["31901963"],"award-info":[{"award-number":["31901963"]}]},{"name":"earmarked fund","award":["31972356"],"award-info":[{"award-number":["31972356"]}]},{"name":"earmarked fund","award":["CARS 26"],"award-info":[{"award-number":["CARS 26"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To address the demands of precision agriculture and the measurement of plant photosynthetic response and nitrogen status, it is necessary to employ advanced methods for estimating chlorophyll content quickly and non-destructively at a large scale. Therefore, we explored the utilization of both linear regression and machine learning methodology to improve the prediction of leaf chlorophyll content (LCC) in citrus trees through the analysis of hyperspectral reflectance data in a field experiment. And the relationship between phenology and LCC estimation was also tested in this study. The LCC of citrus tree leaves at five growth seasons (May, June, August, October, and December) were measured alongside measurements of leaf hyperspectral reflectance. The measured LCC data and spectral parameters were used for evaluating LCC using univariate linear regression (ULR), multivariate linear regression (MLR), random forest regression (RFR), K-nearest neighbor regression (KNNR), and support vector regression (SVR). The results revealed the following: the MLR and machine learning models (RFR, KNNR, SVR), in both October and December, performed well in LCC estimation with a coefficient of determination (R2) greater than 0.70. In August, the ULR model performed the best, achieving an R2 of 0.69 and root mean square error (RMSE) of 8.92. However, the RFR model demonstrated the highest predictive power for estimating LCC in May, June, October, and December. Furthermore, the prediction accuracy was the best with the RFR model with parameters VOG2 and Carte4 in October, achieving an R2 of 0.83 and RMSE of 6.67. Our findings revealed that using just a few spectral parameters can efficiently estimate LCC in citrus trees, showing substantial promise for implementation in large-scale orchards.<\/jats:p>","DOI":"10.3390\/rs15204934","type":"journal-article","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T12:46:13Z","timestamp":1697114773000},"page":"4934","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Utilizing Hyperspectral Reflectance and Machine Learning Algorithms for Non-Destructive Estimation of Chlorophyll Content in Citrus Leaves"],"prefix":"10.3390","volume":"15","author":[{"given":"Dasui","family":"Li","sequence":"first","affiliation":[{"name":"College of Horticulture & Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingqing","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Horticulture & Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siqi","family":"Ruan","sequence":"additional","affiliation":[{"name":"College of Horticulture & Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"East China Academy of Inventory and Planning of NFGA, Hangzhou 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinzhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Horticulture & Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"National Key Laboratory for Germplasm Innovation & Utilization of Horticultural Crops, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chungen","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Horticulture & Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"National Key Laboratory for Germplasm Innovation & Utilization of Horticultural Crops, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2279-3123","authenticated-orcid":false,"given":"Yongzhong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Horticulture & Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"National Key Laboratory for Germplasm Innovation & Utilization of Horticultural Crops, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4957-3681","authenticated-orcid":false,"given":"Yuanyong","family":"Dian","sequence":"additional","affiliation":[{"name":"College of Horticulture & Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"Hubei Engineering Technology Research Center for Forestry Information, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Horticulture & Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"Hubei Engineering Technology Research Center for Forestry Information, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Peng, Y., Nguy-Robertson, A., Arkebauer, T., and Gitelson, A. (2017). Assessment of Canopy Chlorophyll Content Retrieval in Maize and Soybean: Implications of Hysteresis on the Development of Generic Algorithms. Remote Sens., 9.","DOI":"10.3390\/rs9030226"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/BF00377192","article-title":"Photosynthesis and Nitrogen Relationships in Leaves of C3 Plants","volume":"78","author":"Evans","year":"1989","journal-title":"Oecologia"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.envexpbot.2019.03.003","article-title":"Morphological and Physiological Responses to Contrasting Nitrogen Regimes in Populus Cathayana Is Linked to Resources Allocation and Carbon\/Nitrogen Partition","volume":"162","author":"Luo","year":"2019","journal-title":"Environ. Exp. Bot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4065","DOI":"10.1093\/jxb\/eru191","article-title":"Linking Chlorophyll a Fluorescence to Photosynthesis for Remote Sensing Applications: Mechanisms and Challenges","volume":"65","author":"Atherton","year":"2014","journal-title":"J. Exp. Bot."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3513","DOI":"10.1111\/gcb.13599","article-title":"Leaf Chlorophyll Content as a Proxy for Leaf Photosynthetic Capacity","volume":"23","author":"Croft","year":"2017","journal-title":"Glob. Chang. Biol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1104\/pp.85.2.355","article-title":"The Nitrogen Use Efficiency of C3 and C4 Plants: III. Leaf Nitrogen Effects on the Activity of Carboxylating Enzymes in Chenopodium album (L.) and Amaranthus retroflexus (L.)","volume":"85","author":"Sage","year":"1987","journal-title":"Plant Physiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1093\/treephys\/tpv091","article-title":"Global Poplar Root and Leaf Transcriptomes Reveal Links between Growth and Stress Responses under Nitrogen Starvation and Excess","volume":"35","author":"Luo","year":"2015","journal-title":"Tree Physiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106775","DOI":"10.1016\/j.compag.2022.106775","article-title":"UAV-Based Chlorophyll Content Estimation by Evaluating Vegetation Index Responses under Different Crop Coverages","volume":"196","author":"Qiao","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Aasen, H., Honkavaara, E., Lucieer, A., and Zarco-Tejada, P. (2018). Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote Sens., 10.","DOI":"10.3390\/rs10071091"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ta, N., Chang, Q., and Zhang, Y. (2021). Estimation of Apple Tree Leaf Chlorophyll Content Based on Machine Learning Methods. Remote Sens., 13.","DOI":"10.3390\/rs13193902"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhou, J.-J., Zhang, Y.-H., Han, Z.-M., Liu, X.-Y., Jian, Y.-F., Hu, C.-G., and Dian, Y.-Y. (2021). Evaluating the Performance of Hyperspectral Leaf Reflectance to Detect Water Stress and Estimation of Photosynthetic Capacities. Remote Sens., 13.","DOI":"10.3390\/rs13112160"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1007\/s10712-019-09511-5","article-title":"Assessing Vegetation Function with Imaging Spectroscopy","volume":"40","author":"Gamon","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"112724","DOI":"10.1016\/j.rse.2021.112724","article-title":"Transfer-Learning-Based Approach for Leaf Chlorophyll Content Estimation of Winter Wheat from Hyperspectral Data","volume":"267","author":"Zhang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2016.03.024","article-title":"Seasonal Stability of Chlorophyll Fluorescence Quantified from Airborne Hyperspectral Imagery as an Indicator of Net Photosynthesis in the Context of Precision Agriculture","volume":"179","author":"Fereres","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/S0034-4257(98)00046-7","article-title":"Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll a+b, and Total Carotenoid Content in Eucalyptus Leaves","volume":"66","author":"Datt","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"17360","DOI":"10.1038\/s41598-020-73745-2","article-title":"Dissection of Hyperspectral Reflectance to Estimate Nitrogen and Chlorophyll Contents in Tea Leaves Based on Machine Learning Algorithms","volume":"10","author":"Yamashita","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Poobalasubramanian, M., Park, E.-S., Faqeerzada, M.A., Kim, T., Kim, M.S., Baek, I., and Cho, B.-K. (2022). Identification of Early Heat and Water Stress in Strawberry Plants Using Chlorophyll-Fluorescence Indices Extracted via Hyperspectral Images. Sensors, 22.","DOI":"10.3390\/s22228706"},{"key":"ref_18","first-page":"1546","article-title":"Influence of smooth, 1st derivative and baseline correction on the near-infrared spectrum analysis with PLS","volume":"24","author":"Zheng","year":"2004","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/S1881-8366(10)80001-1","article-title":"Cover Crop Nutrient and Biomass Assessment System Using Portable Hyperspectral Camera and Laser Distance Sensor","volume":"3","author":"Zhao","year":"2010","journal-title":"Eng. Agric. Environ. Food"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhao, T., Nakano, A., Iwaski, Y., and Umeda, H. (2020). Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories. Appl. Sci., 10.","DOI":"10.3390\/app10134665"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.2112\/JCOASTRES-D-12-00209.1","article-title":"Explaining the Spectral Red-Edge Features of Inundated Marsh Vegetation","volume":"290","author":"Turpie","year":"2013","journal-title":"J. Coast. Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shah, S.H., Angel, Y., Houborg, R., Ali, S., and McCabe, M.F. (2019). A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat. Remote Sens., 11.","DOI":"10.3390\/rs11080920"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cui, B., Zhao, Q., Huang, W., Song, X., Ye, H., and Zhou, X. (2019). A New Integrated Vegetation Index for the Estimation of Winter Wheat Leaf Chlorophyll Content. Remote Sens., 11.","DOI":"10.3390\/rs11080974"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105786","DOI":"10.1016\/j.compag.2020.105786","article-title":"Estimating Leaf Chlorophyll Content of Crops via Optimal Unmanned Aerial Vehicle Hyperspectral Data at Multi-Scales","volume":"178","author":"Zhu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.agrformet.2017.10.017","article-title":"Wavelet-Based Coupling of Leaf and Canopy Reflectance Spectra to Improve the Estimation Accuracy of Foliar Nitrogen Concentration","volume":"248","author":"Wang","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.cj.2016.01.008","article-title":"Estimation of Biomass in Wheat Using Random Forest Regression Algorithm and Remote Sensing Data","volume":"4","author":"Wang","year":"2016","journal-title":"Crop J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.compag.2017.06.012","article-title":"Contactless and Non-Destructive Chlorophyll Content Prediction by Random Forest Regression: A Case Study on Fresh-Cut Rocket Leaves","volume":"140","author":"Cavallo","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","first-page":"132","article-title":"Estimation of Chlorophyll Content in Maize Canopy Using Wavelet Denoising and SVR Method","volume":"11","author":"Liu","year":"2018","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Maji, A.K., Saha, G., Das, S., Basu, S., and Tavares, J.M.R.S. (2021). Proceedings of the International Conference on Computing and Communication Systems, Springer.","DOI":"10.1007\/978-981-33-4084-8"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Narmilan, A., Gonzalez, F., Salgadoe, A.S.A., Kumarasiri, U.W.L.M., Weerasinghe, H.A.S., and Kulasekara, B.R. (2022). Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14051140"},{"key":"ref_31","unstructured":"Ma, R., Tang, T., and Wang, X. (2023). Correlation Analysis of Citrus Chlorophyll Content based on Machine Learning. Sci. Technol. Innov., 72\u201375."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kong, W., Huang, W., Zhou, X., Ye, H., Dong, Y., and Casa, R. (2017). Off-Nadir Hyperspectral Sensing for Estimation of Vertical Profile of Leaf Chlorophyll Content within Wheat Canopies. Sensors, 17.","DOI":"10.3390\/s17122711"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4589","DOI":"10.1109\/JSTARS.2014.2360069","article-title":"Newly Combined Spectral Indices to Improve Estimation of Total Leaf Chlorophyll Content in Cotton","volume":"7","author":"Jin","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.fcr.2012.06.017","article-title":"Comparison of Two Methods for Estimation of Leaf Total Chlorophyll Content Using Remote Sensing in Wheat","volume":"135","author":"Jin","year":"2012","journal-title":"Field Crops Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"110024","DOI":"10.1016\/j.scienta.2021.110024","article-title":"Remotely Sensed Real-Time Quantification of Biophysical and Biochemical Traits of Citrus (Citrus sinensis L.) Fruit Orchards\u2014A Review","volume":"282","author":"Ali","year":"2021","journal-title":"Sci. Hortic."},{"key":"ref_36","first-page":"1093","article-title":"Relationship between Chlorophyll Content and Canopy Reflectance in Washington Navel Orange Trees (Citrus sinensis (L.) Osbeck)","volume":"37","author":"Sari","year":"2005","journal-title":"Pak. J. Bot."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Osco, L.P., Ramos, A.P.M., Faita Pinheiro, M.M., Moriya, \u00c9.A.S., Imai, N.N., Estrabis, N., Ianczyk, F., de Ara\u00fajo, F.F., Liesenberg, V., and de Castro Jorge, L.A. (2020). A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements. Remote Sens., 12.","DOI":"10.3390\/rs12060906"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gerhards, M., Schlerf, M., Mallick, K., and Udelhoven, T. (2019). Challenges and Future Perspectives of Multi-\/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review. Remote Sens., 11.","DOI":"10.3390\/rs11101240"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, F., Wang, L., Liu, J., Wang, Y., and Chang, Q. (2019). Evaluation of Leaf N Concentration in Winter Wheat Based on Discrete Wavelet Transform Analysis. Remote Sens., 11.","DOI":"10.3390\/rs11111331"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1562\/0031-8655(2001)0740038OPANEO2.0.CO2","article-title":"Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves","volume":"74","author":"Gitelson","year":"2007","journal-title":"Photochem. Photobiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1080\/01431161.2016.1186850","article-title":"Estimating Crop Chlorophyll Content with Hyperspectral Vegetation Indices and the Hybrid Inversion Method","volume":"37","author":"Liang","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","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":"Carter","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/S0176-1617(99)80314-9","article-title":"A New Reflectance Index for Remote Sensing of Chlorophyll Content in Higher Plants: Tests Using Eucalyptus Leaves","volume":"154","author":"Datt","year":"1999","journal-title":"J. Plant Physiol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/0034-4257(94)90018-3","article-title":"Development of Vegetation and Soil Indices for MODIS-EOS","volume":"49","author":"Huete","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral Vegetation Indices and Novel Algorithms for Predicting Green LAI of Crop Canopies: Modeling and Validation in the Context of Precision Agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1029\/1999GL010993","article-title":"Cloud-Vegetation Interaction: Use of Normalized Difference Cloud Index for Estimation of Cloud Optical Thickness","volume":"27","author":"Marshak","year":"2000","journal-title":"Geophys. Res. Lett."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/0034-4257(94)00114-3","article-title":"Estimating PAR Absorbed by Vegetation from Bidirectional Reflectance Measurements","volume":"51","author":"Roujean","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/978-0-585-33173-7_11","article-title":"Imaging Spectrometry in Agriculture\u2014Plant Vitality and Yield Indicators","volume":"Volume 4","author":"Hill","year":"1994","journal-title":"Imaging Spectrometry\u2014A Tool for Environmental Observations"},{"key":"ref_51","unstructured":"Vincini, M., Frazzi, E., and D\u2019Alessio, P. (2006, January 19\u201321). Angular Dependence of Maize and Sugar Beet VIs from Directional CHRIS\/Proba Data. Proceedings of the 4th ESA CHRIS PROBA Workshop, ESRIN, Frascati, Italy."},{"key":"ref_52","first-page":"221","article-title":"Semi-Empirical Indices to Assess Carotenoids\/Chlorophyll Alpha Ratio from Leaf Spectral Reflectance","volume":"31","author":"Penuelas","year":"1995","journal-title":"Photosynthetica"},{"key":"ref_53","unstructured":"Lichtenthaler, H.K., Lang, M., Stober, F., Sowinska, M., Heisel, F., and Miehe, J.A. (1995, January 4\u20136). Detection of Photosynthetic Parameters and Vegetation Stress via a a New High Resolution Fluorescence Imaging-System. Proceedings of the EARSeL, Basel, Switzerland."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/0034-4257(94)90125-2","article-title":"Distinguishing Nitrogen Fertilization Levels in Field Corn (Zea mays L.) with Actively Induced Fluorescence and Passive Reflectance Measurements","volume":"47","author":"McMurtrey","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1080\/014311697217558","article-title":"Remote Estimation of Chlorophyll Content in Higher Plant Leaves","volume":"18","author":"Gitelson","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"27921","DOI":"10.1029\/1999JD900161","article-title":"Land Cover Mapping at BOREAS Using Red Edge Spectral Parameters from CASI Imagery","volume":"104","author":"Miller","year":"1999","journal-title":"J. Geophys. Res."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S0034-4257(02)00018-4","article-title":"Integrated Narrow-Band Vegetation Indices for Prediction of Crop Chlorophyll Content for Application to Precision Agriculture","volume":"81","author":"Haboudane","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of Soil-Adjusted Vegetation Indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_60","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_61","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1071\/BT98042","article-title":"Remote Sensing of Water Content in Eucalyptus Leaves","volume":"47","author":"Datt","year":"1999","journal-title":"Aust. J. Bot."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1080\/014311698215919","article-title":"Spectral Indices for Estimating Photosynthetic Pigment Concentrations: A Test Using Senescent Tree Leaves","volume":"19","author":"Blackburn","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","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_64","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.fcr.2010.01.010","article-title":"Measuring and Predicting Canopy Nitrogen Nutrition in Wheat Using a Spectral Index\u2014The Canopy Chlorophyll Content Index (CCCI)","volume":"116","author":"Fitzgerald","year":"2010","journal-title":"Field Crops Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/0143116042000274015","article-title":"The MERIS Terrestrial Chlorophyll Index","volume":"25","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2006GL026457","DOI":"10.1029\/2006GL026457","article-title":"Three-band Model for Noninvasive Estimation of Chlorophyll, Carotenoids, and Anthocyanin Contents in Higher Plant Leaves","volume":"33","author":"Gitelson","year":"2006","journal-title":"Geophys. Res. Lett."},{"key":"ref_67","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_68","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/S1671-2927(07)60098-4","article-title":"Relationship Between Hyperspectral Parameters and Physiological and Biochemical Indexes of Flue-Cured Tobacco Leaves","volume":"6","author":"Li","year":"2007","journal-title":"Agric. Sci. China"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"022022","DOI":"10.1088\/1742-6596\/1168\/2\/022022","article-title":"An Overview of Overfitting and Its Solutions","volume":"1168","author":"Ying","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_70","unstructured":"Fonti, V., and Belitser, E. (2017). Paper in Business Analytics Feature Selection Using LASSO, VU Amsterdam."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Zhang, C., and Ma, Y. (2012). Ensemble Machine Learning: Methods and Applications, Springer.","DOI":"10.1007\/978-1-4419-9326-7"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2017.10.018","article-title":"Quantification of Sawgrass Marsh Aboveground Biomass in the Coastal Everglades Using Object-Based Ensemble Analysis and Landsat Data","volume":"204","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A Tutorial on Support Vector Regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.ecolind.2014.12.028","article-title":"A Comparative Assessment of Support Vector Regression, Artificial Neural Networks, and Random Forests for Predicting and Mapping Soil Organic Carbon Stocks across an Afromontane Landscape","volume":"52","author":"Were","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2005.12.011","article-title":"A New Technique for Extracting the Red Edge Position from Hyperspectral Data: The Linear Extrapolation Method","volume":"101","author":"Cho","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"3239","DOI":"10.1007\/s12652-018-1043-5","article-title":"Estimating Leaf Chlorophyll Content in Tobacco Based on Various Canopy Hyperspectral Parameters","volume":"10","author":"Guo","year":"2019","journal-title":"J. Ambient Intell. Hum. Comput."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"106654","DOI":"10.1016\/j.compag.2021.106654","article-title":"Hyperspectral Estimation of Canopy Chlorophyll of Winter Wheat by Using the Optimized Vegetation Indices","volume":"193","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Shi, H., Guo, J., An, J., Tang, Z., Wang, X., Li, W., Zhao, X., Jin, L., Xiang, Y., and Li, Z. (2023). Estimation of Chlorophyll Content in Soybean Crop at Different Growth Stages Based on Optimal Spectral Index. Agronomy, 13.","DOI":"10.3390\/agronomy13030663"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.compag.2016.05.008","article-title":"Evaluating Chlorophyll Density in Winter Oilseed Rape (Brassica napus L.) Using Canopy Hyperspectral Red-Edge Parameters","volume":"126","author":"Li","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.rse.2004.01.017","article-title":"Hyperspectral Indices and Model Simulation for Chlorophyll Estimation in Open-Canopy Tree Crops","volume":"90","author":"Miller","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Bhadra, S., Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Newcomb, M., Shakoor, N., and Mockler, T.C. (2020). Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning. Remote Sens., 12.","DOI":"10.3390\/rs12132082"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"722442","DOI":"10.3389\/fpls.2022.722442","article-title":"Machine Learning Strategies for the Retrieval of Leaf-Chlorophyll Dynamics: Model Choice, Sequential Versus Retraining Learning, and Hyperspectral Predictors","volume":"13","author":"Angel","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"An, G., Xing, M., He, B., Liao, C., Huang, X., Shang, J., and Kang, H. (2020). Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data. Remote Sens., 12.","DOI":"10.3390\/rs12183104"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Maimaitijiang, M., Sagan, V., Sidike, P., Daloye, A.M., Erkbol, H., and Fritschi, F.B. (2020). Crop Monitoring Using Satellite\/UAV Data Fusion and Machine Learning. Remote Sens., 12.","DOI":"10.3390\/rs12091357"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, Y., Jiang, D., Zhang, Z., and Chang, Q. (2023). Quantitative Assessment of Apple Mosaic Disease Severity Based on Hyperspectral Images and Chlorophyll Content. Remote Sens., 15.","DOI":"10.3390\/rs15082202"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/20\/4934\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:05:51Z","timestamp":1760130351000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/20\/4934"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,12]]},"references-count":85,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15204934"],"URL":"https:\/\/doi.org\/10.3390\/rs15204934","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,12]]}}}