{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T15:34:56Z","timestamp":1773848096156,"version":"3.50.1"},"reference-count":84,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation of China","award":["41801243"],"award-info":[{"award-number":["41801243"]}]},{"name":"National Science Foundation of China","award":["317387"],"award-info":[{"award-number":["317387"]}]},{"name":"Academy of Finland","award":["41801243"],"award-info":[{"award-number":["41801243"]}]},{"name":"Academy of Finland","award":["317387"],"award-info":[{"award-number":["317387"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate estimation of canopy chlorophyll content (CCC) is critically important for agricultural production management. However, vegetation indices derived from canopy reflectance are influenced by canopy structure, which limits their application across species and seasonality. For horizontally homogenous canopies such as field crops, LAI and leaf inclination angle distribution or leaf mean tilt angle (MTA) are two biophysical characteristics determining canopy structure. Since CCC is relevant to LAI, MTA is the only structural parameter affecting the correlation between CCC and vegetation indices. To date, there are few vegetation indices designed to minimize MTA effects for CCC estimation. Herein, in this study, CCC-sensitive and MTA-insensitive satellite broadband vegetation indices are developed for crop canopy chlorophyll content estimation. The most efficient broadband vegetation indices for four satellite sensors (Sentinel-2, RapidEye, WorldView-2 and GaoFen-6) with red edge channels were identified (in the context of various vegetation index types) using simulated satellite broadband reflectance based on field measurements and validated with PROSAIL model simulations. The results indicate that developed vegetation indices present strong correlations with CCC and weak correlations with MTA, with overall R2 of 0.76\u20130.80 and 0.84\u20130.95 for CCC and R2 of 0.00 and 0.00\u20130.04 in the field measured data and model simulations, respectively. The best vegetation indices identified in this study are the soil-adjusted index type index SAI (B6, B7) for Sentinel-2, Verrelts\u2019s three-band spectral index type index BSI-V (NIR1, Red, Red Edge) for WorldView-2, Tian\u2019s three-band spectral index type index BSI-T (Red Edge, Green, NIR) for RapidEye and difference index type index DI (B6, B4) for GaoFen-6. The identified indices can potentially be used for crop CCC estimation across species and seasonality. However, real satellite datasets and more crop species need to be tested in further studies.<\/jats:p>","DOI":"10.3390\/rs15051234","type":"journal-article","created":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T01:37:52Z","timestamp":1677202672000},"page":"1234","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Potential of Satellite Spectral Resolution Vegetation Indices for Estimation of Canopy Chlorophyll Content of Field Crops: Mitigating Effects of Leaf Angle Distribution"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiaochen","family":"Zou","sequence":"first","affiliation":[{"name":"Technology Innovation Center for Integration Applications in Remote Sensing and Navigation, Ministry of Natural Resources, School of Remote Sensing and Geomatics, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Jun","family":"Jin","sequence":"additional","affiliation":[{"name":"Technology Innovation Center for Integration Applications in Remote Sensing and Navigation, Ministry of Natural Resources, School of Remote Sensing and Geomatics, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2745-1966","authenticated-orcid":false,"given":"Matti","family":"M\u00f5ttus","sequence":"additional","affiliation":[{"name":"VTT Technical Research Centre of Finland Ltd., P.O. Box 1000, FI-02044 Espoo, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1046\/j.0028-646X.2001.00289.x","article-title":"An Evaluation of Noninvasive Methods to Estimate Foliar Chlorophyll Content","volume":"153","author":"Richardson","year":"2002","journal-title":"New Phytol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"S67","DOI":"10.1016\/j.rse.2008.10.019","article-title":"Retrieval of Foliar Information about Plant Pigment Systems from High Resolution Spectroscopy","volume":"113","author":"Ustin","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.rse.2006.07.014","article-title":"Neural Network Estimation of LAI, FAPAR, FCover and LAI\u00d7Cab, from Top of Canopy MERIS Reflectance Data: Principles and Validation","volume":"105","author":"Bacour","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ecolind.2011.08.018","article-title":"The Potential of the Satellite Derived Green Chlorophyll Index for Estimating Midday Light Use Efficiency in Maize, Coniferous Forest and Grassland","volume":"14","author":"Wu","year":"2012","journal-title":"Ecol. Indic."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3983","DOI":"10.1093\/jxb\/ert208","article-title":"Chlorophyll Fluorescence Analysis: A Guide to Good Practice and Understanding Some New Applications","volume":"64","author":"Murchie","year":"2013","journal-title":"J. Exp. Bot."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.agrformet.2017.09.012","article-title":"Incorporating Leaf Chlorophyll Content into a Two-Leaf Terrestrial Biosphere Model for Estimating Carbon and Water Fluxes at a Forest Site","volume":"248","author":"Luo","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1093\/jxb\/erl231","article-title":"Quantification of Plant Stress Using Remote Sensing Observations and Crop Models: The Case of Nitrogen Management","volume":"58","author":"Baret","year":"2007","journal-title":"J. Exp. Bot."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1080\/01431161003614382","article-title":"Nondestructive Estimation of Canopy Chlorophyll Content Using Hyperion and Landsat\/TM Images","volume":"31","author":"Wu","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1093\/oxfordjournals.aob.a083148","article-title":"Comparative Physiological Studies on the Growth of Field Crops: I. Variation in Net Assimilation Rate and Leaf Area between Species and Varieties, and within and between Years","volume":"11","author":"Watson","year":"1947","journal-title":"Ann. Bot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2609","DOI":"10.1111\/pce.12815","article-title":"Simple and Robust Methods for Remote Sensing of Canopy Chlorophyll Content: A Comparative Analysis of Hyperspectral Data for Different Types of Vegetation","volume":"39","author":"Inoue","year":"2016","journal-title":"Plant Cell Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1080\/01140671.2011.588713","article-title":"Early Detection of Canopy Nitrogen Deficiency in Winter Wheat (Triticum aestivum L.) Based on Hyperspectral Measurement of Canopy Chlorophyll Status","volume":"39","author":"Zhao","year":"2011","journal-title":"N. Z. J. Crop Hortic. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1111\/j.1745-4549.2011.00653.x","article-title":"Effect of Preliminary and Technological Treatments on the Content of Chlorophylls and Carotenoids in Kale (Brassica oleracea L. Var","volume":"37","author":"Korus","year":"2013","journal-title":"Acephala). J. Food Process. Preserv."},{"key":"ref_14","first-page":"47","article-title":"Remote Estimation of Nitrogen and Chlorophyll Contents in Maize at Leaf and Canopy Levels","volume":"25","author":"Schlemmer","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"D08S11","DOI":"10.1029\/2005JD006017","article-title":"Relationship between Gross Primary Production and Chlorophyll Content in Crops: Implications for the Synoptic Monitoring of Vegetation Productivity","volume":"111","author":"Gitelson","year":"2006","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1016\/j.rse.2010.12.001","article-title":"Remote Estimation of Gross Primary Production in Maize and Support for a New Paradigm Based on Total Crop Chlorophyll Content","volume":"115","author":"Peng","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.3389\/fpls.2019.01672","article-title":"High-Throughput Field Phenotyping Traits of Grain Yield Formation and Nitrogen Use Efficiency: Optimizing the Selection of Vegetation Indices and Growth Stages","volume":"10","author":"Prey","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1007\/s12524-015-0537-2","article-title":"Influence of Spectral Bandwidth and Position on Chlorophyll Content Retrieval at Leaf and Canopy Levels","volume":"44","author":"Dian","year":"2016","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_19","first-page":"41","article-title":"A Hyperspectral Index Sensitive to Subtle Changes in the Canopy Chlorophyll Content under Arsenic Stress","volume":"36","author":"Li","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/JSTARS.2012.2186118","article-title":"Inversion of a Radiative Transfer Model for Estimation of Rice Canopy Chlorophyll Content Using a Lookup-Table Approach","volume":"5","author":"Darvishzadeh","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ali, A.M., Darvishzadeh, R., Skidmore, A., Heurich, M., Paganini, M., Heiden, U., and M\u00fccher, S. (2020). Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes. Remote Sens., 12.","DOI":"10.3390\/rs12111788"},{"key":"ref_22","first-page":"165","article-title":"A Review on Reflective Remote Sensing and Data Assimilation Techniques for Enhanced Agroecosystem Modeling","volume":"9","author":"Dorigo","year":"2007","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1080\/014311699213730","article-title":"Impacts of Model Parameter Uncertainties on Crop Reflectance Estimates: A Regional Case Study on Wheat","volume":"20","author":"Moulin","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111467","DOI":"10.1016\/j.rse.2019.111467","article-title":"Spectral Vegetation Indices of Wetland Greenness: Responses to Vegetation Structure, Composition, and Spatial Distribution","volume":"234","author":"Taddeo","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_25","first-page":"102198","article-title":"Angle Effects of Vegetation Indices and the Influence on Prediction of SPAD Values in Soybean and Maize","volume":"93","author":"Mao","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sun, Q., Jiao, Q., Qian, X., Liu, L., Liu, X., and Dai, H. (2021). Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations. Remote Sens., 13.","DOI":"10.3390\/rs13030470"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/1011-1344(93)06963-4","article-title":"Quantitative Estimation of Chlorophyll-a Using Reflectance Spectra: Experiments with Autumn Chestnut and Maple Leaves","volume":"22","author":"Gitelson","year":"1994","journal-title":"J. Photochem. Photobiol. B"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/JSTARS.2011.2176468","article-title":"Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content","volume":"5","author":"Clevers","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4059","DOI":"10.1080\/01431161.2018.1454620","article-title":"Using Wavelet Analysis of Hyperspectral Remote-Sensing Data to Estimate Canopy Chlorophyll Content of Winter Wheat under Stripe Rust Stress","volume":"39","author":"He","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","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_32","unstructured":"Okuda, K., Taniguchi, K., Miura, M., Obata, K., and Yoshioka, H. (September, January 28). Application of Vegetation Isoline Equations for Simultaneous Retrieval of Leaf Area Index and Leaf Chlorophyll Content Using Reflectance of Red Edge Band. Proceedings of the Remote Sensing and Modeling of Ecosystems for Sustainability XIII, San Diego, CA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Peng, Y., Nguy-Robertson, A., Arkebauer, T., and Gitelson, A.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_34","doi-asserted-by":"crossref","unstructured":"Clevers, J., Kooistra, L., and van den Brande, M. (2017). Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop. Remote Sens., 9.","DOI":"10.3390\/rs9050405"},{"key":"ref_35","first-page":"187","article-title":"Retrieval of Crop Biophysical Parameters from Sentinel-2 Remote Sensing Imagery","volume":"80","author":"Xie","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"87","DOI":"10.2134\/agronj1971.00021962006300010027x","article-title":"Effects of Leaf Nodal Position on Absorption and Scattering Coefficients and Infinite Reflectance of Cotton Leaves, Gossypium hirsutum L.","volume":"63","author":"Gausman","year":"1971","journal-title":"Agron. J."},{"key":"ref_37","unstructured":"Asrar, G. (1989). Vegetation-Canopy Spectral Reflectance and Biophysical Processes, John Wiley and Sons."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/S0034-4257(98)00014-5","article-title":"Biophysical and Biochemical Sources of Variability in Canopy Reflectance","volume":"64","author":"Asner","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"6031","DOI":"10.1080\/01431161.2015.1110262","article-title":"Retrieval of Leaf Chlorophyll Content in Field Crops Using Narrow-Band Indices: Effects of Leaf Area Index and Leaf Mean Tilt Angle","volume":"36","author":"Zou","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zou, X., and M\u00f5ttus, M. (2017). Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops. Remote Sens., 9.","DOI":"10.3390\/rs9100994"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ross, J. (1981). The Radiation Regime and Architecture of Plant Stands, Springer.","DOI":"10.1007\/978-94-009-8647-3"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.agrformet.2015.12.058","article-title":"Measuring Leaf Angle Distribution in Broadleaf Canopies Using UAVs","volume":"218","author":"McNeil","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.agrformet.2015.02.016","article-title":"Retrieving Crop Leaf Tilt Angle from Imaging Spectroscopy Data","volume":"205","author":"Zou","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zou, X., Zhu, S., and M\u00f5ttus, M. (2022). Estimation of Canopy Structure of Field Crops Using Sentinel-2 Bands with Vegetation Indices and Machine Learning Algorithms. Remote Sens., 14.","DOI":"10.3390\/rs14122849"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Jiao, Q., Sun, Q., Zhang, B., Huang, W., Ye, H., Zhang, Z., Zhang, X., and Qian, B. (2022). A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle. Remote Sens., 14.","DOI":"10.3390\/rs14010098"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.agrformet.2013.09.010","article-title":"Photographic Measurement of Leaf Angles in Field Crops","volume":"184","author":"Zou","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.agrformet.2009.08.007","article-title":"How to Quantify Tree Leaf Area Index in an Open Savanna Ecosystem: A Multi-Instrument and Multi-Model Approach","volume":"150","author":"Ryu","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1007\/s00468-011-0566-6","article-title":"Estimating Leaf Inclination and G-Function from Leveled Digital Camera Photography in Broadleaf Canopies","volume":"25","author":"Pisek","year":"2011","journal-title":"Trees"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.agrformet.2012.10.011","article-title":"Is the Spherical Leaf Inclination Angle Distribution a Valid Assumption for Temperate and Boreal Broadleaf Tree Species?","volume":"169","author":"Pisek","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/0168-1923(90)90030-A","article-title":"Derivation of an Angle Density Function for Canopies with Ellipsoidal Leaf Angle Distributions","volume":"49","author":"Campbell","year":"1990","journal-title":"Agric. For. Meteorol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1007\/BF00032301","article-title":"Calibration of the Minolta SPAD-502 Leaf Chlorophyll Meter","volume":"46","author":"Markwell","year":"1995","journal-title":"Photosynth. Res."},{"key":"ref_52","first-page":"71","article-title":"Applying Different Inversion Techniques to Retrieve Stand Variables of Summer Barley with PROSPECT+SAIL.","volume":"12","author":"Vohland","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3030","DOI":"10.1016\/j.rse.2008.02.012","article-title":"PROSPECT-4 and 5: Advances in the Leaf Optical Properties Model Separating Photosynthetic Pigments","volume":"112","author":"Feret","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/0034-4257(84)90057-9","article-title":"Light Scattering by Leaf Layers with Application to Canopy Reflectance Modeling: The SAIL Model","volume":"16","author":"Verhoef","year":"1984","journal-title":"Remote Sens. Environ."},{"key":"ref_55","unstructured":"Kuusk, A. (1991). Photon-Vegetation Interactions, Springer."},{"key":"ref_56","unstructured":"Hosgood, B., Jacquemoud, S., Andreoli, G., Verdebout, J., Pedrini, G., and Schmuck, G. (1994). Leaf Optical Properties EXperiment 93 (LOPEX93), Office for Official Publications of the European Communities."},{"key":"ref_57","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_58","first-page":"168","article-title":"Growth Response of Pea and Summer Turnip Rape to Foliar Application of Glycinebetaine","volume":"47","author":"Kleemola","year":"1997","journal-title":"Acta Agric. Scand. Sect. B Soil Plant Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1006\/anbo.1998.0709","article-title":"Use of the Expolinear Growth Model to Analyse the Growth of Faba Bean, Peas and Lentils at Three Densities: Predictive Use of the Model","volume":"82","author":"Dennett","year":"1998","journal-title":"Ann. Bot."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2705","DOI":"10.1093\/jxb\/eri263","article-title":"Sugar Metabolism in Developing Lupin Seeds Is Affected by a Short-Term Water Deficit","volume":"56","author":"Pinheiro","year":"2005","journal-title":"J. Exp. Bot."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1093\/aob\/mci264","article-title":"Specific Leaf Area and Dry Matter Content Estimate Thickness in Laminar Leaves","volume":"96","author":"Vile","year":"2005","journal-title":"Ann. Bot."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/36.581987","article-title":"Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: An Overview","volume":"35","author":"Vermote","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","unstructured":"Rouse, J.W. (1973). Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation, NASA. NASA\/GSFC, Type II."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a Two-Band Enhanced Vegetation Index without a Blue Band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_66","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_67","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_68","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0034-4257(98)00059-5","article-title":"Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches","volume":"66","author":"Blackburn","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_69","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_70","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1016\/j.agrformet.2008.03.005","article-title":"Estimating Chlorophyll Content from Hyperspectral Vegetation Indices: Modeling and Validation","volume":"148","author":"Wu","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_71","unstructured":"Barnes, E., Clarke, T.R., Richards, S.E., Colaizzi, P., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., and Thompson, T.L. (2000, January 16\u201319). Coincident Detection of Crop Water Stress, Nitrogen Status, and Canopy Density Using Ground Based Multispectral Data. Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between Leaf Chlorophyll Content and Spectral Reflectance and Algorithms for Non-Destructive Chlorophyll Assessment in Higher Plant Leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_73","unstructured":"Rouse, J.W., Haas, R.H., Deering, D.W., Schell, J.A., and Harlan, J.C. (1974). Monitoring the Vernal Advancement of Retrogradation (Green Wave Effect) of Natural Vegetation, NASA. NASA\/GSFC, Type III, Final Report."},{"key":"ref_74","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_75","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A Soil-Adjusted Vegetation Index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A Modified Soil Adjusted Vegetation Index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_77","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_78","doi-asserted-by":"crossref","first-page":"026457","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_79","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s11104-013-1937-0","article-title":"Comparison of Different Hyperspectral Vegetation Indices for Canopy Leaf Nitrogen Concentration Estimation in Rice","volume":"376","author":"Tian","year":"2014","journal-title":"Plant Soil"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.isprsjprs.2015.04.013","article-title":"Experimental Sentinel-2 LAI Estimation Using Parametric, Non-Parametric and Physical Retrieval Methods\u2014A Comparison","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.fcr.2012.01.014","article-title":"Estimating Leaf Nitrogen Concentration with Three-Band Vegetation Indices in Rice and Wheat","volume":"129","author":"Wang","year":"2012","journal-title":"Field Crops Res."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"3846","DOI":"10.1016\/j.rse.2008.06.005","article-title":"Calibration and Validation of Hyperspectral Indices for the Estimation of Broadleaved Forest Leaf Chlorophyll Content, Leaf Mass per Area, Leaf Area Index and Leaf Canopy Biomass","volume":"112","author":"Soudani","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2018.12.032","article-title":"Assessment of Red-Edge Vegetation Indices for Crop Leaf Area Index Estimation","volume":"222","author":"Dong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1038\/s43017-022-00298-5","article-title":"Optical Vegetation Indices for Monitoring Terrestrial Ecosystems Globally","volume":"3","author":"Zeng","year":"2022","journal-title":"Nat. Rev. Earth Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1234\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:40:38Z","timestamp":1760121638000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1234"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,23]]},"references-count":84,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051234"],"URL":"https:\/\/doi.org\/10.3390\/rs15051234","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,23]]}}}