A new vegetation index combination for leaf carotenoid-to-chlorophyll ratio: minimizing the effect of their correlation

References

• Blackburn, G. A. 1998. “Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves.” International Journal of Remote Sensing 19 (4): 657–675. doi: 10.1080/014311698215919.
   Web of Science ®Google Scholar
• Broge, N. H., and E. Leblanc. 2001. “Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density.” Remote Sensing of Environment 76 (2): 156–172. doi: 10.1016/S0034-4257(00)00197-8.
   Web of Science ®Google Scholar
• Chappelle, E. W., M. S. Kim, and J. E. McMurtrey III. 1992. “Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves.” Remote Sensing of Environment 39 (3): 239–247. doi: 10.1016/0034-4257(92)90089-3.
   Web of Science ®Google Scholar
• Chen, Y., J. Sun, L. Wang, S. Shi, W. Gong, S. Wang, and T. Tagesson. 2021. “Optimized Estimation of Leaf Mass per Area with a 3D Matrix of Vegetation Indices.” Remote Sensing 13 (18): 3761. doi: 10.3390/rs13183761.
   Web of Science ®Google Scholar
• Croft, H., and J. M. Chen. 2018. “Leaf Pigment Content.” Comprehensive Remote Sensing 3: 117–142. doi: 10.1016/B978-0-12-409548-9.10547-0.
   Google Scholar
• Croft, H., J. M. Chen, R. Wang, G. Mo, S. Luo, X. Luo, L. He, et al. 2020. “The global distribution of leaf chlorophyll content.” Remote Sensing of Environment 236: 111497. doi: 10.1016/j.rse.2019.111479.
   Web of Science ®Google Scholar
• Croft, H., J. M. Chen, Y. Zhang, A. Simic, T. L. Noland, N. Nesbitt, and J. Arabian. 2015. “Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework.” ISPRS Journal of Photogrammetry and Remote Sensing 102: 85–95. doi: 10.1016/j.isprsjprs.2015.01.008.
   Web of Science ®Google Scholar
• Cui, B., Q. Zhao, W. Huang, X. Song, H. Ye, and X. Zhou. 2019. “A new integrated vegetation index for the estimation of winter wheat leaf chlorophyll content.” Remote Sensing 11 (8): 974. doi: 10.3390/rs11080974.
   Web of Science ®Google Scholar
• Danner, Martin, Katja Berger, Matthias Wocher, Wolfram Mauser, and Tobias Hank. 2019. “Fitted PROSAIL Parameterization of Leaf Inclinations, Water Content and Brown Pigment Content for Winter Wheat and Maize Canopies.” Remote Sensing 11: 10. doi: 10.3390/rs11101150.
   Web of Science ®Google Scholar
• Danner, M., K. Berger, M. Wocher, W. Mauser, and T. Hank. 2021. “Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops.” ISPRS Journal of Photogrammetry and Remote Sensing 173: 278–296. doi: 10.1016/j.isprsjprs.2021.01.017.
   Web of Science ®Google Scholar
• Daughtry, C. S., C. L. Walthall, M. S. Kim, E. B. De Colstoun, and J. E. McMurtrey Iii. 2000. “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance.” Remote Sensing of Environment 74 (2): 229–239. doi: 10.1016/S0034-4257(00)00113-9.
   Web of Science ®Google Scholar
• Demmig-Adams, B., and W. W. Adams III. 1996. “The role of xanthophyll cycle carotenoids in the protection of photosynthesis.” Trends in Plant science 1 (1): 21–26. doi: 10.1016/S1360-1385(96)80019-7.
   Web of Science ®Google Scholar
• Esteban, R., O. Barrutia, U. Artetxe, B. Fernandez-Marin, A. Hernandez, and J. I. Garcia-Plazaola. 2015. “Internal and external factors affecting photosynthetic pigment composition in plants: a meta-analytical approach.” New Phytologist 206 (1): 268–280. doi: 10.1111/nph.13186.
   PubMed Web of Science ®Google Scholar
• Féret, Jean-Baptiste, Katja Berger, Florian de Boissieu, and Zbyněk Malenovský. 2021. “PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents.” Remote Sensing of Environment 252), doi: 10.1016/j.rse.2020.112173.
   PubMed Web of Science ®Google Scholar
• Féret, Jean-Baptiste, Christophe François, Gregory P. Asner, Anatoly A. Gitelson, Roberta E. Martin, Luc P. R. Bidel, Susan L. Ustin, Guerric le Maire, and Stéphane Jacquemoud. 2008. “PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments.” Remote Sensing of Environment 112 (6): 3030–3043. doi: 10.1016/j.rse.2008.02.012.
   Web of Science ®Google Scholar
• Féret, Jean-Baptiste, Christophe François, Anatoly Gitelson, Gregory P. Asner, Karen M. Barry, Cinzia Panigada, Andrew D. Richardson, and Stéphane Jacquemoud. 2011. “Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling.” Remote Sensing of Environment 115 (10): 2742–2750. doi: 10.1016/j.rse.2011.06.016.
   Web of Science ®Google Scholar
• Féret, Jean-Baptiste, A. A. Gitelson, S. D. Noble, and S. Jacquemoud. 2017. “PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle.” Remote Sensing of Environment 193: 204–215. doi: 10.1016/j.rse.2017.03.004.
   Web of Science ®Google Scholar
• Filella, I., A. Porcar-Castell, S. Munné-Bosch, J. Bäck, M. F. Garbulsky, and J. Peñuelas. 2009. “PRI assessment of long-term changes in carotenoids/chlorophyll ratio and short-term changes in de-epoxidation state of the xanthophyll cycle.” International Journal of Remote Sensing 30 (17): 4443–4455. doi: 10.1080/01431160802575661.
   Web of Science ®Google Scholar
• Gamon, J. A. 2015. “Optical sampling of the flux tower footprint.” Biogeosciences Discussions 12: 6. doi: 10.5194/bgd-12-4973-2015.
   Web of Science ®Google Scholar
• Gamon, J. A., K. F. Huemmrich, C. Y. Wong, I. Ensminger, S. Garrity, D. Y. Hollinger, A. Noormets, and J. Penuelas. 2016. “A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers.” Proceedings of the National Academy of Sciences 113 (46): 13087–13092. doi: 10.1073/pnas.1606162113.
   PubMed Web of Science ®Google Scholar
• Gamon, J. A., J. Penuelas, and C. B. Field. 1992. “A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency.” Remote Sensing of Environment 41 (1): 35–44. doi: 10.1016/0034-4257(92)90059-S.
   Web of Science ®Google Scholar
• Gitelson, Anatoly. 2005. “Remote estimation of canopy chlorophyll content in crops.” Geophysical Research Letters 32: 8. doi: 10.1029/2005gl022688.
   Web of Science ®Google Scholar
• Gitelson, Anatoly. 2020. “Towards a generic approach to remote non-invasive estimation of foliar carotenoid-to-chlorophyll ratio.” Journal of plant physiology 252: 153227. doi: 10.1016/j.jplph.2020.153227.
   PubMed Web of Science ®Google Scholar
• Gitelson, A. A., G. P. Keydan, and M. N. Merzlyak. 2006. “Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves.” Geophysical Research Letters 33: 11. doi: 10.1029/2006gl026457.
   Web of Science ®Google Scholar
• Gitelson, A. A., and M. N. Merzlyak. 1994a. “Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation.” Journal of plant physiology 143 (3): 286–292. doi: 10.1016/S0176-1617(11)81633-0.
   Web of Science ®Google Scholar
• Gitelson, A., and M. N. Merzlyak. 1994b. “Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves.” Journal of Photochemistry and Photobiology B: Biology 22 (3): 247–252. doi: 10.1016/1011-1344(93)06963-4.
   Web of Science ®Google Scholar
• Gitelson, A. A., M. N. Merzlyak, and O. B. Chivkunova. 2001. “Optical properties and nondestructive estimation of anthocyanin content in plant leaves.” Photochemistry and photobiology 74 (1): 38–45. doi: 10.1562/0031-8655(2001)0740038OPANEO2.0.CO2.
   PubMed Web of Science ®Google Scholar
• Gitelson, A. A., Y. Zur, O. B. Chivkunova, and M. N. Merzlyak. 2002. “Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy.” Photochemistry and photobiology 75 (3): 272–281. doi: 10.1562/0031-8655(2002)0750272ACCIPL2.0.CO2.
   PubMed Web of Science ®Google Scholar
• Haboudane, D. 2004. “Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture.” Remote Sensing of Environment 90 (3): 337–352. doi: 10.1016/j.rse.2003.12.013.
   Web of Science ®Google Scholar
• Haboudane, D., J. R. Miller, N. Tremblay, P. J. Zarco-Tejada, and L. Dextraze. 2002. “Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture.” Remote Sensing of Environment 81 (2-3): 416–426. doi: 10.1016/S0034-4257(02)00018-4.
   Web of Science ®Google Scholar
• Hennessy, Andrew, Kenneth Clarke, and Megan Lewis. 2020. “Hyperspectral classification of plants: A review of waveband selection generalisability.” Remote Sensing 12 (1): 113. doi: 10.3390/rs12010113.
   Web of Science ®Google Scholar
• Hernández-Clemente, R., R. M. Navarro-Cerrillo, L. Suárez, F. Morales, and P. J. Zarco-Tejada. 2011. “Assessing structural effects on PRI for stress detection in conifer forests.” Remote Sensing of Environment 115 (9): 2360–2375. doi: 10.1016/j.rse.2011.04.036.
   Web of Science ®Google Scholar
• Hernández-Clemente, Rocío, Rafael M. Navarro-Cerrillo, and Pablo J. Zarco-Tejada. 2012. “Carotenoid content estimation in a heterogeneous conifer forest using narrow-band indices and PROSPECT + DART simulations.” Remote Sensing of Environment 127: 298–315. doi: 10.1016/j.rse.2012.09.014.
   Web of Science ®Google Scholar
• Ivanov, L. A., D. A. Ronzhina, P. K. Yudina, N. V. Zolotareva, I. V. Kalashnikova, and L. A. Ivanova. 2020. “Seasonal Dynamics of the Chlorophyll and Carotenoid Content in the Leaves of Steppe and Forest Plants on Species and Community Level.” Russian Journal of Plant Physiology 67 (3): 453–462. doi: 10.1134/s1021443720030115.
   Web of Science ®Google Scholar
• Jacquemoud, S., and F. Baret. 1990. “PROSPECT: A model of leaf optical properties spectra.” Remote Sensing of Environment 34 (2): 75–91. doi: 10.1016/0034-4257(90)90100-Z.
   Web of Science ®Google Scholar
• Kattenborn, Teja, Fabian Ewald Fassnacht, Sebastian Schmidtlein, Harini Nagendra, and Kate He. 2018. “Differentiating plant functional types using reflectance: which traits make the difference?” Remote Sensing in Ecology and Conservation 5 (1): 5–19. doi: 10.1002/rse2.86.
   Web of Science ®Google Scholar
• Kattenborn, T., F. Schiefer, and S. Schmidtlein. 2017. “Leaf reflectance plant functional gradient IFGG/KIT” (dataset). https://ecosis.org/package/leaf-reflectance-plant-functional-gradient-ifgg-kit.
   Google Scholar
• Kong, Weiping, Wenjiang Huang, Xianfeng Zhou, Xiaoyu Song, and Raffaele Casa. 2016. “Estimation of carotenoid content at the canopy scale using the carotenoid triangle ratio index fromin situand simulated hyperspectral data.” Journal of Applied Remote Sensing 10 (2): 026035. doi: 10.1117/1.Jrs.10.026035.
   Google Scholar
• Kothari, S., R. Beauchamp-Rioux, E. Laliberté, and J. Cavender-Bares. 2022. “Fresh-leaf CABO spectra from herbarium project” (dataset). https://ecosis.org/package/fresh-leaf-cabo-spectra-from-herbarium-project.
   Google Scholar
• Li, Dong, Jing M Chen, Xiao Zhang, Yan Yan, Jie Zhu, Hengbiao Zheng, Kai Zhou, et al. 2020. “Improved estimation of leaf chlorophyll content of row crops from canopy reflectance spectra through minimizing canopy structural effects and optimizing off-noon observation time.” Remote Sensing of Environment 248: 111985. doi: 10.1016/j.rse.2020.111985.
   Web of Science ®Google Scholar
• Li, P., and Q. Wang. 2001. “Retrieval of leaf biochemical parameters using PROSPECT inversion: A new approach for alleviating ill-posed problems.” IEEE Transactions on Geoscience and Remote Sensing 49 (7): 2499–2506. doi: 10.1109/TGRS.2011.2109390.
   Google Scholar
• Liang, Liang, Liping Di, Lianpeng Zhang, Meixia Deng, Zhihao Qin, Shuhe Zhao, and Hui Lin. 2015. “Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method.” Remote Sensing of Environment 165: 123–134. doi: 10.1016/j.rse.2015.04.032.
   Web of Science ®Google Scholar
• Liang, Liang, Hui Lin, Lianpeng Zhang, Zhihao Qin, Shuhe Zhao, Liping Di, Chao Zhang, Lijuan Wangb, and Zhixiao Liug. 2016. “Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method.” International Journal of Remote Sensing 37 (13): 2923–2949. doi: 10.1080/01431161.2016.1186850.
   Web of Science ®Google Scholar
• Lightenthaler, H. K. 1987. “Chlorophylls and carotenoids: pigments of photosynthetic biomembranes.” Methods in enzymology 148: 350–382. doi: 10.1016/0076-6879(87)48036-1.
   Web of Science ®Google Scholar
• Locherer, Matthias, Tobias Hank, Martin Danner, and Wolfram Mauser. 2015. “Retrieval of Seasonal Leaf Area Index from Simulated EnMAP Data through Optimized LUT-Based Inversion of the PROSAIL Model.” Remote Sensing 7 (8): 10321–10346. doi: 10.3390/rs70810321.
   Web of Science ®Google Scholar
• Maćkiewicz, A., and W. Ratajczak. 1993. “Principal components analysis (PCA).” Computers & Geosciences 19 (3): 303–304. doi: 10.1016/0098-3004(93)90090-R.
   Web of Science ®Google Scholar
• Marshak, A., Y. Knyazikhin, A. B. Davis, W. J. Wiscombe, and P. Pilewskie. 2000. “Cloud-vegetation interaction: Use of normalized difference cloud index for estimation of cloud optical thickness.” Geophysical Research Letters 27 (12): 1695–1698. doi: 10.1029/1999gl010993.
   Web of Science ®Google Scholar
• Merzlyak, M. N., A. A. Gitelson, O. B. Chivkunova, and V. Y. Rakitin. 1999. “Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening.” Physiologia plantarum 106 (1): 135–141. doi: 10.1034/j.1399-3054.1999.106119.x.
   Web of Science ®Google Scholar
• Miraglio, Thomas, Karine Adeline, Margarita Huesca, Susan Ustin, and Xavier Briottet. 2019. “Monitoring LAI, chlorophylls, and carotenoids content of a woodland savanna using hyperspectral imagery and 3D radiative transfer modeling.” Remote Sensing 12 (1): 28. doi: 10.3390/rs12010028.
   Web of Science ®Google Scholar
• Penuelas, J., F. Baret, and I. Filella. 1995. “Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance.” Photosynthetica 31 (2): 221–230.
   Web of Science ®Google Scholar
• Qiu, Feng, Jing M. Chen, Weimin Ju, Jun Wang, Qian Zhang, and Meihong Fang. 2018. “Improving the PROSPECT Model to Consider Anisotropic Scattering of Leaf Internal Materials and Its Use for Retrieving Leaf Biomass in Fresh Leaves.” IEEE Transactions on Geoscience and Remote Sensing 56 (6): 3119–3136. doi: 10.1109/tgrs.2018.2791930.
   Web of Science ®Google Scholar
• Rouse Jr, J. W., R. H. Haas, D. W. Deering, J. A. Schell, and J. C. Harlan. 1974. “Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation.” NASA/GSFCT Type III Final Report, Greenbelt, MD, USA. https://ntrs.nasa.gov/search.jsp?R=19740022555
   Google Scholar
• Sims, Daniel A., and John A. Gamon. 2002. “Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages.” Remote Sensing of Environment 81 (2-3): 337–354. doi: 10.1016/s0034-4257(02)00010-x.
   Web of Science ®Google Scholar
• Song, Guangman, and Quan Wang. 2022. “Developing Hyperspectral Indices for Assessing Seasonal Variations in the Ratio of Chlorophyll to Carotenoid in Deciduous Forests.” Remote Sensing 14 (6): 1324. doi: 10.3390/rs14061324.
   Web of Science ®Google Scholar
• Sonobe, Rei, Hiroto Yamashita, Harumi Mihara, Akio Morita, and Takashi Ikka. 2020. “Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance.” Remote Sensing 12 (19): 3265. doi: 10.3390/rs12193265.
   Web of Science ®Google Scholar
• Spafford, Lynsay, Guerric le Maire, Andrew MacDougall, Florian de Boissieu, and Jean-Baptiste Féret. 2021. “Spectral subdomains and prior estimation of leaf structure improves PROSPECT inversion on reflectance or transmittance alone.” Remote Sensing of Environment 252: 112176. doi: 10.1016/j.rse.2020.112176.
   Web of Science ®Google Scholar
• Sun, Jia, Shuo Shi, Jian Yang, Wei Gong, Feng Qiu, Lunche Wang, Lin Du, and Biwu Chen. 2019. “Wavelength selection of the multispectral lidar system for estimating leaf chlorophyll and water contents through the PROSPECT model.” Agricultural and Forest Meteorology 266: 43–52. doi: 10.1016/j.agrformet.2018.11.035.
   Web of Science ®Google Scholar
• Van der Tol, C., W. Verhoef, J. Timmermans, A. Verhoef, and Z. Su. 2009. “An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance.” Biogeosciences (online) 6 (12): 3109–3129. doi: 10.5194/bg-6-3109-2009.
   Web of Science ®Google Scholar
• Verstraete, M. M., and B. Pinty. 1996. “Designing optimal spectral indexes for remote sensing applications.” IEEE Transactions on Geoscience and Remote Sensing 34 (5): 1254–1265. doi: 10.1109/36.536541.
   Web of Science ®Google Scholar
• Vincini, M., E. Frazzi, and P. D’Alessio. 2006. “Angular dependence of maize and sugar beet VIs from directional CHRIS/Proba data.” Proc. 4th ESA CHRIS PROBA Workshop 2006:19-21.
   Google Scholar
• Wan, Liang, Zheng Tang, Jiafei Zhang, Shuobo Chen, Weijun Zhou, and Haiyan Cen. 2021. “Upscaling from leaf to canopy: Improved spectral indices for leaf biochemical traits estimation by minimizing the difference between leaf adaxial and abaxial surfaces.” Field Crops Research 274: 108330. doi: 10.1016/j.fcr.2021.108330.
   Web of Science ®Google Scholar
• Yang, F., J. Li, X. Gan, Y. Qian, X. Wu, and Q. Yang. 2010. “Assessing nutritional status of Festuca arundinacea by monitoring photosynthetic pigments from hyperspectral data.” Computers and Electronics in Agriculture 70 (1): 52–59. doi: 10.1016/j.compag.2009.08.010.
   Web of Science ®Google Scholar
• Yang, Peiqi, Wout Verhoef, and Christiaan van der Tol. 2017. “The mSCOPE model: A simple adaptation to the SCOPE model to describe reflectance, fluorescence and photosynthesis of vertically heterogeneous canopies.” Remote Sensing of Environment 201: 1–11. doi: 10.1016/j.rse.2017.08.029.
   Web of Science ®Google Scholar
• You, Wenqiang, Zheyi Wang, Fan Lu, Yunsheng Zhao, and Shan Lu. 2017. “Spectral indices to assess the carotenoid/chlorophyll ratio from adaxial and abaxial leaf reflectance.” Spectroscopy Letters 50 (7): 387–393. doi: 10.1080/00387010.2017.1335753.
   Web of Science ®Google Scholar
• Zhong, Rui-Sen, Qiu-Xiang Yi, Ying Liu, and Cun Chang. 2020. “Estimation of cotton Car/Chla ratio by hyperspectral vegetation indices and partial least square regression.” Acta Agronomica Sinica 46 (8): 1266–1274. doi: 10.3724/sp.J.1006.2020.94157.
   Google Scholar
• Zhou, Xianfeng, Wenjiang Huang, Weiping Kong, Huichun Ye, Yingying Dong, and Raffaele Casa. 2017. “Assessment of leaf carotenoids content with a new carotenoid index: Development and validation on experimental and model data.” International Journal of Applied Earth Observation and Geoinformation 57: 24–35. doi: 10.1016/j.jag.2016.12.005.
   Web of Science ®Google Scholar
• Zhou, Xianfeng, Wenjiang Huang, Jingcheng Zhang, Weiping Kong, Raffaele Casa, and Yanbo Huang. 2019. “A novel combined spectral index for estimating the ratio of carotenoid to chlorophyll content to monitor crop physiological and phenological status.” International Journal of Applied Earth Observation and Geoinformation 76: 128–142. doi: 10.1016/j.jag.2018.10.012.
   Web of Science ®Google Scholar