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Front. Plant Sci. | doi: 10.3389/fpls.2019.01047

Estimation of Corn Canopy Chlorophyll Content Using Derivative Spectra in the O2–A Absorption Band

 Xuehong Zhang1*, Fan Xu2,  Yang He2, Xinhui Li2, Dongmei Chen3, Guojie Wang4 and Iixin Shi5
  • 1Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, China
  • 2School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, China
  • 3Department of Geography and Planning, Faculty of Arts and Science, Queen’s University, Canada
  • 4School of Geographical Sciences, Nanjing University of Information Science and Technology, China
  • 5Key Laboratory of Meteorology and Ecological Environment of Hebei Province, Meteorological Institute of Hebei Province, China

Chlorophyll (Chl) is one of the most important classes of light-absorbing pigments in photosynthesis and the proportion of Chl in leaves is closely related to vegetation nutrient status. Remote sensing-based estimation of Chl content holds great potential for evaluating crop growth status in agricultural management, precision farming and ecosystem monitoring. Recent studies have shown that steady-state fluorescence contributed up to 2% on the apparent reflectance in the 750-nm spectral region of plant and also provided additional evidence for fluorescence in-filling of the atmospheric oxygen absorption band at central wavelength of 760 nm (O2–A band). In this study, an in situ hyperspectral remote sensing approach was employed to estimate corn Chl content at the canopy level by using chlorophyll fluorescence (ChlF) signals in the O2–A absorption band. Two new spectral indices, REArea760 (sum of first derivative reflectance between 755 and 763 nm) and REA760 (maximum of first derivative reflectance between 755 and 763 nm), derived from the first derivative spectra in the O2–A band, were proposed for estimating the corn canopy Chl content (CCC). They were compared with the performance of published indices measured at ground level, including MERIS Terrestrial Chlorophyll Index (MTCI), Optimized Soil-Adjusted Vegetation Index 2 (OSAVI2), Modified Chlorophyll Absorption Ratio Index 2 (MCARI2), SR710, REArea (sum of first derivative reflectance between 680 and 780 nm), REA (maximum value of first derivative reflectance between 680 and 780 nm), and mND705. The results indicated that corn Chl content at the canopy level was better predicted by the new indices (with R2=0.835) than the published indices (with R2 ranging from 0.676 to 0.826). The two new indices ranked in the top four according to their summed ranks by integrating the ranks of RMSE and R2 of CCC linear regression models. ChlF originates only from chlorophyll in the photosynthetic apparatus and therefore is less sensitive to soil, wood, and dead biomass interference. Moreover, due to the fluorescence in-filling of the O2–A band and the amplified effect on spectrum signals by derivative operation, the spectral derivative indices in the O2–A band have great potential of estimating the CCC.

Keywords: corn, Chlorophyll content, Chlorophyll Fluorescence, Derivative spectra, Oxygen absorption band

Received: 15 Oct 2018; Accepted: 29 Jul 2019.

Edited by:

Guijun Yang, Beijing Research Center for Information Technology in Agriculture, China

Reviewed by:

Esa Tyystjärvi, University of Turku, Finland
Zhenhai Li, Beijing Research Center for Information Technology in Agriculture, China  

Copyright: © 2019 Zhang, Xu, He, Li, Chen, Wang and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Xuehong Zhang, Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China, zxhbnu@126.com