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Chlorophyll content retrieval from hyperspectral remote sensing imagery

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Abstract

Chlorophyll content is the essential parameter in the photosynthetic process determining leaf spectral variation in visible bands. Therefore, the accurate estimation of the forest canopy chlorophyll content is a significant foundation in assessing forest growth and stress affected by diseases. Hyperspectral remote sensing with high spatial resolution can be used for estimating chlorophyll content. In this study, the chlorophyll content was retrieved step by step using Hyperion imagery. Firstly, the spectral curve of the leaf was analyzed, 25 spectral characteristic parameters were identified through the correlation coefficient matrix, and a leaf chlorophyll content inversion model was established using a stepwise regression method. Secondly, the pixel reflectance was converted into leaf reflectance by a geometrical-optical model (4-scale). The three most important parameters of reflectance conversion, including the multiple scattering factor (M 0 ), and the probability of viewing the sunlit tree crown (P T ) and the background (P G ), were estimated by leaf area index (LAI), respectively. The results indicated that M 0 , P T , and P G could be described as a logarithmic function of LAI, with all R 2 values above 0.9. Finally, leaf chlorophyll content was retrieved with RMSE = 7.3574 μg/cm2, and canopy chlorophyll content per unit ground surface area was estimated based on leaf chlorophyll content and LAI. Chlorophyll content mapping can be useful for the assessment of forest growth stage and diseases.

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Acknowledgments

The authors would like to thank the two anonymous reviewers for their valuable comments. This work was sponsored by “the Fundamental Research Funds for the Central Universities” (DL13BAX07, DL13BAX09) of China.

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The authors declare that they have no competing interests.

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Authors and Affiliations

  1. Northeast Forestry University, 26 Hexing rd., 150040, Harbin, China

    Xiguang Yang, Ying Yu & Wenyi Fan

  2. Key Laboratory of Saline-Alkali Vegetation Ecology Restoration in Oil Field (SAVER), Ministry of Education, Alkali Soil Natural Environmental Science Center (ASNESC), Northeast Forestry University, 26 Hexing rd., 150040, Harbin, China

    Xiguang Yang

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  1. Xiguang Yang
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Correspondence to Xiguang Yang.

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Yang, X., Yu, Y. & Fan, W. Chlorophyll content retrieval from hyperspectral remote sensing imagery. Environ Monit Assess 187, 456 (2015). https://doi.org/10.1007/s10661-015-4682-4

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