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Evaluated the performance of 3 billion+ band combinations of 9 vegetation index types by conducting regression analysis to determine the optimal chlorophyll index for chlorophyll content estimation.

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Sarah018/Linear-Regression-Best-Index

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Linear-Regression-Best-Index

Evaluated the performance of 3 billion+ band combinations of 9 vegetation index types by conducting regression analysis to determine the optimal chlorophyll index for chlorophyll content estimation.

Methodology described in the paper by Zhao, Yunxia, et al., 2019

1. Requirements

The project was implemented and tested in Matlab.

2. Datasets

In this study, we collected fifty-two, fifty-four, fifty-six and fifty-eight leaf samples for Kandelia candel, Avicennia marina, Aegiceras corniculatum, and Sonneratia apetala, respectively, resulting in 220 leaves.

3. Nine commonly used vegetation indexes examined in our study

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4. Flow chart of data analysis and processing

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Evaluated the performance of 3 billion+ band combinations of 9 vegetation index types by conducting regression analysis to determine the optimal chlorophyll index for chlorophyll content estimation.

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