Study on Hyperspectral Models in estimating Chlorophyll Content of Corn Based on Hyperspectral measured data and HJ-HSI

DOI

10.2991/rsete.2013.98How to use a DOI?

Keywords

Vegetation indices; HJ-HSI; three-band model; chlorophyll content

Abstract

Chlorophyll is one of the most important pigments for plant photosynthesis as a proxy of health of vegetation and photosynthetic capacity. In this study, estimation of chlorophyll content of corn of western regions of Jilin Province is conducted based on both the measured hyperspectral data and the environmental satellite hyperspectral image data (HJ-HSI). Four kinds of vegetation indices including NDVI, MSR, MCAVI/OSAVI and TCAVI/OSAVI, are used for the inversion of chlorophyll. Among these vegetation indices, NDVI performs the best in estimating chlorophyll, followed by MSR and TCAVI/OSAVI. Otherwise, using HSI data based on vegetation indices in modeling performs not very well but the verification accuracy is rather high (R2>0.9). And the slopes of validated models are all less than 0.4, which indicate that these estimates are lower than the measured results.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tian-tian Shao
AU  - Kai-shan Song
AU  - Bai Zhang
PY  - 2013/08
DA  - 2013/08
TI  - Study on Hyperspectral Models in estimating Chlorophyll Content of Corn Based on Hyperspectral measured data and HJ-HSI
BT  - Proceedings of the 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013)
PB  - Atlantis Press
SP  - 401
EP  - 404
SN  - 1951-6851
UR  - https://doi.org/10.2991/rsete.2013.98
DO  - 10.2991/rsete.2013.98
ID  - Shao2013/08
ER  -