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Sensors 2014, 14(10), 19910-19925; doi:10.3390/s141019910

Plant Leaf Chlorophyll Content Retrieval Based on a Field Imaging Spectroscopy System

1
,
2,3,* , 4
,
1,†
and
2,3,†
1
Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection, Nanjing 210042, China
2
Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
3
Huanjiang Observation and Research Station for Karst Ecosystems, Chinese Academy of Sciences, Hechi 547100, China
4
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 17 July 2014 / Revised: 27 September 2014 / Accepted: 17 October 2014 / Published: 23 October 2014
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [2037 KB, uploaded 23 October 2014]   |  

Abstract

A field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed for agriculture applications. In this study, FISS was used to gather spectral information from soybean leaves. The chlorophyll content was retrieved using a multiple linear regression (MLR), partial least squares (PLS) regression and support vector machine (SVM) regression. Our objective was to verify the performance of FISS in a quantitative spectral analysis through the estimation of chlorophyll content and to determine a proper quantitative spectral analysis method for processing FISS data. The results revealed that the derivative reflectance was a more sensitive indicator of chlorophyll content and could extract content information more efficiently than the spectral reflectance, which is more significant for FISS data compared to ASD (analytical spectral devices) data, reducing the corresponding RMSE (root mean squared error) by 3.3%–35.6%. Compared with the spectral features, the regression methods had smaller effects on the retrieval accuracy. A multivariate linear model could be the ideal model to retrieve chlorophyll information with a small number of significant wavelengths used. The smallest RMSE of the chlorophyll content retrieved using FISS data was 0.201 mg/g, a relative reduction of more than 30% compared with the RMSE based on a non-imaging ASD spectrometer, which represents a high estimation accuracy compared with the mean chlorophyll content of the sampled leaves (4.05 mg/g). Our study indicates that FISS could obtain both spectral and spatial detailed information of high quality. Its image-spectrum-in-one merit promotes the good performance of FISS in quantitative spectral analyses, and it can potentially be widely used in the agricultural sector. View Full-Text
Keywords: field imaging spectroscopy system; spectral sensor; chlorophyll; spectral analysis field imaging spectroscopy system; spectral sensor; chlorophyll; spectral analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Liu, B.; Yue, Y.-M.; Li, R.; Shen, W.-J.; Wang, K.-L. Plant Leaf Chlorophyll Content Retrieval Based on a Field Imaging Spectroscopy System. Sensors 2014, 14, 19910-19925.

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