Estimating chlorophyll-a concentration based on a four-band model using field spectral measurements and HJ-1A hyperspectral data of Qiandao Lake, China

作者:Feng, QL (Feng, Quanlong)[ 1 ] ; Gong, JH (Gong, Jianhua)[ 1,2 ] ; Wang, Y (Wang, Ying)[ 3 ] ; Liu, JT (Liu, Jiantao)[ 1 ] ; Li, Y (Li, Yi)[ 1 ] ; Ibrahim, AN (Ibrahim, A. N.)[ 1,2 ] ; Liu, QG (Liu, Qigen)[ 3 ] ; Hu, ZJ (Hu, Zhongjun)[ 3 ]

REMOTE SENSING LETTERS

卷: 6  期: 10  页: 735-744

DOI: 10.1080/2150704X.2015.1054044

出版年: OCT 3 2015

摘要

Accurate estimation of phytoplankton chlorophyll-a (chl-a) concentration from remote sensing data is challenging due to the complex optical properties of case II waters. Recently, a novel semi-analytical four-band model was developed to estimate chl-a concentration in turbid productive waters. The objective of this study was to evaluate the performance of the four-band model and extend its application to hyperspectral satellite data for estimating chl-a concentration in Qiandao Lake of China. Based on field spectral measurements and in situ water sampling, the four-band model expressed as [R-rs(-1)(661.6) - R-rs(-1)(706.7)] [R-rs(-1)(714.8) - R-rs(-1)(682.2)](-1) was calibrated after band tuning, where R-rs(-1) represents the reciprocal of the remote sensing reflectance. The spectral-based four-band model accounted for more than 88% of variance in chl-a concentration with a root mean square error (RMSE) of 1.47g l(-1). To justify the potential of this model with satellite data, comparable wavelengths selected from HJ-1A Hyperspectral Imager (HSI) imagery were utilized to calibrate the four-band model. The HSI-based model explained about 80% of chl-a variation with an RMSE of 1.35g l(-1). Experimental results also showed that the four-band model outperformed its three-band counterpart. The results validated the rationale of the four-band model and demonstrated the effectiveness of this model for estimating chl-a concentration from both in situ spectral data and HJ-1A hyperspectral satellite imagery.

通讯作者地址: Gong, JH (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China

[ 2 ] Zhejiang CAS Applicat Ctr Geoinformat, Jiaxing, Peoples R China

[ 3 ] Shanghai Ocean Univ, Coll Fisheries & Life Sci, Shanghai, Peoples R China