DocumentCode
3088424
Title
Estimating Secchi depth by simulated HSI of HJ-1A from in situ hyperspectral data: A case study in Sishili Bay, China
Author
Dingfeng Yu ; Qianguo Xing ; Ping Shi ; Dingfeng Vu
Author_Institution
Key Lab. of Coastal Zone Environ. Process, Yantai mstitute of Coastal Zone Res., Yantai, China
fYear
2012
fDate
16-18 Dec. 2012
Firstpage
291
Lastpage
295
Abstract
We study the use of simulated Hyper-S pectral Imaging sensor (HSI) on Environmental Satellite 1A (HJ-1A) satellite remote sensing data for estimating Secchi depth of coastal waters. Field data such as Secchi depth of the Sishili Bay in Yantai´s coastal waters were collected, meanwhile, hyperspectral remote sensing data were measured with Ocean Optics TSB4000 spectrometer during two cruises carried out on 22nd and 23rd June 2009. The coastal water-leaving reflectance of HSI was simulated from in situ hyperspectral remote sensing spectrum with 0.19 nm spectral resolution. According to the spectral simulation, characteristics bands of HSI were identified for Secchi depth estimation model. On the basis of correlation analysis between Secchi depth and simulation spectra, a single band model for retrieving Secchi depth was established. Moreover, the relationship between Secchi depth and all the band combinations (band addition, band subtraction, and band ratio) were analyzed, and then a linear regression model of Secchi depth using simulated HSI was developed, which performed better than other models in Sishili Bay, with a mean relative error (RE) of 5.2% and relative mean square error (RMSE) of 0.28 m. The result indicates that the band ratio model of Rrs(508.42)/Rrs(513.56) of HSI could be used to estimate Secchi depth in coastal waters.
Keywords
geophysical image processing; hyperspectral imaging; image resolution; oceanographic techniques; regression analysis; remote sensing; Secchi depth estimation model; Secchi depth retrieval; band ratio model; coastal waters; correlation analysis; environmental satellite; hyperspectral data; hyperspectral remote sensing data; hyperspectral remote sensing spectrum; linear regression model; relative mean square error; simulated hyperspectral imaging sensor; simulation spectra; single band model; spectral resolution; spectral simulation; Analytical models; Optical imaging; Optical reflection; Optical sensors; HSI; Secchi depth; Sishili Bay; coastal waters; hyperspectral;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision in Remote Sensing (CVRS), 2012 International Conference on
Conference_Location
Xiamen
Print_ISBN
978-1-4673-1272-1
Type
conf
DOI
10.1109/CVRS.2012.6421277
Filename
6421277
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