Principal component analysis of TM images for monitoring inland water quality

Xiaozhou Shu; Yin Qiu; Dingbo Kuang

doi:10.1117/12.373102

17 December 1999 Principal component analysis of TM images for monitoring inland water quality

Xiaozhou Shu, Yin Qiu, Dingbo Kuang

Author Affiliations +

Proceedings Volume 3868, Remote Sensing for Earth Science, Ocean, and Sea Ice Applications; (1999) https://doi.org/10.1117/12.373102
Event: Remote Sensing, 1999, Florence, Italy

Abstract

TM data have been used by many researchers for remote sensing of monitoring chlorophyll-a concentration, which is strongly correlated with trophic state of surface water. In this paper, principal component analysis (PCA) is employed for extracting alga production in lake water from TM data. Input images are chosen as radiance ratios TM2/TM1, TM2/TM3 and TM4/TM3, instead of four independent band data. Regression analysis gives relationship between the first principal component (PCA1) and chlorophyll-a concentrations as: Chl((mu) g/L) equals -56.69+135.68X(PCA1), where PCA1 equals 0.146 X(TM2/TM1)-0.274X(TM4/TM2)+0.951X(TM4/TM 3). Chlorophyll-a concentrations estimated from TM image are compared with field measurements. The chlorophyll-a algorithm is also applied to TM images of the same lake acquired at different time. Multi-temporal chlorophyll-a distributions in the lake are mapped.

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Xiaozhou Shu, Xiaozhou Shu, Yin Qiu, Yin Qiu, Dingbo Kuang, Dingbo Kuang, } "Principal component analysis of TM images for monitoring inland water quality", Proc. SPIE 3868, Remote Sensing for Earth Science, Ocean, and Sea Ice Applications, (17 December 1999); doi: 10.1117/12.373102; https://doi.org/10.1117/12.373102

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