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2 December 2005 A new approach to estimate chlorophyll a concentration using COCTS
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Proceedings Volume 6045, MIPPR 2005: Geospatial Information, Data Mining, and Applications; 60452B (2005) https://doi.org/10.1117/12.651418
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China
Abstract
Chlorophyll a concentration estimated from satellite is very helpful to assess marine environmental pollution, particularly to detect and monitor algal bloom. On basis of spectral feature of chlorophyll, a new index, i.e. Normalized Difference Pigment Index (NDPI), which is a combination of blue band (its center wavelength 443 nm) and green band (its center wavelength 565 nm), was constructed to indicate the phytoplankton. COCTS (Chinese Ocean Color and Temperature Scanner) imagery obtained from the first ocean satellite HY-1 of China was calibrated, atmospherically corrected, registered and cloud masked. Firstly, NDPI was calculated. Secondly, correlation between NDPI and in situ chlorophyll a concentration measured in Liaodong Gulf was analyzed. R2 is 0.692 (p<0.05). Finally band ratio was correlated with in situ chlorophyll a. And modified SeaWiFS OC4v4 algorithm was also used. Comparison shows that NDPI algorithm performed better than band ratio algorithm and the modified SeaWiFS OC4v4 algorithm. Estimated chlorophyll a concentration from NDPI algorithm shows good consistency with previous research based on field survey both in distribution trend and in magnitude. The result demonstrates the capability of COCTS in HY-1 for estimating chlorophyll a concentration. It is also shown that NDPI algorithm is applicable and encouraging for estimating chlorophyll a concentration in sea area.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wenpeng Lin, Pifu Cong, Limei Qu, Qingyuan Zhang, and Chenli Wang "A new approach to estimate chlorophyll a concentration using COCTS", Proc. SPIE 6045, MIPPR 2005: Geospatial Information, Data Mining, and Applications, 60452B (2 December 2005); https://doi.org/10.1117/12.651418
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