Inversion of suspended sediment concentration at the Hangzhou Bay based on the high-resolution satellite HJ-1A/B imagery

Qinghui Meng; Zhihua Mao; Haiqing Huang; Yuzhang Shen

doi:10.1117/12.2022698

24 September 2013 Inversion of suspended sediment concentration at the Hangzhou Bay based on the high-resolution satellite HJ-1A/B imagery

Qinghui Meng, Zhihua Mao, Haiqing Huang, Yuzhang Shen

Author Affiliations +

Proceedings Volume 8869, Remote Sensing and Modeling of Ecosystems for Sustainability X; 88690W (2013) https://doi.org/10.1117/12.2022698
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

Abstract

HJ-1A/B were two small satellite constellations, that were launched for the environment and disaster monitoring and forecasting on September 6th, 2008. Based on the advantages of high temporal and spacial resolution of the HJ CCD data, this paper aims at evaluate four inversion algorithms of suspended sediments concentration by the remote sensing reflectance in the Hangzhou Bay. First, the atmospheric correction of HJ-1 A/B CCD imagery was carried out using fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) model, in which aerosol optical depth was retrieved from synchronous Terra/MODIS data. Then, four classical band ratio algorithms were evaluated. Results show that He’s GOCI model was better than others with 12.75% relative error. At last, we compare the discrepancy between He’s GOCI algorithm and any other three model, results indicate that the other three empirical algorithms performance not very good because of the difference come from different satellite data, various study area, different research season, distinct correction result. This research has an important practical significance to improving the SSC inversion in the HZB.

Citation Download Citation

Qinghui Meng, Zhihua Mao, Haiqing Huang, and Yuzhang Shen "Inversion of suspended sediment concentration at the Hangzhou Bay based on the high-resolution satellite HJ-1A/B imagery", Proc. SPIE 8869, Remote Sensing and Modeling of Ecosystems for Sustainability X, 88690W (24 September 2013); https://doi.org/10.1117/12.2022698

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