Modified optical remote sensing algorithms for the Pearl River Estuary

Abstract

This study aims to develop new algorithms to retrieve sea surface parameters including concentrations of Chlorophyll a (Chl a) and Suspended Particulate Matter (SPM), and absorbance of Colored Dissolved Organic Matter (aCDOM) by incorporating the contribution of red bands to make them more adaptable to case 2 waters. Optical remote sensing algorithms have demonstrated efficient retrieval of Chl a, SPM, and aCDOM, yet they are not very accurate especially for coastal areas. It has also been found that the default algorithm has overestimated Chl a in the Pearl River Estuary, and shown poor correlation for CDOM absorbance. By incorporating the red band ratios into the algorithm, a correction effect has been shown, which improves the accuracy of quantifying the actual concentration. Modeling and data fitting of the algorithm have been done based on 61 data samples collected in the Pearl River estuary during a cruise from 3 to 11 May 2014. The study also attempts to modify the aerosol correction bands used in SeaDAS to prevent saturation of these bands. The modified algorithms showed an R-Square value of 0.7289 for Chl a fitting, and 0.7338 for CDOM fitting, and corrected overestimation of Chl a concentration in the Pearl River estuary.

Keywords

optical remote sensing algorithm / Pearl River Estuary

References

[1] Arellano A R (2013). Investigation of Colored Dissolved Organic Matter (CDOM) Optical Properties, Nutrients, and Salinity in Coastal Florida: Springshed to Estuaries. University of South Florida Scholar Commons, Graduate Theses and Dissertations

[2] Chen X, Chen L, Yu Z, Tian L, Zhang W (2009). Chromophoric dissolved organic matter optical characteristics and spatial distribution in the lakes of the middle reaches of YangTze River. Journal of Lake Science, 21(2): 248–254

[3] Gilerson A A, Gitelson A A, Zhou J, Gurlin D, Moses W, Ioannou I, Ahmed S A (2010). Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands. Opt Express, 18(23): 24109–24125

[4] Ioannou I, Gilerson A, Gross B, Moshary F, Ahmed S (2013). Deriving ocean color products using neural networks. Remote Sens Environ, 134: 78–91

[5] Lee Z, Carder K L (2000). Band-ratio or spectral-curvature algorithms for satellite remote sensing. Applied Optics, 39(24): 4377–4380

[6] Lee Z, Carder K L, Mobley C D, Steward R G, and Patch J S (1998). Hyperspectral remote sensing for shallow waters. I. A semianalytical model. Applied Optics, 37(27): 6329–6338

[7] Matsuoka A, Bricaud A, Benner R, Para J, Sempéré R, Prieur L, Bélanger S, Babin M (2012). Tracing the transport of colored dissolved organic matter in water masses of the Southern Beaufort Sea: relationship with hydrographic characteristics. Biogeosciences, 9(3): 925–940

[8] Mobley C D (1999). Estimation of the remote-sensing reflectance from above-surface measurements. Appl Opt, 38(36): 7442–7455

[9] Nababan B (2008). Comparison of chlorophyll concentration estimation using two different algorithms and the effect of coloured dissolved organic matter. International Journal of Remote Sensing and Earth Sciences, 5: 92–101

[10] Neville R A, Gower J F R (1977). Passive remote sensing of phytoplankton via chlorophyll a fluorescence. Journal of Geophysical Research, 82(24): 3487–3493

[11] O’Reilly J E, Maritorena S, Mitchell B G, Siegel D A, Carder K L, Garver S A, Kahru M, McClain C (1998). Ocean Color Chlorophyll algorithms for SeaWiFS. J Geophys Res, 103(C11): 24937–24953

[12] Ruddick K, Nechad B, Neukermans G, Park Y, Doxaran D, Sirjacobs D, Beckers J M (2008). Remote Sensing of Suspended Particulate Matter in Turbid Waters: State of the Art and Future Perspectives. CDROM Proceedings of the Ocean Optics XIX conference held in Barga, 2008-October-6

[13] Tang J (2004). The methods of water spectra measurement and analysis I: above-water method. Journal of Remote Sensing, 8(1): 37–44

[14] Wang M, Shi W (2007). The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing. Opt Express, 15(24): 15722–15733

Acknowledgement

This work is supported by the Hong Kong Innovation and Technology Fund under grants of ITS/272/11 and ITS/259/12, the General Research Fund of Hong Kong Research Grants Council (RGC) under grants CUHK 402912 and 403113, the National Natural Science Foundation of China (Grant No. 41376035), and the direct grants of the Chinese University of Hong Kong. The authors are grateful to Dr. Chunyan Shen, who provided with substantial supports to the in-situ data collection.

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2015 Higher Education Press and Springer-Verlag Berlin Heidelberg