Hyperspectral remote sensing offers an effective approach for frequent, synoptic water quality measurements over a large spatial extent. However, the optical complexity of case 2 water makes the water quality monitoring by remote sensing in estuarine water a challenge. The prime objective of this study was to develop algorithms for hyperspectral remote sensing of water quality based on in situ spectral measurement of water reflectance. In this study, water reflectance spectra R(λ) were acquired by a pair of Ocean Optic 2000 spectroradiometers during the summers from 2008 to 2011 at Patuxent River, a tributary of Chesapeake Bay, USA. Simultaneously, concentrations of chlorophyll a and total suspended solids (TSS), as well as absorption of colored dissolved organic matter (CDOM) were measured. Empirical models that based on spectral features of water reflectance generally showed good correlations with water quality parameters. The retrieval model that using spectral bands at red/NIR showed a high correlation with chlorophyll a concentration (R2 = 0.81). The ratio of green to blue spectral bands is the best predictor for TSS (R2 = 0.75), and CDOM absorption is best correlated with spectral features at blue and NIR regions (R2 = 0.85). These empirical models were further applied to the ASIA Eagle hyperspectral aerial imagery to demonstrate the feasibility of hyperspectral remote sensing of water quality in the optical complex estuarine waters.
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Dall’Olmo, G., Gitelson, A. A., Rundquist, D. C., Leavitt, B., Barrow, T., & Holz, J. C. (2005). Assessing the Potential of SeaWiFS and MODIS for Estimating Chlorophyll Concentration in Turbid Productive Waters Using Red and Near-Infrared Bands. Remote Sensing of Environment, 96, 176-187. http://dx.doi.org/10.1016/j.rse.2005.02.007
Doxaran, D., Cherukuru, R., & Lavender, S. (2005). Use of Reflectance Band Ratios to Estimate Suspended and Dissolved Matter Concentrations in Estuarine Waters. International Journal of Remote Sensing, 26, 1763-1769.
Fan, C., Glibert, P. M., & Burkholder, J. M. (2003). Characteri-zation of the Affinity for Nitrogen, Uptake Kinetics, and Environmental Relationships for Prorocentrum Minimum in Natural Blooms and Laboratory Cultures. Harmful Algae, 2, 283-299. http://dx.doi.org/10.1016/S1568-9883(03)00047-7
Hunter, P. D., Tyler, A. N., Carvalho, L., Codd, G. A., & Maberly, S. C. (2010). Hyperspectral Remote Sensing of Cyanobacterial Pigments as Indicators for Cell Populations and Toxins in Eutrophic Lakes. Remote Sensing of Environment, 114, 2705-2718. http://dx.doi.org/10.1016/j.rse.2010.06.006
Legleiter, C. J., & Roberts, D. A. (2005). Effects of Channel Morphology and Sensor Spatial Resolution on Image-Derived Depth Estimates. Remote Sensing of Environment, 95, 231-247. http://dx.doi.org/10.1016/j.rse.2004.12.013
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Olmanson, L. G., Brezonik, P. L., & Bauer, M. E. (2013). Airborne Hyperspectral Remote Sensing to Assess Spatial Distribution of Water Quality Characteristics in Large Rivers: The Mississippi River and Its Tributaries in Minnesota. Remote Sensing of Environment, 130, 254-265. http://dx.doi.org/10.1016/j.rse.2012.11.023
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