Spectral Analysis of Water Reflectance for Hyperspectral Remote Sensing of Water Quailty in Estuarine Water


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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Fan, C. (2014) Spectral Analysis of Water Reflectance for Hyperspectral Remote Sensing of Water Quailty in Estuarine Water. Journal of Geoscience and Environment Protection, 2, 19-27. doi: 10.4236/gep.2014.22004.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Anderson, D. M., Burkholder, J. M., Cochlan, W. P., Glibert, P. M., Gobler, C. J., Heil, C. A., Kudela, R. M., Parsons, M. L., Rensel, J. E. J., Townsend, D. W., Trainer, V. L., & Vargo, G. A. (2008). Harmful Algal Blooms and Eutrophication: Examining Linkages from Selected Coastal Regions of the United States. Harmful Algae, 8, 39-53. http://dx.doi.org/10.1016/j.hal.2008.08.017
[2] Brando, V. E., & Dekker, A. G. (2003). Satellite Hyperspectral Remote Sensing for Estimating Estuarine and Coastal Water Quality. IEEE Transactions on Geoscience and Remote Sensing, 41, 1378-1387. http://dx.doi.org/10.1109/TGRS.2003.812907
[3] Campbell, G., Phinn, S. R., Dekker, A. G., & Brando, V. E. (2011). Remote Sensing of Water Quality in an Australian Tropical Freshwater Impoundment Using Matrix Inversion and MERIS Images. Remote Sensing of Environment, 115, 2402-2414. http://dx.doi.org/10.1016/j.rse.2011.05.003
[4] Chang, G., Dickey, T., & Lewis, M. (2006). Toward a Global Ocean System for Measurements of Optical Properties Using Remote Sensing and in Situ Observations. Remote Sensing of the Marine Environment: Manual of Remote Sensing, 6, 285-326.
[5] 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
[6] 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. http://dx.doi.org/10.1080/01431160512331314092
[7] 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
[8] 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
[9] Kendrick, P. (1976). Remote Sensing and Water Quality. Journal (Water Pollution Control Federation), 48, 2243-2246.
[10] 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
[11] Lubac, B., & Loisel, H. (2007). Variability and Classification of Remote Sensing Reflectance Spectra in the Eastern English Channel and Southern North Sea. Remote Sensing of Environment, 110, 45-58. http://dx.doi.org/10.1016/j.rse.2007.02.012
[12] 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
[13] Schalles, J. F. (2006). Optical Remote Sensing Techniques to Estimate Phytoplankton Chlorophyll a Concentrations in Coastal. Remote Sensing of Aquatic Coastal Ecosystem Processes, Springer, 27-79.
[14] Senay, G. B., Shafique, N. A., Autrey, B. C., Fulk, F., & Cormier S. M. (2002). The Selection of Narrow Wavebands for Optimizing Water Quality Monitoring on the Great Miami River, Ohio Using Hyperspectral Remote Sensor Data. Journal of Spatial Hydrology, 1.
[15] Toole, D. A., & Siegel, D. A. (2001). Modes and Mechanisms of Ocean Color Variability in the Santa Barbara Channel. Journal of Geophysical Research: Oceans (1978-2012), 106, 26985-27000.
[16] Warner, R. A., & Fan, C. (2013). Optical Spectra of Phytoplankton Cultures for Remote Sensing Applications: Focus on Harmful Algal Blooms. International Journal of Environmental Science and Development, 94-98.

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