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Son, S; Wang, MH (2012). Water properties in Chesapeake Bay from MODIS-Aqua measurements. REMOTE SENSING OF ENVIRONMENT, 123, 163-174.

This study evaluates the performance of ocean color products derived from measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua using the standard near-infrared (NIR) and the shortwave infrared (SWIR)-based atmospheric correction algorithms in the Chesapeake Bay. The MODIS-Aqua-derived normalized water-leaving radiances, nL(w)(lambda), and chlorophyll-a (Chl-a) data are compared with in situ radiometric measurements from the NASA SeaWiFS Bio-optical Archive and Storage System (SeaBASS) database and Chl-a data from the Chesapeake Bay Water Quality Database. Results show that, using the NIR-SWIR combined ocean color data processing, improved nL(w)(lambda) and Chl-a data products can be produced in the Chesapeake Bay. However, Chl-a data are still overestimated in some Chesapeake Bay regions, in particular, in the upper bay region where waters are strongly influenced by the total suspended sediment (TSS) concentration. Specifically, using the NIR-SWIR approach, mean ratios of MODIS-derived and in situ-measured nL(w)(lambda) at wavelengths of 412, 443, 488, 531, 551, and 667 nm for the Chesapeake Bay are 1.288, 1.093, 0.998, 0.946, 0.908, and 0.865, respectively, while mean Chl-a values over the region from satellite-derived and in situ-measure data are 11.14 and 10.28 mg. m(-3), respectively. Based on a strong correlation relationship between TSS and water diffuse attenuation coefficient, a regional TSS algorithm for the Chesapeake Bay has been developed and validated, with mean ratio of 1.064 between MODIS-derived and in situ-measured TSS data. Therefore, using the NIR-SWIR algorithm for MODIS-Aqua ocean color data processing, nL(w)(lambda), Chl-a, and TSS data from 2002 to 2010 for the Chesapeake Bay have been generated and used for characterizing the water properties in the region, showing strong seasonal and interannual variability, as well as important spatial variations in the region. Published by Elsevier Inc.



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