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Current Ocean Color (OC) algorithms for the estimation of chlorophyll-a concentration (Chla) from satellite imagery, which are based primarily on 440, 490 and 550 nm bands, work well in the open ocean areas and often break in coastal waters because of failure of the atmospheric correction in blue bands and water complexity. A recently developed neural network (NN) algorithm for VIIRS avoids blue bands utilizing 490, 550 and 671 nm bands. This algorithm was proven to work well to detect Karenia brevis algal blooms along the West Florida Shelf even near the coast and to determine Chla in many other areas but is limited to Chla below 10-15 mg/m3. Algorithms that are based on the red-NIR bands, work well at high Chla for the near surface measurements. However, they require a band near 709 nm which is available only on the Sentinel-3 OLCI satellite sensor, and the current OLCI atmospheric correction is not reliable enough at this band. To detect algal blooms in very complex areas like the Chesapeake Bay, a new approach is presented, which avoids blue bands but includes data from the VIIRS I1 imaging band 600 – 680 nm with a center at 638 nm. Remote sensing reflectance for this I1 band is now routinely included in the standard set of processed VIIRS bands at NOAA CoastWatch with 750 m spatial resolution. This band integrates part of the remote sensing reflectance spectrum with variable phytoplankton absorption together with a complex combination of spectra from other water components and is helpful in the estimation of Chla in a broad range. Results are compared with the performance of the OLCI red-NIR algorithm with an alternative atmospheric correction, showing good qualitative agreements in the Chesapeake Bay area in conditions from clear waters to algal blooms.
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