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.
States can adopt numeric water quality criteria into their water quality standards to protect the designated uses of their coastal waters from eutrophication impacts. The first objective of this study was to provide an approach for developing numeric water quality criteria for coastal waters based on archived SeaWiFS ocean color satellite data. The second objective was to develop an approach for transferring water quality criteria assessments to newer ocean color satellites, such as MODIS and MERIS. Measures of SeaWiFS, MODIS, and MERIS chlorophyll-a (Chl RS -a , mgm −3 ) were resolved across Florida’s coastal waters between 1998 and 2009. Annual geometric means of SeaWiFS Chl RS -a were evaluated to determine a quantitative reference baseline from the 90th percentile of the annual geometric means. A method for transferring to multiple ocean color sensors was implemented with SeaWiFS as the reference instrument. The Chl RS -a annual geometric means for each coastal segment from MODIS and MERIS were regressed against SeaWiFS to provide a similar response among all three satellites. Standardization factors for each coastal segment were calculated based on the differences between 90th percentile from SeaWiFS to MODIS and SeaWiFS to MERIS. This transfer approach was allowed for future assessments, typically with <7% difference in the calculated criteria.
Harmful algal blooms (HABs) have impacts on coastal economies, public health, and various endangered species. HABs are caused by a variety of organisms, most commonly dinoflagellates, diatoms, and cyanobacteria. In the late 1970's, optical remote sensing was found to have a potential for detecting the presence of blooms of Karenia brevis on the US Florida coast. Due to the nearly annual frequency of these blooms and the ability to note them with ocean color imagery, K. brevis blooms have strongly influenced the field of HAB remote sensing. However, with the variability between phytoplankton blooms, heir environment and their relatively narrow range of pigment types, particularly between toxic and non-toxic dinoflagellates and diatoms, techniques beyond optical detection are required for detecting and monitoring HABs. While satellite chlorophyll has some value, ecological or environmental characteristics are required to use chlorophyll. For example, identification of new blooms can be an effective means of identifying HABs that are quie intense, also blooms occurring after specific rainfall or wind events can be indicated as HABs. Several HAB species do not bloom in the traditional sense, in that they do not dominate the biomass. In these cases, remote sensing of SST or chlorophyll can be coupled with linkages to seasonal succession, changes in circulation or currents, and wind-induced transport--including upwelling and downwelling, to indicate the potential for a HAB to occur. An effective monitoring and forecasting system for HABs will require the coupling of remote sensing with an environmental and ecological understanding of the organism.
To enable the production of the best chlorophyll products from SeaWiFS data NOAA (Coastwatch and NOS) evaluated the various atmospheric correction algorithms by comparing the satellite derived water reflectance derived for each algorithm with in situ data. Gordon and Wang (1994) introduced a method to correct for Rayleigh and aerosol scattering in the atmosphere so that water reflectance may be derived from the radiance measured at the top of the atmosphere. However, since the correction assumed near infrared scattering to be negligible in coastal waters an invalid assumption, the method over estimates the atmospheric contribution and consequently under estimates water reflectance for the lower wavelength bands on extrapolation. Several improved methods to estimate near infrared correction exist: Siegel et al. (2000); Ruddick et al. (2000); Stumpf et al. (2002) and Stumpf et al. (2003), where an absorbing aerosol correction is also applied along with an additional 1.01% calibration adjustment for the 412 nm band. The evaluation show that the near infrared correction developed by Stumpf et al. (2003) result in an overall minimum error for U.S. waters. As of July 2004, NASA (SEADAS) has selected this as the default method for the atmospheric correction used to produce chlorophyll products.
Conference Committee Involvement (1)
Remote Sensing of the Coastal Oceanic Environment
31 July 2005 | San Diego, California, United States
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.