Environmental Science & Technology met all standard quality control flags from level-1 to level-3 8- day composites. SeaWiFS OC4 derived chlorophyll (ChlRS-a)7 and light attenuation (KdRSPAR)16 were validated against field chlor- ophyll (Chl-a) and light attenuation (KdPAR) measurements using the native resolution of the sensor. The OC4 was selected because it was a universal algorithm that could be applied to locations beyond Florida, and it was an algorithm packaged within the SeaDAS l2gen program so processing could be completed in SeaDAS by managers. Satellite match-ups were evaluated following Bailey and Werdell17 with a geometric mean (Type II) linear regression18 between a 3 × 3 pixel extraction of satellite data centered at the corresponding field measurement location. Field data used for satellite validation were from the following: the Northeastern Gulf of Mexico project (NEGOM), obtained from the NOAA National Oceanographic Data Center (NODC); the Ecology and Oceanography of Harmful Algal Blooms project (ECOHAB); the Fish and Wildlife Research Institute (FWRI); Mote Marine Laboratory; and SeaWiFS Bio-optical Archive and Storage System.19,20 ChlRS-a values within coastal segments were extracted by matching segment polygon vertex coordinates with correspond- ing satellite image pixel and line values on 8-day composites within SeaDAS. The satellite image pixel and line locations were used to build a polygon using the 8-day array with Interactive Data Language (IDL, ITT VIS). Values from the 8- day array were then averaged if unmasked bins were completely contained within the coastal segment polygon using IDL’s region of interest (ROI, Figure S1 of the Supporting Information, SI). Averages were calculated from the beginning of the satellite mission (September 14, 1997) until January 1, 2010. The relationship between bathymetry and satellite pene- tration depth were examined within each coastal segment to evaluate the potential for interference from bottom reflectance. Median depth of each coastal segment was calculated using 90 m resolution bathymetry data (NOAA National Geophysical Data Center). PAR integrated satellite penetration depths were calculated as the inverse of KdRSPAR. To assess the extent to which ChlRS-a varied with river discharge in various coastal regions of Florida, model II regression analyses were conducted on log10 transformed discharge and ChlRS-a. Daily discharge data were obtained from near coastal USGS gauges on dominant rivers. Discharge data were binned into 8-day averages that matched averaging periods for ChlRS-a. Weekly K. brevis cell counts for the entire state of Florida were acquired from FWRI. Satellites detect K. brevis blooms when cell counts were above 50 000 cells L−1.10,21 Coastal segments with an FWRI count greater than 50 000 cells L−1 during an 8-day composite were flagged. In addition, the same segment was flagged one week prior to and after a bloom was detected to provide a temporal buffer as blooms were transported along the coast. Criteria were defined as a specific concentration of ChlRS-a and a frequency with which that concentration may be exceeded in the future. Cumulative distribution functions were calculated for ChlRS-a in each coastal segment for coastal numeric criteria development. Criteria values were selected as the 90th percentile from the trailing 3-year 50th and 75th percentile values in each segment. No more than 50% and 25% of 8-day composite ChlRS-a values, within a trailing 3-year Article assessment period, are expected to exceed the criteria levels. The statistical significance of the proportion of 8-day composites exceeding the criteria could be evaluated using a binomial test. A time series of ChlRS-a in each segment was used to calculate criteria based on observations from the reference period (1997 to 2010). All data were used, including data flagged during K. brevis bloom events. Medians and upper quartiles (75th percentile) of ChlRS-a were computed for each segment within trailing 3-year periods, beginning with 1997− 1999, 1998−2000, etc. Criteria values were calculated from the 90th percentile of the median and 75th percentile within each segment during the reference period using x+̅ t0.2,N‑1s, where x̅ and s are the mean and sample standard deviation of the median or 75th percentile, and t0.2,N‑1 is the Student’s t-statistic given α = 0.2 and N = 11 3-year periods evaluated. Once criteria values were determined, observations from within the reference period were tested against the criteria with a binomial test to ensure the data within the reference period did not exceed the criteria concentration or frequency.22 ■ RESULTS Field data included >5500 Chl-a observations, which were reduced to 1947 after filtering for surface samples (0 to 2 m depth) and satellite overpass time (±3 h). Fewer KdPAR observations (429) were available for satellite match-up. Within the 3 NM limit, 62 Chl-a field observations were paired with ChlRS-a data and 34 KdPAR field observations were paired with KdRSPAR (Figure 2 and Figure S2 of the SI). Within the 3 Figure 2. SeaWiFS observations of ChlRS-a compared to in situ Chl-a from stations within coastal segments (A) and for all the stations (B). Gray dashed line is 1:1 fit and black line is regression slope. Plots are presented in log space, but regression coefficients have been converted to linear space to represent a linear regression formula of y = slope*x + intercept. NM limit Chl-a and ChlRS-a were significantly correlated (slope = 0.85, R2 = 0.52, RMSE = 0.23, p < 0.01, N = 62) as were KdPAR and KdRSPAR (slope = 0.85, R2 = 0.49, RMSE = 0.10, p < 0.01, N = 34). The regression coefficients from these relationships were used to adjust all derived ChlRS-a and KdRSPAR to better represent field data prior to the 918 dx.doi.org/10.1021/es2014105 | Environ. Sci. Technol. 2012, 46, 916−922