A standard algorithm for determining depth in clear water from passive sensors exists; but it requires tuning of five parameters and does not retrieve depths where the bottom has an extremely low albedo. To address these issues, we developed an empirical solution using a ratio of reflectances that has only two tunable parameters and can be applied to low-albedo features. The two algorithms-the standard linear transform and the new ratio transformwere compared through analysis of IKONOS satellite imagery against lidar bathymetry. The coefficients for the ratio algorithm were tuned manually to a few depths from a nautical chart, yet performed as well as the linear algorithm tuned using multiple linear regression against the lidar. Both algorithms compensate for variable bottom type and albedo (sand, pavement, algae, coral) and retrieve bathymetry in water depths of less than 10-15 m. However, the linear transform does not distinguish depths Ͼ15 m and is more subject to variability across the studied atolls. The ratio transform can, in clear water, retrieve depths in Ͼ25 m of water and shows greater stability between different areas. It also performs slightly better in scattering turbidity than the linear transform. The ratio algorithm is somewhat noisier and cannot always adequately resolve fine morphology (structures smaller than 4-5 pixels) in water depths Ͼ15-20 m. In general, the ratio transform is more robust than the linear transform.Since the first use of aerial photography over clear shallow water, it has been recognized that water depth can be estimated in some way by remote sensing. The theory developed by Lyzenga (1978Lyzenga ( , 1981 and expanded by Philpot (1989) and Maritorena et al. (1994) demonstrated the validity of, and problems involved with, using passive remote sensing for determination of water depth. The use of two or more bands allows separation of variations in depth from variations in bottom albedo, but compensation for turbidity, while tractable, can be problematic. Although passive optical systems are limited in depth penetration and constrained by water turbidity, the use of such satellite data might be the only viable way to characterize either extensive or remote coral reef environments. Besides the obvious need for bathymetric information in many remote areas, mapping of coral reefs and characterization of potential for bleaching requires information on water depth. Coral reefs, by their nature, strongly influence the physical structure of their environment, and water depth information is fundamental to discriminating and characterizing coral reef habitat, such as patch reef, spur-and-groove, and seagrass beds. Knowledge of water depth also allows estimation of bottom albedo, which can improve habitat mapping (Mumby et al. 1998). Knowledge of the detailed structure of the bottom helps in 1 Corresponding author (richard.stumpf@noaa.gov). AcknowledgmentsThis effort was funded by the NOAA, National Ocean Service, Coral Reef Mapping Program. Steve Rohmann provided overall coordi...

After a 20-year absence, severe cyanobacterial blooms have returned to Lake Erie in the last decade, in spite of negligible change in the annual load of total phosphorus (TP). Medium-spectral Resolution Imaging Spectrometer (MERIS) imagery was used to quantify intensity of the cyanobacterial bloom for each year from 2002 to 2011. The blooms peaked in August or later, yet correlate to discharge (Q) and TP loads only for March through June. The influence of the spring TP load appears to have started in the late 1990 s, after Dreissenid mussels colonized the lake, as hindcasts prior to 1998 are inconsistent with the observed blooms. The total spring Q or TP load appears sufficient to predict bloom magnitude, permitting a seasonal forecast prior to the start of the bloom.

Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multi-metric and user-dependent approach that can be applied within science, modeling, and resource management communities.

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