An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters.
Bio-optical models are based on relationships between the spectral remote sensing reflectance and optical properties of in-water constituents. The wavelength range where this information can be exploited changes depending on the water characteristics. In low chlorophyll-awaters, the blue/green region of the spectrum is more sensitive to changes in chlorophyll-aconcentration, whereas the red/NIR region becomes more important in turbid and/or eutrophic waters. In this work we present an approach to manage the shift from blue/green ratios to red/NIR-based chlorophyll-aalgorithms for optically complex waters. Based on a combinedin situdata set of coastal and inland waters, measures of overall algorithm uncertainty were roughly equal for two chlorophyll-aalgorithms-the standard NASA OC4 algorithm based on blue/green bands and a MERIS 3-band algorithm based on red/NIR bands-with RMS error of 0.416 and 0.437 for each in log chlorophyll-aunits, respectively. However, it is clear that each algorithm performs better at different chlorophyll-aranges. When a blending approach is used based on an optical water type classification, the overall RMS error was reduced to 0.320. Bias and relative error were also reduced when evaluating the blended chlorophyll-aproduct compared to either of the single algorithm products. As a demonstration for ocean color applications, the algorithm blending approach was applied to MERIS imagery over Lake Erie. We also examined the use of this approach in several coastal marine environments, and examined the long-term frequency of the OWTs to MODIS-Aqua imagery over Lake Erie.
Ocean Process Analysis Laboratory, University of New Hampshire, Durham, NH 03824, USA.
Timothy S
TS
Moore
European Commission-Joint Research Centre, Institute for Environment and Sustainability, TP 272, via E. Fermi 2749, I-21027 Ispra, VA, Italy.
Mark D
MD
Dowell
Department of Biological Sciences, University of New Hampshire, Durham, NH 03824, USA.
Shane
S
Bradt
Image Processing Laboratory (IPL). Universitat de València Catedrático José Beltrán, 2E-46980 Paterna (València), Spain.
Antonio Ruiz
AR
Verdu
Bio-optics; Biosignatures and proxies; Computational methods and data processing; Instruments sensors, and techniques; Remote sensing