Marine Science Faculty Publications

Comparison of Two Atmospheric Correction Approaches Applied to MODIS Measurements Over North American Waters

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Atmospheric correction, POLYMER, SeaDAS, MODIS, Ocean color

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Using in situ data of spectral remote sensing reflectance (Rrs, sr−1) collected over North American oceanic, coastal and estuarine waters between 2002 and 2016 (N = 942), we evaluate two atmospheric correction approaches applied to MODIS measurements. One is the POLYnomial based approach originally designed for MERIS (POLYMER) but adopted and implemented for MODIS, and the other is the traditional Gordon and Wang (1994b) near-infrared (NIR) approach with iteration to account for non-negligible NIR water-leaving radiance, which is currently embedded in the SeaWiFS Data Analysis System (SeaDAS) software package and used operationally by NASA for processing MODIS data (termed as NASA standard atmospheric correction or NSAC). The approaches are evaluated for both quality and quantity of their retrieved Rrs in the visible domain. The quality is gauged through three statistical measures between in situ and MODIS-retrieved Rrs: root mean square error (RMSE, sr−1), unbiased root mean square (uRMS), and mean bias (δ, sr−1). For common points where both approaches yield valid Rrs retrievals, POLYMER shows worse performance than NSAC for blue bands (<488 nm) and comparable performance for green and red bands. However, POLYMER shows the ability to retrieve more valid Rrs data points (2–3 folds) than NSAC for this evaluation dataset primarily because the latter fails over strong sun glint regions where the MODIS NIR bands saturate but the former is designed to work over sun glint regions using non-saturation MODIS bands. For those data points where only POLYMER yield valid Rrs retrievals, data quality is slightly worse than from the common data points. Although these results may vary slightly among individual subregions, it is generally true that POLYMER may be used as a surrogate of NSAC for atmospheric correction of MODIS when data quantity is significantly limited due to perturbations of sun glint and thin clouds that are typical for subtropical and tropical regions.

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Citation / Publisher Attribution

Remote Sensing of Environment, v. 216, p. 442-455