Improving Satellite Global Chlorophyll a Data Products Through Algorithm Refinement and Data Recovery

Authors

Chuanmin Hu, University of South FloridaFollow
Lian Feng, University of South Florida
Zhongping Lee, University of Massachusetts Boston
Bryan A. Franz, NASA GSFC
Sean W. Bailey, NASA GSFC
Jeremy Werdell, NASA GSFC
Christopher W. Proctor, NASA GSFC

Document Type

Article

Publication Date

2-2019

Digital Object Identifier (DOI)

https://doi.org/10.1029/2019JC014941

Abstract

A recently developed algorithm to estimate surface ocean chlorophyll a concentrations (Chl in mg m⁻³), namely, the ocean color index (OCI) algorithm, has been adopted by the U.S. National Aeronautics and Space Administration to apply to all satellite ocean color sensors to produce global Chl maps. The algorithm is a hybrid between a band-difference color index algorithm for low-Chl waters and the traditional band-ratio algorithms (OCx) for higher-Chl waters. In this study, the OCI algorithm is revisited for its algorithm coefficients and for its algorithm transition between color index and OCx using a merged data set of high-performance liquid chromatography and fluorometric Chl. Results suggest that the new OCI algorithm (OCI2) leads to lower Chl estimates than the original OCI (OCI1) for Chl < 0.05 mg m⁻³, but smoother algorithm transition for Chl between 0.25 and 0.40 mg m⁻³. Evaluation using in situ data suggests that similar to OCI1, OCI2 has significantly improved image quality and cross-sensor consistency between SeaWiFS, MODISA, and VIIRS over the OCx algorithms for oligotrophic oceans. Mean cross-sensor difference in monthly Chl data products over global oligotrophic oceans reduced from ~10% for OCx to 1–2% for OCI2. More importantly, data statistics suggest that the current straylight masking scheme used to generate global Chl maps can be relaxed from 7 × 5 to 3 × 3 pixels without losing data quality in either Chl or spectral remote sensing reflectance (Rrs(λ), sr⁻¹) for not just oligotrophic oceans but also more productive waters. Such a relaxed masking scheme yields an average relative increase of 39% in data quantity for global oceans, thus making it possible to reduce data product uncertainties and fill data gaps.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

Journal of Geophysical Research: Oceans, v. 124, issue 3, p. 1524-1543

©2019. American Geophysical Union. All Rights Reserved.

Scholar Commons Citation

Hu, Chuanmin; Feng, Lian; Lee, Zhongping; Franz, Bryan A.; Bailey, Sean W.; Werdell, Jeremy; and Proctor, Christopher W., "Improving Satellite Global Chlorophyll a Data Products Through Algorithm Refinement and Data Recovery" (2019). Marine Science Faculty Publications. 2005.
https://digitalcommons.usf.edu/msc_facpub/2005