Publications

Hu, CM; Feng, L; Lee, ZP; Franz, BA; Bailey, SW; Werdell, PJ; Proctor, CW (2019). Improving Satellite Global Chlorophyll a Data Products Through Algorithm Refinement and Data Recovery. JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 124(3), 1524-1543.

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<0.05mg m(-3), but smoother algorithm transition for Chl between 0.25 and 0.40mg 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 similar to 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 7x5 to 3x3 pixels without losing data quality in either Chl or spectral remote sensing reflectance (R-rs(lambda), 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. Plain Language Summary There are generally two issues with any remote sensing data product: data quality (accuracy) and data quantity (coverage). In this work, these two issues for global ocean data products of chlorophyll a concentrations (Chl in mg m(-3)) are investigated through revisiting a recently developed algorithm concept and statistical analyses of cloud-adjacent data. The use of more data in algorithm development leads to slightly different algorithm coefficients and smoother transition between clear and turbid waters, and a new straylight masking scheme is proposed to recover some of the previously masked data in the global data products. The new algorithm leads to significantly improved cross-sensor consistency (SeaWiFS, MODIS, VIIRS) as compared to the traditional band-ratio algorithms, with mean monthly difference reduced from 10% to 1-2%. The new straylight masking scheme leads to relative increase of 39% in data quantity in the global ocean without losing data quality. These improvements are expected to reduce uncertainties and fill gaps in the global data products.

DOI:
10.1029/2019JC014941

ISSN:
2169-9275