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

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.

The fifth shade of green: A novel approach to phytoplankton color index assessment in an oligotrophic system

“…Despite the extended spatial and temporal coverage, the quantity and quality of oceanographic satellite data is subject to inherent methodological and environmental conditions. For instance, atmospheric conditions, such as cloud coverage and atmospheric dust, obscure the visual path of imaging instruments (Hu et al 2019). Hence in essence, there is a discrepancy in the availability of satellite information seasonally, with less information available in winter months.…”

An Assessment and Improvement of Satellite Ocean Color Algorithms for the Tropical Pacific Ocean

“…The original CI coefficients resulted in slight underestimation for all sensors, with more data points located above than below the blue Hu et al (2012) line. MERIS' best fit is closest to the updated Hu et al (2019) coefficients; however, the CI for MERIS is the least useful of any sensor (Table 3). The OCI linear fit was among the best performing CI coefficients for all three sensors during the model combination iterator.…”

“…The CI coefficient provided for all sensors is from Hu et al (2012). Hu et al (2019) provide an updated set of sensor independent coefficients (OCI2).…”

“…MERIS had the best overall performance with a median bias of 0.973, a 2.7% underestimation compared to SeaWiFS' bias of 0.92 (an underestimation of 7.6%), and MODIS-Aqua which underestimated by 10.6%, particularly at high Table 4. The last two listed models for each sensor are the current NASA algorithm (Hu et al, 2012) and the proposed new OCI2 algorithm with updated CI coefficients and blending window (Hu et al, 2019).…”

“…The linear reference between blue and the red baseline creates a background from which the relative height of green (λ555) can derive chl more accurately at low concentrations (Hu et al, 2012). Chl CI may perform marginally better at low chl compared to chl OCx by being more responsive to changes in chl-specific backscatter and non-phytoplankton absorption (Hu et al, 2019). Its major advantage is that Chl CI is less sensitive to errors induced by instrument noise and imperfect corrections for the atmosphere, sun-glint, or whitecaps, leading to cleaner imagery and improved cross-sensor consistency (Hu et al, 2012 Chl CI is merged with chl OCx using an algorithm called the Ocean Color Index (OCI) which uses a blending window to best represent open ocean, case 1 waters (Morel & Prieur, 1977).…”