Recovery of pixels with extremely turbid waters and intensive floating algae from false cloud masking in satellite ocean color remote sensing

Abstract

We describe our work to improve the cloud masking for satellite ocean color data processing over extremely turbid waters and intensive algae blooms (or floating algae), which are often identified as cloud mistakenly. An improved cloud masking approach is proposed using additional information of the Alternate Floating Algae Index (AFAI) and a new normalized AFAI (nAFAI), as well as ratios of the Rayleigh-corrected reflectance ε^(RC)(λ_i, λ_j) from the blue and near-infrared bands. Specifically, the proposed algorithm adds a recovery procedure after the original cloud masking to retrieve falsely masked pixels and identifies these pixels as turbid waters, floating algae or absorbing aerosols, from which ocean color products can be further derived. The new cloud masking algorithm has been implemented in the NOAA Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system for routine global data processing for various satellite ocean color sensors, e.g., the Visible Infrared Imaging Radiometer Suite (VIIRS), the Ocean and Land Colour Instrument (OLCI), the Geostationary Ocean Color Imager (GOCI), etc. Results show that the new cloud masking has remarkably improved ocean color data coverage, particularly over highly turbid coastal and inland waters, as well as intensive floating algae, eliminating almost all false cloud pixels. For example, using the new cloud masking algorithm, the falsely masked pixels are recovered, reducing cloud masking pixels by ∼30–40% and ∼40–50% over highly turbid China East Coast and China’s Lake Taihu, respectively.