A novel algorithm for ocean chlorophyll-a concentration using MODIS Aqua data

Abstract

The ability to infer ocean chlorophyll-a concentrations (Chla) from spaceborne instruments is key to assessments of global ocean productivity and monitoring of water quality. Here, we present a novel parametric algorithm, OCG, trained on a set of global in situ high-performance liquid chromatography (HPLC) data that leverages Level-3 remote sensing reflectance (Rrs) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. The OCG algorithm leverages more bands than existing algorithms and also provides pixel-wise uncertainty assessments that enable the calculation of the probability of exceeding specific Chla thresholds. This feature has significant implications for water quality management, particularly in monitoring harmful algal blooms. The OCG surpasses existing algorithms in bias and accuracy without overfitting, especially in coastal areas, where it outperforms the current standard product (CI OC3) by 20 % in median symmetric accuracy. Moreover, the OCG reduces the signed symmetric percentage bias (SSPB) in coastal regions from 41 % (CI OC3) to below 5 %. Globally, the OCG algorithm yields lower Chla in coastal regions, the Southern Ocean and the Mediterranean Sea, and higher values in the open ocean, particularly in ocean gyres and polar regions. For the Chesapeake Bay and the Baltic Sea, for example, daily OCG estimates for 2002 to 2021 are, on average, 2.9 g/L and 3.7 g/L lower than CI OC3 estimates, respectively. The presented approach also shows great potential for other existing and upcoming sensors, enabling widespread application in remote sensing.