On the consistency of MODIS chlorophyll $a$ products in the northern South China Sea
- 1Key Laboratory of Underwater Acoustic Communication and Marine Information Technology (Xiamen University), Ministry of Education of China, Xiamen 361005, China
- 2Research and Development Center for Ocean Observation Technologies, Xiamen University, Xiamen 361005, China
- 3College of Marine Science, University of South Florida, 140 Seventh Ave. S., St. Petersburg, FL 33701, USA
Abstract. Chlorophyll a (Chl) concentrations derived from satellite measurements have been used in oceanographic research, for example to interpret eco-responses to environmental changes on global and regional scales. However, it is unclear how existing Chl products compare with each other in terms of accuracy and consistency in revealing temporal and spatial patterns, especially in the optically complex marginal seas. In this study, we examined three MODIS (Moderate Resolution Imaging Spectroradiometer) Chl data products that have been made available to the community by the US National Aeronautics and Space Administration (NASA) using community-accepted algorithms and default parameterization. These included the products derived from the OC3M (ocean chlorophyll three-band algorithm for MODIS), GSM (Garver–Siegel–Maritorena model) and GIOP (generalized inherent optical properties) algorithms. We compared their temporal variations and spatial distributions in the northern South China Sea. We found that the three products appeared to capture general features such as unique winter peaks at the Southeast Asian Time-series Study station (SEATS, 18° N, 116° E) and the Pearl River plume associated blooms in summer. Their absolute magnitudes, however, may be questionable in the coastal zones. Additional error statistics using field measured Chl as the truth demonstrated that the three MODIS Chl products may contain high degree of uncertainties in the study region. Root mean square error (RMSE) of the products from OC3M and GSM (on a log scale) was about 0.4 and average percentage error (ε) was ~ 115% (Chl between 0.05–10.41 mg m−3, n = 114). GIOP with default parameterization led to higher errors (ε = 329%). An attempt to tune the algorithms based on a local coastal-water bio-optical data set led to reduced errors for Chl retrievals, indicating the importance of local tuning of globally-optimized algorithms. Overall, this study points to the need of continuous improvements for algorithm development and parameterization for the coastal zones of the study region, where quantitative interpretation of the current Chl products requires extra caution.