POSTER Session 1

Assessment of algorithms for estimating inherent optical properties (IOPs) based on water mass classification in highly turbid water regions

The inherent optical properties (IOPs) are crucial parameters for estimating primary production and dissolved carbon content, etc. in ocean color remote sensing. While previous studies have focused on developing and validating IOPs estimation algorithms using global data, there remains uncertainty about their applicability in complex coastal regions due to limited in situ measurements. This study evaluates the performance and characteristics of five different IOPs estimation algorithms (GIOP, GSM, LMI, PML, QAA) using optical measurements from various highly turbid coastal areas and lakes. Additionally, a method to enhance IOPs estimation accuracy by integrating these algorithms is explored. Optical measurements, including remote sensing reflectance (Rrs), absorption coefficients of phytoplankton (aph), colored dissolved organic matter plus non-algal particles (adg), and backscattering coefficient of particles (bbp), were obtained from the coastal datasets of Valente et al., (2022), and the JAXA Ocean Group. The JAXA dataset, with IOPs median values 2 to 6 times larger than those from Valente et al., (2022), proved effective for validating IOP estimation accuracy in highly turbid waters. Hierarchical clustering based on aph(443), adg(443), and bbp(565) categorized water masses into eight distinct optical characteristic clusters. Taylor diagrams were used to assess the applicability of the five IOPs estimation algorithms to these clusters, revealing varying effectiveness across different optical characteristics. Subsequently, principal component analysis was applied to the IOPs estimation values to create uncorrelated components, which, when used as explanatory variables in multiple regression analysis, resulted in a combined algorithm showing higher accuracy in IOPs estimation compared to individual algorithms.

Hiroto Higa, Yokohama National University, Japan

Salem Ibrahim Salem, Kyoto University of Advanced Science, Japan

Joji Ishizaka, Nagoya University, Japan

Victor Kuwahara, Soka University, Japan

Code of Conduct
Privacy Policy