Performance Evaluation of Inherent Optical Property Algorithms and Identification of Potential Water Quality Indicators Using GCOM-C Data in Eutrophic Lake Kasumigaura, Japan

• Published in: Remote sensing (Basel, Switzerland) Vol. 17; no. 9; p. 1621
• Main Authors: Choto, Misganaw, Higa, Hiroto, Salem, Salem Ibrahim, Siswanto, Eko, Suzuki, Takayuki, Mäll, Martin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 02.05.2025
• Subjects: Absorption; Accuracy; Algae; Algorithms; Analysis; Aquatic ecosystems; Biodiversity; Biogeochemistry; Chlorophyll; Climate change; Correlation; Dissolved organic matter; Environmental monitoring; Eutrophic environments; Eutrophic lakes; Eutrophication; GCOM-C/SGLI; In situ measurement; Indicators; inherent optical properties (IOPs) algorithm; Inland waters; Lakes; Machine learning; Optical properties; Performance evaluation; Phytoplankton; Plankton; Quality assessment; Quality control; Reflectance; Remote sensing; remote sensing reflectance; Root-mean-square errors; Satellite observation; Satellites; SeaDAS; Solid suspensions; Suspended solids; Water; Water monitoring; Water quality; Water quality assessments; Water quality management; Water resources management; Japan; Lake Kasumigaura
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs17091621

Lake Kasumigaura, one of Japan’s largest lakes, presents significant challenges for remote sensing due to its eutrophic conditions and complex optical properties. Although the Global Change Observation Mission-Climate (GCOM-C)/Second-generation Global Imager (SGLI)-derived inherent optical properties (IOPs) offer water quality monitoring potential, their performance in such turbid inland waters remains inadequately validated. This study evaluated five established IOP retrieval algorithms, including the quasi-analytical algorithm (QAA_V6), Garver–Siegel–Maritorena (GSM), generalized IOP (GIOP-DC), Plymouth Marine Laboratory (PML), and linear matrix inversion (LMI), using measured remote sensing reflectance (Rrs) and corresponding IOPs between 2017–2018. The results demonstrated that the QAA had the highest performance for retrieving absorption of particles (ap) with a Pearson correlation (r) = 0.98, phytoplankton (aph) with r = 0.97, and non-algal particles (anap) with r = 0.85. In contrast, the GSM algorithm exhibited the best accuracy for estimating absorption by colored dissolved organic matter (aCDOM), with r = 0.87, along with the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE). Additionally, a strong correlation (r = 0.81) was observed between SGLI satellite-derived remote-sensing reflectance (Rrs) and in situ measurements. Notably, a high correlation was observed between the aph (443 nm) and the chlorophyll a (Chl-a) concentration (r = 0.84), as well as between the backscattering coefficient (bbp) at 443 nm and inorganic suspended solids (r = 0.64), confirming that IOPs are reliable water quality assessment indicators. Furthermore, the use of IOPs as variables for estimating water quality parameters such as Chl-a and suspended solids showed better performance compared to empirical methods.