Ocean transparency from space: Validation of algorithms estimating Secchi depth using MERIS, MODIS and SeaWiFS data

• Published in: Remote sensing of environment Vol. 115; no. 12; pp. 2986 - 3001
• Main Authors: Doron, Maéva, Babin, Marcel, Hembise, Odile, Mangin, Antoine, Garnesson, Philippe
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 15.12.2011; Elsevier
• Subjects: Algorithms; Animal, plant and microbial ecology; Applied geophysics; Baltic Sea; Biological and medical sciences; climatology; Coastal; color; data collection; Earth sciences; Earth, ocean, space; environmental policy; Estimates; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; In situ measurement; Internal geophysics; linear models; MERIS; moderate resolution imaging spectroradiometer; MODIS; North Sea; Ocean color; Oceans; radiometry; Remote sensing; Satellites; SeaWiFS; Secchi; Semi-analytical algorithm; Sensors; Teledetection and vegetation maps; Transparency; turbidity; Visibility; water quality; Northeast Atlantic; North Atlantic; North Sea; Atlantic Ocean; Baltic Sea; ANE, North Sea; ANE, Baltic Sea; SeaWiFS; Transparency; Semi-analytical algorithm; Secchi; Coastal; MERIS; Visibility; Ocean color; Remote-sensing; MODIS; algorithms; color; data; Environmental policy; adaptation; Index; measurement sensor; productivity; radiometry; Disk; satellites; Validation; development; Empirical method; remote sensing; biogeochemistry; in situ; Measurement in situ; depth; marine environment; ocean; water quality; Environment; Measurement method; properties
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2011.05.019

Ocean transparency, often measured using Secchi disk, is a useful index of water quality or productivity and is used in many environmental studies. The spaceborne ocean color sensors provide synoptic and regular radiometric data and can be used for applying environmental policies if the data is converted into relevant biogeochemical properties. We adapted and developed semi-analytical and empirical algorithms to estimate the Secchi depth from satellite ocean color data in both coastal and oceanic waters. The development of the algorithms is based on the use of a comprehensive in situ bio-optical dataset. The algorithms are validated using an extensive set of coincident satellite estimates and in situ measurements of the Secchi depth (so-called matchups). More than 400 matchups are compiled for the MERIS, MODIS and SeaWiFS sensors. The comparison between Secchi depth retrievals from remote sensing data and in situ measurements yields determination coefficients (R 2) between 0.50 and 0.73, depending on the sensor and algorithm. The type II linear regression slopes and intercepts vary between 0.95 and 1.46, and between − 0.8 and 6.2 m, respectively. While semi-analytical algorithms provide the most promising results on in situ data, the empirical one proves to be more robust on remote sensing data because it is less sensitive to error due to erroneous atmospheric corrections. Using ocean color archives, one can derive maps of ocean transparency for different areas. Our climatology of the Secchi depth based on ocean color for the transition zone between the North Sea and Baltic Sea is compared to an historical dataset. ► Estimation of ocean transparency (Secchi depth) with satelliteborne ocean color data. ► A semi-analytical algorithm is developed, using an in situ bio-optical database. ► 3 algo (2 semi-analytical, 1 empirical) tested on satellite/in situ match-up database. ► 3 algorithms validated for MERIS, MODIS, SeaWiFS, with R2 between 0.5 and 0.73. ► Satellite climatology compared with historical climatology for Skagerrak and Kattegat.