Details

Autor(en) / Beteiligte

• Fan, Yongzhen
• Li, Wei
• Chen, Nan
• Ahn, Jae-Hyun
• Park, Young-Je
• Kratzer, Susanne
• Schroeder, Thomas
• Ishizaka, Joji
• Chang, Ryan
• Stamnes, Knut

Titel

OC-SMART: A machine learning based data analysis platform for satellite ocean color sensors

Ist Teil von

• Remote sensing of environment, 2021-02, Vol.253, p.112236, Article 112236

Ort / Verlag

New York: Elsevier Inc

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• We introduce a new platform, Ocean Color - Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART), for analysis of data obtained by satellite ocean color sensors. OC-SMART is a multi-sensor data analysis platform which supports heritage, current, and possible future multi-spectral and hyper-spectral sensors from US, EU, Korea, Japan, and China, including SeaWiFS, Aqua/MODIS, SNPP/VIIRS, ISS/HICO, Landsat8/OLI, DSCOVR/EPIC, Sentinel-2/MSI, Sentinel-3/OLCI, COMS/GOCI, GCOM-C/SGLI and FengYun-3D/MERSI2. The products provided by OC-SMART include spectral normalized remote sensing reflectances (Rrs values), chlorophyll_a (CHL) concentrations, and spectral in-water inherent optical properties (IOPs) including absorption coefficients due to phytoplankton (aph), absorption coefficients due to detritus and Gelbstoff (adg) and backscattering coefficients due to particulates (bbp). Spectral aerosol optical depths (AODs) and cloud mask results are also provided by OC-SMART. The goal of OC-SMART is to improve the quality of global ocean color products retrieved from satellite sensors, especially under complex environmental conditions, such as coastal/inland turbid water areas and heavy aerosol loadings. Therefore, the atmospheric correction (AC) and ocean IOP algorithms in OC-SMART are driven by extensive radiative transfer (RT) simulations in conjunction with powerful machine learning techniques.To simulate top of the atmosphere (TOA) radiances, we solve the radiative transfer equation pertinent for the coupled atmosphere-ocean system. For each sensor, we have created about 13 million RT simulations and comprehensive training datasets to support the development of the machine learning AC and in-water IOP algorithms. The results, as demonstrated in this paper, are very promising. Not only does OC-SMART improve the quality of the retrieved water products, it also resolves the negative water-leaving radiance problem that has plagued heritage AC algorithms. The comprehensive training datasets created using multiple atmosphere, aerosol, and ocean IOP models ensure global and generic applicability of OC-SMART. The use of machine learning algorithms makes OC-SMART roughly 10 times faster than NASA's SeaDAS platform. OC-SMART also includes an advanced cloud screening algorithm and is resilient to the contamination by weak to moderate sunglint and cloud edges. It is therefore capable of recovering large amounts of data that are discarded by other algorithms (such as those implemented in NASA's SeaDAS package), especially in coastal areas. OC-SMART is currently available as a standalone Python package or as a plugin that can be installed in ESA's Sentinel Application Platform (SNAP). •OC-SMART, a new satellite data analysis platform for ocean color was developed.•Machine learning algorithms driven by coupled atmosphere-ocean RT simulations.•Global application, multi-sensor support, reliable, robust and efficient.•Completely resolved negative water-leaving signal issue.•Superior noise handling capability and resilient to sunglint and adjacency effects.