期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 59, 期 6, 页码 4590-4607出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.3016473
关键词
Oceans; Machine learning algorithms; Water; Machine learning; Image color analysis; Sea measurements; Sensors; Algorithm; chlorophyll a; empirical; machine learning; Moderate Resolution Spectroradiometer (MODIS); ocean color; remote sensing; Sea-viewing Wide Field-of-view Sensor (SeaWiFS); support vector regression (SVR); Visible Infrared Imaging Radiometer Suite (VIIRS)
类别
资金
- Joint Polar Satellite System Funding for the National Oceanic and Atmospheric Administration (NOAA) Ocean Color Calibration and Validation (Cal/Val) [NA15OAR4320064]
A machine learning approach, specifically support vector regression (SVR), was developed to estimate surface ocean chlorophyll a concentrations globally, showing better performance and extended applicability compared to traditional algorithms. SVR not only improves algorithm performance, but also reduces image noise and enhances cross-sensor consistency between different satellite sensors, making it a potential alternative for global data processing.
Various approaches have been proposed to estimate surface ocean chlorophyll a concentrations (Chl, mg m-3) from spectral reflectance measured either in the field or from space, each with its own strengths and limitations. Here, we develop a machine learning approach to reduce the impact of spectral noise and improve algorithm performance at the global scale for multiple satellite sensors. Among several candidates, the support vector regression (SVR) approach was found to yield the best algorithm performance as gauged by several statistical measures against field-measured Chl. While statistically the performance of the SVR is slightly worse than the empirical color index (CI) algorithm proposed in Hu et al. (2012) for Chl < 0.25 mg m-3, its applicability to global waters is much extended, from the CIs 0.01-0.25 mg m-3 (about 75% of the global oceans) to its 0.01-1 mg-3 [about 96% of global oceans according to Sea-viewing Wide Field-of-view Sensor (SeaWiFS) statistics]. Within this range, not only does the SVR show much improved performance over the traditional band-ratio OCx approaches, but the SVR leads to much reduced image noise and much improved cross-sensor consistency between SeaWiFS and Moderate Resolution Spectroradiometer (MODIS)/Aqua and between MODIS/Aqua and Visible Infrared Imaging Radiometer Suite (VIIRS). Furthermore, compared with the hybrid Ocean CI ( OCI) algorithm currently used by the U.S. NASA as the default algorithm for all mainstream ocean color sensors, the SVR avoids the need to merge two different algorithms for intermediate Chl (band subtraction for CI and band ratio for OCx), thus may serve as an alternative approach for global data processing.
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