Abstract
The accuracy of remote-sensing reflectance ( R r s ) estimated from ocean color imagery through the atmospheric correction step is essential in conducting quantitative estimates of the inherent optical properties and biogeochemical parameters of seawater. Therefore, finding the main source of error is the first step toward improving the accuracy of R r s . However, the classic validation exercises provide only the total error of the retrieved R r s . They do not reveal the error sources. Moreover, how to effectively improve this satellite algorithm remains unknown. To better understand and improve various aspects of the satellite atmospheric correction algorithm, the error budget in the validation is required. Here, to find the primary error source from the OLCI R r s , we evaluated the OLCI R r s product with in-situ data around the China Sea from open ocean to coastal waters and compared them with the MODIS-AQUA and VIIRS products. The results show that the performances of OLCI are comparable to those MODIS-AQUA. The average percentage difference (APD) in R r s is lowest at 490 nm (18%), and highest at 754 nm (79%). A more detailed analysis reveals that open ocean and coastal waters show opposite results: compared to coastal waters the satellite R r s in open seas are higher than the in-situ measured values. An error budget for the three satellite-derived R r s products is presented, showing that the primary error source in the China Sea was the aerosol estimation and the error on the Rayleigh-corrected radiance for OLCI, as well as for MODIS and VIIRS. This work suggests that to improve the accuracy of Sentinel-3A in the coastal waters of China, the accuracy of aerosol estimation in atmospheric correction must be improved.
Highlights
The first global chlorophyll-a concentration image from the NASA Coastal Zone Color Scanner (CZCS, 1978–1986) launched efforts to begin conducting optical remote sensing of the world’s oceans
Many ocean color sensors have been launched since CZCS, including the NASA Sea-viewing Wide Field-of-view Sensor (SeaWiFS, 1997–2010), the NASA Moderate Resolution Imaging Spectroradiometer (MODIS-T, 1999–present on board the Terra platform, and MODIS-A, 2002–present on board the Aqua platform), the European Space Agency (ESA) Medium Resolution Imaging Spectrometer (MERIS, 2001–2012), NOAA Visible Infrared Imaging Radiometer Suite (VIIRS, 2011–present on board the Suomi NPP) and the most recent sensor, the ESA Sentinel-3 Ocean and Land Color Instrument (OLCI, 2016–present)
We focused on the standard Ocean and Land Color Imager (OLCI) radiometric product obtained with the standard OLCI atmospheric correction algorithm and the MODIS-AQUA and VIIRS radiometric products obtained with the NASA standard atmospheric correction algorithm [24]
Summary
The first global chlorophyll-a concentration image from the NASA Coastal Zone Color Scanner (CZCS, 1978–1986) launched efforts to begin conducting optical remote sensing of the world’s oceans. Differences among the atmospheric correction schemes, which affect the magnitude and shape of the retrieved marine signals (i.e., Rrs) [11,12,13], may introduce errors when estimating Rrs [9]. The differences in Rrs values may further affect the subsequent optical (absorption and back-scattering coefficients) and biogeochemical products (i.e., chlorophyll concentration, Kd_490, primary production, etc.) [14], which may induce bias in the studies of the impact of anthropogenic carbon on the climate and environment [15,16]. Achieving highly accurate Rrs is critical for ocean color
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