Abstract
NASA’s current Atmospheric Correction (AC) algorithm for ocean color utilizes two bands and their ratio in the Near Infrared (NIR) to estimate aerosol reflectance and aerosol type. The algorithm then extrapolates the spectral dependence of aerosol reflectance to the visible wavelengths based on modeled spectral dependence of the identified aerosol type. Future advanced ocean color sensors, such as the Ocean Color Instrument (OCI) that will be carried on the Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) satellite, will be capable of measuring the hyperspectral radiance from 340 to 890 nm at 5-nm spectral resolution and at 7 discrete Short-wave Infrared (SWIR) channels: 940, 1038, 1250, 1378, 1615, 2130, and 2260 nm. To optimally employ this unprecedented instrument capability, we propose an improved AC algorithm that utilizes all atmospheric-window channels in the NIR to SWIR spectral range to reduce the uncertainty in the AC process. A theoretical uncertainty analysis of this, namely Multi-Band AC (MBAC), indicates that the algorithm can reduce the uncertainty in remote sensing reflectance (Rrs) retrievals of the ocean caused by sensor random noise. Furthermore, in optically complex waters, where the NIR signal is affected by contributions from highly-reflective turbid waters, the MBAC algorithm can be adaptively weighted to the strongly-absorbing SWIR channels to enable improved ocean color retrievals in coastal waters. We provide here a description of the algorithm and demonstrate the improved performance in ocean color retrievals, relative to the current NASA standard AC algorithm, through comparison with field measurements and assessment of propagated uncertainties in applying the MBAC algorithm to MODIS and simulated PACE OCI data.
Highlights
Ocean color retrieval algorithms require an atmospheric correction (AC) process to separate the radiometric contribution of the atmosphere from the ocean, given the radiance measured at the top of atmosphere (TOA)
To demonstrate how the fitting of the spectral information improves the determination of the aerosol spectral dependence, we show in Figure 3 an example of two retrievals of the aerosol reflectance for a 50% fine-mode fraction case and coarsemode dominant aerosol case, 10% fine-mode fraction, at 77.5% relative humidity (RH), using the 2-band and the 6-band multiband AC (MBAC) algorithms
The utilization of multibands from the near infrared (NIR) to short-wave infrared (SWIR) in the AC reduces the uncertainty in the retrieval of ocean color reflectance due to sensor random and systematic noise
Summary
Ocean color retrieval algorithms require an atmospheric correction (AC) process to separate the radiometric contribution of the atmosphere from the ocean, given the radiance measured at the top of atmosphere (TOA). The primary challenge is determining the contribution of aerosols (a primary source of uncertainty in the AC) to the atmospheric path radiance, which is highly variable and, must be inferred from observation This approach takes advantage of the strong absorption of water in the Near Infrared to Shortwave Infrared (NIR-SWIR) (longward of 700 nm) to separate the atmospheric and oceanic signals (Gordon and Wang, 1994; Wang et al, 2009; Ahmad et al, 2010). A recent study by Liu et al (2019) indicated that the turbidity index, responsible for the switching, varies with the aerosol size distribution, optical depth, and observing geometry, rendering the effectiveness of the NIR-SWIR switching questionable
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