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Automated Atmospheric Correction of Nanosatellites Using Coincident Ocean Color Radiometer Data

Authors :
Sean McCarthy
Summer Crawford
Christopher Wood
Mark D. Lewis
Jason K. Jolliff
Paul Martinolich
Sherwin Ladner
Adam Lawson
Marcos Montes
Source :
Journal of Marine Science and Engineering; Volume 11; Issue 3; Pages: 660
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Here we present a machine-learning-based method for utilizing traditional ocean-viewing satellites to perform automated atmospheric correction of nanosatellite data. These sensor convolution techniques are required because nanosatellites do not usually possess the wavelength combinations required to atmospherically correct upwelling radiance data for oceanographic applications; however, nanosatellites do provide superior ground-viewing spatial resolution (~3 m). Coincident multispectral data from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (Suomi NPP VIIRS; referred to herein as “VIIRS”) were used to remove atmospheric contamination at each of the nanosatellite’s visible wavelengths to yield an estimate of spectral water-leaving radiance [Lw(l)], which is the basis for surface ocean optical products. Machine learning (ML) algorithms (KNN, decision tree regressors) were applied to determine relationships between Lw and top-of-atmosphere (Lt)/Rayleigh (Lr) radiances within VIIRS training data, and then applied to test cases for (1) the Marine Optical Buoy (MOBY) in Hawaii and (2) the AErosol RObotic Network Ocean Color (AERONET-OC), Venice, Italy. For the test cases examined, ML-based methods appeared to improve statistical results when compared to alternative dark spectrum fitting (DSF) methods. The results suggest that ML-based sensor convolution techniques offer a viable path forward for the oceanographic application of nanosatellite data streams.

Details

ISSN :
20771312
Volume :
11
Database :
OpenAIRE
Journal :
Journal of Marine Science and Engineering
Accession number :
edsair.doi.dedup.....1b0a8d6e0b3352aa73e0a59d2f634212
Full Text :
https://doi.org/10.3390/jmse11030660