TITLE:
Evaluation of Atmospheric Correction Algorithms for Landsat-8 OLI and MODIS-Aqua to Study Sediment Dynamics in the Northern Gulf of Mexico
AUTHORS:
Nazanin Chaichitehrani, Erin Lee Hestir, Chunyan Li
KEYWORDS:
Suspended Particulate Matter, Remote Sensing, Atmospheric Correction Algorithms, River Plume
JOURNAL NAME:
Advances in Remote Sensing,
Vol.7 No.2,
June
22,
2018
ABSTRACT: Suspended
particulate matter (SPM) is regarded as an energy source and a water quality
indicator in coastal and marine ecosystems. To estimate SPM from ocean color
sensors and land observing satellites, an accurate and robust atmospheric
correction must be done. We evaluated the capabilities of ocean color and land
observing satellite for estimation of SPM concentrations over Louisiana
continental shelf in the northern Gulf of Mexico, using the Operational Land
Imager (OLI) on Landsat-8, and Moderate Resolution Imaging Spectroradiometer
(MODIS) on Aqua. In high turbidity waters, the traditional atmospheric correction
algorithms based on near-infrared (NIR) bands underestimate SPM concentrations
due to the inaccurate removal of the aerosol contribution to the top of
atmosphere signals. Therefore, atmospheric correction in high turbidity waters
is a challenge. Four atmospheric correction algorithms were implemented on
remote sensing reflectance (Rrs) values to select suitable atmospheric
correction algorithms for each sensor in our study area. We evaluated
short-wave infrared (SWIR) and NIR atmospheric correction algorithms on Rrs
products from Landsat-8 OLI and Management Unit of the North Sea Mathematical
Models (MUMM) and SWIR.NIR atmospheric correction algorithms on Rrs products from MODIS-Aqua.
SPM was retrieved from a band-ratio SPM-retrieval algorithm for each sensor.
Our results indicated that SWIR atmospheric correction algorithm was the
suitable algorithm for Landsat-8 OLI and SWIR.NIR atmospheric correction
algorithm outperformed MUMM algorithm for MODIS.