remote sensing Article Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing Christopher O. Ilori 1,*, Nima Pahlevan 2,3 and Anders Knudby 4 1 Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada 2 NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA;
[email protected] 3 Science Systems and Applications, Inc., 10210 Greenbelt Road, Suite 600 Lanham, MD 20706, USA 4 University of Ottawa, 60 University Private, Ottawa, ON K1N 6N5, Canada;
[email protected] * Correspondence:
[email protected]; Tel.: +1-778-929-5350 Received: 28 January 2019; Accepted: 18 February 2019; Published: 25 February 2019 Abstract: Ocean colour (OC) remote sensing is important for monitoring marine ecosystems. However, inverting the OC signal from the top-of-atmosphere (TOA) radiance measured by satellite sensors remains a challenge as the retrieval accuracy is highly dependent on the performance of the atmospheric correction as well as sensor calibration. In this study, the performances of four atmospheric correction (AC) algorithms, the Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI), Atmospheric Correction for OLI ‘lite’ (ACOLITE), Landsat 8 Surface Reflectance (LSR) Climate Data Record (Landsat CDR), herein referred to as LaSRC (Landsat 8 Surface Reflectance Code), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), implemented for Landsat 8 Operational Land Imager (OLI) data, were evaluated. The OLI-derived remote sensing reflectance (Rrs) products (also known as Level-2 products) were tested against near-simultaneous in-situ data acquired from the OC component of the Aerosol Robotic Network (AERONET-OC).