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https://doi.org/10.1016/S0967-0645(04)00107-9

has been cited by the following article:

• 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.