Comprehensive comparison performances of Landsat-8 OLI atmospheric correction methods for inland and coastal waters
Authors: Tian Shang , Guo Hongwei , Huang Jinhui Jeanne , Zhu Xiaotong , Zhang zijie 
Published: March 2022

&lt;p&gt;Remote sensing is important for&amp;#160;aquatic&amp;#160;environment monitoring. The Operational Land Imager (OLI) sensor onboard Landsat-8 has been proved to be able to monitor water&amp;#160;quality&amp;#160;of&amp;#160;inland&amp;#160;and&amp;#160;coastal&amp;#160;waters. Atmospheric correction (AC) is a&amp;#160;crucial&amp;#160;step in the quantitative research of remote sensing, and its accuracy is the key to the quantitative analysis of inland&amp;#160;and&amp;#160;coastal&amp;#160;waters.&amp;#160;However, the optical complexity of inland and coastal waters remains a major challenge for&amp;#160;AC of Landsat-8 imagery, which in turn affects the retrieval&amp;#160;accuracy of optically active constituents (OACs). A variety&amp;#160;of AC algorithms had&amp;#160;developed specifically for water&amp;#160;application. However, comprehensive comparative studies of AC methods for both inland and coastal waters&amp;#160;with a gradient of turbidity levels are lacking. Meanwhile&amp;#160;the comparation of different AC algorithms coupled&amp;#160;with Landsat-8 Chlorophyll-a (Chl-a) retrieval&amp;#160;algorithm are also limited. In this study, the&amp;#160;performances of six water-based AC methods were evaluated by using&amp;#160;multiple&amp;#160;global&amp;#160;datasets&amp;#160;(&lt;em&gt;N&lt;/em&gt;&amp;#160;= 139). The AC methods include the default&amp;#160;and Management Unit Mathematics Models (MUMM) algorithms integrated into Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), the Dark Spectrum Fitting (DSF) and Exponential&amp;#160;Extrapolation (EXP) algorithms&amp;#160;integrated into the Atmospheric Correction for OLI &amp;#8216;lite&amp;#8217; (ACOLITE), image correction for atmospheric effects (iCOR), and the Case 2 Regional CoastColour processor (C2RCC). Four evaluation&amp;#160;strategies were&amp;#160;applied&amp;#160;in this&amp;#160;study including&amp;#160;spectral&amp;#160;similarity and&amp;#160;Chl-a retrieval. The results showed that SeaDAS and DSF performed the best in term of analytical match, band ratio, Chl-a retrieval&amp;#160;and spectral similarity for&amp;#160;all matchups. SeaDAS had&amp;#160;the lowest root-mean-square-error (RMSE) in the blue-green bands of 0.0036 sr&lt;sup&gt;-1&lt;/sup&gt;&amp;#160;and 0.0043 sr&lt;sup&gt;-1&lt;/sup&gt;&amp;#160;respectively. SeaDAS also showed good consistency across the spectra with the lowest median spectral angle of 7&amp;#176;.&amp;#160;It should note that DSF performed best in high turbid waters, but was not as accurate as SeaDAS for remote sensing reflectance (&lt;em&gt;R&lt;/em&gt;&lt;sub&gt;rs&lt;/sub&gt;) retrievals in most low-to-moderately turbid waters. For&amp;#160;the retrieval of Chl-a using&amp;#160;OC3 and Clark algorithm, all AC methods except iCOR and EXP gave similar performance compared to in-situ measurements. SeaDAS coupled with OC3 and Clark algorithms had the lowest RMSE of 1.3359 and 1.4250 mg m&lt;sup&gt;-3&lt;/sup&gt;&amp;#160;respectively, which showed the advantage of SeaDAS in Chl-a retrieval.&amp;#160;This study provides&amp;#160;scientific basis&amp;#160;for&amp;#160;choosing&amp;#160;AC methods&amp;#160;of&amp;#160;Landsat-8 OLI data for aquatic&amp;#160;environment monitoring.&lt;/p&gt;