Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using iCOR algorithm: validation for coastal and inland waters

Abstract

Image correction for atmospheric effects (iCOR) is an atmospheric correction tool that can process satellite data collected over coastal, inland or transitional waters and land. The tool is adaptable with minimal effort to hyper-or multi-spectral radiometric sensors. By using a single atmospheric correction implementation for land and water, discontinuities in reflectance within one scene are reduced. iCOR derives aerosol optical thickness from the image and allows for adjacency correction, which is SIMilarity Environmental Correction (SIMEC) over water. This paper illustrates the performance of iCOR for Landsat-8 OLI and Sentinel-2 MSI data acquired over water. An intercomparison of water leaving reflectance between iCOR and Aerosol Robotic Network-Ocean Color provided a quantitative assessment of performance and produced coefficient of determination (R 2) higher than 0.88 in all wavebands except the 865 nm band. For inland waters, the SIMEC adjacency correction improved results in the rededge and near-infrared region in relation to optical in situ measurements collected during field campaigns.

Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data

“…Bad results over water Type 1 and Type 2 could be explained by this factor. In the work of De Keukelaere et al [58], observations are made over different lakes of Europe. They reported promising results for S2-MSI, except for band 443 nm, with and without adjacency correction, especially for Lake Marken.…”

“…In the NIR, results are less promising, but with the SIMilarity Environment Correction (SIMEC) on, the adjacency correction seems to have a positive effect. De Keukelaere et al [58] pointed out several important issues with iCOR. First, the surface reflectance should be representable by a linear combination of two pure green and a bare soil endmembers and the ocean and inland water do not meet this requirement unless there is some land within the scene.…”

Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers

“…One of the algorithm limitations is difficulty in the derivation of reliable aerosol model estimation and the assumption that surface reflectance should be displayed as a linear combination of two pure green vegetation and a bare soil endmember. Another limitation of iCOR is that it does not correct the image for sun glint effects [26]. SREF and STDSREF perform well only with SVM classification with and without radiometric indices and partially for XGB classification with STDSREF atmospheric correction.…”

“…It uses image data and precalculated Look-up-tables (LUT) for deriving required parameters for the method. It calculates correction in four steps: (1) identification and distinction of land and water pixels; (2) calculation of Aerosol optical thickness (AOT) which is derived from land pixels using an adapted version of method developed by Guanter [23], and extending to water pixel assuming spatially homogenous atmosphere; (3) adjacency correction which is calculated using similarity environment correction (SIMEC) [24] over water and over land targets user defines fixed range [25]; (4) the radiative transfer equation calculation [26]. For computation time minimalization MODTRAN LUT is used, while additional information for atmospheric correction is solar and viewing zenith and azimuth angle and digital elevation model (DEM) [27].…”

Determinação das Áreas Atingidas por Queimadas em Bacias Hidrográficas por meio do Índice De Queimada (NBR), Estudo de Caso da Bacia do Rio Piracicaba-MG

“…Apesar da disponibilidade de dados de sensoriamento remoto e eficiência das técnicas de aplicação é recomendado o pré tratamento das cenas a serem utilizadas (Chen et al, 2005;Nia et al, 2015) por meio da realização de correção radiométrica para minimizar inconsistência radiométrica entre os alvos e divergências entre as características do sensor, condições atmosféricas e angulação solar e de visada (Chen et al, 2005;Laybros et al, 2019), visto que os efeitos atmosféricos modificam as medidas radiométricas do sensor (Bernardo et al, 2016;Keukelaere et al, 2018). Essa técnica consiste na conversão de número digital (ND) ou níveis de cinza de cada pixel em radiância espectral (Chen et al, 2005;Thompson et al, 2018), utiliza os coeficientes de redimensionamento radiométrico fornecidos no arquivo metadados disponibilizados simultaneamente à disponibilização das bandas (arquivo MTL.txt no caso das imagens LandSat 8) e permite minimizar os erros causados pela atmosfera, ângulo solar e geometria de visada (Chen et al, 2005).…”