Atmospheric Correction

• Published in: Hyperspectral Satellites and System Design Vol. 1; pp. 561 - 584
• Main Author: Qian, Shen-En
• Format: Book Chapter
• Language: English
• Published: United Kingdom CRC Press 2020; Taylor & Francis Group
• Edition: 1
• Subjects: Atmospheric Correction Algorithm; Radiative Transfer Code; Dense Dark Vegetation; Reflectance Spectra; Aerosol Optical Thickness; Elm; Aerosol Model; TOA Reflectance; Black Pixel Assumption; Atmospheric Correction; Aerosol Single Scattering Albedo; Aerosol Scattering; Hyperspectral Data; Radiative Transfer; Atmospheric Correction Processes; Surface Reflectance Spectra; SWIR Band; Meris Image; Water Vapor Absorption Bands; Aerosol Scattering Phase Function; Solar Zenith Angle; SWIR Region; Atmospheric Correction Method; Surface Reflectance Retrieval; VNIR Region
• ISBN: 9780367217907; 0367217902
• DOI: 10.1201/9780429266201-14

Radiance data measured by a hyperspectral sensor contain atmospheric effects, which include absorption by atmospheric water vapor and gases (e.g., oxygen and ozone), atmospheric molecular scattering (Rayleigh effect), and aerosol absorption and scattering. These atmospheric effects need to be corrected by converting the at-sensor radiance data to surface reflectance in order for hyperspectral data to be used for quantitative remote sensing. This chapter begins with an introduction of atmospheric effects on hyperspectral data. It describes statistics-based atmospheric correction approaches, including empirical line method, internal average relative reflectance, flat-field correction, cloud shadow method, and dense dark vegetation algorithm. The chapter describes the radiative transfer modeling based atmospheric correction techniques for land and water/ocean applications separately, because water/ocean surfaces are much darker than land surfaces and the air-water interface is not Lambertian, very accurate modeling of atmospheric absorption, scattering effects, and the specular water surface reflection effects is required. The chapter describes six popular radiative modeling based atmospheric correction techniques for land applications. It describes various radiative modeling based atmospheric correction techniques for water/ocean applications, including black-pixel NIR algorithm, NIR similarity spectrum algorithm, NIR-SWIR algorithm using turbid water index, self-contained atmospheric parameter estimation, modified black-pixel NIR algorithm, and direct inversion approach using neural network. This chapter begins with an introduction of atmospheric effects on hyperspectral data. It describes six popular radiative modeling based atmospheric correction techniques for land applications. The chapter also describes various radiative modeling based atmospheric correction techniques for water/ocean applications, including black-pixel NIR algorithm, NIR similarity spectrum algorithm, NIR-SWIR algorithm using turbid water index, self-contained atmospheric parameter estimation, modified black-pixel NIR algorithm, and direct inversion approach using neural network. It explains statistics-based atmospheric correction approaches, including empirical line method, internal average relative reflectance, flat-field correction, cloud shadow method, and dense dark vegetation algorithm. The application of atmospheric correction to retrieve surface reflectance can also lead to better characterization of the atmosphere. Secondary products of the physics-based atmospheric correction approaches can include a map of the integrated column water vapor and scene aerosol type and visibility. A Lambertian surface is assumed for land scenes in the modeling of radiative transfer effects.