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Atmospheric Correction of Moderate- and High-Resolution Satellite Imagery
Author(s): Alain Sei
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Book Description

This Spotlight provides a comprehensive overview of atmospheric correction techniques used for the exploitation of satellite imagery over land surfaces. It starts with radiative transfer theory, then derives computationally efficient atmospheric correction algorithms, and finally surveys applications of these algorithms through derived gridded land products, such as vegetation index or albedo products.

Book Details

Date Published: 1 May 2019
Pages: 62
ISBN: 9781510628311
Volume: SL49

Table of Contents
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1 Introduction

2 From the Radiative Transfer Equation to Chandrasekhar's Formula
2.1 Introduction
     2.1.1 Motivation
     2.1.2 Layout of the section
2.2 Derivation of the exact solution
     2.2.1 Total radiance field: direct and diffuse radiances
2.3 Diffuse radiance: sum of atmospheric and surface radiances
     2.3.1 Green's function for the surface radiance problem
     2.3.2 Integral equation for the boundary value and its solution
     2.3.3 Rigorous expression of the top-of-atmosphere radiance

3 Atmospheric Correction Based on Chandrasekhar's Formula
3.1 Atmospheric constituents and their impacts on satellite measurements
3.2 Standard atmospheric correction based on Chandrasekhar's formula
3.3 Aerosol retrieval assumptions about surface reflectance
3.4 Standard atmospheric correction extension for pressure variation

4 Adjacency Effects and Their Mitigation
4.1 Introduction
4.2 Adjacency problem based on the point spread function
     4.2.1 Environment reflectance and point spread function
     4.2.2 Integral equation for the surface reflectance
     4.2.3 Efficient computation of singular integrals
4.3 Neumann series, analytic continuation, and Padé approximants
4.4 Examples of applications
     4.4.1 Clear and turbid atmospheres
     4.4.2 Landsat-based examples

5 Applications of Atmospheric Correction
5.1 Derived gridded land products: Albedo and vegetation index
     5.1.1 Albedo/BRDF gridded products
     5.1.2 Gridded NBAR NDVI
     5.1.3 Max/Min NDVI product

6 Conclusions

7 Appendix A: Notations

8 Appendix B: Closed-Form Solution Derivation


This Spotlight stems from my work on land-products algorithms for the Suomi National Polar-Orbiting Partnership (SNPP) program. As the land-products lead for the prime contractor Northrop Grumman Aerospace Systems, I was responsible for the end-to-end performance of the surface reflectance, vegetation index, surface albedo, surface type, and fire mask products. This led to a careful study of the heritage algorithms used for these products in order to evaluate their performance against the requirements levied on the SNPP program. This Spotlight focuses on atmospheric correction algorithms, which are at the core of many land products, such as surface reflectance, vegetation index, surface albedo, and surface type.

Atmospheric correction consists of processing satellite data collected by airborne or spaceborne instruments to infer surface characteristics. Broadly speaking, it amounts to bringing the instruments down to the surface to remove the effects introduced in the measurements by the atmosphere. The full understanding of the connection between surface characteristics and satellite measurements requires going back to first principles, which, in this case, means using radiative transfer theory. This well-established theory fully describes the physics of atmosphere- surface interactions. Once the exact solution of the radiative transfer equation is established, approximations and simplifications to particular cases can be derived. As these derivations are the basis of the operational atmospheric correction algorithms, a complete understanding is then at hand for evaluating the algorithms' performance and limitations.

This Spotlight aims at providing a self-contained review of atmospheric correction algorithms over land surfaces in the solar spectrum from their derivations to their applications. It addresses mathematical aspects by deriving exact analytical formulas linking the top of atmosphere measurements to the surface characteristics, numerical aspects by focusing on the computational cost of implementation of atmospheric correction algorithms, and finally, applications' aspects by giving examples of gridded products derived from atmospherically corrected data. I hope it will be useful to scientists and engineers interested in processing or using satellite imagery.

Many of my colleagues at Northrop Grumman helped to shape the material presented in this Spotlight. I would like, in particular, to thank Dr. Bruce Hauss for stimulating conversations on this and other topics, and Dr. Merit Shoucri for his unwavering support of this work. Finally, I would like to thank my family for their love and support, my parents for their devotion, and my amazing wife and my three daughters for indulging in my numerous puns and other so-called "daddy" jokes over the years.

Alain Sei
April 2019

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