Pat S. Chavez, Jr. 1996. Image-Based Atmospheric Corrections – Revisited and Improved. Photogrammetric Engineering and Remote Sensing 62(9): 1025-1036.

Geog 577 Paper Discussion

Dahl Winters

December 6, 2006

Background

Radiometric Correction Models

The objective of a radiometric atmospheric correction procedure is to devise a model to convert a satellite’s digital photon counts (DCs) to ground reflectances. Derivation of the different model parameters depends on the information available (in-situ ground data or entirely image-based data).

1. DCs converted to at-satellite radiances by removing the gain and offset effects introduced by the imaging system.

2. At-satellite radiances converted to surface reflectances by correcting for both solar and atmospheric effects.

Objective

The objective is to develop a purely image-based atmospheric correction method for satellite images. This would enable easier use of historical satellite images, or images taken in inaccessible areas where field measurements cannot be done.

The DOS (dark-object subtraction) method is strictly image-based, but has unacceptable accuracy because it corrects only for the additive scattering effect of the atmosphere, and not for its multiplicative transmittance effect.

In this paper, the DOS model is expanded upon by including a simple correction for the multiplicative transmittance effect, which is caused by both scattering and absorption.

Methods

Data: Spectral data from Moran et al (1992) suitable for testing multiple atmospheric correction methods under a variety of conditions.

Two ways of deriving the required multiplicative transmittance-correction coefficient are presented: the COST and default TAUz methods.

COST: uses the cosine of the solar zenith angle, which is a good first-order approximation of the atmospheric transmittance for the study sites and dates.

TAUz: uses the average of the transmittance values computed using in-situ atmospheric field measurements made during 7 different satellite overflights.

Results

Two entirely image-based radiometric correction models are presented, generating results with comparable accuracy to those developed from models using in-situ field measurements. These are variations of the DOS (dark-object subtraction) model with the addition of a multiplicative transmittance correction.

The corrections generated by the entirely image-based COST model are as accurate as those generated by models using in-situ atmospheric field measurements.

This means, at least for the atmospheric conditions existing at the study sites and times, the COST model can be used for atmospheric correction without the need of doing field measurements.

Figure 1

Figures 2 and 3

Table 4 Moran et al. 1992, Uncorrected/Apparent Reflectance

Table 5 Moran et al. 1992, DOS 1-percent reflectance

Table 6 Moran et al. 1992, SSD Reflectance

Table 7 Moran et al. 1992, HBC Reflectance

Table 8 COST Reflectance, New Image-Based Model

Table 9 Default TAUz Reflectance, New Image-Based Model

Discussion

The COST model works well for images taken of a semi-arid to arid environment. Both the COST and TAUz models approximate the transmittance values for a non-arid environment. Further testing is needed for non-arid environments, different atmospheric conditions, and images with > 55 degree solar zenith angles.

Overcorrections: • The COST model uses a cosine-function correction for multiplicative transmittance

that may overcorrect at higher zenith angles, making the TAUz model more appropriate for such images.

• Tables and scatter plots show that all models overcorrect for very low reflectances, with the DOS model overcorrecting the least.

The additive scattering correction is more important for darker reflectances, while the multiplicative transmittance correction is more important for brighter reflectances.

Future Research: The data used (TM bands 1-4) did not cover the full range of reflectances; future studies should use targets including more of the dynamic range of reflectances in all TM bands. Accuracy differences between the visible and IR bands or soils vs. vegetation may actually be a situation of bright vs. dark.