Image-Based Atmospheric Corrections - Revisited and Improved

TLDR: In this paper, an image-based procedure that expands on the ~10s model by including a simple multiplicative correction for the effect of atmospheric transmittance was presented, and the results were compared with those generated by the models that used in-situ atmospheric field measurements and RTC software.

Abstract: A major benefit of multitemporal, remotely sensed images is their applicability to change detection over time. Because of concerns about global and environmental change, these data are becoming increasingly more important. However, to maximize the usefulness of the data from a multitemporal point of view, an easy-to-use, cost-effective, and accurate radiometric calibration and correction procedure is needed. The atmosphere effects the radiance received at the satellite by scattering, absorbing, and refracting light; corrections for these effects, as well as for sensor gains and offsets, solar irradiance, and solar zenith angles, must be included in radiometric correction procedures that are used to convert satellite-recorded digital counts to ground reflectances. To generate acceptable radiometric correction results, a model is required that typically uses in-situ atmospheric measurements and radiative transfer code (RTC) to correct for atmospheric effects. The main disadvantage of this type of correction procedure is that it requires in-situ field measurements during each satellite overflight. This is unacceptable for many applications and is often impossible, as when using historical data or when working in very remote locations. The optimum radiometric correction procedure is one based solely on the digital image and requiring no in-situ field measurements during the satellite overflight. The darkobject subtraction (DOS) method, a strictly image-based technique, is an attempt to achieve this ideal procedure. However, the accuracy is not acceptable for many applications, mostly because it corrects only for the additive scattering effect and not for the multiplicative transmittance effect. This paper presents an entirely image-based procedure that expands on the ~10s model by including a simple multiplicative correction for the effect of atmospheric transmittance. Two straightforward methods to derive the multiplicative transmittance-correction coefficient are presented. The COSITZ) or COST method uses the cosine of the solar zenith angle, which, to a first order, is a good approximation of the atmospheric transmittance for the dates und sites used in this study. The default TAUS method uses the average of the transmittance values computed by using in-situ atmospheric field measurements made during seven different satellite overflights. Published and unpublished data made available for this study by Moran et al. (1992) are used, and my model results are compared with their results. The corrections generated by the entirely image-based COST model are as accurate as those generated by the models that used in-situ atmospheric field measurements and RTC software.