Atmospheric correction

Introduction

The radiance values of reflected polychromatic solar radiation and/or the emitted thermal and microwave radiances from a certain target area on the Earth’s surface are for researchers the most valuable information obtainable from a remote sensor. In the absence of an atmosphere, the radiance for any wavelength on the ground would be the same as the radiance at the sensor. No atmosphere would make RS easier - but life impossible. We have to figure out how we can convert remotely detected radiances to radiances at ground level.

The presence of a heterogeneous, dense and layered terrestrial atmosphere composed of water vapour, aerosols and gases disturbs the signal reaching sensors in many ways. Therefore, methods of atmospheric corrections (AC) are needed to “clean” the images from these disturbances, in order to allow the retrieval of pure ground radiances from the target.

Explanation

The physics behind AC techniques in visible and thermal ranges is essentially the same, meaning that the same AC procedures that are applicable to one also apply to the other. However, there are a number of reasons for making a distinction between techniques applied to visible data and thermal data:

  • Incident and reflected solar radiation and terrestrial thermal emissions belong to very different parts of the spectrum.

  • Solar emission and reflection depends on the position of the Sun and the satellite at the time of image acquisition. Thermal emission is theoretically less dependent on this geometry.

  • Solar rays travel twice through the atmosphere before they reach the sensor (Top of the Atmosphere (TOA)–ground–sensor), whereas ground thermal emissions only pass through the atmosphere once (ground–sensor).

  • Solar reflection at the Earth’s surface depends on material reflectance (ρ). Thermal emission from the Earth depends on the emissivity of the surface materials (ϵ). Since solar reflection and Earth thermal emission occur at different wavelengths, the behaviour of one is not an indication of the other.

  • The processes of atmospheric attenuation, i.e. scattering and absorption, are both wavelength dependent and affect the two sectors of the spectrum differently.

  • As a result of the previous point, AC techniques are applied at a monochromatic level (individual wavelengths). This means that attenuation of radiation is calculated at every individual wavelength and then integrated across the spectrum of the sensor by mathematical integration.

  • Atmospheric components affect different areas of the spectrum in different ways, meaning that some components can be neglected when dealing with data belonging to the thermal or the visible part of the spectrum.

A classification of different AC methods allows us to assess what kind of effort is needed to correct raw data for the particular application at hand. Some RS applications do not require AC procedures at all, except for some “cosmetics”, while others call for rigorous and complex procedures. “Intermediate” solutions are sufficient for many applications.

In general, applications for which the actual radiance at ground level is not needed do not require atmospheric correction. Some “cosmetic” and/or image enhancement procedures may suffice: for instance, mapping applications where visual interpretation and image geometry are important, but not the chemical properties of surface material.

Applications that require the quantification of radiation at ground level must include rigorous atmospheric correction procedures. Quantification of evapotranspiration or CO2 sequestration, or surface temperature and reflectivity mapping, are examples of such applications.

Applications concerned with the evolution of certain parameters or land properties over time, rather than their absolute quantification, are “intermediate” cases. For these, knowledge of the relative trend may suffice. Such procedures apply mainly when the mapping parameters do not really have a meaningful physical value, simply because they were designed primarily for multi-temporal relative comparison. Index evolution and correlation procedures, where radiances are associated with the evolution of a certain parameter (e.g. turbidity) are examples of this category. Be aware that some indexes such as NDVI typically require some absolute atmospheric correction.

The “effort” required is commensurate with the amount of information required to describe the components of the atmosphere at different altitudes (atmospheric profiling) at the moment and position at which the image is taken, and less so with sophistication of the AC procedure itself. State-of-the-art atmospheric models allow the “cleaning” of any cloudless image regardless of sensor type, as long as atmospheric profile data are available. Unfortunately, such detailed atmospheric information can only be obtained through atmospheric sounding procedures, which use a series of instruments to sample the atmosphere at fixed intervals while being transported vertically by a balloon, or sounding sensors on board a satellite. This kind of profiling is carried out daily (at fixed times) at some atmospheric centres, regardless of satellite overpass times. However, the atmosphere is dynamic. Atmospheric processes and composition change rapidly, mainly at low altitudes (water vapour and aerosols), meaning that soundings made somewhere close to the target and near the time of a satellite overpass might not be enough to ensure an adequate atmospheric description. As as rule of thumb regarding AC techniques, first consider the objectives of the project, then identify the appropriate AC procedure, and finally establish the effort, i.e. the required information to execute the chosen correction procedure.

Examples

An atmospheric degradation effect, which is already disturbing when extracting information from one RS image, is atmospheric scattering. Sky radiance at the detector causes haze in the image and reduces contrast. Converting DNs to radiances on the ground becomes relevant if we want to compare RS data with ground measurements, or if we want to compare data acquired at different times by different sensors to detect change.

Prior knowledge

Outgoing relations

Incoming relations

Learning paths