Atmospheric Correction

What is an Atmospheric Correction?

As the name suggests, atmospheric correction is the process of removing the effects of the atmosphere on the reflectance values of remotely sensed images. The atmospheric effects may be referred to as the presence of gas absorption, molecule, and aerosol scattering that can influence incident and reflected radiation the atmosphere can have a high impact on the reflectance values of images (especially those taken from space). Atmospheric correction is likely to give better results while using multiple images from different dates while determining biophysical parameters using imagery or creating derivative bands (ratios) such as vegetation indices.

“While atmospheric correction may not be important for certain applications (e.g., when conducting land cover classification for a single year), it is absolutely necessary when performing a time-series analysis in crop growth. For example, in comparing spectral characteristics of a pixel or group of pixels (an object) acquired on different dates, removal of the influence of atmospheric conditions prior to comparison is crucial.”

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Atmospheric correction algorithms use mathematical models to estimate the atmospheric effects and remove them from the imagery. The goal of atmospheric correction is to retrieve accurate and reliable information about the Earth's surface, such as land cover, vegetation, and water quality.

Phases of Atmospheric Correction

Before diving into it, let's recap how sensors work. A sensor records radiance. Radiance is the combination of the electromagnetic energy being reflected by the objects minus the energy absorbed by the atmosphere plus the energy scattered into the path of the sensor by the atmosphere. Reflectance is the proportion of the amount of radiation hitting an object often referred to as ratio value and gives a better representation of the physical properties of the objects because it has been corrected to compensate for the atmospheric impacts.

Atmospheric correction is done in 2 phases/steps. In the first phase, the Digital Numbers (DNs) are converted to radiance, and then to the top of atmosphere radiance. In the next phase, the top-of-atmosphere reflectance is converted to surface reflectance (also known as bottom-of-atmosphere reflectance, or top-of-canopy reflectance, or in vegetation studies). The resulting image is called atmospherically corrected. Some image provider agencies (like the United States Geological Survey) now deliver atmospherically corrected images free of charge on their EarthExplorer website.

Types of Atmospheric Correction

Atmospheric correction is of 2 types: 1. Relative correction, and 2. Absolute correction.

1. Relative Atmospheric Correction

Relative correction normalizes the images and is easier to achieve because normalization makes the images directly comparable to other images rather than removing the atmospheric effects. One of the most common relative correction is Dark Object Subtraction (DOS) where the dark objects (object with very low reflectance) is subtracted from the image which not only normalizes the image but also removes the atmospheric effect. Another process could be multiplying the images to normalize each other so that they can be directly compared without the impact of the atmosphere. In this process, we use a regression model to transform the spectral characteristics of the other images to the base image (images which has the least atmospheric effect or one that has been previously atmospherically corrected).

2. Absolute Atmospheric Correction

In absolute atmospheric correction, the atmospheric profile obtained on the same day at the same location is used in conjunction with an algorithm to compensate for the atmosphere. The unavailability of this information makes it difficult to perform the absolute atmospheric correction. This information is usually obtained from one of the two very robust and time-tested radiative transfer models: MODTRAN (MODerate resolution atmospheric TRNAsmission) or 6s (Second Simulation of the Satellite Signal in the Solar Spectrum). These models store information on the location, time, average ground elevation, altitude of the sensor, and band wavelength ranges. These characteristics are used for absolute atmospheric correction. The most common algorithms used to perform this correction are Atmospheric CORrection Now (ACORN), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), and Atmospheric CORrection (ATCOR).

Importance of Atmospheric Correction

It is with no doubt that the importance of atmospheric correction is to gain better results—in the sense that atmospheric correction removes the atmospheric noises and provides the true surface reflectance. The presence of aerosols makes it difficult to read the true reflectance values which hinders the accurate study of, for example, water depth. Atmospheric correction gives better results while using multiple images from different dates while determining biophysical parameters using imagery, or creating derivative bands (ratios) such as vegetation indices.

Atmospheric correction is an important step in remote sensing analysis, and it has both advantages and disadvantages. Some of the advantages and disadvantages are:

Advantages

1. Improved Data Quality: Atmospheric correction can significantly improve the quality of remote sensing data by removing or compensating for atmospheric effects such as scattering, absorption, and reflection.
2. Accurate Surface Reflectance: Atmospheric correction enables the extraction of accurate surface reflectance values, which are essential for a range of remote sensing applications such as land cover classification, vegetation analysis, and water quality assessment.
3. Standardization: Atmospheric correction provides a standardized approach to processing remote sensing data, making it possible to compare and combine data from different sources and times.
4. Increased Sensitivity: Atmospheric correction can increase the sensitivity of remote sensing data to subtle changes in the Earth's surface, allowing for more precise monitoring and analysis of environmental conditions.

Disadvantages

1. Data Availability: Atmospheric correction requires accurate information about the atmospheric conditions at the time of image acquisition. This information may not always be available or may be difficult to obtain, especially in remote areas or during extreme weather conditions.
2. Model Assumptions: Atmospheric correction algorithms are based on assumptions about the atmospheric and surface properties, which may not always be accurate. Errors in the model assumptions can lead to errors in the atmospheric correction.
3. Complexity: Some atmospheric correction methods, such as radiative transfer models, are complex and computationally intensive, requiring specialized knowledge and expertise to apply correctly.
4. Uncertainty: Atmospheric correction introduces some degree of uncertainty into the remote sensing data, which can affect the accuracy and reliability of downstream analyses.

In summary, while atmospheric correction can improve the quality and accuracy of remote sensing data, it also has some limitations and challenges that need to be carefully considered when applying the technique.