Spectrum of Choices: Navigating Atmospheric Correction Methods for Enhanced Remote Sensing Accuracy

Atmospheric correction plays a crucial role in enhancing the accuracy and reliability of remote sensing data for various applications. In this dynamic landscape of atmospheric correction methods, researchers and practitioners are presented with a spectrum of choices that can significantly impact the outcomes of remote sensing endeavors. This paper explores five prominent atmospheric correction approaches, namely Radiative Transfer Models (RTMs), machine learning integrated with radiative transfer models, and the Atmospheric Radiative Transfer Simulator (ARTS). Each method is evaluated based on its strengths, weaknesses, functional basis, theoretical philosophy, limitations, advantages, and disadvantages. Radiative Transfer Models (RTMs) are recognized for their accuracy and versatility in modeling a wide range of atmospheric conditions. However, their computational complexity and the need for expert knowledge pose challenges in their practical implementation. On the other hand, machine learning methods trained by radiative transfer model inversion offer a promising avenue for generating global land and atmospheric estimates from satellite observations. Integrating radiative transfer models with machine learning techniques has shown potential in predicting diffuse solar radiation and surface solar radiation, enhancing the capabilities of atmospheric correction processes. The Atmospheric Radiative Transfer Simulator (ARTS) developed by Olivier Hagolle and collaborators is designed to account for various atmospheric parameters such as aerosols, water vapor, and gases to retrieve accurate surface reflectance values. While ARTS excels in handling a range of atmospheric conditions and is adaptable to various satellite sensors, challenges exist in obtaining accurate input parameters and the assumption of a plane-parallel atmosphere, which may not always reflect real-world conditions accurately. The significance of choosing the right atmospheric correction method extends to various remote sensing applications, including environmental monitoring, agriculture, land cover analysis, and target detection. By understanding the strengths and limitations of each method, researchers and practitioners can make informed decisions to ensure the accuracy and reliability of remote sensing data for their specific applications. Breifly, the diverse array of atmospheric correction methods presented in this paper offers a pathway to refined and impactful remote sensing endeavors. By leveraging the strengths of each method and addressing their limitations, researchers can enhance the quality of remote sensing data, ultimately contributing to advancements in environmental monitoring, agriculture, and other critical fields.