Effects of Atmospheric Correction on Remote Sensing Statistical Inference in an Aquatic Environment

Atmospheric correction (AC) plays a critical role in the preprocessing of remote sensing images. Although AC is necessary for applications based on remote sensing inversion, it is not always required for those based on remote sensing classification. Recently, remote sensing statistical inference has been proposed for evaluating water quality. However, input data for these models have always been remote sensing reflectance (R-rs), which requires AC. This raises the question of whether AC is necessary for remote sensing statistical inference. We conducted a theoretical analysis and image validations by testing 24 water bodies observed by Landsat-8 and compared their spectral probability distributions (SPDs) calculated from R-rs before and after AC (using the ACOLITE model). Additionally, we tested and found that, if we use remote sensing inference as a tool to quantitatively infer statistical parameters of a specific waterbody, it is better to perform atmospheric correction. However, if the quantitative inference is applied to a large number of water bodies and high inference accuracy is not required, atmospheric correction may not be necessary, and a quick calculation based on the strong correlations between R-rs at the surface and sensor-observed reflectance can be used as a substitute.

Automated summary

Atmospheric correction (AC) is crucial for preprocessing remote sensing images, but it is not always necessary for remote sensing classification. A recent approach of remote sensing statistical inference for assessing water quality always requires AC. To determine whether AC is necessary for remote sensing statistical inference, we conducted a theoretical analysis and image validations using Landsat-8 data. The results show that AC is better when performing quantitative inference on a specific water body, but it may not be necessary for a large number of water bodies with low inference accuracy requirements.