Abstract
The atmospheric correction of satellite images based on radiative transfer calculations is a prerequisite for many remote sensing applications. The software package ATCOR, developed at the German Aerospace Center (DLR), is a versatile atmospheric correction software, capable of processing data acquired by many different optical satellite sensors. Based on this well established algorithm, a new Python-based atmospheric correction software has been developed to generate L2A products of Sentinel-2, Landsat-8, and of new space-based hyperspectral sensors such as DESIS (DLR Earth Sensing Imaging Spectrometer) and EnMAP (Environmental Mapping and Analysis Program). This paper outlines the underlying algorithms of PACO, and presents the validation results by comparing L2A products generated from Sentinel-2 L1C images with in situ (AERONET and RadCalNet) data within VNIR-SWIR spectral wavelengths range.
Highlights
Earth remote sensing data are based on the terrestrial reflection of the incident solar radiation.This radiation is especially affected by different absorption and scattering processes caused by diverse atmospheric constituents on its way down to the Earth surface and up towards the Earth observation sensors
This paper presents the software characteristics and its performance validating the atmospheric and surface reflectance L2A products, obtained with PACO for the multi-spectral sensors Sentinel-2 (A and B) and Landsat-8
The accuracy (A), precision (P), and uncertainty (U) (Equation (4)) have been calculated for the AOT and WV centered in Sentinel-2 and Landsat-8 scenes
Summary
Earth remote sensing data are based on the terrestrial reflection of the incident solar radiation. This radiation is especially affected by different absorption and scattering processes caused by diverse atmospheric constituents on its way down to the Earth surface and up towards the Earth observation sensors (airborne or in orbit). The spatial and temporal variation in composition and properties of some of such constituents makes the compensation of atmospheric effects an important step in the remote sensing applications to retrieve consistent surface properties [1,2,3]. The atmospheric correction is important when comparing remote sensing data (surface reflectance) from different sensors in-orbit under realistic atmospheric conditions [15,16,17]. The term “surface reflectance” is used for simplicity, while, strictly speaking, the hemispherical-directional reflectance factor (HDRF) is retrieved
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
