Abstract
Physically-based atmospheric correction of optical Earth Observation satellite data is used to accurately derive surface biogeophysical parameters free from the atmospheric influence. While water vapor or surface pressure can be univocally characterized, the compensation of aerosol radiometric effects relies on assumptions and parametric approximations of their properties. To determine the validity of these assumptions and approximations in the atmospheric correction of ESA’s FLEX/Sentinel-3 tandem mission, a systematic error analysis of simulated FLEX data within the O 2 absorption bands was conducted. This paper presents the impact of key aerosol parameters in atmospherically-corrected FLEX surface reflectance and the subsequent Sun-Induced Fluorescence retrieval (SIF). We observed that: (1) a parametric characterization of aerosol scattering effects increases the accuracy of the atmospheric correction with respect to the commonly implemented discretization of aerosol optical properties by aerosol types and (2) the Ångström exponent and the aerosol vertical distribution have a residual influence in the atmospherically-corrected surface reflectance. In conclusion, a multi-parametric aerosol characterization is sufficient for the atmospheric correction of FLEX data (and SIF retrieval) within the mission requirements in nearly 85% (70%) of the cases with average aerosol load conditions. The future development of the FLEX atmospheric correction algorithm would therefore gain from a multi-parametric aerosol characterization based on the synergy of FLEX and Sentinel-3 data.
Highlights
Atmospheric correction of optical Earth observation data is one of the critical steps in the data processing chain of a satellite mission as it allows deriving surface biogeophysical parameters free from the atmospheric influence [1,2]
We studied the accuracy of using approximations and assumptions related to aerosol properties to describe their net radiometric effects through their impact in synthetic atmospherically-corrected FLuORescence Imaging Spectrometer (FLORIS) data and subsequent retrieved Sun-induced
We used a newly developed Atmospheric Look-up Table Generator (ALG) tool to simulate three synthetic datasets based on the execution of MODerate resolution atmospheric TRANsmission (MODTRAN) Radiative Transfer Model (RTM) coupled with OPAC aerosol database
Summary
Atmospheric correction of optical Earth observation data is one of the critical steps in the data processing chain of a satellite mission as it allows deriving surface biogeophysical parameters free from the atmospheric influence [1,2]. Atmospheric correction methods can be classified in two main families [3,4]: (1) empirical and (2) physically-based inversion methods. Empirical methods are based on mathematical approximations of the atmospheric radiative transfer equation and the inversion of surface properties directly from sensor data through empirical assumptions [5,6]. Physically-based methods [7,8,9,10] typically comprise two main steps: (1) derivation of key atmospheric parameters and (2) decoupling of surface properties from atmospheric effects by inversion of a Radiative Transfer Model (RTM). While empirical methods have a lower computation burden than physically-based inversion methods, their accuracy is generally lower [1,6]. When it comes to missions that require an accurate determination of surface properties, physically-based inversion methods are preferred
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