Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach

Nicola Acito,Marco Diani,Gregorio Procissi,Giovanni Corsini

doi:10.3390/rs13152967

Abstract

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.

Highlights

Hyperspectral sensors (HSs) offer the opportunity of analyzing the chemical and physical composition of the remote sensed scene thanks to their ability of measuring the spectrum of the observed pixels in a large number of contiguous and narrow spectral channels [1]
In this sub-section, we present the results obtained by applying the CWV retrieval algorithm described in Section 3 and based on the learning-based atmospheric compensation (LBAC)
Such results are compared with the CWV estimates included in the PRISMA L2C product and with those obtained by applying the well-known Atmospherically Pre-corrected Differential Absorption (APDA)

Summary

Introduction

Hyperspectral sensors (HSs) offer the opportunity of analyzing the chemical and physical composition of the remote sensed scene thanks to their ability of measuring the spectrum of the observed pixels in a large number of contiguous and narrow spectral channels [1]. Spaceborne sensors allow the exploitation of the potential of hyperspectral technology for large-scale monitoring of the earth [2]. Hyperspectral satellites are still poorly represented in spaceborne missions compared to multispectral ones [2]. PRISMA, launched in March 2019 [11], includes a pushbroom hyperspectral camera covering the portion of the electromagnetic spectrum ranging from 400 nm to 2500 nm with 10 nm spectral

Methods

Results

Conclusion