PRISMA is the hyperspectral mission from the Italian Space Agency (ASI) launched in 2019. It samples the solar irradiance reflected and diffused by the earth-atmosphere system between 400 nm and 2500 nm with a spectral distance better than 11nm and a 30m Ground Sampling Distance. To answer the demanding need of hyperspectral applications, a high absolute radiometric accuracy is required and reached through the combination of on board and natural targets based calibration.
This paper describes PRISMA mission and focuses on the natural targets based calibration methods used to assess the instrument sensitivity. Two methods are used:
- PICS (Pseudo Invariant Calibration Sites) allow to cross-calibrate PRISMA with SENTINEL-2 and 3 ESA missions.
- Gobabeb and La Crau Instrumented sites known as RadCalNet sites which provide a BOA spectral BRDF through a dedicated acquisition protocol and processing as well as atmospheric parameters simultaneously to the satellite pass.
The adaptation of these methods to hyperspectral sensors calibration is presented. The calibrations results which show the very good temporal stability of PRISMA instrument are discussed as well as the methods and in situ instrumentation evolutions planned to improve the calibration of hyperspectral sensors using natural targets.
A new model has been developed to estimate irradiance at ground level over a rugged terrain in the reflective spectral domain in order to be used in an hyperspectral inversion code. Modtran4 allows to calculate atmospheric parameters over a flat scene which are then used to estimate the four components of irradiance over a mountainous area (direct, diffuse, reflected and coupling irradiance). This method have been compared with an accurate radiative transfer code called AMARTIS. Simulations are done at three wavelengths and for two solar configurations over a relief composed of two hills and flat terrain. Irradiances obtained with our model are in good agreement with this reference code except in shadow areas in the SWIR. Our model is also compared with a currently used model developed by Sandmeier whose results are worse than our model's results. Current relative errors of our diffuse, reflected and coupling irradiance calculation model do not have much influence on total irradiance in most of the cases. This influence become significant for high beam incidence angles where Digital Elevation Model errors can be much more important.
The Optics Department of ONERA has developed and implemented an inverse algorithm, COSHISE, to correct hyperspectral images of the atmosphere effects in the visible-NIR-SWIR domain (0,4-2,5 micrometers ). This algorithm automatically determine the integrated water-vapor content for each pixel, from the radiance at sensor level by using a LIRR-type (Linear Regression Ratio) technique. It then retrieves the spectral reflectance at ground level using atmospheric parameters computed with Modtran4, included the water-vapor spatial dependence as obtained in the first stop. The adjacency effects are taken into account using spectral kernels obtained by two Monte-Carlo codes. Results obtained with COCHISE code on real hyperspectral data are first compared to ground based reflectance measurements. AVIRIS images of Railroad Valley Playa, CA, and HyMap images of Hartheim, France, are use. The inverted reflectance agrees perfectly with the measurement at ground level for the AVIRIS data set, which validates COCHISE algorithm/ for the HyMap data set, the results are still good but cannot be considered as validating the code. The robustness of COCHISE code is evaluated. For this, spectral radiance images are modeled at the sensor level, with the direct algorithm COMANCHE, which is the reciprocal code of COCHISE. The COCHISE algorithm is then used to compute the reflectance at ground level from the simulated at-sensor radiance. A sensitivity analysis has been performed, as a function of errors on several atmospheric parameter and instruments defaults, by comparing the retrieved reflectance with the original one. COCHISE code shows a quite good robustness to errors on input parameter, except for aerosol type.
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