The knowledge of atmospheric composition is extremely important for several applications. Indeed, the application of hyperspectral images in the fields of geology, mineral exploration, agriculture, forestry and environmental researches need to be compensated for atmospheric effects. In these cases, atmospheric correction becomes a necessary pre-requisite for any further processing and accurate interpretation of spectra relevant to different surface materials/objects. In particular, atmospheric water vapour, mineral dust, CO2, particulate matter clouds and pollen are key variables in most of the environmental applications.
As regards the water vapour in the atmosphere, that is one of the most important greenhouse gases, it has been observed a high variability both in space and in time, due to the meteorological conditions and the amount of undelying land use. A Mineral dust and particulate matter clouds originating from sand storms or fires involving biomass have a highly variable temporal and spatial distribution as well. Information on the regional distribution of atmospheric water vapor and on the mineral composition of the transported dust is extremely important to improve atmospheric correction and to separate atmospheric influences from actual ground signals. At this aim, the great number of PRISMA spectral channels ranging from visible to short wave infrared will allow an efficient removal of the atmospheric effects on the acquired images and a good estimation of the abundance of major atmospheric constituents. In particular, PRISMA spectral configuration permits to retrieve the atmospheric water vapor by means of the 0.94 -1,13 μm bands.
Aside from the application relevant to atmospheric effect correction, hyperspectral data are highly meaningful for the climate change topic. The evidences that human activities are impacting the climate system are continually increasing, but more work is needed to reduce the level of uncertainties of Earth System processes that drive climate change. Among those uncertainties, the effects of atmospheric aerosol and their impact on clouds and radiation are currently on the top of the uncertainties ranking. Hyperspectral satellite observations may be fruitfully applied to better characterize atmospheric aerosol and their effects on the climate system. Hyperspectral data have been applied to the retrieval of the atmosphere characteristics mainly for the study of atmospheric aerosol. It has also been demonstrated that Hyperspectral images may be usefully applied to the detection of key minerals in dust suspended over deserts. In particular, it has been possible to associate the spectral features of atmospheric dust in the SWIR (2080-2370 nm) to main mineral components. Morever, it has been theoretically proven that hyperspectral observations have the potential of classifying the atmospheric aerosol in several components.
As widely known, the carbon dioxide (CO2) gas, by absorbing electromagnetic radiation in several regions of solar spectrum, plays an important role on the earth radiation budget and in the global warming process, although his concentration is low compared to other atmospheric gases. Thus the monitoring of CO2 emissions both in a local and a regional scale, enabled by the PRISMA mission, will provide significant insights in this field.
|Identification of key minerals in desert dust suspended in the atmosphere from a Hyperion image during the Saharan dust event of August 2001 over Southern Italy. The MODIS images on the left show the daily sequence of the Aerosol Optical Depth (AOD) over South-Western Europe. The Hyperion scene in the centre was acquired north of Sicily, and the reflectance between 2100 and 2350 nm over the sea is analyzed. Key minerals in dust were recognized by Spectral Angle Mapper (SAM) method, and the relative abundance by Linear Spectral Unmixing (LSU). The uncertainty in the estimated abundance is very high due to the instrument noise.|
Thus, summarizing, in the context of atmospheric retrieval PRISMA can contribute to the:
· Clouds, Water vapor and aerosol measurements
· Fine dust composition evaluation and monitoring
· Detection of CO2 emission in a local/regional scale
· Characterization of atmospheric aerosol distribution and radiative properties
· Improvement of knowledge on the aerosol-cloud interaction.
 Evaluation of atmospheric corrections on hyperspectral data with special reference to mineral mapping. Nisha Rani, Venkata Ravibabu Mandla, Tejpal Singh. Geoscience Frontiers xxx (2016) 1-12
 Del Frate, F., Di Noia, A., Sellitto, P., Curci, G. (2010), Feasibility of aerosol type identification from hyperspectral data. In: Hyperspectral Workshop 2010. ESA-ESRIN Frascati, 17-19 March 2010
 Chudnovsky, A., Ben-Dor, E., Kostinski, A. B., and Koren, I. (2009), Mineral content analysis of atmospheric dust using hyperspectral information from space, Geophys. Res. Lett., 36, L15811, doi:10.1029/2009GL037922
 Nichol, J. E., Wong, M. S., and Chan, Y. Y. (2008), Fine Resolution Air Quality Monitoring from a Small Satellite: CHRIS/PROBA, Sensors 2008, 8, 7581-7595, doi:10.3390/s8127581