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CNES is currently carrying out a Phase A study to assess the feasibility of a future hyperspectral imaging sensor (10 m spatial resolution) combined with a panchromatic camera (2.5 m spatial resolution). This mission focuses on both high spatial and spectral resolution requirements, as inherited from previous French studies such as HYPEX, HYPXIM, and BIODIVERSITY. To meet user requirements, cost, and instrument compactness constraints, CNES asked the French hyperspectral Mission Advisory Group (MAG), representing a broad French scientific community, to provide recommendations on spectral sampling, particularly in the Short Wave InfraRed (SWIR) for various applications.This paper presents the tests carried out with the aim of defining the optimal spectral sampling and spectral resolution in the SWIR domain for quantitative estimation of physical variables and classification purposes. The targeted applications are geosciences (mineralogy, soil moisture content), forestry (tree species classification, leaf functional traits), coastal and inland waters (bathymetry, water column, bottom classification in shallow water, coastal habitat classification), urban areas (land cover), industrial plumes (aerosols, methane and carbon dioxide), cryosphere (specific surface area, equivalent black carbon concentration), and atmosphere (water vapor, carbon dioxide and aerosols). All the products simulated in this exercise used the same CNES end-to-end processing chain, with realistic instrument parameters, enabling easy comparison between applications. 648 simulations were carried out with different spectral strategies, radiometric calibration performances and signal-to-noise Ratios (SNR): 24 instrument configurations × 25 datasets (22 images + 3 spectral libraries).The results show that spectral sampling up to 20 nm in the SWIR range is sufficient for most applications. However, 10 nm spectral sampling is recommended for applications based on specific absorption bands such as mineralogy, industrial plumes or atmospheric gases. In addition, a slight performance loss is generally observed when radiometric calibration accuracy decreases, with a few exceptions in bathymetry and in the cryosphere for which the observed performance is severely degraded. Finally, most applications can be achieved with a realistic SNR, with the exception of bathymetry, shallow water classification, as well as carbon dioxide and methane estimation, which require the optimistic SNR level tested. On the basis of these results, CNES is currently evaluating the best compromise for designing the future hyperspectral sensor to meet the objectives of priority applications.

This paper presents MARMIT-2, a radiative transfer model that predicts the spectral reflectance of soils in the solar domain (0.4–2.5 μm) as a function of their surface moisture. This is an improved version of MARMIT (multilayer radiative transfer model of soil reflectance) that represents a wet soil as a dry soil topped with a thin layer of liquid water. The changes brought in this article concern the mixing of the spectral reflectance of the dry and wet soil areas, the transmission of diffuse light in the water layer, and the inclusion of soil particles in the water layer. Wet soil reflectance is now expressed in terms of dry soil reflectance and three free parameters: the thickness of the water layer, the surface fraction of the wet soil, and a new parameter, the volume fraction of soil particles in the water layer. With more accurate physical modeling, MARMIT-2 simulates soil spectral reflectance with better accuracy than MARMIT. In particular, the fit of the soil reflectance spectra is much better for high water contents, both in the visible range (0.4–0.7 μm) and in the water absorption bands around 1.45 μm and 1.95 μm. The average root mean square error between measured and predicted reflectance obtained on a set of 225 soil samples is about 0.8% with MARMIT-2 versus 1.8% with MARMIT. • MARMIT-2, an improved version of the MARMIT model, is presented. • The spectral reflectance of wet soils is predicted with better accuracy. • The optical properties of a water layer containing soil particles are calculated. • A mixing function is used to calculate an effective complex refractive index. • A database of 225 soil samples measured in the laboratory is used for validation. [ABSTRACT FROM AUTHOR]