FN Archimer Export Format
PT J
TI End-to-end simulations to optimize imaging spectroscopy mission requirements for seven scientific applications
BT 
AF Briottet, X.
   Adeline, K.
   Bajjouk, Touria
   Carrère, V.
   Chami, M.
   Constans, Y.
   Derimian, Y.
   Dupiau, A.
   Dumont, Marie
   Doz, S.
   Fabre, S.
   Foucher, P.Y.
   Herbin, H.
   Jacquemoud, S.
   Lang, M.
   Le Bris, A.
   Litvinov, P.
   Loyer, S.
   R, Marion
   Minghelli, A.
   Miraglio, T.
   Sheeren, D.
   Szymanski, B.
   Romand, F.
   Desjardins, C.
   Rodat, D.
   Cheul, B.
AS 1:1;2:1;3:2;4:3;5:4;6:1;7:5;8:1,6;9:7;10:1;11:1;12:1;13:5;14:6;15:8;16:9;17:16;18:10;19:11;20:12;21:1;22:8;23:13;24:15;25:14;26:14;27:14;
FF 1:;2:;3:PDG-ODE-DYNECO-LEBCO;4:;5:;6:;7:;8:;9:;10:;11:;12:;13:;14:;15:;16:;17:;18:;19:;20:;21:;22:;23:;24:;25:;26:;27:;
C1 Université de Toulouse, ONERA DOTA, Toulouse, France
   Ifremer, DYNECO, LEBCO, Plouzané, France
   Nantes Université, Laboratoire de Planétologie et Géosciences, UMR 6112, Nantes, France
   Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Sorbonne Université, Laboratoire Lagrange, Nice, France
   Université Lille, CNRS, UMR 8518, LOA, France
   Université Paris Cité, Institut de Physique du Globe de Paris, CNRS, Paris, France
   Université Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France
   Université de Toulouse, INRAE, UMR DYNAFOR; , Castanet-Tolosane, France
   Université Gustave Eiffel, LASTIG, ENSG, IGN, Saint-Mandé, France
   SHOM, Brest, France
   CEA/DAM/DIF, Arpajon, France
   Université de Toulon, CNRS, SeaTech, LIS laboratory, UMR 7020, Toulon, France
   DGA, Paris, France
   CNES, Toulouse, France
   ACRI-ST, Sophia-Antipolis, France
   GRASP SAS, Villeneuve d’Ascq, France
C2 UNIV TOULOUSE, FRANCE
   IFREMER, FRANCE
   UNIV NANTES, FRANCE
   UNIV COTE D’AZUR, FRANCE
   UNIV LILLE, FRANCE
   UNIV PARIS CITE, FRANCE
   UNIV GRENOBLE ALPES, FRANCE
   UNIV TOULOUSE, FRANCE
   UNIV GUSTAVE EIFFEL, FRANCE
   SHOM, FRANCE
   CEA, FRANCE
   UNIV TOULON, FRANCE
   DGA, FRANCE
   CNES, FRANCE
   ACRI-ST, FRANCE
   GRASP SAS, FRANCE
SI BREST
SE PDG-ODE-DYNECO-LEBCO
TC 0
UR https://archimer.ifremer.fr/doc/00881/99268/109211.pdf
LA English
DT Article
DE ;imaging spectroscopy;signal-to-noise ratio;spectral sampling;image quality;mineralogy;soil moisture content;tree species;leaf functional traits;bottom classification;shallow water;bathymetry;seabed;urban land cover;plume;aerosols;methane;cryosphere;water vapor;aerosols
AB 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. 
PY 2024
PD APR
SO ISPRS Open Journal of Photogrammetry and Remote Sensing
SN 2667-3932
PU Elsevier BV
VL 12
DI 10.1016/j.ophoto.2024.100060
ID 99268
ER

EF