{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T21:53:06Z","timestamp":1776721986022,"version":"3.51.2"},"reference-count":139,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,15]],"date-time":"2019-10-15T00:00:00Z","timestamp":1571097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Imaging spectrometry from aerial or spaceborne platforms, also known as hyperspectral remote sensing, provides dense sampled and fine structured spectral information for each image pixel, allowing the user to identify and characterize Earth surface materials such as minerals in rocks and soils, vegetation types and stress indicators, and water constituents. The recently launched DLR Earth Sensing Imaging Spectrometer (DESIS) installed on the International Space Station (ISS) closes the long-term gap of sparsely available spaceborne imaging spectrometry data and will be part of the upcoming fleet of such new instruments in orbit. DESIS measures in the spectral range from 400 and 1000 nm with a spectral sampling distance of 2.55 nm and a Full Width Half Maximum (FWHM) of about 3.5 nm. The ground sample distance is 30 m with 1024 pixels across track. In this article, a detailed review is given on the applicability of DESIS data based on the specifics of the instrument, the characteristics of the ISS orbit, and the methods applied to generate products. The various DESIS data products available for users are described with the focus on specific processing steps. The results of the data quality and product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements. The limitations of the DESIS data products are also subject to a critical examination.<\/jats:p>","DOI":"10.3390\/s19204471","type":"journal-article","created":{"date-parts":[[2019,10,16]],"date-time":"2019-10-16T03:32:54Z","timestamp":1571196774000},"page":"4471","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":154,"title":["Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS)"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2469-8290","authenticated-orcid":false,"given":"Kevin","family":"Alonso","sequence":"first","affiliation":[{"name":"Remote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 We\u00dfling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8381-7662","authenticated-orcid":false,"given":"Martin","family":"Bachmann","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center, DLR, Oberpfaffenhofen, 82234 We\u00dfling, Germany"}]},{"given":"Kara","family":"Burch","sequence":"additional","affiliation":[{"name":"Innovative Imaging and Research, Corp. (I2R), Building 1103, Suite 140C, Stennis Space Center, Hancock County, MS 39529, USA"}]},{"given":"Emiliano","family":"Carmona","sequence":"additional","affiliation":[{"name":"Remote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 We\u00dfling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2984-8315","authenticated-orcid":false,"given":"Daniele","family":"Cerra","sequence":"additional","affiliation":[{"name":"Remote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 We\u00dfling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0485-9552","authenticated-orcid":false,"given":"Raquel","family":"de los Reyes","sequence":"additional","affiliation":[{"name":"Remote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 We\u00dfling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2682-6172","authenticated-orcid":false,"given":"Daniele","family":"Dietrich","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center, DLR, Oberpfaffenhofen, 82234 We\u00dfling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3865-1912","authenticated-orcid":false,"given":"Uta","family":"Heiden","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center, DLR, Oberpfaffenhofen, 82234 We\u00dfling, Germany"}]},{"given":"Andreas","family":"H\u00f6lderlin","sequence":"additional","affiliation":[{"name":"Technology Marketing, DLR, Linder H\u00f6he, 51147 K\u00f6ln, Germany"}]},{"given":"Jack","family":"Ickes","sequence":"additional","affiliation":[{"name":"Teledyne Brown Engineering (TBE), 300 Sparkman Drive, Huntsville, AL 35805, USA"}]},{"given":"Uwe","family":"Knodt","sequence":"additional","affiliation":[{"name":"Strategic services, DLR, Linder H\u00f6he, 51147 K\u00f6ln, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1720-1197","authenticated-orcid":false,"given":"David","family":"Krutz","sequence":"additional","affiliation":[{"name":"Institute of Optical Sensor Systems, DLR, Rutherfordstra\u00dfe 2, 12489 Berlin, Germany"}]},{"given":"Heath","family":"Lester","sequence":"additional","affiliation":[{"name":"Teledyne Brown Engineering (TBE), 300 Sparkman Drive, Huntsville, AL 35805, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3288-5814","authenticated-orcid":false,"given":"Rupert","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"Remote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 We\u00dfling, Germany"}]},{"given":"Mary","family":"Pagnutti","sequence":"additional","affiliation":[{"name":"Innovative Imaging and Research, Corp. (I2R), Building 1103, Suite 140C, Stennis Space Center, Hancock County, MS 39529, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8122-1475","authenticated-orcid":false,"given":"Peter","family":"Reinartz","sequence":"additional","affiliation":[{"name":"Remote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 We\u00dfling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8499-4780","authenticated-orcid":false,"given":"Rudolf","family":"Richter","sequence":"additional","affiliation":[{"name":"Remote Sensing Technology Institute, DLR, Oberpfaffenhofen, 82234 We\u00dfling, Germany"}]},{"given":"Robert","family":"Ryan","sequence":"additional","affiliation":[{"name":"Innovative Imaging and Research, Corp. (I2R), Building 1103, Suite 140C, Stennis Space Center, Hancock County, MS 39529, USA"}]},{"given":"Ilse","family":"Sebastian","sequence":"additional","affiliation":[{"name":"Institute of Optical Sensor Systems, DLR, Rutherfordstra\u00dfe 2, 12489 Berlin, Germany"}]},{"given":"Mirco","family":"Tegler","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center, DLR, Oberpfaffenhofen, 82234 We\u00dfling, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S123","DOI":"10.1016\/j.rse.2009.03.001","article-title":"Earth system science related imaging spectroscopy\u2014An assessment","volume":"113","author":"Schaepman","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.rse.2014.06.024","article-title":"Mapping of NiCu-PGE ore hosting ultramafic rocks using airborne and simulated EnMAP hyperspectral imagery, Nunavik, Canada","volume":"152","author":"Rogge","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1016\/j.rse.2007.11.016","article-title":"Invasive species detection in Hawaiian rainforests using airborne imaging spectroscopy and LiDAR","volume":"112","author":"Asner","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.rse.2007.04.008","article-title":"Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data","volume":"111","author":"Heiden","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/JSTARS.2011.2176468","article-title":"Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content","volume":"5","author":"Clevers","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Inoue, Y., Gu\u00e9rif, M., Frederic, B., Skidmore, A., Gitelson, A., Schlerf, M., Darvishzadeh, R., and Olioso, A. (2016). Simple and robust methods for remote sensing of canopy chlorophyll content: A comparative analysis of hyperspectral data for different types of vegetation. Plant Cell Environ., 39.","DOI":"10.1111\/pce.12815"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.geomorph.2010.11.008","article-title":"Linking spatial patterns of soil organic carbon to topography\u2014A case study from south-eastern Spain","volume":"126","author":"Schwanghart","year":"2011","journal-title":"Geomorphology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2174","DOI":"10.2136\/sssaj2012.0054","article-title":"Soil organic carbon predictions by airborne imaging spectroscopy: Comparing cross-validation and validation","volume":"76","author":"Stevens","year":"2012","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s10712-018-9476-0","article-title":"Imaging Spectrometry of Inland and Coastal Waters: State of the Art, Achievements and Perspectives","volume":"40","author":"Giardino","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Priem, F., and Canters, F. (2016). Synergistic Use of LiDAR and APEX Hyperspectral Data for High-Resolution Urban Land Cover Mapping. Remote Sens., 8.","DOI":"10.3390\/rs8100787"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3997","DOI":"10.1109\/JSTARS.2016.2585674","article-title":"Combining Field and Imaging Spectroscopy to Map Soil Organic Carbon in a Semiarid Environment","volume":"9","author":"Bayer","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"S5","DOI":"10.1016\/j.rse.2007.12.014","article-title":"Three decades of hyperspectral remote sensing of the Earth: A personal view","volume":"113","author":"Goetz","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1512","DOI":"10.1109\/TGRS.2004.827260","article-title":"The PROBA\/CHRIS mission: A low-cost smallsat for hyperspectral multiangle observations of the Earth surface and atmosphere","volume":"42","author":"Barnsley","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/JSTARS.2013.2267204","article-title":"Progress in Hyperspectral Remote Sensing Science and Technology in China Over the Past Three Decades","volume":"7","author":"Tong","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.isprsjprs.2015.11.004","article-title":"Earth observation from the manned low Earth orbit platforms","volume":"115","author":"Guo","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Guarini, R., Loizzo, R., Longo, F., Mari, S., Scopa, T., and Varacalli, G. (2017, January 23\u201328). Overview of the prisma space and ground segment and its hyperspectral products. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8126986"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8830","DOI":"10.3390\/rs70708830","article-title":"The EnMAP spaceborne imaging spectroscopy mission for earth observation","volume":"7","author":"Guanter","year":"2015","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Coppo, P., Taiti, A., Pettinato, L., Francois, M., Taccola, M., and Drusch, M. (2017). Fluorescence Imaging Spectrometer (FLORIS) for ESA FLEX Mission. Remote Sens., 9.","DOI":"10.3390\/rs9070649"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nieke, J., and Rast, M. (2018, January 23\u201327). Towards the copernicus hyperspectral imaging mission for the environment (CHIME). Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518384"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lee, C., Cable, M., Hook, S., Green, R., Ustin, S., Mandl, D., and Middleton, E. (2015). An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities. Remote Sens. Environ., 167.","DOI":"10.1016\/j.rse.2015.06.012"},{"key":"ref_21","unstructured":"Conticello, S., Manzillo, P., Dijk, C., Vercruyssen, N., Esposito, M., Baeck, P.J., Benhadj, I., Livens, S., Delaur\u00e9, B., and Soukup, M. (June, January 30). Hyperspectral Imaging for real time land and vegetation inspection. Proceedings of the Small Satellites, System & Services Symposium (4S), Valletta, Malta."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Blommaert, J., Delaur\u00e9, B., Livens, S., Nuyts, D., Tack, K., Lambrechts, A., Paola, R.D., Moreau, V., Callut, E., and Habay, G. (August, January 28). Csimba: Towards a Smart-Spectral Cubesat Constellation. Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898081"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Matsunaga, T., Iwasaki, A., Tsuchida, S., Iwao, K., Nakamura, R., Yamamoto, H., Kato, S., Obata, K., Kashimura, O., and Tanii, J. (2018, January 22\u201327). HISUI status toward FY2019 launch. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518639"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6703","DOI":"10.1109\/TGRS.2015.2446197","article-title":"CLARREO Reflected Solar Spectrometer: Restrictions for Instrument Sensitivity to Polarization","volume":"53","author":"Lukashin","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","first-page":"461","article-title":"The New Hyperspectral Sensor DESIS on the Multi-Payload Platform MUSES Installed on the ISS","volume":"41","author":"Avbelj","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Krutz, D., M\u00fcller, R., Knodt, U., G\u00fcnther, B., Walter, I., Sebastian, I., S\u00e4uberlich, T., Reulke, R., Carmona, E., and Eckardt, A. (2019). The Instrument Design of the DLR Earth Sensing Imaging Spectrometer (DESIS). Sensors, 19.","DOI":"10.3390\/s19071622"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Perkins, R., Galloway, P., Miller, R., and Graham, L. (2017, January 23\u201328). Teledyne\u2019s muses mission on the ISS: Enabling flexible and reconfigurable earth observation from space. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127167"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.rse.2019.01.007","article-title":"Gradients in urban material composition: A new concept to map cities with spaceborne imaging spectroscopy data","volume":"223","author":"Jilge","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_29","unstructured":"Dekker, A.G., Pinnel, N., Gege, P., Giardino, C., Briottet, X., Court, A., Peters, S., Turpie, K., Sterckx, S., and Costa, M. (2019, January 6\u20138). Feasibility study for an aquatic ecosystem earth observing system. Proceedings of the 11th Earsel SIG IS Workshop, Brno, Czech Republic."},{"key":"ref_30","first-page":"10","article-title":"Chemical ecology and marine biodiversity: Insights and products from the sea","volume":"9","author":"Hay","year":"1996","journal-title":"Oceanogr. Wash. Oceanogr. Soc."},{"key":"ref_31","unstructured":"Turpie, K., Allen, D.W., Ackelson, S., Bell, T., Dierssen, H., Cavanaugh, K., Fisher, J.B., Goodman, J., Guild, L., and Hochberg, E. (2015). New Need to Understand Changing Coastal and Inland Aquatic Ecosystem Services."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2015.05.023","article-title":"Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission","volume":"167","author":"Hestir","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1002\/eap.1682","article-title":"Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems","volume":"28","author":"Hestir","year":"2018","journal-title":"Ecol. Appl."},{"key":"ref_34","unstructured":"Bernard, S., Binding, C., Brockmann, C., Dekker, A., DiGiacomo, P., Greb, S., Griffith, D., Groom, S., Hestir, E., and Hunter, P. (2018). Earth Observations in Support of Global Water Quality Monitoring, International Ocean-Colour Coordinating Group. Vol. IOCCG Report 17; Reports and Monographs of the International Ocean Colour Coordinating Group."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2015.02.001","article-title":"Aquatic color radiometry remote sensing of coastal and inland waters: Challenges and recommendations for future satellite missions","volume":"160","author":"Mouw","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1109\/TGRS.2003.812907","article-title":"Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality","volume":"41","author":"Brando","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2006.12.017","article-title":"Assessment of water quality in Lake Garda (Italy) using Hyperion","volume":"109","author":"Giardino","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_38","unstructured":"Pinnel, N., Gege, P., and G\u00f6ritz, A. (2018, January 23\u201326). Sensitivity study for aquatic ecosystem monitoring with the DESIS hyperspectral sensor. Proceedings of the WHISPERS 2018, Amsterdam, The Netherlands."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2192","DOI":"10.1364\/AO.50.002192","article-title":"Sources of variance of downwelling irradiance in water","volume":"50","author":"Gege","year":"2011","journal-title":"Appl. Opt."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1016\/j.rse.2009.12.001","article-title":"A two-step optimization procedure for assessing water constituent concentrations by hyperspectral remote sensing techniques: An application to the highly turbid Venice lagoon waters","volume":"114","author":"Santini","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Mishra, D.R., Ogashawara, I., and Gitelson, A.A. (2017). Chapter 2\u2014Radiative Transfer Theory for Inland Waters. Bio-Optical Modeling and Remote Sensing of Inland Waters, Elsevier.","DOI":"10.1016\/B978-0-12-804644-9.00001-X"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4086","DOI":"10.1016\/j.rse.2007.12.013","article-title":"A 20-year Landsat water clarity census of Minnesota\u2019s 10,000 lakes","volume":"112","author":"Olmanson","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.rse.2014.10.010","article-title":"Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters","volume":"156","author":"Matthews","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_44","unstructured":"Pinnel, N. (2007). A Method for Mapping Submerged Macrophytes in Lakes Using Hyperspectral Remote Sensing. [Ph.D. Thesis, Technische Universit\u00e4t M\u00fcnchen]."},{"key":"ref_45","first-page":"13","article-title":"Applied marine hyperspectral imaging; Coral Bleaching from a spectral viewpoint","volume":"31","author":"Teague","year":"2019","journal-title":"Spectrosc. Eur."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/S0034-4257(00)00111-5","article-title":"A Hyperspectral Method for Remotely Sensing the Grain Size of Snow","volume":"74","author":"Nolin","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"025204","DOI":"10.1088\/1748-9326\/5\/2\/025204","article-title":"Enhanced surface warming and accelerated snow melt in the Himalayas and Tibetan Plateau induced by absorbing aerosols","volume":"5","author":"Lau","year":"2010","journal-title":"Environ. Res. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.5194\/acp-11-1929-2011","article-title":"Sensitivity studies on the impacts of Tibetan Plateau snowpack pollution on the Asian hydrological cycle and monsoon climate","volume":"11","author":"Qian","year":"2011","journal-title":"Atmos. Chem. Phys."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1073\/pnas.2237157100","article-title":"Soot climate forcing via snow and ice albedos","volume":"101","author":"Hansen","year":"2004","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"S25","DOI":"10.1016\/j.rse.2007.07.029","article-title":"Interpretation of snow properties from imaging spectrometry","volume":"113","author":"Dozier","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Painter, T.H., Barrett, A.P., Landry, C.C., Neff, J.C., Cassidy, M.P., Lawrence, C.R., McBride, K.E., and Farmer, G.L. (2007). Impact of disturbed desert soils on duration of mountain snow cover. Geophys. Res. Lett., 34.","DOI":"10.1029\/2007GL030284"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"9511","DOI":"10.1002\/jgrd.50520","article-title":"Imaging spectroscopy of albedo and radiative forcing by light-absorbing impurities in mountain snow","volume":"118","author":"Painter","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rse.2016.06.018","article-title":"The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo","volume":"184","author":"Painter","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1029\/2018EA000506","article-title":"The GEDI Simulator: A Large-Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions","volume":"6","author":"Hancock","year":"2019","journal-title":"Earth Space Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2905","DOI":"10.1111\/j.1365-2486.2011.02451.x","article-title":"TRY\u2014A global database of plant traits","volume":"17","author":"Kattge","year":"2011","journal-title":"Glob. Chang. Biol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1111\/j.1466-8238.2011.00717.x","article-title":"Going beyond limitations of plant functional types when predicting global ecosystems atmosphere fluxes: Exploring the merits of traits-based approaches","volume":"21","author":"Douma","year":"2012","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1007\/s10712-019-09511-5","article-title":"Assessing Vegetation Function with Imaging Spectroscopy","volume":"40","author":"Gamon","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2014.11.011","article-title":"Quantifying forest canopy traits: Imaging spectroscopy versus field survey","volume":"158","author":"Asner","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_59","first-page":"281","article-title":"Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV)","volume":"171\u2013172","author":"Catalina","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1016\/S0034-4257(00)00147-4","article-title":"Deriving Water Content of Chaparral Vegetation from AVIRIS Data","volume":"74","author":"Serrano","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3934","DOI":"10.3390\/rs70403934","article-title":"Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe","volume":"7","author":"Hank","year":"2015","journal-title":"Remote Sens."},{"key":"ref_62","unstructured":"Schlerf, M., Buddenbaum, H., Vohland, M., Werner, W., Dong, P., Hill, J., Erasmi, S., Cuffka, B., and Kappas, M. (2004, January 7\u20138). Assessment of forest productivity using an ecosystem process model, remotely sensed LAI maps and FIELD data. Proceedings of the 1st G\u00f6ttingen GIS & Remote Sensing Days: Environmental Studies, G\u00f6ttingen, Germany."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1890\/1051-0761(2002)012[1286:DEOAFP]2.0.CO;2","article-title":"Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen","volume":"12","author":"Smith","year":"2002","journal-title":"Ecol. Appl."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"387","DOI":"10.14214\/sf.244","article-title":"A global forest growing stock, biomass and carbon map based on FAO statistics","volume":"42","author":"Kindermann","year":"2008","journal-title":"Silva Fenn."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1890\/02-0344","article-title":"Using a generalized vegetation model to simulate vegetation dynamics in northeastern USA","volume":"85","author":"Hickler","year":"2004","journal-title":"Ecology"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"4360","DOI":"10.1109\/JSTARS.2017.2725825","article-title":"ISS as a Platform for Optical Remote Sensing of Ecosystem Carbon Fluxes: A Case Study Using HICO","volume":"10","author":"Huemmrich","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1007\/s10712-018-9478-y","article-title":"Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods","volume":"40","author":"Verrelst","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.rse.2004.04.010","article-title":"Hyperspectral versus multispectral data for estimating leaf area index in four different biomes","volume":"91","author":"Lee","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_69","unstructured":"Ustin, S.L., Zarco-tejada, P.J., and Asner, G.P. (2001). The Role of Hyperspectral Data in Understanding the Global Carbon Cycle."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/s10712-018-9492-0","article-title":"Spaceborne Imaging Spectroscopy for Sustainable Agriculture: Contributions and Challenges","volume":"40","author":"Hank","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_71","unstructured":"FAO, and ITPS (2019, October 14). Available online: http:\/\/www.fao.org\/3\/a-i5126e.pdf."},{"key":"ref_72","unstructured":"Jeffery, S., Hiederer, R., L\u00fckewille, A., Strassburger, T., Panagos, P., Herv\u00e1s, J., Barcelo, S., Jones, A., Yigini, Y., and Erhard, M. (2010). A Contribution of the JRC to the European Environment Agency\u2019s, JRC. Environment State and Outlook Report\u2014SOER 2010\u2014Study."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Nocita, M., Stevens, A., van Wesemael, B., Aitkenhead, M., Bachmann, M., Barthes, B., Ben-Dor, E., Brown, D., Clairotte, M., and Csorba, A. (2015). Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring. Advances in Agronomy, Academic Press.","DOI":"10.1016\/bs.agron.2015.02.002"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"S38","DOI":"10.1016\/j.rse.2008.09.019","article-title":"Using imaging spectroscopy to study soil properties","volume":"113","author":"Chabrillat","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10712-019-09524-0","article-title":"Imaging Spectroscopy for Soil Mapping and Monitoring","volume":"40","author":"Chabrillat","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_76","unstructured":"Chabrillat, S., Guillaso, S., Rabe, A., Foerster, S., and Guanter, L. (2016, January 17\u201322). From HYSOMA to ENSOMAP\u2014A new open source tool for quantitative soil properties mapping based on hyperspectral imagery from airborne to spaceborne applications. Proceedings of the EGU General Assembly Conference, Vienna, Austria."},{"key":"ref_77","first-page":"90","article-title":"An advanced analytical approach for spectral-based modelling of soil properties","volume":"7","author":"Carmon","year":"2017","journal-title":"Int. J. Emerg. Technol. Adv. Eng."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1109\/JSTARS.2015.2462125","article-title":"Characterization of Soil Erosion Indicators Using Hyperspectral Data From a Mediterranean Rainfed Cultivated Region","volume":"9","author":"Schmid","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/S0034-4257(98)00106-0","article-title":"Remote sensing of soils in the Santa Monica Mountains: II. Hierarchical foreground and background analysis","volume":"68","author":"Pinzon","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_80","unstructured":"Gomez, C., Oltra-Carrio, R., Bacha, S., Lagacherie, P., and Briottet, X. (2014, January 15\u201316). Sensitivity of Soil Property Prediction Obtained from Hyperspectral Vis-NIR Imagery to Atmospheric Effects and Degradation in Image Spatial Resolutions. Proceedings of the EGU General Assembly Conference, Vienna, Austria."},{"key":"ref_81","first-page":"81","article-title":"Soil organic carbon mapping of partially vegetated agricultural fields with imaging spectroscopy","volume":"13","author":"Bartholomeus","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Stevens, A., Nocita, M., Toth, G., Montanarella, L., and van Wesemael, B. (2013). Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0066409"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.geoderma.2008.06.011","article-title":"Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study","volume":"146","author":"Gomez","year":"2008","journal-title":"Geoderma"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1080\/01431160010006962","article-title":"Mapping of several soil properties using DAIS-7915 hyperspectral scanner data\u2014A case study over clayey soils in Israel","volume":"23","author":"Patkin","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Liu, Y., Pan, X., Wang, C., Li, Y., and Shi, R. (2015). Predicting soil salinity with Vis\u2013NIR spectra after removing the effects of soil moisture using external parameter orthogonalization. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0140688"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/01431160701294695","article-title":"Surface soil moisture quantification models from reflectance data under field conditions","volume":"29","author":"Haubrock","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/0034-4257(87)90073-3","article-title":"A model for soil surface roughness influence on the spectral response of bare soils in the visible and near-infrared range","volume":"23","author":"Cierniewski","year":"1987","journal-title":"Remote Sens. Environ."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2019.02.014","article-title":"Retrieving soil surface roughness with the Hapke photometric model: Confrontation with the ground truth","volume":"225","author":"Labarre","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Yamamoto, H., Obata, K., Tsuchida, S., Kerr, G.H.G., and Bachmann, M. (2016, January 11\u201315). Cross-sensor calibration and validation between DESIS and HUSUI on the international space station (ISS). Proceedings of the IGARSS 2016, Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729496"},{"key":"ref_90","unstructured":"Heiden, U., and M\u00fcller, R. (2019, September 15). DESIS Mission. Available online: https:\/\/www.dlr.de\/eoc\/desktopdefault.aspx\/tabid-13614."},{"key":"ref_91","unstructured":"(2019, September 15). Geospatial Solutions. Available online: https:\/\/https:\/\/tbe.com\/geospatial\/MUSES."},{"key":"ref_92","unstructured":"(2019, September 15). Optical Etaloning in Charge Coupled Devices. Available online: https:\/\/andor.oxinst.com\/learning\/view\/article\/optical-etaloning-in-charge-coupled-devices."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"5481","DOI":"10.1109\/TGRS.2018.2818258","article-title":"Research on the Etalon Effect in Dispersive Hyperspectral VNIR Imagers Using Back-Illuminated CCDs","volume":"56","author":"Hu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"61","DOI":"10.14358\/PERS.78.1.61","article-title":"Automated Georeferencing of Optical Satellite Data with Integrated Sensor Model Improvement","volume":"78","author":"Schneider","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_95","first-page":"148","article-title":"A program for direct georeferencing of airborne and spaceborne line scanner images","volume":"34","author":"Muller","year":"2002","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"131","DOI":"10.14358\/PERS.81.2.131","article-title":"Validation of Geometric Accuracy of Global Land Survey (GLS) 2000 Data","volume":"81","author":"Rengarajan","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Leutenegger, S., Chli, M., and Siegwart, R.Y. (2011, January 7). BRISK: Binary Robust invariant scalable keypoints. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126542"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Lehner, M. (1994, January 17). Stereoscopic evaluation of combined stereoscopic (Along-Track) and multispectral data of the MOMS-02 Sensor. Proceedings of the ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision, Munich, Germany.","DOI":"10.1117\/12.182811"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_100","unstructured":"De los Reyes, R., Richter, R., Langheinrich, M., Pflug, B., and Schwind, P. (2019, October 14). Validation of a New Atmospheric Correction Software Using AERONET Reference Data. Available online: https:\/\/earth.esa.int\/documents\/700255\/3506752\/Poster27_Validation_AC_LPVE_v04.pdf."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"4004","DOI":"10.1364\/AO.37.004004","article-title":"Correction of satellite imagery over mountainous terrain","volume":"37","author":"Richter","year":"1998","journal-title":"Appl. Opt."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Doxani, G., Vermote, E., Roger, J.C., Gascon, F., Adriaensen, S., Frantz, D., Hagolle, O., Hollstein, A., Kirches, G., and Li, F. (2018). Atmospheric Correction Inter-Comparison Exercise. Remote Sens., 10.","DOI":"10.3390\/rs10020352"},{"key":"ref_103","unstructured":"Berk, A. (2019, October 14). MODTRAN 5.4.0 User\u2019s Manual. Available online: ftp:\/\/ftp.pmodwrc.ch\/stealth\/132250_claus\/MODTRAN5\/Manual\/MODTRAN(R)5.2.0.0.pdf."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"D20108","DOI":"10.1029\/2011JD016032","article-title":"High-resolution solar spectral irradiance from extreme ultraviolet to far infrared","volume":"116","author":"Fontenla","year":"2011","journal-title":"J. Geophys. Res."},{"key":"ref_105","unstructured":"Wan, Z., and Hook, S.H.G. (2019, October 25). NASA EOSDIS LP DAAC. Available online: https:\/\/doi.org\/10.5067\/MODIS\/MYD11C2."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1080\/01431160500486690","article-title":"An automatic atmospheric correction algorithm for visible\/NIR imagery","volume":"27","author":"Richter","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/S0034-4257(98)00044-3","article-title":"Atmospheric Precorrected Differential Absorption Technique to Retrieve Columnar Water Vapor","volume":"65","author":"Borel","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1109\/TGRS.2012.2228654","article-title":"Overview of Intercalibration of Satellite Instruments","volume":"51","author":"Chander","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1080\/01431161.2014.883094","article-title":"The PROBA-V mission: Image processing and calibration","volume":"35","author":"Sterckx","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_110","unstructured":"(2019, September 05). RadCalNet Portal. Available online: https:\/\/www.radcalnet.org\/."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1023\/A:1024048429145","article-title":"The Solar Spectral Irradiance from 200 to 2400 nm as Measured by the SOLSPEC Spectrometer from the Atlas and Eureca Missions","volume":"214","author":"Thuillier","year":"2003","journal-title":"Sol. Phys."},{"key":"ref_112","unstructured":"(2019, September 05). CEOS Recommended Solar Irradiance Spectrum for Use in Earth Observation Applications. Available online: https:\/\/eocalibration.wordpress.com\/2006\/12\/15\/ceos-recommended-solar-irradiance-spectrum-for-use-in-earth-observation-applications\/."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Jing, X., Leigh, L., Pinto, C.T., and Helder, D. (2019). Evaluation of RadCalNet Output Data Using Landsat 7, Landsat 8, Sentinel 2A, and Sentinel 2B Sensors. Remote Sens., 11.","DOI":"10.3390\/rs11050541"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"12275","DOI":"10.3390\/rs61212275","article-title":"Landsat-8 operational land imager radiometric calibration and stability","volume":"6","author":"Markham","year":"2014","journal-title":"Remote Sens."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"147","DOI":"10.4236\/ars.2017.62011","article-title":"Sentinel-2 MSI radiometric characterization and cross-calibration with Landsat-8 OLI","volume":"6","author":"Li","year":"2017","journal-title":"Adv. Remote Sens."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Janesick, J.R. (2007). Photon Transfer, SPIE Press.","DOI":"10.1117\/3.725073"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Ponomarenko, N.N., Lukin, V.V., Zriakhov, M.S., Kaarna, A., and Astola, J. (2008, January 8\u201311). Automatic Approaches to On-Land\/On-Board Filtering and Lossy Compression of AVIRIS Images. Proceedings of the IGARSS 2008\u20142008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4779331"},{"key":"ref_118","unstructured":"Rao, K.R., and Yip, P. (2014). Discrete Cosine Transform: Algorithms, Advantages, Applications, Academic Press."},{"key":"ref_119","unstructured":"Taylor, J. (2009). Error Analysis, University Science Books."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Sebastian, I., Krutz, D., Eckardt, A., Venus, H., Walter, I., G\u00fcnther, B., Neidhardt, M., Reulke, R., M\u00fcller, R., and Uhlig, M. (2018, January 22\u201326). On-Ground Calibration of DESIS: DLR\u015b Earth Sensing Imaging Spectrometer for the International Space Station ISS. Proceedings of the SPIE Photonics Europe 2018, Strasbourg, France.","DOI":"10.1117\/12.2307188"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1364\/AO.45.002360","article-title":"Spectral calibration of hyperspectral imagery using atmospheric absorption features","volume":"45","author":"Guanter","year":"2006","journal-title":"Appl. Opt."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1016\/j.rse.2017.09.015","article-title":"Imaging spectrometer stray spectral response: In-flight characterization, correction, and validation","volume":"204","author":"Thompson","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_123","unstructured":"Bachmann, M., M\u00fcller, R., Schneider, M., Walzel, T., Habermeyer, M., Storch, T., Kaufmann, H., Segl, K., and Rogass, C. (2014, January 28\u201330). Data Quality Assurance for hyperspectral L1 and L2 products\u2014Cal\/Val\/Mon procedures within the EnMAP Ground Segment. Proceedings of the LPVE Workshop, Frascati, Italy."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Kardan, N., Dabney, P., and Babu, S. (2018, January 23\u201327). Landsat Missions to Sustainable Land Imaging Technology Program. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517943"},{"key":"ref_125","first-page":"50","article-title":"Landsat 7 On-Orbit Modulation Transfer Function Estimation","volume":"4540","author":"Storey","year":"2001","journal-title":"Sens. Syst. Next-Gener. Satell. V"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"583","DOI":"10.5589\/m10-078","article-title":"Targets, methods, and sites for assessing the in-flight spatial resolution of electro-optical data products","volume":"36","author":"Pagnutti","year":"2010","journal-title":"Can. J. Remote Sens."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(98)00031-5","article-title":"AERONET\u2014A Federated Instrument Network and Data Archive for Aerosol Characterization","volume":"66","author":"Holben","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"1958","DOI":"10.1109\/TGRS.2008.916470","article-title":"Considerations on Water Vapor and Surface Reflectance Retrievals for a Spaceborne Imaging Spectrometer","volume":"46","author":"Richter","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Obreg\u00f3n, M., Rodrigues, G., Costa, M., Potes, M., and Silva, A. (2019). Validation of ESA Sentinel-2 L2A Aerosol Optical Thickness and Columnar Water Vapour during 2017\u20132018. Remote Sens., 11.","DOI":"10.3390\/rs11141649"},{"key":"ref_130","unstructured":"(2019, October 14). CEOS Reference: QA4EO-WGCV-IVO-CSP-002-LCFR. Available online: https:\/\/www.radcalnet.org\/sites\/LCFR\/documentation\/Site%20documentation\/QA4EO-WGCV-IVO-CSP-002_LCFR_20180405.pdf."},{"key":"ref_131","unstructured":"(2019, October 14). CEOS Reference: QA4EO-WGCV-IVO-CSP-002-RVUS. Available online: https:\/\/www.radcalnet.org\/sites\/RVUS\/documentation\/Site%20documentation\/QA4EO-WGCV-IVO-CSP-002_RVUS_20180404.pdf."},{"key":"ref_132","unstructured":"(2019, October 14). CEOS Reference: QA4EO-WGCV-IVO-CSP-002-GONA. Available online: https:\/\/www.radcalnet.org\/sites\/GONA\/documentation\/Site%20documentation\/QA4EO-WGCV-IVO-CSP-002_GONA_20180405.pdf."},{"key":"ref_133","unstructured":"Platnick, S., King, M., and Hubanks, P. (2015, March 09). MODIS Atmosphere L3 Eight-Day Product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center. Available online: https:\/\/doi.org\/10.5067\/MODIS\/MOD08_E3.006."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Saunier, S., Mannan, R., Schwind, P., Mueller, R., Storch, T., Biasutti, R., Gascon, F., Goryl, P., and Meloni, M. (2018, January 10\u201312). Bulk reprocessing of the ALOS PRISM\/AVNIR-2 archive of the European Space Agency: Level 1 orthorectified data processing and data quality evaluation. Proceedings of the Image and Signal Processing for Remote Sensing XXIV, Berlin, Germany.","DOI":"10.1117\/12.2325618"},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Bieniarz, J., M\u00fcller, R., Zhu, X.X., and Reinartz, P. (2014, January 13\u201318). Hyperspectral image resolution enhancement based on joint sparsity spectral unmixing. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6947017"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/MGRS.2016.2637824","article-title":"Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature","volume":"5","author":"Yokoya","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/TGRS.2011.2161320","article-title":"Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion","volume":"50","author":"Yokoya","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1109\/JSTARS.2012.2208449","article-title":"Cross-Calibration for Data Fusion of EO-1\/Hyperion and Terra\/ASTER","volume":"6","author":"Yokoya","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS)\u2014Interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/20\/4471\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:26:32Z","timestamp":1760189192000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/20\/4471"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,15]]},"references-count":139,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19204471"],"URL":"https:\/\/doi.org\/10.3390\/s19204471","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,15]]}}}