{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T12:02:46Z","timestamp":1771329766801,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2015,6,26]],"date-time":"2015-06-26T00:00:00Z","timestamp":1435276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Surface reflectance has a central role in the analysis of land surface for a broad variety of Earth System studies. An accurate atmospheric correction, obtained by an appropriate selection of aerosol model, is the first requirement for reliable surface reflectance estimation. In the aerosol model, the type is defined by the physical and chemical properties, while the loading is usually described by the optical thickness at 550 nm. The aim of this work is to evaluate the radiative impact of the aerosol model on the surface reflectance obtained from Compact High Resolution Imaging Spectrometer (CHRIS) hyperspectral data over land by using the specifically developed algorithm CHRIS Atmospherically Corrected Reflectance Imagery (CHRIS@CRI) based on the 6SV radiative transfer model. We employed five different aerosol models: one provided by the AERONET inversion products (used as reference), three standard aerosol models in 6SV, and one obtained from the output of the GEOS-Chem global chemistry-transport model (CTM). The results obtained for the two case studies selected over Benelux show that in the absence of AERONET data on the scene, the best performing aerosol model is the one derived from CTM output.<\/jats:p>","DOI":"10.3390\/rs70708391","type":"journal-article","created":{"date-parts":[[2015,6,26]],"date-time":"2015-06-26T10:24:46Z","timestamp":1435314286000},"page":"8391-8415","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Effect of the Aerosol Model Assumption on the Atmospheric Correction over Land: Case Studies with CHRIS\/PROBA Hyperspectral Images over Benelux"],"prefix":"10.3390","volume":"7","author":[{"given":"Cecilia","family":"Tirelli","sequence":"first","affiliation":[{"name":"CETEMPS, University of L'Aquila, Via Vetoio, Coppito, 67100 L'Aquila, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9871-5570","authenticated-orcid":false,"given":"Gabriele","family":"Curci","sequence":"additional","affiliation":[{"name":"CETEMPS, University of L'Aquila, Via Vetoio, Coppito, 67100 L'Aquila, Italy"},{"name":"Department of Physical and Chemical Sciences, University of L'Aquila, Via Vetoio, Coppito, 67100 L'Aquila, Italy"}]},{"given":"Ciro","family":"Manzo","sequence":"additional","affiliation":[{"name":"Institute for Atmospheric Pollution Research (IIA), Italian National Research Council (CNR), Research Area of Rome 1, Via Salaria km 29,300, Monterotondo Scalo, 00016 Rome, Italy"}]},{"given":"Paolo","family":"Tuccella","sequence":"additional","affiliation":[{"name":"CETEMPS, University of L'Aquila, Via Vetoio, Coppito, 67100 L'Aquila, Italy"},{"name":"Sorbonne Universit\u00e9s, UPMC Univ. Paris 06, Universit\u00e9 Versailles St-Quentin, CNRS\/INSU, LATMOS-IPSL, UMR, 8190 Paris, France"},{"name":"NUMTECH, 6 Allee Alan Turing, CS 60242, 63178 Aubiere, France"},{"name":"Laboratoire de M\u00e9t\u00e9orologie Dynamique, Ecole Polytechnique, 91128 Palaiseau, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4431-0273","authenticated-orcid":false,"given":"Cristiana","family":"Bassani","sequence":"additional","affiliation":[{"name":"Institute for Atmospheric Pollution Research (IIA), Italian National Research Council (CNR), Research Area of Rome 1, Via Salaria km 29,300, Monterotondo Scalo, 00016 Rome, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2015,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bach, H., Verhoef, W., and Schneider, K. (2001). Coupling remote sensing observation models and a growth model for improved retrieval of (geo)-biophysical information from optical remote sensing data. Proc. 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