An automatic atmospheric correction algorithm for visible/NIR imagery

The automatic correction of atmospheric effects currently requires visible to short-wave spectral bands (400–2500nm) to derive high accuracy surface reflectance data. Common techniques employ spectral correlations of dark targets in the short-wave infrared (SWIR, around 2.2 mm), blue (480 nm) and red (660 nm) regions to derive the aerosol optical depth. A large number of current Earth-observing satellite sensors have only three or four spectral channels in the visible and near-infrared (VNIR) region (400–1000nm), making an automatic image-based atmospheric correction very difficult. This contribution presents a new algorithm and first results with VNIR imagery. The method starts with the assumption of average clear atmospheric conditions (aerosol optical depth AOD50.27, corresponding to a visibility of 23km) and calculates the surface reflectance in the red and near-infrared (NIR) bands. The second step derives a mask of dark vegetation pixels. It is calculated using multiple thresholds of vegetation index combined with red and NIR surface reflectance values. Then the red band surface reflectance for the dark pixels is estimated from the NIR reflectance as rred50.1 rnir, from which the aerosol optical depth (or visibility) can be calculated. The core of the VNIR algorithm consists of two subsequent iteration loops (visibility and rred) to improve the visibility estimate. Results of the VNIR method are presented for Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper Plus (ETM + ) imagery using only the first four bands. The performance of the method is compared to the established dark pixel technique where the SWIR bands are included. Results show that the deviation between both methods is usually less than 0.005 reflectance units if measured in terms of the scene-average reflectance, indicating a useful potential for this approach. DOI: https://doi.org/10.1080/01431160500486690 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-62118 Published Version Originally published at: Richter, R; Schläpfer, D; Müller, A (2006). An automatic atmospheric correction algorithm for visible/NIR imagery. International Journal of Remote Sensing, 27(10):2077-2085. DOI: https://doi.org/10.1080/01431160500486690 This article was downloaded by: [ETH Zurich] On: 06 December 2011, At: 00:29 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK International Journal of Remote Sensing Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tres20 An automatic atmospheric correction algorithm for visible/NIR imagery R. Richter a , D. Schläpfer b & A. Müller a a DLR, German Aerospace Center, Remote Sensing Data Center, D‐82234 Wessling, Germany b University of Zurich, Remote Sensing Laboratories, Winterthurerstr. 190, CH‐8057‐Zurich, Switzerland Available online: 22 Feb 2007 To cite this article: R. Richter, D. Schläpfer & A. Müller (2006): An automatic atmospheric correction algorithm for visible/NIR imagery, International Journal of Remote Sensing, 27:10, 2077-2085 To link to this article: http://dx.doi.org/10.1080/01431160500486690 PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: http://www.tandfonline.com/page/terms-andconditions This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. An automatic atmospheric correction algorithm for visible/NIR imagery R. RICHTER*{, D. SCHLÄPFER{ and A. MÜLLER{ {DLR, German Aerospace Center, Remote Sensing Data Center, D-82234 Wessling, Germany {University of Zurich, Remote Sensing Laboratories, Winterthurerstr. 190, CH-8057-