Influence of aerosol and surface reflectance variability on hyperspectral observed radiance

Abstract. Current aerosol retrievals based on visible and near infrared remote-sensing, are prone to loss of accuracy, where the assumptions of the applied algorithm are violated. This happens mostly over land and it is related to misrepresentation of specific aerosol conditions or surface properties. New satellite missions, based on high spectral resolution instruments, such as PRISMA (Hyperspectral Precursor of the Application Mission), represent a valuable opportunity to improve the accuracy of τ a 550 retrievable from a remote-sensing system developing new atmospheric measurement techniques. This paper aims to address the potential of these new observing systems in more accurate retrieving τ a 550 , specifically over land in heterogeneous and/or homogeneous areas composed by dark and bright targets. The study shows how the variation of the hyperspectral observed radiance can be addressed to recognise a variation of Δτ a 550 = 0.02. The goal has been achieved by using simulated radiances by combining two aerosol models (urban and continental) and two reflecting surfaces: dark (represented by water) and bright (represented by sand) for the PRISMA instrument, considering the environmental contribution of the observed radiance, i.e., the adjacency effect. Results showed that, in the continental regime, the expected instrument sensitivity would allow for retrieval accuracy of the aerosol optical thickness at 550 nm of 0.02 or better, with a dark surface surrounded by dark areas. The study also showed that for the urban regime, the surface plays a more significant role, with a bright surface surrounded by dark areas providing favourable conditions for the aerosol load retrievals, and dark surfaces representing less suitable situations for inversion independently of the surroundings. However, over all, the results obtained provide evidence that high resolution observations of Earth spectrum between 400 and 1000 nm would allow for a significant improvement of the accuracy of the τ a 550 for anthropogenic/natural aerosols over land.

[1]  T. Eck,et al.  Global evaluation of the Collection 5 MODIS dark-target aerosol products over land , 2010 .

[2]  H. L. Miller,et al.  Climate Change 2007: The Physical Science Basis , 2007 .

[3]  Luis Guanter,et al.  Simulation of Optical Remote-Sensing Scenes With Application to the EnMAP Hyperspectral Mission , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Lorraine Remer,et al.  The MODIS 2.1-μm channel-correlation with visible reflectance for use in remote sensing of aerosol , 1997, IEEE Trans. Geosci. Remote. Sens..

[5]  Y. Kaufman,et al.  Passive remote sensing of tropospheric aerosol and atmospheric , 1997 .

[6]  Frederic Teston,et al.  The PROBA/CHRIS mission: a low-cost smallsat for hyperspectral multiangle observations of the Earth surface and atmosphere , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Felix C. Seidel,et al.  Critical surface albedo and its implications to aerosol remote sensing , 2011 .

[8]  L. Guanter,et al.  Spectral calibration and atmospheric correction of ultra-fine spectral and spatial resolution remote sensing data. Application to CASI-1500 data , 2007 .

[9]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.

[10]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[11]  Eric Vermote,et al.  Atmospheric correction for the monitoring of land surfaces , 2008 .

[12]  村井 俊治 EARSeL〔European Association of Remote Sensing Laboratories〕シンポジウムに参加して , 1990 .

[13]  A. Kokhanovsky,et al.  Aerosol remote sensing over land: A comparison of satellite retrievals using different algorithms and instruments , 2007, Atmospheric Research.

[14]  Peter R. J. North,et al.  The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light , 2009 .

[15]  E. Vermote,et al.  Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer , 1997 .

[16]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

[17]  Alexei Lyapustin,et al.  Radiative transfer codes for atmospheric correction and aerosol retrieval: intercomparison study. , 2008, Applied optics.

[18]  Alexandre Alakian,et al.  Remote sensing of aerosol plumes: a semianalytical model. , 2008, Applied optics.

[19]  Alexander A. Kokhanovsky,et al.  Aerosol Optics: Light Absorption and Scattering by Particles in the Atmosphere , 2008 .

[20]  Patrick Hostert,et al.  Environmental Mapping and Analysis Program (EnMAP) - Recent Advances and Status , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[21]  J. Lenoble Radiative transfer in scattering and absorbing atmospheres: Standard computational procedures , 1985 .

[22]  Stefano Pignatti,et al.  Aerosol Optical Retrieval and Surface Reflectance from Airborne Remote Sensing Data over Land , 2010, Sensors.

[23]  Yoram J. Kaufman,et al.  Relationship between surface reflectance in the visible and mid‐IR used in MODIS aerosol algorithm ‐ theory , 2002 .

[24]  A. Goetz,et al.  Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean , 2009 .

[25]  D. Tanré,et al.  Remote Sensing of Tropospheric Aerosols from Space: Past, Present, and Future. , 1999 .

[26]  Andrea Sacchetti,et al.  The PRISMA Program , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[27]  Eric P. Shettle,et al.  Atmospheric Aerosols: Global Climatology and Radiative Characteristics , 1991 .