A new approach to estimate the aerosol scattering ratios for the atmospheric correction of satellite remote sensing data in coastal regions

Abstract Aerosol scattering reflectance is the most uncertain term to be determined in the atmospheric correction of satellite remote sensing data. The values in the visible bands depend on the aerosol scattering ratios (the epsilon spectrum). The epsilon value in the Near-infrared (NIR) band is estimated on the dark pixel assumption of the water-leaving reflectance in the two NIR bands and then the epsilon spectrum is determined from the aerosol models. This assumption usually becomes invalid for turbid coastal waters, leading to lost regions in the satellite imagery masked by the failure of the atmospheric correction. A new approach was developed to accurately estimate epsilon from turbid coastal waters. This method is based on the idea that the aerosol scattering reflectance and the epsilon values can be obtained from the known water-leaving reflectance of in situ measurements. The water-leaving reflectance is determined from the choice of a look-up table of the water-leaving reflectance based on the Angstrom law of the candidate aerosol scattering reflectance using the best non-linear least squares fit function. In this approach, the entire epsilon spectra can be obtained and used to determine the two closest aerosol models which are used to interpolate the actual epsilon values. It is demonstrated that the results from matching the entire spectra are more robust than that obtained from using only one epsilon value. The performance of the approach was evaluated using the simulated reflectance at the top of the atmosphere, the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery, and in situ measured aerosol optical thickness. This approach is based on the assumption of the aerosol scattering reflectance following the Angstrom law instead of the standard dark pixel assumption, named as the ENLF model. This new assumption is valid for both Case 1 and Case 2 waters, even over terrestrial regions. Therefore, the ENLF model provides a potential approach for a universal algorithm of the atmospheric correction of satellite remote sensing data.

[1]  Davide D'Alimonte,et al.  Validation of satellite ocean color primary products at optically complex coastal sites: Northern Adriatic Sea, Northern Baltic Proper and Gulf of Finland , 2009 .

[2]  H. Gordon,et al.  Analysis of the influence of O(2) A-band absorption on atmospheric correction of ocean-color imagery. , 1995, Applied optics.

[3]  Menghua Wang,et al.  An efficient method for multiple radiative transfer computations and the lookup table generation , 2003 .

[4]  F. Muller‐Karger,et al.  Atmospheric Correction of SeaWiFS Imagery over Turbid Coastal Waters: A Practical Method , 2000 .

[5]  Menghua Wang,et al.  Detection of turbid waters and absorbing aerosols for the MODIS ocean color data processing , 2007 .

[6]  Jim Aiken,et al.  The atmospheric correction of water colour and the quantitative retrieval of suspended particulate matter in Case II waters: Application to MERIS , 1999 .

[7]  G. Zibordi,et al.  An Evaluation of Above- and In-Water Methods for Determining Water-Leaving Radiances , 2002 .

[8]  Delu Pan,et al.  A regional remote sensing algorithm for total suspended matter in the East China Sea , 2012 .

[9]  Fang Gong,et al.  Establishment of a hyperspectral evaluation model of ocean color satellite-measured reflectance , 2010, Science China Information Sciences.

[10]  Sindy Sterckx,et al.  In situ evidence of non-zero reflectance in the OLCI 1020 nm band for a turbid estuary , 2012 .

[11]  H. Gordon,et al.  Surface-roughness considerations for atmospheric correction of ocean color sensors. II: Error in the retrieved water-leaving radiance. , 1992, Applied optics.

[12]  H. Gordon,et al.  Aerosol analysis with the Coastal Zone Color Scanner: a simple method for including multiple scattering effects. , 1989, Applied optics.

[13]  W. Munk,et al.  Measurement of the Roughness of the Sea Surface from Photographs of the Sun’s Glitter , 1954 .

[14]  T. Eck,et al.  Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) Sun and sky radiance measurements , 2000 .

[15]  Menghua Wang,et al.  A simple, moderately accurate, atmospheric correction algorithm for SeaWiFS☆ , 1994 .

[16]  M. Wang,et al.  Validation study of the SeaWiFS oxygen A-band absorption correction: comparing the retrieved cloud optical thicknesses from SeaWiFS measurements. , 1999, Applied optics.

[17]  Alexander Smirnov,et al.  Validation of SeaWiFS and MODIS aerosol products with globally distributed AERONET data , 2010 .

[18]  Hui Feng,et al.  AERONET-OC: A Network for the Validation of Ocean Color Primary Products , 2009 .

[19]  R. W. Austin,et al.  Ocean Optics Protocols for Satellite Ocean Color Sensor Validation , 2013 .

[20]  Menghua Wang.  Correction of artifacts in the SeaWiFS atmospheric correction: removing discontinuity in the derived products , 2003 .

[21]  Menghua Wang,et al.  Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm. , 1994, Applied optics.

[22]  J. Ryu,et al.  Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI) , 2012, Ocean Science Journal.

[23]  H. Gordon,et al.  Coastal Zone Color Scanner atmospheric correction algorithm: multiple scattering effects. , 1987, Applied optics.

[24]  Alexander Smirnov,et al.  Cloud-Screening and Quality Control Algorithms for the AERONET Database , 2000 .

[25]  Y. Ahn,et al.  New atmospheric correction technique to retrieve the ocean colour from SeaWiFS imagery in complex coastal waters , 2007 .

[26]  Menghua Wang.  Remote sensing of the ocean contributions from ultraviolet to near-infrared using the shortwave infrared bands: simulations. , 2007, Applied optics.

[27]  Menghua Wang,et al.  Estimation of ocean contribution at the MODIS near‐infrared wavelengths along the east coast of the U.S.: Two case studies , 2005 .

[28]  Delu Pan,et al.  THE ATMOSPHERIC CORRECTION ALGORITHM OF SeaWiFS DATA IN CHINA SEAS , 2001 .

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

[30]  K. Ruddick,et al.  Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters. , 2000, Applied optics.