Aerosol optical depth retrieval over China from NOAA AVHRR data

A new algorithm for Land Aerosol property and Bidirectional reflectance Inversion by Time Series technique (LABITS) is presented and applied to National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) data over China. Based on the assumptions that the surface bidirectional reflective property are not varying during one day and aerosol characteristics are constant in 0.1° × 0.1° window, we inverse the aerosol optical depth (AOD) and bidirectional reflectance distribution function (BRDF) parameters. Preliminary AOD validation with Aerosol Robotic Network (AERONET) data shows that the correlation coefficient, R2, is 0.79, the root-mean-square error, RMSE, is 0.13 and the uncertainty is Δτ= ±0.05 ± 0.20Δ. Comparing with MODIS AOD product, it is found that both the AOD results are consistent very well. The R2 is 0.80 and RMSE is 0.10. The algorithm is flexible and appropriate for aerosol retrieval over both dark and bright land surface. It is potential to retrieve long term global AOD over land from NOAA AVHRR data since 1980s and to study aerosol climatology and global climate change well.

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

[2]  V. Soufflet,et al.  Remote sensing of aerosols over boreal forest and lake water from AVHRR data , 1997 .

[3]  Alexander Ignatov,et al.  Aerosol Retrievals from Individual AVHRR Channels. Part I: Retrieval Algorithm and Transition from Dave to 6S Radiative Transfer Model , 2002 .

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

[5]  Y. Kaufman,et al.  Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery , 1988 .

[6]  J. Muller,et al.  MODIS BRDF / Albedo Product : Algorithm Theoretical Basis Document Version 5 . 0 , 1999 .

[7]  A. Hauser,et al.  Validation of a modified AVHRR aerosol optical depth retrieval algorithm over Central Europe , 2010 .

[8]  Alexander Ignatov,et al.  Operational Aerosol Observations (AEROBS) from AVHRR/3 On Board NOAA-KLM Satellites , 2004 .

[9]  W. Weaver,et al.  Two-Stream Approximations to Radiative Transfer in Planetary Atmospheres: A Unified Description of Existing Methods and a New Improvement , 1980 .

[10]  Michael J. Pavolonis,et al.  Daytime Cloud Overlap Detection from AVHRR and VIIRS , 2004 .

[11]  Larry L. Stowe,et al.  Scientific basis and initial evaluation of the CLAVR-1 global clear cloud classification algorithm f , 1999 .

[12]  A. Mccarthy.  Development , 1996, Current Opinion in Neurobiology.

[13]  Alan H. Strahler,et al.  An analytic BRDF model of canopy radiative transfer and its inversion , 1993, IEEE Trans. Geosci. Remote. Sens..

[14]  A. Hauser,et al.  NOAA AVHRR derived aerosol optical depth over land , 2005 .

[15]  Alexander Ignatov,et al.  Development, validation, and potential enhancements to the second‐generation operational aerosol product at the National Environmental Satellite, Data, and Information Service of the National Oceanic and Atmospheric Administration , 1997 .

[16]  A. Ignatov,et al.  Aerosol Retrievals from Individual Avhrr Channels: Ii. Quality Control, Probability Distribution Functions, Information Content, and Consistency Checks of Retrievals , 2001 .

[17]  A. Strahler,et al.  Retrieval of surface BRDF from multiangle remotely sensed data , 1994 .

[18]  Oleg Dubovik,et al.  Global aerosol optical properties and application to Moderate Resolution Imaging Spectroradiometer aerosol retrieval over land , 2007 .