Use of MODIS-derived surface reflectance data in the ORAC-AATSR aerosol retrieval algorithm: Impact of differences between sensor spectral response functions
The aerosol component of the Oxford-Rutherford Appleton Laboratory (RAL) Aerosol and Clouds (ORAC) retrieval scheme for the Advanced Along-Track Scanning Radiometer (AATSR) uses data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to constrain the brightness of the surface. Ho...
| Egile Nagusiak: | , , , , , |
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| Formatua: | Journal article |
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Elsevier
2011
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| _version_ | 1826278522661896192 |
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| author | Sayer, A Thomas, G Grainger, R Carboni, E Poulsen, C Siddans, R |
| author_facet | Sayer, A Thomas, G Grainger, R Carboni, E Poulsen, C Siddans, R |
| author_sort | Sayer, A |
| collection | OXFORD |
| description | The aerosol component of the Oxford-Rutherford Appleton Laboratory (RAL) Aerosol and Clouds (ORAC) retrieval scheme for the Advanced Along-Track Scanning Radiometer (AATSR) uses data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to constrain the brightness of the surface. However, the spectral response functions of the channels used (centred near 550 nm, 660 nm, 870 nm, and 1.6 μm) do not exactly match between the two sensors. It is shown that failure to account for differences between the instruments' spectral response functions leads to errors of typically 0.001–0.01 in spectral surface albedo, and distinct biases, dependent on wavelength and surface type. A technique based on singular value decomposition (SVD) is used to reduce these random errors by an average of 35% at 670 nm and over 60% at the other wavelengths used. The technique reduces the biases so that they are negligible. In principle, the method can be extended to any combination of sensors. The SVD-based scheme is applied to AATSR data from the month of July 2008 and found to increase the number of successful aerosol retrievals, the speed of retrieval convergence, and improve the level of consistency between the measurements and the retrieved state. Additionally, retrieved aerosol optical depth at 550 nm shows an improvement in correspondence when compared to Aerosol Robotic Network (AERONET) data. |
| first_indexed | 2024-03-06T23:45:11Z |
| format | Journal article |
| id | oxford-uuid:70a6e13a-b62d-4012-a721-3be9517c7f98 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T23:45:11Z |
| publishDate | 2011 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:70a6e13a-b62d-4012-a721-3be9517c7f982022-03-26T19:38:34ZUse of MODIS-derived surface reflectance data in the ORAC-AATSR aerosol retrieval algorithm: Impact of differences between sensor spectral response functionsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:70a6e13a-b62d-4012-a721-3be9517c7f98Symplectic Elements at OxfordElsevier2011Sayer, AThomas, GGrainger, RCarboni, EPoulsen, CSiddans, RThe aerosol component of the Oxford-Rutherford Appleton Laboratory (RAL) Aerosol and Clouds (ORAC) retrieval scheme for the Advanced Along-Track Scanning Radiometer (AATSR) uses data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to constrain the brightness of the surface. However, the spectral response functions of the channels used (centred near 550 nm, 660 nm, 870 nm, and 1.6 μm) do not exactly match between the two sensors. It is shown that failure to account for differences between the instruments' spectral response functions leads to errors of typically 0.001–0.01 in spectral surface albedo, and distinct biases, dependent on wavelength and surface type. A technique based on singular value decomposition (SVD) is used to reduce these random errors by an average of 35% at 670 nm and over 60% at the other wavelengths used. The technique reduces the biases so that they are negligible. In principle, the method can be extended to any combination of sensors. The SVD-based scheme is applied to AATSR data from the month of July 2008 and found to increase the number of successful aerosol retrievals, the speed of retrieval convergence, and improve the level of consistency between the measurements and the retrieved state. Additionally, retrieved aerosol optical depth at 550 nm shows an improvement in correspondence when compared to Aerosol Robotic Network (AERONET) data. |
| spellingShingle | Sayer, A Thomas, G Grainger, R Carboni, E Poulsen, C Siddans, R Use of MODIS-derived surface reflectance data in the ORAC-AATSR aerosol retrieval algorithm: Impact of differences between sensor spectral response functions |
| title | Use of MODIS-derived surface reflectance data in the ORAC-AATSR aerosol retrieval algorithm: Impact of differences between sensor spectral response functions |
| title_full | Use of MODIS-derived surface reflectance data in the ORAC-AATSR aerosol retrieval algorithm: Impact of differences between sensor spectral response functions |
| title_fullStr | Use of MODIS-derived surface reflectance data in the ORAC-AATSR aerosol retrieval algorithm: Impact of differences between sensor spectral response functions |
| title_full_unstemmed | Use of MODIS-derived surface reflectance data in the ORAC-AATSR aerosol retrieval algorithm: Impact of differences between sensor spectral response functions |
| title_short | Use of MODIS-derived surface reflectance data in the ORAC-AATSR aerosol retrieval algorithm: Impact of differences between sensor spectral response functions |
| title_sort | use of modis derived surface reflectance data in the orac aatsr aerosol retrieval algorithm impact of differences between sensor spectral response functions |
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