A Geostatistics-Based Method to Determine the Pixel Distance in a Structure Function Model for Aerosol Optical Depth Inversion
Inversion of aerosol optical depth (AOD) over bright land surface by optical remote sensing is particularly challenging because surface reflectance dominates the satellite signal. A structure function method is suitable and can effectively solve aerosol optical depth inversion in high reflectance ar...
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ftdoajarticles:oai:doaj.org/article:9db13ebcfdb84a73a01ea5083bb57582 2023-05-15T13:06:26+02:00 A Geostatistics-Based Method to Determine the Pixel Distance in a Structure Function Model for Aerosol Optical Depth Inversion Guanhua Zhou Yue Zhang Zhongqi Ma Wujian Xu Kai Zhang Junhui Liu Yumin Tan 2017-01-01T00:00:00Z https://doi.org/10.3390/atmos8010006 https://doaj.org/article/9db13ebcfdb84a73a01ea5083bb57582 EN eng MDPI AG http://www.mdpi.com/2073-4433/8/1/6 https://doaj.org/toc/2073-4433 2073-4433 doi:10.3390/atmos8010006 https://doaj.org/article/9db13ebcfdb84a73a01ea5083bb57582 Atmosphere, Vol 8, Iss 1, p 6 (2017) aerosol optical depth (AOD) structure function geostatistics pixel distance Meteorology. Climatology QC851-999 article 2017 ftdoajarticles https://doi.org/10.3390/atmos8010006 2022-12-31T15:01:22Z Inversion of aerosol optical depth (AOD) over bright land surface by optical remote sensing is particularly challenging because surface reflectance dominates the satellite signal. A structure function method is suitable and can effectively solve aerosol optical depth inversion in high reflectance areas. How to select d (the pixel distance between two pixels) value is one of the key problems with the structure function method. We present a method based on geostatistics, where variogram theory is referenced, to determine the pixel distance in structure function model for aerosol optical depth inversion. This method was validated by the Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km resolution level 1B data from Beijing, China. The results indicate that the relationship between variogram and d in four different directions can be fitted by exponential function, of which correlation coefficients are all above 0.9. Compared with the MODIS aerosol product (MOD 04 product), the inversion AOD has higher accuracy, with an absolute error of −0.00187 instead of −0.00854 and a relative error of 0.99% instead of 4.35%, based on AERosol RObotic NETwork (AERONET) observations. For validation, we applied this new method separately to both Beijing–Tianjin–Hebei region and MODIS 500 m resolution images for region and resolution validation. The results show that inversion AOD has higher accuracy and more efficient pixels. Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Atmosphere 8 12 6 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
aerosol optical depth (AOD) structure function geostatistics pixel distance Meteorology. Climatology QC851-999 |
spellingShingle |
aerosol optical depth (AOD) structure function geostatistics pixel distance Meteorology. Climatology QC851-999 Guanhua Zhou Yue Zhang Zhongqi Ma Wujian Xu Kai Zhang Junhui Liu Yumin Tan A Geostatistics-Based Method to Determine the Pixel Distance in a Structure Function Model for Aerosol Optical Depth Inversion |
topic_facet |
aerosol optical depth (AOD) structure function geostatistics pixel distance Meteorology. Climatology QC851-999 |
description |
Inversion of aerosol optical depth (AOD) over bright land surface by optical remote sensing is particularly challenging because surface reflectance dominates the satellite signal. A structure function method is suitable and can effectively solve aerosol optical depth inversion in high reflectance areas. How to select d (the pixel distance between two pixels) value is one of the key problems with the structure function method. We present a method based on geostatistics, where variogram theory is referenced, to determine the pixel distance in structure function model for aerosol optical depth inversion. This method was validated by the Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km resolution level 1B data from Beijing, China. The results indicate that the relationship between variogram and d in four different directions can be fitted by exponential function, of which correlation coefficients are all above 0.9. Compared with the MODIS aerosol product (MOD 04 product), the inversion AOD has higher accuracy, with an absolute error of −0.00187 instead of −0.00854 and a relative error of 0.99% instead of 4.35%, based on AERosol RObotic NETwork (AERONET) observations. For validation, we applied this new method separately to both Beijing–Tianjin–Hebei region and MODIS 500 m resolution images for region and resolution validation. The results show that inversion AOD has higher accuracy and more efficient pixels. |
format |
Article in Journal/Newspaper |
author |
Guanhua Zhou Yue Zhang Zhongqi Ma Wujian Xu Kai Zhang Junhui Liu Yumin Tan |
author_facet |
Guanhua Zhou Yue Zhang Zhongqi Ma Wujian Xu Kai Zhang Junhui Liu Yumin Tan |
author_sort |
Guanhua Zhou |
title |
A Geostatistics-Based Method to Determine the Pixel Distance in a Structure Function Model for Aerosol Optical Depth Inversion |
title_short |
A Geostatistics-Based Method to Determine the Pixel Distance in a Structure Function Model for Aerosol Optical Depth Inversion |
title_full |
A Geostatistics-Based Method to Determine the Pixel Distance in a Structure Function Model for Aerosol Optical Depth Inversion |
title_fullStr |
A Geostatistics-Based Method to Determine the Pixel Distance in a Structure Function Model for Aerosol Optical Depth Inversion |
title_full_unstemmed |
A Geostatistics-Based Method to Determine the Pixel Distance in a Structure Function Model for Aerosol Optical Depth Inversion |
title_sort |
geostatistics-based method to determine the pixel distance in a structure function model for aerosol optical depth inversion |
publisher |
MDPI AG |
publishDate |
2017 |
url |
https://doi.org/10.3390/atmos8010006 https://doaj.org/article/9db13ebcfdb84a73a01ea5083bb57582 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Atmosphere, Vol 8, Iss 1, p 6 (2017) |
op_relation |
http://www.mdpi.com/2073-4433/8/1/6 https://doaj.org/toc/2073-4433 2073-4433 doi:10.3390/atmos8010006 https://doaj.org/article/9db13ebcfdb84a73a01ea5083bb57582 |
op_doi |
https://doi.org/10.3390/atmos8010006 |
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Atmosphere |
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8 |
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12 |
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6 |
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1766005439859785728 |