A MISR-Based Method for the Estimation of Particle Size Distribution: Comparison with AERONET over China
Aerosol particle size has a crucial impact on the environment and public health. Current satellite-based regression models focus on the total amount of particles and are limited by surface observations. This study proposes an algorithm to derive the long-term normalized volume size distribution (VSD...
Published in: | Journal of Remote Sensing |
---|---|
Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
American Association for the Advancement of Science (AAAS)
2023
|
Subjects: | |
Online Access: | http://dx.doi.org/10.34133/remotesensing.0032 https://spj.science.org/doi/pdf/10.34133/remotesensing.0032 |
id |
craaas:10.34133/remotesensing.0032 |
---|---|
record_format |
openpolar |
spelling |
craaas:10.34133/remotesensing.0032 2024-06-23T07:44:59+00:00 A MISR-Based Method for the Estimation of Particle Size Distribution: Comparison with AERONET over China Shao, Yanchuan Liu, Riyang Li, Weihan Bi, Jun Ma, Zongwei 2023 http://dx.doi.org/10.34133/remotesensing.0032 https://spj.science.org/doi/pdf/10.34133/remotesensing.0032 en eng American Association for the Advancement of Science (AAAS) Journal of Remote Sensing volume 3 ISSN 2694-1589 journal-article 2023 craaas https://doi.org/10.34133/remotesensing.0032 2024-06-06T04:01:35Z Aerosol particle size has a crucial impact on the environment and public health. Current satellite-based regression models focus on the total amount of particles and are limited by surface observations. This study proposes an algorithm to derive the long-term normalized volume size distribution (VSD) of aerosol particles, which is independent of ground measurements. The size distribution and aerosol optical depth of Multi-angle Imaging SpectroRadiometer (MISR) components are employed. We find the estimated MISR VSD is consistent with Aerosol Robotic Network (AERONET) observations, with R = 0.56, 0.54, 0.59, and 0.68 for daily, monthly, seasonal, and annual levels. The stratified validations of radius, stations, and years further confirm the stable performance of derived VSD ( R = 0.28 to 0.73). The application of the random forest model demonstrates the potential improvements of predicted VSD by 10-fold cross-validation R = 0.86 at the monthly level. We apply MISR VSD to quantify the normalized volume of fractional aerosol particles at a resolution of 0.2° × 0.2° during 2004 to 2016 in China. We also calculate the proportion of small and medium particles to indicate the contribution of anthropogenic aerosols. The highest ratios are concentrated in the northeastern regions especially during winter while relatively lower in the Taklamakan Desert of western China. The case study demonstrates that the application of MISR data can yield valuable and resolved size distributions of aerosol particles. Article in Journal/Newspaper Aerosol Robotic Network AAAS Resource Center (American Association for the Advancement of Science) Journal of Remote Sensing 3 |
institution |
Open Polar |
collection |
AAAS Resource Center (American Association for the Advancement of Science) |
op_collection_id |
craaas |
language |
English |
description |
Aerosol particle size has a crucial impact on the environment and public health. Current satellite-based regression models focus on the total amount of particles and are limited by surface observations. This study proposes an algorithm to derive the long-term normalized volume size distribution (VSD) of aerosol particles, which is independent of ground measurements. The size distribution and aerosol optical depth of Multi-angle Imaging SpectroRadiometer (MISR) components are employed. We find the estimated MISR VSD is consistent with Aerosol Robotic Network (AERONET) observations, with R = 0.56, 0.54, 0.59, and 0.68 for daily, monthly, seasonal, and annual levels. The stratified validations of radius, stations, and years further confirm the stable performance of derived VSD ( R = 0.28 to 0.73). The application of the random forest model demonstrates the potential improvements of predicted VSD by 10-fold cross-validation R = 0.86 at the monthly level. We apply MISR VSD to quantify the normalized volume of fractional aerosol particles at a resolution of 0.2° × 0.2° during 2004 to 2016 in China. We also calculate the proportion of small and medium particles to indicate the contribution of anthropogenic aerosols. The highest ratios are concentrated in the northeastern regions especially during winter while relatively lower in the Taklamakan Desert of western China. The case study demonstrates that the application of MISR data can yield valuable and resolved size distributions of aerosol particles. |
format |
Article in Journal/Newspaper |
author |
Shao, Yanchuan Liu, Riyang Li, Weihan Bi, Jun Ma, Zongwei |
spellingShingle |
Shao, Yanchuan Liu, Riyang Li, Weihan Bi, Jun Ma, Zongwei A MISR-Based Method for the Estimation of Particle Size Distribution: Comparison with AERONET over China |
author_facet |
Shao, Yanchuan Liu, Riyang Li, Weihan Bi, Jun Ma, Zongwei |
author_sort |
Shao, Yanchuan |
title |
A MISR-Based Method for the Estimation of Particle Size Distribution: Comparison with AERONET over China |
title_short |
A MISR-Based Method for the Estimation of Particle Size Distribution: Comparison with AERONET over China |
title_full |
A MISR-Based Method for the Estimation of Particle Size Distribution: Comparison with AERONET over China |
title_fullStr |
A MISR-Based Method for the Estimation of Particle Size Distribution: Comparison with AERONET over China |
title_full_unstemmed |
A MISR-Based Method for the Estimation of Particle Size Distribution: Comparison with AERONET over China |
title_sort |
misr-based method for the estimation of particle size distribution: comparison with aeronet over china |
publisher |
American Association for the Advancement of Science (AAAS) |
publishDate |
2023 |
url |
http://dx.doi.org/10.34133/remotesensing.0032 https://spj.science.org/doi/pdf/10.34133/remotesensing.0032 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Journal of Remote Sensing volume 3 ISSN 2694-1589 |
op_doi |
https://doi.org/10.34133/remotesensing.0032 |
container_title |
Journal of Remote Sensing |
container_volume |
3 |
_version_ |
1802643909588811776 |