Exploring Global Land Coarse-Mode Aerosol Changes from 2001-2021 Using a New Spatiotemporal Coaction Deep-Learning Model

被引：4

作者：

Zang, Zhou ^{[1
]}

Zhang, Yue ^{[1
]}

Zuo, Chen ^{[1
]}

Chen, Jiayi ^{[1
]}

He, Bin ^{[2
]}

Luo, Nana ^{[3
]}

Zou, Junxiao ^{[1
]}

Zhao, Wenji ^{[4
]}

Shi, Wenzhong ^{[5
]}

Yan, Xing ^{[1
]}

机构：

[1] Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[2] Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China

[3] Beijing Univ Civil Engn & Architecture, Sch Geomat & Urban Spatial Informat, Beijing 102612, Peoples R China

[4] Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China

[5] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Hong Kong, Peoples R China

来源：

ENVIRONMENTAL SCIENCE & TECHNOLOGY | 2023年 / 57卷 / 48期

基金：

中国国家自然科学基金;

关键词：

cAOD; deep learning; MODIS; globalland; DUST OPTICAL DEPTH; CHINA; PM2.5; RETRIEVALS; TRENDS;

D O I：

10.1021/acs.est.3c07982

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Coarse-mode aerosol optical depths (cAODs) are critical for understanding the impact of coarse particle sizes, especially dust aerosols, on climate. Currently, the limited data length and high uncertainty of satellite products diminish the applicability of cAOD for climate research. Here, we propose a spatiotemporal coaction deep-learning model (SCAM) for the retrieval of global land cAOD (500 nm) from 2001-2021. In contrast to conventional deep-learning models, the SCAM considers the impacts of spatiotemporal feature interactions and can simultaneously describe linear and nonlinear relationships for retrievals. Based on these unique characteristics, the SCAM considerably improved global daily cAOD accuracies and coverages (R = 0.82, root-mean-square error [RMSE] = 0.04). Compared to official products from the multiangle imaging spectroradiometer (MISR), the moderate resolution imaging spectroradiometer (MODIS), and the polarization and directionality of Earth's reflectances (POLDER) instrument, as well as the physical-deep learning (Phy-DL) derived cAOD, the SCAM cAOD improved the monthly R from 0.44 to 0.88 and more accurately captured over the desert regions. Based on the SCAM cAOD, daily dust cases decreased over the Sahara, Thar Desert, Gobi Desert, and Middle East during 2001-2021 (>3 x 10(-3)/year). The SCAM-retrieved cAOD can contribute considerably to resolving the climate change uncertainty related to coarse-mode aerosols. Our proposed method is highly valuable for reducing uncertainties regarding coarse aerosols and climate interactions.

引用

页码：19881 / 19890

页数：10