Continental aerosol properties and absorption retrieval using random forest machine learning method specific to geostationary remote sensing

Bao, Fangwen1; Wu, Shengbiao2; Gao, Jinhui3; Yuan, Shuyun4; Liu, Yiwen1; Huang, Kai5

2024-09-01

10.1016/j.rse.2024.114275

REMOTE SENSING OF ENVIRONMENT   影响因子和分区

0034-4257

1879-0704

The utilization of satellite remote sensing images for retrieving aerosol optical parameters has been extensively discussed over the past few decades. While employing machine learning models is indeed a viable approach, a significant portion of these studies still rely on redundant data. Moreover, the discussion regarding aerosol absorption, a crucial factor for determining aerosol radiative impact and distinguishing aerosol components, is limited in current machine learning studies. In this study, we propose a random forest model to retrieve high-precision aerosol properties and their absorption over land from Himawari-8 geostationary satellite images. Remarkably, this model attains a high degree of accuracy in estimating aerosol optical depth (AOD), absorption aerosol optical depth (AAOD), and single scattering albedo (SSA) of heavy air mass using only seven primary predictors (observational radiances or their mathematical combinations, geometries, and wavelength). For AOD, the new random forest model demonstrates excellent performance on an hourly scale (R-2 >= 0.89, MAE < 0.07, RMSE <0.13), >80% of the samples fall within the expected error (EE) range. Concerning AAOD, the validation indicates that at least 65% of AAODs have a bias of <= 50%, with an R-2 exceeding 0.78, MAE <= 0.008 and RMSE <= 0.016. SSA also demonstrates a high accuracy (R-2 >= 0.57, MAE < 0.03, RMSE <0.05), with >70% of the results have an error <= 0.03. Through more comprehensive independent spatiotemporal cross validation, it can be determined that the model also offers reliable spatial and temporal predictions. The proposed RF model is capable of learning aerosol properties under most atmosphere scenarios, providing a reasonable conversion from predictors to AOD and AAOD/SSA under high aerosol loadings. The spatial patterns of these parameters suggest that the retrievals show considerable potential in capturing high aerosol loading in East Asia and biomass burning in Southeast Asia. The method introduced in this study offers a new approach to obtaining aerosol properties from geostationary satellite remote sensing, featuring a flexible process, simple inputs, high accuracy, and enhanced robustness. Additionally, it furnishes supplementary insights into aerosol absorption, presenting new possibilities in determining aerosol radiative impact and distinguishing aerosol components.

Random Forest Aerosol optical depth Absorption aerosol optical depth Single scattering albedo Himawari AHI

SCI ; EI

英语

第一 ; 通讯

Foundation of China["42105124","41905114"] ; Scientific Research Foundation of Chengdu University of Information Technology[KYTZ202121]

Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology

Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology

WOS:001266752300001

ELSEVIER SCIENCE INC

20242616508777

Machine learning ; Optical properties ; Optical remote sensing ; Water absorption

Artificial Intelligence:723.4 ; Light/Optics:741.1 ; Optical Devices and Systems:741.3 ; Chemical Operations:802.3 ; Agricultural Equipment and Methods; Vegetation and Pest Control:821

GEOSCIENCES

Web of Science

1.Southern Univ Sci & Technol, Dept Ocean Sci & Engn, Shenzhen, Peoples R China
2.Univ Hong Kong, Fac Architecture, Div Landscape Architecture, Future Urban & Sustainable Environm FUSE Lab, Hong Kong, Peoples R China
3.Chengdu Univ Informat Technol, Sch Atmospher Sci, Plateau Atmosphere & Environm Key Lab Sichuan Prov, Chengdu, Peoples R China
4.Shenzhen Polytech Univ, Sch Automot & Transportat Engn, Shenzhen, Peoples R China
5.Honor Device Co Ltd, Shenzhen, Peoples R China

Bao, Fangwen,Wu, Shengbiao,Gao, Jinhui,et al. Continental aerosol properties and absorption retrieval using random forest machine learning method specific to geostationary remote sensing[J]. REMOTE SENSING OF ENVIRONMENT,2024,311.

Bao, Fangwen,Wu, Shengbiao,Gao, Jinhui,Yuan, Shuyun,Liu, Yiwen,&Huang, Kai.(2024).Continental aerosol properties and absorption retrieval using random forest machine learning method specific to geostationary remote sensing.REMOTE SENSING OF ENVIRONMENT,311.

Bao, Fangwen,et al."Continental aerosol properties and absorption retrieval using random forest machine learning method specific to geostationary remote sensing".REMOTE SENSING OF ENVIRONMENT 311(2024).