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Retrieval of Aerosol Single-Scattering Albedo from MODIS Data Using an Artificial Neural Network

文献类型：期刊论文

作者Qi, Lin1,2; Liu, Ronggao2; Liu, Yang2
刊名REMOTE SENSING
出版日期2022-12-01
卷号14期号:24页码:20
关键词remote sensing retrieval thick aerosol single-scattering albedo intelligent computing artificial neural network
DOI10.3390/rs14246341
通讯作者Liu, Ronggao(liurg@igsnrr.ac.cn)
英文摘要Aerosol single-scattering albedo (SSA) is one of the largest sources of uncertainty in the evaluation of the aerosol radiative forcing effect. The SSA signal, coupled with aerosol optical depth (AOD) and surface reflectance in satellite images, is difficult to retrieve by the look-up table approach. In this study, we proposed an artificial neural network- (ANN) based approach that retrieves SSA over land based on MODIS (moderate resolution imaging spectroradiometer) visible (red band) reflectance variations among nearby pixels that have different surface reflectivities. Using the training dataset generated by the radiative transfer model, the ANN model was trained to establish the relationship among SSA, surface reflectance, and top of atmosphere (TOA) reflectance. Then, based on the trained ANN model, SSA can be retrieved using the surface and apparent reflectance of several heterogeneous pixels. According to sensitivity analysis, this method works well on nonuniform land surfaces with high AODs. The root mean square error (RMSE) of retrieved and measured SSA (from 28 sites of AErosol RObotic NETwork, AERONET) was 0.042, of which the results with an error less than 0.03 accounted for 51%. In addition, the SSA retrieval method was applied to several thick aerosol layer events over different areas (South Asia, South America, and North China Plain) and compared with the ozone monitoring instrument near-UV aerosol data product (OMAERUV). The comparison results of the images show that the retrieval method of visible wavelength proposed in this study has similar outcomes to those from the ultraviolet wavelengths in these regions. The retrieval algorithm we propose provides an effective way to produce an SSA product in visible wavelength and might help to better estimate the aerosol radiative and optical properties over high heterogeneous areas, which is important for the aerosol radiative impact estimate at a regional scale.
WOS关键词OPTICAL DEPTH ; SATELLITE MEASUREMENTS ; ATMOSPHERIC CORRECTION ; AERONET ; CLIMATE ; ABSORPTION ; ALGORITHM ; RADIATION ; POLLUTION ; DUST
资助项目Strategic Priority Research Program of the Chinese Academy Sciences ; [XDA19080303]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000902699000001
出版者MDPI
资助机构Strategic Priority Research Program of the Chinese Academy Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/188319]  
专题中国科学院地理科学与资源研究所
通讯作者Liu, Ronggao
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Qi, Lin,Liu, Ronggao,Liu, Yang. Retrieval of Aerosol Single-Scattering Albedo from MODIS Data Using an Artificial Neural Network[J]. REMOTE SENSING,2022,14(24):20.
APA Qi, Lin,Liu, Ronggao,&Liu, Yang.(2022).Retrieval of Aerosol Single-Scattering Albedo from MODIS Data Using an Artificial Neural Network.REMOTE SENSING,14(24),20.
MLA Qi, Lin,et al."Retrieval of Aerosol Single-Scattering Albedo from MODIS Data Using an Artificial Neural Network".REMOTE SENSING 14.24(2022):20.

入库方式： OAI收割

来源：地理科学与资源研究所

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