Remote sensing of atmospheric particulate mass of dry PM2.5 near the ground: Method validation using ground-based measurements

Li, Zhengqiang; Zhang, Ying; Shao, Jie; Li, Baosheng; Hong, Jin; Liu, Dong; Li, Donghui; Wei, Peng; Li, Wei; Li, Lei; Zhang, Fengxia; Guo, Jie; Deng, Qian; Wang, Bangxin; Cui, Chaolong; Zhang, Wanchun; Wang, Zhenzhu; Yang, Leiku; Xu, Hua; Chen, Xingfeng; Li, Li; Qie, Lili

doi:10.1016/j.rse.2015.11.019

Cited by 101 publications

(43 citation statements)

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“…The satellite‐derived aerosol optical depth (AOD) products have been extensively employed to expand PM 2.5 estimation beyond that only provided by ground monitoring stations (Hoff & Christopher, ; Lary et al, , ; Z. Li et al, ). The AOD products used include those retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS; Fang et al, ), the Multiangle Imaging Spectroradiometer (You et al, ), the Visible Infrared Imaging Radiometer Suite (Wu et al, ), and the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (Paciorek et al, ), and so forth.…”

Section: Introductionmentioning

confidence: 99%

Estimating Regional Ground‐Level PM_2.5 Directly From Satellite Top‐Of‐Atmosphere Reflectance Using Deep Belief Networks

et al. 2018

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Almost all remote sensing atmospheric PM2.5 estimation methods need satellite aerosol optical depth (AOD) products, which are often retrieved from top‐of‐atmosphere (TOA) reflectance via an atmospheric radiative transfer model. Then, is it possible to estimate ground‐level PM2.5 directly from satellite TOA reflectance without a physical model? In this study, this challenging work was achieved based on a machine learning model. Specifically, we established the relationship between PM2.5, satellite TOA reflectance, observation angles, and meteorological factors in a deep learning architecture (denoted as Ref‐PM modeling). This relationship was trained with station PM2.5 measurements, and then the PM2.5 values of those locations without stations could be retrieved. Taking the Wuhan Urban Agglomeration as a case study, the results demonstrate that, compared with AOD‐PM modeling, the Ref‐PM modeling obtains a competitive performance, with sample‐based cross‐validated R2 and root‐mean‐square error values of 0.87 and 9.89 μg/m3, respectively. Also, the TOA‐reflectance‐derived PM2.5 has a finer resolution and a larger spatial coverage than the AOD‐derived PM2.5. This work provides an alternative technique to estimate ground‐level PM2.5, and may have the potential to promote the application in atmospheric environmental monitoring.

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Section: Introductionmentioning

confidence: 99%

Estimating Regional Ground‐Level PM_2.5 Directly From Satellite Top‐Of‐Atmosphere Reflectance Using Deep Belief Networks

et al. 2018

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“…Aerosol optical depth (AOD) is an important aerosol optical parameter with many applications in the fields of atmospheric studies and climate change [1][2][3][4][5][6][7]. Recently, we proposed an aerosol fine-mode fraction (FMF) retrieval method [8] which uses the ratio of the fine-mode AOD (AOD f ) to the total AOD to obtain FMF.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Aerosol Optical Depth Using the Empirical Orthogonal Functions (EOFs) Based on PARASOL Multi-Angle Intensity Data

Zhang

Qie

et al. 2017

Remote Sensing

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Aerosol optical depth (AOD) is a widely used aerosol optical parameter in atmospheric physics. To obtain this parameter precisely, many institutions plan to launch satellites with multi-angle measurement sensors, but one important step in aerosol retrieval, the estimation of surface reflectance, is still a pressing issue. This paper presents an AOD retrieval method based on the multi-angle intensity data from the Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) platform using empirical orthogonal functions (EOFs), which can be universally applied to multi-angle observations. The function of EOFs in this study is to estimate surface intensity contributions, associated with aerosol lookup tables (LUTs), so that the retrieval of AOD can be implemented. A comparison of the retrieved AODs for the Beijing, Xianghe, Taihu, and Hongkong_PolyU sites with those from the Aerosol Robotic Network (AERONET) ground-based observations produced high correlation coefficients (r) of 0.892, 0.915, 0.831, and 0.897, respectively, while the corresponding root mean square errors (RMSEs) are 0.095, 0.093, 0.099, and 0.076, respectively.

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“…This method can be applied in atmospheric environmental studies and can contribute significantly to the estimation of anthropogenic aerosol contents and fine particulate matter concentrations [37,40]. Additionally, the satellite-derived aerosol parameters are used for Data Assimilation (DA) in many studies [41][42][43][44], and our work has implications beyond diagnosing air quality.…”

Section: Discussionmentioning

confidence: 99%

Retrieval of Aerosol Fine-Mode Fraction from Intensity and Polarization Measurements by PARASOL over East Asia

Zhang

Qie

et al. 2016

Remote Sensing

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Abstract:The fine-mode fraction (FMF) of aerosol optical depth (AOD) is a key optical parameter that represents the proportion of fine particles relative to total aerosols in the atmosphere. However, in comparison to ground-based measurements, the FMF is still difficult to retrieve from satellite observations, as attempted by a Moderate-resolution Imaging Spectroradiometer (MODIS) algorithm. In this paper, we introduce the retrieval of FMF based on Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) data. This method takes advantage of the coincident multi-angle intensity and polarization measurements from a single satellite platform. In our method, we use intensity measurements to retrieve the total AOD and polarization measurements to retrieve the fine-mode AOD. The FMF is then calculated as the ratio of the retrieved fine-mode AOD to the total AOD. The important processes in our method include the estimation of the surface intensity and polarized reflectance by using two semi-empirical models, and the building of two sets of aerosol retrieval lookup tables for the intensity and polarized measurements via the 6SV radiative transfer code. We apply this method to East Asia, and comparisons of the retrieved FMFs for the Beijing, Xianghe and Seoul_SNU sites with those of the Aerosol Robotic Network (AERONET) ground-based observations produce correlation coefficients (R 2 ) of 0.838, 0.818, and 0.877, respectively. However, the comparison results are relatively poor (R 2 = 0.537) in low-AOD areas, such as the Osaka site, due to the low signal-to-noise ratio of the satellite observations.

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Remote sensing of atmospheric particulate mass of dry PM2.5 near the ground: Method validation using ground-based measurements

Cited by 101 publications

References 50 publications

Estimating Regional Ground‐Level PM_2.5 Directly From Satellite Top‐Of‐Atmosphere Reflectance Using Deep Belief Networks

Estimating Regional Ground‐Level PM_2.5 Directly From Satellite Top‐Of‐Atmosphere Reflectance Using Deep Belief Networks

Retrieval of Aerosol Optical Depth Using the Empirical Orthogonal Functions (EOFs) Based on PARASOL Multi-Angle Intensity Data

Retrieval of Aerosol Fine-Mode Fraction from Intensity and Polarization Measurements by PARASOL over East Asia

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Remote sensing of atmospheric particulate mass of dry PM2.5 near the ground: Method validation using ground-based measurements

Cited by 101 publications

References 50 publications

Estimating Regional Ground‐Level PM2.5 Directly From Satellite Top‐Of‐Atmosphere Reflectance Using Deep Belief Networks

Estimating Regional Ground‐Level PM2.5 Directly From Satellite Top‐Of‐Atmosphere Reflectance Using Deep Belief Networks

Retrieval of Aerosol Optical Depth Using the Empirical Orthogonal Functions (EOFs) Based on PARASOL Multi-Angle Intensity Data

Retrieval of Aerosol Fine-Mode Fraction from Intensity and Polarization Measurements by PARASOL over East Asia

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Estimating Regional Ground‐Level PM_2.5 Directly From Satellite Top‐Of‐Atmosphere Reflectance Using Deep Belief Networks

Estimating Regional Ground‐Level PM_2.5 Directly From Satellite Top‐Of‐Atmosphere Reflectance Using Deep Belief Networks