Validation and Accuracy Analysis of Global MODIS Aerosol Products over Land

Wang, Qingxin; Sun, Lin; Wei, Jing; Yang, Yikun; Li, Ruibo; Liu, Qinhuo; Chen, Liangfu

doi:10.3390/atmos8080155

Cited by 23 publications

(15 citation statements)

References 34 publications

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“…As LSR increases, the sensitivity of AOD retrieval to surface reflection also increases. Quantitatively, when AOD is equal to 1.0 with TOA reflectance of 0.16, the surface reflectance error of 0.01 can cause an AOD retrieval error of approximately 0.08 (LSR ~ 0.04), while the error of AOD retrieval would reach to approximately 0.15 (LSR ~ 0.09), similar to previous simulation results (Kaufman, Wald, et al, ; Tian et al, ; Wang, Sun, et al, ). Therefore, it is critical to estimate the LSR especially in urban areas.…”

Section: Discussionsupporting

confidence: 87%

Improved Aerosol Retrievals Over Complex Regions Using NPP Visible Infrared Imaging Radiometer Suite Observations

Yang

Zhao

Sun

et al. 2019

Earth and Space Science

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As a new generation of polar‐orbiting satellites, NPP VIIRS (National Polar‐orbiting Partnership Visible Infrared Imaging Radiometer Suite) provides important supports for the studies of local and global scale climate change, environmental monitoring, and radiant energy balance. This paper proposed an improved deep blue aerosol retrieval algorithm over complex regions based on NPP VIIRS observations. Two improvements were proposed and implemented to the traditional aerosol optical depth (AOD) retrieval algorithm in order to improve the retrieval accuracy, including the construction of the surface reflectance conversion model and aerosol optical parameter acquisition. The retrieval results were extensively evaluated. First, the accuracy of the algorithm was evaluated using AErosol RObotic NETwork ground‐based observations, including conditions over different underlying surfaces, different seasons, and different AOD values. Then, the cross validation was carried out between our AOD retrievals and NPP VIIRS aerosol products. The verification results show that our AOD retrievals agree very well with the AErosol RObotic NETwork AOD with coefficient of determination (R2) ~ 0.85. The improved deep blue algorithm performs well overall under different surface conditions and seasons, while the quality of performance gradually decreases with the increase of AOD. Moreover, the improved algorithm is robust with less bias, root‐mean‐square error, and relative mean bias in the AOD retrievals and can provide more detailed aerosol information compared with the NPP VIIRS aerosol products. These results suggest that the improved deep blue algorithm can obtain high‐precision AOD information, providing better data basis for related researches on air pollution and climate change.

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Section: Discussionsupporting

confidence: 87%

Improved Aerosol Retrievals Over Complex Regions Using NPP Visible Infrared Imaging Radiometer Suite Observations

Yang

Zhao

Sun

et al. 2019

Earth and Space Science

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“…Aerosol Robotic Network (AERONET) AOD products (version 3) were collected to fuse DT and DB AOD, and the products were used to validate the fused result. The AERONET AOD includes three levels: Level 1.0 (unscreened), Level 1.5 (cloud-screened and quality controlled), and Level 2.0 (quality-assured) [ 39 ]. MODIS AOD data were validated by AERONET level 2.0 observed at three AERONET stations: Beijing (116.38° E, 39.97° N), Beijing_CAMS (116.32° E, 39.93° N), and Xianghe (116.96° E, 39.75° N).…”

Section: Methodsmentioning

confidence: 99%

Estimating PM2.5 Concentrations Based on MODIS AOD and NAQPMS Data over Beijing–Tianjin–Hebei

Wang

Zeng

Tao

et al. 2019

Sensors

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Accurately estimating fine ambient particulate matter (PM2.5) is important to assess air quality and to support epidemiological studies. To analyze the spatiotemporal variation of PM2.5 concentrations, previous studies used different methodologies, such as statistical models or neural networks, to estimate PM2.5. However, there is little research on full-coverage PM2.5 estimation using a combination of ground-measured, satellite-estimated, and atmospheric chemical model data. In this study, the linear mixed effect (LME) model, which used the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), meteorological data, normalized difference vegetation index (NDVI), and elevation data as predictors, was fitted for 2017 over Beijing–Tianjin–Hebei (BTH). The LME model was used to calibrate the PM2.5 concentration using the nested air-quality prediction modeling system (NAQPMS) simulated with ground measurements. The inverse variance weighting (IVW) method was used to fuse satellite-estimated and model-calibrated PM2.5. The results showed a strong agreement with ground measurements, with an overall coefficient (R2) of 0.78 and a root-mean-square error (RMSE) of 26.44 μg/m3 in cross-validation (CV). The seasonal R2 values were 0.75, 0.62, 0.80, and 0.78 in the spring, summer, autumn, and winter, respectively. The fusion results supplement the lack of satellite estimates and can capture more detailed information than the NAQPMS model. Therefore, the results will be helpful for pollution process analyses and health-related studies.

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“…Meanwhile, other scholars think that there still are many uncertainties in satellite aerosol data sets (Li et al, ; Tao et al, ; Tao et al, ). However, numerous scholars have compared the MODIS AOD products to the observations from Aerosol Robotic NETwork (AERONET) or China Aerosol Robot Sunphotometer Network (Fan et al, ; Hsu et al, ; Sayer et al, ; Sayer et al, ; Tao et al, ; Wang et al, ; Wei et al, ; Xie et al, ; Yan et al, ), and most of their MODIS aerosol retrievals are from DT algorithm or DB algorithm of C5.1 and C6. Besides, the present studies mainly focus on the eastern coastal, the Beijing‐Tianjin‐Hebei Region as well as the whole of China, and overseas (Boiyo et al, ; Bouaziz et al, ; Tao et al, ; Tian et al, ; Wang et al, ), and due to the scarcity of aerosol observation, little research has been done on the region of northwestern China (Li et al, ; Tao, Chen, et al, ).…”

Section: Introductionmentioning

confidence: 99%

Validation and Accuracy Analysis of the Collection 6.1 MODIS Aerosol Optical Depth Over the Westernmost City in China Based on the Sun‐Sky Radiometer Observations From SONET

Huang

Chen

et al. 2020

Earth and Space Science

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Toward the Kashi region in northwest of China, the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals from Collection 6 (C6) MYD, Collection 6.1 (C6.1) MOD, and C6.1 MYD during 2016–2017 are compared with ground‐based measurements from the Sun‐sky Radiometer Network (SONET), and the first comprehensive evaluation of the Dark Target (DT) and Deep Blue (DB) retrievals with a 10‐km spatial resolution in the latest C6.1 MYD AOD data set during 2016–2018 is presented. In general, C6.1 MYD AOD products (both of DT and DB algorithm) are the most effective in Kashi of the three collections, and there is an overall underestimation of DB AOD, while DT AOD that slightly outperformed DB AOD in Kashi is overestimated on the whole. As to the factors that influence the accuracy of MODIS AOD, for DB algorithm, the overestimations of the surface reflectance and Single Scattering Albedo that DB aerosol model assumed can cause underestimation of DB AOD retrievals over Kashi, while the ones for DT algorithm are opposite. Besides, the coarse dust particles with lower veracity are predominant in Kashi region, which illustrated that the errors of particle size assumption in C6.1 MYD DT and DB algorithms will make large inversion error of MODIS AOD. Moreover, whatever DB or DT algorithm, the accuracy of AOD is diminished as the aerosol loading increases. The more realistic aerosol models and surface characterizations are necessary during the process of generating the MODIS aerosol retrievals in Kashi region.

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Validation and Accuracy Analysis of Global MODIS Aerosol Products over Land

Cited by 23 publications

References 34 publications

Improved Aerosol Retrievals Over Complex Regions Using NPP Visible Infrared Imaging Radiometer Suite Observations

Improved Aerosol Retrievals Over Complex Regions Using NPP Visible Infrared Imaging Radiometer Suite Observations

Estimating PM2.5 Concentrations Based on MODIS AOD and NAQPMS Data over Beijing–Tianjin–Hebei

Validation and Accuracy Analysis of the Collection 6.1 MODIS Aerosol Optical Depth Over the Westernmost City in China Based on the Sun‐Sky Radiometer Observations From SONET

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