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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“…However, these anomaly patterns do not correspond well to the cloud‐fraction anomalies over the Ross Sea, Bellingshausen Sea, and the Amundsen Sea, where positive net radiative flux anomalies of approximately 8 W/m 2 occur. This result indicates that the net radiative flux anomalies are partially influenced by cloud cover in winter over the Southern Ocean around Antarctica, and they are also affected by other atmospheric components, such as water vapor, aerosols, carbon dioxide, and ozone, which are not the focus of this paper (Jing & Lin, ; Lin et al, ; Mei et al, ; Sun et al, ; Wang et al, ; Wu et al, ). The cloud‐cover anomalies resulted in strong negative cloud forcing in winter 2011, which interrupted the surface energy balance and had an important influence on the sea‐ice growth.…”

Section: Cloud Forcing Anomaly At the Surfacementioning

confidence: 94%

“…Ocean around Antarctica, and they are also affected by other atmospheric components, such as water vapor, aerosols, carbon dioxide, and ozone, which are not the focus of this paper (Jing & Lin, 2017;Lin et al, 2016;Mei et al, 2012;Sun et al, 2015;Wang et al, 2017;Wu et al, 2017). The cloud-cover anomalies resulted in strong negative cloud forcing in winter 2011, which interrupted the surface energy balance and had an important influence on the sea-ice growth.…”

Section: Journal Of Geophysical Research: Atmospheresmentioning

confidence: 99%

The Contributions of Winter Cloud Anomalies in 2011 to the Summer Sea‐Ice Rebound in 2012 in the Antarctic

Wang

Yuan

et al. 2019

JGR Atmospheres

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Unlike the rapid decline of Arctic sea ice in the warming climate, Antarctic sea‐ice extent exhibits a modest positive trend in the period of near four decades. In recent years, the fluctuation in Antarctic sea ice has been strengthened, including a decrease toward the lowest sea‐ice extent in February 2011 for the period of 1978–2016 and a strong rebound in the summer of 2012. The sea‐ice recovery mainly occurs in the Weddell Sea, Bellingshausen Sea, Amundsen Sea, southern Ross Sea, and the eastern Somov Sea. This study offers a new mechanism for this summertime sea‐ice rebound. We demonstrate that cloud‐fraction anomalies in winter 2011 contributed to the positive Antarctic sea‐ice anomaly in summer 2012. The results show that the negative cloud‐fraction anomalies in winter 2011 related to the large‐scale atmospheric circulation resulted in a substantial negative surface‐radiation budget, which cooled the surface and promoted more sea‐ice growth. The sea‐ice growth anomalies due to the negative cloud forcing propagated by sea‐ice motion vectors from September 2011 to January 2012. The distribution of the sea‐ice anomalies corresponded well with the sea‐ice concentration anomalies in February 2012 in the Weddell Sea and eastern Somov Sea. Thus, negative cloud‐fraction anomalies in winter can play a vital role in the following summer sea‐ice distribution.

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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

The Contributions of Winter Cloud Anomalies in 2011 to the Summer Sea‐Ice Rebound in 2012 in the Antarctic

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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