An improved dark target method for aerosol optical depth retrieval over China from Himawari-8

Gao, Ling; Lin, Chunye; Li, Jun; Li, Chengcai; Zhu, Lin

doi:10.1016/j.atmosres.2020.105399

Cited by 19 publications

(7 citation statements)

References 43 publications

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“…Assuming that the surface of the Earth is Lambertian, the atmosphere changes uniformly in the vertical direction, the reflectance of the satellite to the top of the atmosphere is as shown in Equation ( 3) [33][34][35]:…”

Section: Aeronet Aod Productsmentioning

confidence: 99%

Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data

Chen

Wang

et al. 2021

Remote Sensing

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High temporal resolution aerosol optical depth (AOD) products are very important for the studies of atmospheric environment and climate change. Geostationary Ocean Color Imager (GOCI) is a suitable data source for AOD retrieval, as it can monitor hourly aerosol changes and make up for the low temporal resolution deficiency of polar orbiting satellite. In this study, we proposed an algorithm for retrieving high temporal resolution AOD using GOCI data and then applied the algorithm in the Yangtze River Delta, a typical region suffering severe air pollution issues. Based on Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance determined by MODIS V5.2 algorithm and MODIS Bidirectional Reflectance Distribution Function (BRDF) data, after spectral conversion between MODIS and GOCI, the GOCI surface reflectance at different solar angles were obtained and used to retrieve AOD. Five indicators including correlation coefficient (R), significant level of the correlation (p value), mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE) were employed to analyze the errors between the Aerosol Robotic Network (AERONET) observed AOD and the GOCI retrieved AOD. The results showed that the GOCI AOD retrieved by the continental aerosol look-up table was consistent with the AERONET AOD (R > 0.7, p ≤ 0.05). The highest R value of Taihu Station and Xuzhou CUMT Station are both 0.84 (8:30 a.m.); the minimum RMSE at Taihu and Xuzhou-CUMT stations were 0.2077 (11:30 a.m.) and 0.1937 (10:30 a.m.), respectively. Moreover, the results suggested that the greater the solar angle of the GOCI sensor, the higher the AOD retrieval accuracy, while the retrieved AOD at noon exhibited the largest error as assessed by MAE and MRE. We concluded that the inaccurate estimation of surface reflectance was the root cause of the retrieval errors. This study has implications in providing a deep understanding of the effects of solar angle changes on retrieving AOD using GOCI.

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Section: Aeronet Aod Productsmentioning

confidence: 99%

Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data

Chen

Wang

et al. 2021

Remote Sensing

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“…With the comparison between Himawari-8 IBAA and other AHI algorithms in previous papers, such as different improved DT algorithms (R = 0.86 and RMSE = 0.12 in [21], 2018; R = 0.9 and RMSE = 0.15 in [22]; R > 0.8 in [23]), an improved time-series algorithm (R > 0.8 and RMSE < 0.2 in [59]), the OE method (R = 0.88, RMSE = 0.17 and 69.9% of retrievals falling within EE = ±(0.05 + 0.2 × AOD AERONET ) in [24]), and the monthly spectral base reflectance ratio library method (R = 0.939, RMSE = 0.113 and 82.5% of retrievals falling within EE = ±(0.05 + 0.2 × AOD AERONET in [25]), the IBAA algorithm has certain precision and a simple process. Due to different time range and regions and different ground-based station observation selected for algorithm validation applied in the above papers, there are great uncertainties in such comparisons.…”

Section: Discussionmentioning

confidence: 99%

“…It is worth noting that Xie et al (2019) derived a global and hourly dataset of AOD from GOES-16, MSG-1, MSG-4, and Himawari-8 [20]. For Himawari-8 AHI AOD, the famous DT algorithm was tested and produced a good result [21][22][23]. She et al (2018) obtained the hourly AOD and surface reflectance by the OE method [24].…”

Section: Introductionmentioning

confidence: 99%

Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization

et al. 2021

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The Advanced Himawari Imager (AHI) aboard the Himawari-8, a new generation of geostationary meteorological satellite, has high-frequency observation, which allows it to effectively capture atmospheric variations. In this paper, we have proposed an Improved Bi-angle Aerosol optical depth (AOD) retrieval Algorithm (IBAA) from AHI data. The algorithm ignores the aerosol effect at 2.3 μm and assumes that the aerosol optical depth does not change within one hour. According to the property that the reflectivity ratio K of two observations at 2.3 μm does not change with wavelength, we constructed the equation for two observations of AHI 0.47 μm band. Then Particle Swarm Optimization (PSO) was used to solve the nonlinear equation. The algorithm was applied to the AHI observations over the Chinese mainland (80°–135°E, 15°–60°N) between April and June 2019 and hourly AOD at 0.47 μm was retrieved. We validated IBAA AOD against the Aerosol Robotic Network (AERONET) sites observation, including surrounding regions as well as the Chinese mainland, and compared it with the AHI L3 V030 hourly AOD product. Validation with AERONET of 2079 matching points shows a correlation coefficient R = 0.82, root-mean-square error RMSE = 0.27, and more than 62% AOD retrieval results within the expected error of ±(0.05 + 0.2 × AODAERONET). Although IBAA does not perform very well in the case of coarse-particle aerosols, the comparison and validation demonstrate it can estimate AHI AOD with good accuracy and wide coverage over land on the whole.

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“…Su et al (2021) [15] proposed a high-precision aerosol retrieval algorithm (HiPARA) that employs a monthly spectral reflectance ratio library and aerosol type from Aerosol Robotic Network (AERONET) statistics to retrieve AOD. Gao et al (2021) [16] improved the surface reflectance estimation of the DT method by taking into account the land cover, NDVI and scattering angle. The retrieved AOD in the study of Su et al (2021) [15] and Gao et al (2021) [16] was more accurate than the JAXA AOD.…”

Section: Introductionmentioning

confidence: 99%

“…Gao et al (2021) [16] improved the surface reflectance estimation of the DT method by taking into account the land cover, NDVI and scattering angle. The retrieved AOD in the study of Su et al (2021) [15] and Gao et al (2021) [16] was more accurate than the JAXA AOD. Huttunen et al (2016) [17] compared 6 methods of AOD retrieval, including one radiative transfer modeling LUT (look up table) method, one non-linear regression method and four machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG): Algorithm Framework Development

Chen¹,

Zheng²,

Wang³

et al. 2021

Preprint

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An improved dark target method for aerosol optical depth retrieval over China from Himawari-8

Cited by 19 publications

References 43 publications

Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data

Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data

Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization

Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG): Algorithm Framework Development

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