Preliminary Investigation of a New AHI Aerosol Optical Depth (AOD) Retrieval Algorithm and Evaluation with Multiple Source AOD Measurements in China

Yang, Fukun; Wang, Yang; Tao, Jianhua; Wang, Zifeng; Fan, Meng; Leeuw, G. de; Chen, Liangfu

doi:10.3390/rs10050748

Cited by 33 publications

(12 citation statements)

References 48 publications

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“…Zhang et al [18] retrieved an AOT dataset over East Asia from the H8/AHI using six typical aerosol models and improved channel relationships. Yang et al [19] retrieved a new H8/AHI AOT dataset over mainland China using a traditional dark target algorithm. Later, the Japan Aerospace Exploration Agency (JAXA) released their H8/AHI aerosol products via the JAXA Himawari Monitor website, including a Level-2 (L2) dataset of 10-minute interval and hourly Level-3 (L3) products with two sub-datasets (AOT pure and AOT merged ) and three versions (010, 020, 030) [20].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of JAXA Himawari-8-AHI Level-3 Aerosol Products over Eastern China

Qin

et al. 2019

Atmosphere

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A novel geostationary satellite, the H8/AHI (Himawari-8/Advanced Himawari Imager), greatly improved the scan times per day covering East Asia, and the operational products have been stably provided for a period of time. Currently, atmospheric aerosol pollution is a major concern in China. H8/AHI aerosol products with a high temporal resolution are helpful for real-time monitoring of subtle aerosol variation. However, the H8/AHI aerosol optical thickness (AOT) product has been updated three times since its launch, and the evaluation of this dataset is currently rare. In order to validate its accuracy, this study compared the H8/AHI Level-3 (L3) hourly AOT products of all versions with measurements obtained from eleven sunphotometer sites located in eastern China from 2015 to 2018. Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 AOT products from the same period were also used for inter-comparison. Although the H8/AHI AOT retrievals in version 010 show a moderate agreement with ground-based observations (correlation coefficient (R): 0.66–0.85), and the time series analysis shows that it can effectively monitor hourly variation, it suffers from an obvious underestimation of 0.3 compared to ground-based and MODIS observations. After the retrieval algorithm updated the predefined aerosol model, the overall underestimation of AHI AOTs was solved (version 010 slope: 0.43–0.62, version 030 slope: 0.75–1.02), and the AOTs in version 030 show a high agreement with observations from ten sites (R: 0.73–0.91). In addition, the surface reflectance dataset derived from the minimum reflectivity model in version 010 is inaccurate in parts of eastern China, for both “bright” and “dark” land surfaces, which leads to the overestimation of the AOT values under low aerosol loads at the Beijing and Xianghe sites. After the update of the surface dataset in version 030, this phenomenon was alleviated, resulting in no significant difference in scatterplots under different surface conditions. The AOTs of H8/AHI version 030 show a significant improvement compared to the previous two versions, but the spatial distribution of AHI is still different from MODIS AOT products due to the differences in sensors and algorithms. Therefore, although the evaluation in this study demonstrates the effectiveness of H8/AHI AOT products for aerosol monitoring at fine temporal resolutions, the performance of H8/AHI AOT products needs further study by considering more conditions.

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Section: Introductionmentioning

confidence: 99%

Evaluation of JAXA Himawari-8-AHI Level-3 Aerosol Products over Eastern China

Qin

et al. 2019

Atmosphere

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“…These include the Japanese Meteorological Agency (JMA) for AHI on Himawari (Uesawa, 2016) and the National Oceanographic and Atmospheric Agency (NOAA) for ABI on the GOES-R se-ries. In addition to these official operational products, other algorithms have been developed that make use of the new generation of GEO observations for aerosol retrievals, especially for AHI data (Sekiyama et al, 2015;Yumimoto et al, 2016;Lim et al, 2016Lim et al, , 2018aZhang et al, 2018;Yoshida et al, 2018;Yang et al, 2018;Shi et al, 2018;Yan et al, 2018;Choi et al, 2019). Some of these alternative aerosol products are research algorithms for specific purposes, while others could be of general interest and could be made public.…”

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confidence: 99%

Applying the Dark Target aerosol algorithm with Advanced Himawari Imager observations during the KORUS-AQ field campaign

et al. 2019

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Abstract. For nearly 2 decades we have been quantitatively observing the Earth's aerosol system from space at one or two times of the day by applying the Dark Target family of algorithms to polar-orbiting satellite sensors, particularly MODIS and VIIRS. With the launch of the Advanced Himawari Imager (AHI) and the Advanced Baseline Imagers (ABIs) into geosynchronous orbits, we have the new ability to expand temporal coverage of the traditional aerosol optical depth (AOD) to resolve the diurnal signature of aerosol loading during daylight hours. The Korean–United States Air Quality (KORUS-AQ) campaign taking place in and around the Korean peninsula during May–June 2016 initiated a special processing of full-disk AHI observations that allowed us to make a preliminary adoption of Dark Target aerosol algorithms to the wavelengths and resolutions of AHI. Here, we describe the adaptation and show retrieval results from AHI for this 2-month period. The AHI-retrieved AOD is collocated in time and space with existing AErosol RObotic NETwork stations across Asia and with collocated Terra and Aqua MODIS retrievals. The new AHI AOD product matches AERONET, and the standard MODIS product does as well, and the agreement between AHI and MODIS retrieved AOD is excellent, as can be expected by maintaining consistency in algorithm architecture and most algorithm assumptions. Furthermore, we show that the new product approximates the AERONET-observed diurnal signature. Examining the diurnal patterns of the new AHI AOD product we find specific areas over land where the diurnal signal is spatially cohesive. For example, in Bangladesh the AOD increases by 0.50 from morning to evening, and in northeast China the AOD decreases by 0.25. However, over open ocean the observed diurnal cycle is driven by two artifacts, one associated with solar zenith angles greater than 70∘ that may be caused by a radiative transfer model that does not properly represent the spherical Earth and the other artifact associated with the fringes of the 40∘ glint angle mask. This opportunity during KORUS-AQ provides encouragement to move towards an operational Dark Target algorithm for AHI. Future work will need to re-examine masking including snow mask, re-evaluate assumed aerosol models for geosynchronous geometry, address the artifacts over the ocean, and investigate size parameter retrieval from the over-ocean algorithm.

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“…This may be because only a maximum of 11 pixels remain in the deselection window, making the product noisier than that at 10 km. Yang et al (2018) conducted a preliminary investigation of an AOD product at 1 km resolution using the geostationary Advanced Himawari Imager (AHI) satellite, based on the DT algorithm, with results showing some overestimation compared to AERONET data, with a correlation coefficient of 0.83 and RMSE of 0.11. Due to the recent availability of AHI, the AOD retrievals could not be thoroughly evaluated but are considered promising.…”

Section: Monitoring Air Quality In Urban Areasmentioning

confidence: 99%

Urban Pollution

et al. 2021

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This chapter depicts the state of the art in remote sensing for urban pollution monitoring, including urban heat islands, urban air quality, and water quality around urban coastlines. Recent developments in spatial and temporal resolutions of modern sensors, and in retrieval methodologies and gap-filling routines, have increased the applicability of remote sensing for urban areas. However, capturing the spatial heterogeneity of urban areas is still challenging, given the spatial resolution limitations of aerosol retrieval algorithms for air-quality monitoring, and of modern thermal sensors for urban heat island analysis. For urban coastal applications, water-quality parameters can now be retrieved with adequate spatial and temporal detail even for localized phenomena such as algal blooms, pollution plumes, and point pollution sources. The chapter reviews the main sensors used, and developments in retrieval algorithms. For urban air quality the MODIS Dark Target (DT), Deep Blue (DB), and the merged DT/DB algorithms are evaluated. For urban heat island and urban climatic analysis using coarse- and medium- resolution thermal sensors, MODIS, Landsat, and ASTER are evaluated. For water-quality monitoring, medium spatial resolution sensors including Landsat, HJ1A/B, and Sentinel 2, are evaluated as potential replacements for expensive routine ship-borne monitoring.

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Preliminary Investigation of a New AHI Aerosol Optical Depth (AOD) Retrieval Algorithm and Evaluation with Multiple Source AOD Measurements in China

Cited by 33 publications

References 48 publications

Evaluation of JAXA Himawari-8-AHI Level-3 Aerosol Products over Eastern China

Evaluation of JAXA Himawari-8-AHI Level-3 Aerosol Products over Eastern China

Applying the Dark Target aerosol algorithm with Advanced Himawari Imager observations during the KORUS-AQ field campaign

Urban Pollution

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