Evaluation of the Moderate Resolution Imaging Spectroradiometer aerosol products at two Aerosol Robotic Network stations in China

Mi, Wen; Li, Zhanqing; Xia, Xiangao; Holben, B. N.; Levy, Robert C.; Zhao, Fengsheng; Chen, Hongbin; Cribb, Maureen

doi:10.1029/2007jd008474

Cited by 95 publications

(71 citation statements)

References 27 publications

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“…Significantly, since 55% of C6 DTDB retrievals derived from C6 DT, they had a good consistency with ground-based observations, accompanied by the abundant matchups, the slopes and intercepts for both satellites being closed to 1 and 0, and more than 61% of data falling within EE envelope. As we all know, the quality of MODIS AOD retrievals is closely related to aerosol types, particle size and surface reflectivity, which all have strong seasonal cycles [32,39]. Therefore, we compared the monthly average MODIS AOD with ground-observed ones in different seasons in Figure 5a-h, which was similar to the daily-scale analysis.…”

Section: The Comparison Of Modis C5 and C6 Products At The Backgroundmentioning

confidence: 78%

“…Interestingly, when considering the aerosol type sorted by ground-base α (α < 0.5 and α > 1.5 represent coarse mode dominated and fine mode dominated aerosol types, respectively, according to Wang et al [10]), all the products we studied here had better performance in fine mode dominated condition (α > 1.5), with 78%~91% of data falling into EE envelope(figures not shown), which further proved the better performance of AOD products in autumn. As we all know, the quality of MODIS AOD retrievals is closely related to aerosol types, particle size and surface reflectivity, which all have strong seasonal cycles [32,39]. Therefore, we compared the monthly average MODIS AOD with ground-observed ones in different seasons in Figure 5a-h, which was similar to the daily-scale analysis.…”

Section: The Comparison Of Modis C5 and C6 Products At The Backgroundmentioning

confidence: 78%

“…The surfaces reflectance is a dynamic function related to regions, seasons, scattering angle and normalized difference vegetation index (NDVI). Additionally, there are five aerosol types over land, which consist of a mixture of fine and coarse models, determined by season and location [8,17,32]. The Deep Blue (DB) algorithm is initially developed to retrieve aerosol properties over bright surface in blue channels (0.412, 0.47 and 0.65 µm), where the surface reflectance is much smaller than that for longer channels.…”

Section: The Modis Productmentioning

confidence: 99%

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The Variations and Trends of MODIS C5 & C6 Products’ Errors in the Recent Decade over the Background and Urban Areas of North China

et al. 2016

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With ten-year (2004With ten-year ( -2013 ground-based observations of Beijing Forest (BJF) and Beijing City (BJC) sites in North China, we validated the high-quality MODerate resolution Imaging Spectroradiometer (MODIS) Collection 5 (C5) and Collection 6 (C6) Aerosol Optical Depth (AOD) products' precision and discussed the sensors degradation issues. The annual mean AOD and Angstrom exponent (α) were 0.20 ± 0.02 and 0.83 ± 0.15 in the background over the past ten years, and they were 0.59 ± 0.07 and 1.13 ± 0.08 in the urban, respectively. Ground-based AOD had both slightly declining trends, with variations of 0.023 and 0.057 over the past decade in the background and urban, respectively. There were large differences among the eight kinds of MODIS AOD products (Terra vs. Aqua, C5 vs. C6, DT (Deep Target) vs. DB (Deep Blue), and DTDB in the background and urban areas), but all the products' monthly errors had larger variations in the spring and summer, and smaller ones in the autumn and winter. In the background, more than 62% of DT matchups for C5 and C6 products were within NASA's expected error (EE) envelope. In the urban, 69%~72% of C6 DB retrievals were falling within EE envelope. The new dataset named C6 DTDB had better performance in the background, whereas it overestimated by 37%~41% in the urban caused by surface reflectivity estimation error. The range of monthly average error varied from −0.21 to 0.28 in the background and from −0.63 to 0.48 in the urban. From the background to the urban areas, the retrieval errors of Terra and Aqua had slightly increased by 0.0023~0.0158 and 0.0011~0.0124 per year, respectively, which implied that the two MODIS instruments had degraded slowly.

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Section: The Comparison Of Modis C5 and C6 Products At The Backgroundmentioning

confidence: 78%

Section: The Comparison Of Modis C5 and C6 Products At The Backgroundmentioning

confidence: 78%

Section: The Modis Productmentioning

confidence: 99%

See 1 more Smart Citation

The Variations and Trends of MODIS C5 & C6 Products’ Errors in the Recent Decade over the Background and Urban Areas of North China

et al. 2016

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“…It was chosen to deal with cities (specially megacities) that are spread across several MODIS pixels (e.g., New York). This scheme is unlikely to affect the AOD and α estimates since neighboring pixels have similar values over an urban area [44]. 2.…”

Section: Airrgb Decomposition and Visualizationmentioning

confidence: 99%

Novel Decomposition Scheme for Characterizing Urban Air Quality with MODIS

2017

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Urban air pollution is one of the most widespread global sustainability problems. Previous research has studied growth or fall of particulate matter (PM) levels using on-ground monitoring stations in urban regions. However, studying this worldwide is difficult because most cities do not have sufficient infrastructure to monitor air quality. Thus, satellite data is increasingly being employed to solve this limitation. In this paper, we use 16 years (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) of aerosol optical depth (AOD) and Angstrom exponent (α) datasets, retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer) sensors on the National Aeronautics and Space Administration's (NASA) Terra satellite to study air quality over 60 locations globally. We propose a novel technique, called AirRGB decomposition, to characterize urban air quality by decomposing AOD and α retrievals into 'components' of three distinct scenarios. In the AirRGB decomposition method, using AOD and α dataset three scenarios were investigated: 'R'-high α and high AOD, 'G'-high α and low AOD, and 'B'-low α and low AOD values. These scenarios were mapped and quantified over a triangular red, green and blue color scale. This visualization easily segregates regions having a high concentration of industrial aerosol from only natural aerosols. Our analysis indicates that a sharp divide exists between North American and European cities and Asian cities in terms of baseline pollution and slopes of R and G trends. We found that while pollution in cities in China has started to decrease (e.g., since 2011 for Beijing), it continues to increase in South Asia and Southeast Asia. e.g., R offset of Beijing and New Delhi was 54.98 and 50.43 respectively but R slope was −0.04 and 0.08 respectively. High offset (≥45) and slope (≥0.025) of B for New York, Tokyo, Sydney and Sao Paolo shows that they have clean air, which is still getting better.

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“…Satellite-derived AOD fills this vacancy with spatially continuous observations, providing better spatial and temporal coverage for regional assessments on a large scale [9]. AOD products from the Moderate Resolution Imaging Spectroradiometer (MODIS) have been widely used in aerosol-related studies, such as regional aerosol optical properties [10] and public environmental health [11], because of their assured quality [12][13][14]. Along with the MODIS Collection 6 (C6) satellite product released in 2014, the second generation of the Deep Blue AOD (AOD DB) retrieved algorithm, which is referred to as enhanced DB, was expanded to cover vegetated land surfaces, urban areas, and brighter deserts by dynamic surface reflectance determination [15,16].…”

Section: Introductionmentioning

confidence: 99%

Improving Spatial Coverage for Aqua MODIS AOD using NDVI-Based Multi-Temporal Regression Analysis

et al. 2017

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Abstract:The Moderate Resolution Imaging Spectroradiometer (MODIS) provides widespread Aerosol Optical Depth (AOD) datasets for climatological and environmental health research. Since MODIS AOD clearly lacks coverage in orbit-scanning gaps and cloud obscuration, some applications will benefit from data recovery using multi-temporal AOD. Aimed at qualitatively describing the relationship between multi-temporal AOD, AOD loadings and Normalized Difference Vegetation Index (NDVI) have been considered based on the mechanism of satellite AOD retrieval. Accordingly, the NDVI-based Weighted Linear Regression (NWLR) has been proposed to recover AOD by synthetically weighing AOD similarity, spatial proximity, and NDVI similarity. To evaluate the performance of AOD recovery, simulated experiments applying gap and window masks were conducted in South Asia and Beijing, respectively. The evaluation results demonstrated that the linear regression R 2 achieved 0.8 and the absolute relative errors remained steady. Further validation was conducted between the recovered and actual AODs using 56 Aerosol Robotic Network (AERONET) sites in East and South Asia from 2013 to 2015, which demonstrated that over 41% of recovered AODs fell within the expected error (EE) envelope. Additional validation conducted in South Asia and Beijing showed that recovery by NWLR did not expand satellite-derived AOD errors, and the accuracy of recovered AOD was consistent with the accuracy of the original Aqua MODIS Deep Blue (DB) AOD. The recovery results illustrated that AOD coverage was improved in most regions, especially in North China, Mongolia, and South Asia, which could provide better support in aerosol spatio-temporal analysis and aerosol data assimilation.

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Evaluation of the Moderate Resolution Imaging Spectroradiometer aerosol products at two Aerosol Robotic Network stations in China

Cited by 95 publications

References 27 publications

The Variations and Trends of MODIS C5 & C6 Products’ Errors in the Recent Decade over the Background and Urban Areas of North China

The Variations and Trends of MODIS C5 & C6 Products’ Errors in the Recent Decade over the Background and Urban Areas of North China

Novel Decomposition Scheme for Characterizing Urban Air Quality with MODIS

Improving Spatial Coverage for Aqua MODIS AOD using NDVI-Based Multi-Temporal Regression Analysis

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