Development of a new dust index NDLI for Asian dust extraction system based on Aqua MODIS data and monitoring of trans-boundary Asian dust events in Japan

A, Kalpoma Kazi; Nagatani, Izumi; Kawano, K.; Kudoh, J.

doi:10.1080/01431161.2018.1524170

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…In particular, a lot of remarkable progress has been made in the remote sensing monitoring of dust events. For example, a new dust index based on Moderate Resolution Imaging Spectroradiometer (MODIS) data has been proposed and evaluated based on ground observations in different locations in Japan, which could help to understand these Asian dust events better [22]. Based on the MODIS data from 2002 to 2011, dust storm detections in Saudi Arabia were studied [23].…”

Section: Introductionmentioning

confidence: 99%

Detection of a Dust Storm in 2020 by a Multi-Observation Platform over the Northwest China

Yang

Huang

et al. 2021

Remote Sensing

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Dust storms have occurred frequently in northwest China and can dramatically reduce visibility and exacerbate air quality in downwind regions through long-range transport. In order to study the distribution characteristics of dust particles sizes, structures and concentrations in the process of dust storm, especially for the vertical distributions, the multi-observation platform composed of six Lidars and nine aerosol analytical instruments is first used to detect a severe dust storm event, which occurred in Northwest China on 3 May 2020. As a strong weather system process, the dust storm has achieved high intensity and wide range. When the intensity of a dust storm is at its strongest, the ratios of PM2.5 (particulate matter with diameter < 2.5 µm) and PM10 (particulate matter with diameter < 10 µm) (PM2.5/PM10) in cities examined were less than 0.2 and the extinction coefficients became greater than 1 km−1 based on Lidar observations. In addition, the growth rates of PM2.5 were higher than that of PM10. The dust particles mainly concentrated at heights of 2 km, after being transported about 200–300 km, vertical height increased by 1–2 km. Meanwhile, the dust concentration decreased markedly. Furthermore, the depolarization ratio showed that dust in the Tengger Desert was dominated by spherical particles. The linear relationships between 532 nm extinction coefficient and the concentration of PM2.5 and PM10 were found firstly and their R2 were 0.706 to 0.987. Our results could give more information for the physical schemes to simulate dust storms in specific models, which could improve the forecast of dust storms.

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Section: Introductionmentioning

confidence: 99%

Detection of a Dust Storm in 2020 by a Multi-Observation Platform over the Northwest China

Yang

Huang

et al. 2021

Remote Sensing

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“…The brightness temperature difference (BTD) (Ackerman, 1997;Prata, 1989) and Normalized Dust Difference Index (NDDI) (Qu et al, 2006) are two major dust detection algorithms that utilize the spectral signature in the thermal and visible to near-infrared regions, respectively. Enhanced methods are developed to employ the merits of BTDs and NDDI including the Brightness Temperature Adjusted Dust Index (BADI) (Yue et al, 2017); Three-band Volcanic Ash Product (TVAP) (Ellrod et al, 2003); Normalized Dust Layer Index (NDLI) (Kazi A et al, 2019). Other dust indices include the Thermal-infrared Dust Index (TDI) (Hao and Qu, 2007), MEDI (Karimi et al, 2012), and D-parameter (Roskovensky and Liou, 2003).…”

Section: Dust Spectral Index Methodsmentioning

confidence: 99%

“…BT11 further decreases as dust layer rises, thus, results in more negative BTD11-12. Generally, BTD11-12 is sensitive to high-density and highaltitude dust storms which can lead to more negative BTD11-12, while being less sensitive to low-density or low-altitude dust cases (Kazi A et al, 2019). BTD11-12 is near zero for most underlying surfaces (except for bright surfaces, e.g., deserts).…”

Section: Brightness Temperature Differencementioning

confidence: 99%

“…Geostationary satellites allow continuous observations, hence being capable of monitoring the formation and transportation of dust storms. MODIS has been extensively used in dust storm studies in previous decades because of its higher spatial resolution and spectral resolution (Baddock et al, 2009;Butt and Mashat, 2018b;Filonchyk et al, 2018;Kazi A et al, 2018;Liu et al, 2013;Park et al, 2014;Prachi and Pravin, 2014;Roskovensky and Liou, 2005;Samadi et al, 2014;Su et al, 2017;Xu et al, 2011a;Yue et al, 2017;Zhao et al, 2010). However, dust storms have a short lifetime (sometimes even last within hours) but large spatial coverage.…”

Section: Satellites and Sensorsmentioning

confidence: 99%

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Review of dust storm detection algorithms for multispectral satellite sensors

Wong

Lee

et al. 2021

Atmospheric Research

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“…Most of the studies focused on assessing the spatial-temporal distribution of SDS and atmospheric monitoring [17,18]. They used national observed SDS records or satellite images [19][20][21][22][23], and identified the relationship between surface features, such as land cover types, vegetation states, soil types, etc. and climate conditions, such as precipitation, land surface temperature, etc.…”

Section: Introductionmentioning

confidence: 99%

Applicability Analysis of Vegetation Condition and Dryness for Sand and Dust Storm (SDS) Risk Reduction in SDS Source and Receptor Region

et al. 2020

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Central Asian countries, which are included the Mid-Latitude Region (MLR), need to develop regional adaptive strategies for reducing Sand and Dust Storm (SDS)-induced negative damages based on adequate information and data. To overcome current limitation about data and assessment approaches in this region, the macroscale verified methodologies were required. Therefore, this study analyzed environmental conditions based on the SDS impacts and regional differences of SDS sources and receptors to support regional SDS adaptation plans. This study aims to identify environmental conditions based on the phased SDS impact and regional differences of SDS source and receptor to support regional adaptation plans in MLR. The Normalized Difference Vegetation Index (NDVI), Aridity Index (AI), and SDS frequency were calculated based on satellite images and observed meteorological data. The relationship among SDS frequency, vegetation, and dryness was determined by performing statistical analysis. In order to reflect phased SDS impact and regional differences, SDS frequency was classified into five classes, and representative study areas were selected by dividing source and receptor in Central Asia and East Asia. The spatial analysis was performed to characterize the effect of phased SDS impact and regional distribution differences pattern of NDVI and AI. The result revealed that vegetation condition was negatively correlated with the SDS frequency, while dryness and the SDS frequency were positively correlated. In particular, the range of dryness and vegetation was related to the SDS frequency class and regional difference based on spatial analysis. Overall, the Aral Sea and the Caspian Sea can be considered as an active source of SDS in Central Asia, and the regions were likely to expand into potential SDS risk areas compared to East Asia. This study presents the possibility of potential SDS risk area using continuously monitored vegetation and dryness index, and aids in decision-making which prioritizes vegetation restoration to prevent SDS damages with the macrolevel approach in the MLR perspective.

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Development of a new dust index NDLI for Asian dust extraction system based on Aqua MODIS data and monitoring of trans-boundary Asian dust events in Japan

Cited by 7 publications

References 17 publications

Detection of a Dust Storm in 2020 by a Multi-Observation Platform over the Northwest China

Detection of a Dust Storm in 2020 by a Multi-Observation Platform over the Northwest China

Review of dust storm detection algorithms for multispectral satellite sensors

Applicability Analysis of Vegetation Condition and Dryness for Sand and Dust Storm (SDS) Risk Reduction in SDS Source and Receptor Region

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