Dust Aerosol Optical Depth Retrieval and Dust Storm Detection for Xinjiang Region Using Indian National Satellite Observations

Abstract

Abstract:The Xinjiang Uyghur Autonomous Region (Xinjiang) is located near the western border of China. Xinjiang has a high frequency of dust storms, especially in late winter and early spring. Geostationary satellite remote sensing offers an ideal way to monitor the regional distribution and intensity of dust storms, which can impact the regional climate. In this study observations from the Indian National Satellite (INSAT) 3D are used for dust storm detection in Xinjiang because of the frequent 30-min observations with six bands. An analysis of the optical properties of dust and its quantitative relationship with dust storms in Xinjiang is presented for dust events in April 2014. The Aerosol Optical Depth (AOD) derived using six predefined aerosol types shows great potential to identify dust events. Cross validation between INSAT-3D retrieved AOD and MODIS AOD shows a high coefficient of determination (R 2 = 0.92). Ground validation using AERONET (Aerosol Robotic Network) AOD also shows a good correlation with R 2 of 0.77. We combined the apparent reflectance (top-of-atmospheric reflectance) of visible and shortwave infrared bands, brightness temperature of infrared bands and retrieved AOD into a new Enhanced Dust Index (EDI). EDI reveals not only dust extent but also the intensity. EDI performed very well in measuring the intensity of dust storms between 22 and 24 April 2014. A visual comparison between EDI and Feng Yun-2E (FY-2E) Infrared Difference Dust Index (IDDI) also shows a high level of similarity. A good linear correlation (R 2 of 0.78) between EDI and visibility on the ground demonstrates good performance of EDI in estimating dust intensity. A simple threshold method was found to have a good performance in delineating the extent of the dust plumes but inadequate for providing information on dust plume intensity.

Dust Detection and Intensity Estimation Using Himawari-8/AHI Observation

“…Di et al [49] proposed an Enhanced Dust Index for dust detection over the Xinjiang region using Indian geostationary satellite-INSAT 3A/D IMGER data. The EDI is calculated using observations at VIS, NIR, and TIR channels, and AOD:…”

“…where A = 1, B = 10, and C = 0.1 and the AHI AOD retrieval is detailed in the following. These coefficients are determined based on Di et al [49] and adapted to AHI data by analysis of AHI dust and non-dust observations. Typical areas with dust, deserts, and non-desert land surface are selected as training samples.…”

“…Miller [15] introduced an index using multi-channels reflectance, and BT at 11 µm and 12 µm to separate dust from other objects. Di et al [49] developed an dust index, called the Enhanced Dust Index (EDI), which is based on aerosol optical depth (AOD), BTD (3)(4)(5)(6)(7)(8)(9)(10)(11), and the reflectance in 0.63 and 1.6 µm channels, and the dust index shows high consistency with visibility data from ground-based measurements.…”

Characterization of aerosol types over Lake Urmia Basin

“…They also studied the role of the lakes in modifying mineral aerosols [2]. Di et al (2016) used Indian National Satellite (INSAT) data to derive AOD. Their results showed that retrieved AOD can detect dust storms in Xinjiang region in China [3].…”

“…Di et al (2016) used Indian National Satellite (INSAT) data to derive AOD. Their results showed that retrieved AOD can detect dust storms in Xinjiang region in China [3]. Hsu et al (2004) retrieved aerosol properties over three areas (arid, semiarid, and urban) using surface reflectance from remote sensing data in visible and NIR (near infrared) wavelengths [4].…”