Retrieval of Aerosol Optical Depth in the Arid or Semiarid Region of Northern Xinjiang, China

Abstract

Satellite remote sensing has been widely used to retrieve aerosol optical depth (AOD), which is an indicator of air quality as well as radiative forcing. The dark target (DT) algorithm is applied to low reflectance areas, such as dense vegetation, and the deep blue (DB) algorithm is adopted for bright-reflecting regions. However, both DT and DB algorithms ignore the effect of surface bidirectional reflectance. This paper provides a method for AOD retrieval in arid or semiarid areas, in which the key points are the accurate estimation of surface reflectance and reasonable assumptions of the aerosol model. To reduce the uncertainty in surface reflectance, a minimum land surface reflectance database at the spatial resolution of 500 m for each month was constructed based on the moderate-resolution imaging spectroradiometer (MODIS) surface reflectance product. Furthermore, a bidirectional reflectance distribution function (BRDF) correction model was adopted to compensate for the effect of surface reflectance anisotropy. The aerosol parameters, including AOD, single scattering albedo, asymmetric factor, Ångström exponent and complex refractive index, are determined based on the observation of two sunphotometers installed in northern Xinjiang from July to August 2014. The AOD retrieved from the MODIS images was validated with ground-based measurements and the Terra-MODIS aerosol product (MOD04). The 500 m AOD retrieved from the MODIS showed high consistency with ground-based AOD measurements, with an average correlation coefficient of~0.928, root mean square error (RMSE) of~0.042, mean absolute error (MAE) of~0.032, and the percentage falling within the expected error (EE) of the collocations is higher than that for the MOD04 DB product. The results demonstrate that the new AOD algorithm is more suitable to represent aerosol conditions over Xinjiang than the DB standard product.

Validation and Comparison of MODIS C6.1 and C6 Aerosol Products over Beijing, China

“…As AERONET provides a point measurement that repeats frequently over time, while satellites provide a snapshot of a larger region at a single time, in order to take both the spatial and temporal variabilities of aerosol distribution into account, the AERONET measurements and the MODIS retrievals need to be co-located in time and space. Previous studies have used the average MODIS AOD in a window of a few pixels (such as three or five pixels) to match the AERONET measured AOD, averaged within 30 min of the satellite overpass [22][23][24][25]. This method can mitigate the effect of variability in the underlying aerosol field and increase the number of matched AODs for those sites without long-term measurements.…”

PM2.5 ∕ PM10 ratio prediction based on a long short-term memory neural network in Wuhan, China

“…Wuhan is the provincial capital of Hubei Province. The administrative extent is between 113.683 and 115.083 • E and 29.967 and 31.367 • N, and the total area is 8494.41 km 2 (Zhou and Chen, 2018). The largest distance is between the eastern and western parts of Wuhan and is 134 km, and the maximum distance from north to south is 155 km.…”

“…The Dark Blue AOD retrieval method was used to complement the Dark Target results by retrieving the AOD over bright arid land surfaces, such as deserts (Sayer et al, 2013). In addition, a new method that considers bidirectional reflectance of the surface was proposed, which is suitable for calculating the AOD in arid or semiarid regions (Xinpeng et al, 2018).…”

Air pollution in the Gobi Desert region: Analysis of dust‐storm events

“…It extends 1,600 km from southwest to northeast and 800 km from north to south. The region experiences sandstorms every spring, usually originating in the Tarim Basin and the Gobi Desert itself (She et al ., 2018; Tian et al ., 2018; Maki et al ., 2019; Meng et al ., 2019; Yu et al ., 2019; Filonchyk et al ., 2020b). Intensive weather fronts, which occur in spring, contribute to sandstorm formation.…”