Abstract
The four wide-field-of-view (WFV) cameras aboard the GaoFen-1 (GF-1) satellite launched by China in April 2013 have been applied to the studies of the atmospheric environment. To highlight the advantages of GF-1 data in the atmospheric environment monitoring, an improved deep blue (DB) algorithm using only four bands (visible–near infrared) of GF-1/WFV was adopted to retrieve the aerosol optical depth (AOD) at ~500 m resolution in this paper. An optimal reflectivity technique (ORT) method was proposed to construct monthly land surface reflectance (LSR) dataset through converting from MODIS LSR product according to the WFV and MODIS spectral response functions to make the relationship more suitable for GF-1/WFV. There is a good spatial coincidence between our retrieved GF-1/WFV AOD results and MODIS/Terra or Himawari-8/AHI AOD products at 550 nm, but GF-1/WFV AOD with higher resolution can better characterized the details of regional pollution. Additionally, our retrieved GF-1/WFV AOD (2016–2019) results showed a good agreement with AERONET ground-based AOD measurements, especially, at low levels of AOD. Based on the same LSR dataset transmitted from 2016–2018 MODIS LSR products, RORT of 2016–2018 and 2019 GF-1/WFV AOD retrievals can reach up to 0.88 and 0.94, respectively, while both of RMSEORT are smaller than 0.13. It is indicated that using the ORT method to deal with LSR information can make GF-1/WFV AOD retrieval algorithm more suitable and flexible.
Highlights
A significant portion of aerosols in the atmosphere sources from anthropogenic activities, including industrial processes, fossil fuel combustion, agricultural operation, construction and mining
The main purpose of this paper is to verify that the aerosol optical depth (AOD) inversion based on a new cloud screening method and land surface reflectance (LSR) database can be more suitable for BTH region
optimal reflectivity technique (ORT) LSR results were firstly compared with the minimum reflectivity technique (MRT) LSR data
Summary
A significant portion of aerosols in the atmosphere sources from anthropogenic activities, including industrial processes, fossil fuel combustion, agricultural operation, construction and mining. China suffered from frequently severe pollution events, especially in winter, which had strong impact on the air environment, climate change, and public health [1,2,3,4]. Due to the heterogeneous distribution of sources, short lifetime, and episodic features of emission events, aerosols exhibit high spatiotemporal variability which can hardly be characterized by the sparsely ground-based measurements. Aerosol optical depth (AOD) retrieved from satellite data has been increasingly used to estimate the surface-level particle concentrations or the air pollution level [5]. Characterization, retrieval algorithm, pixel selection, cloud and other masking) in satellite. AOD retrievals, many relevant studies still been continually developed [6,7,8,9,10]. Dark target (DT) and deep blue (DB) algorithms have been successfully applied to the Moderate
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