Abstract
Aerosol Optical Depth (AOD) is crucial for urban air quality assessment. However, the frequently used moderate-resolution imaging spectroradiometer (MODIS) AOD product at 10 km resolution is too coarse to be applied in a regional-scale study. Gaofen-1 (GF-1) wide-field-of-view (WFV) camera data, with high spatial and temporal resolution, has great potential in estimation of AOD. Due to the lack of shortwave infrared (SWIR) band and complex surface reflectivity brought from high spatial resolution, it is difficult to retrieve AOD from GF-1 WFV data with traditional methods. In this paper, we propose an improved AOD retrieval algorithm for GF-1 WFV data. The retrieved AOD has a spatial resolution of 160 m and covers all land surface types. Significant improvements in the algorithm include: (1) adopting an improved clear sky composite method by using the MODIS AOD product to identify the clearest days and correct the background atmospheric effect; and (2) obtaining local aerosol models from long-term CIMEL sun-photometer measurements. Validation against MODIS AOD and ground measurements showed that the GF-1 WFV AOD has a good relationship with MODIS AOD (R2 = 0.66; RMSE = 0.27) and ground measurements (R2 = 0.80; RMSE = 0.25). Nevertheless, the proposed algorithm was found to overestimate AOD in some cases, which will need to be improved upon in future research.
Highlights
As an important component of atmosphere, aerosols play a vital role in climate change, earth radiation budget and air quality [1,2,3]
To fill in the blank of high resolution Aerosol Optical Depth (AOD) for regional air quality studies, we proposed an improved approach to retrieve AOD from Gaofen-1 Wide-Field-of-View (GF-1 WFV) data
The improved techniques used in the algorithm include: (1) Seasonal aerosol model over Wuhan was obtained and introduced in 6S model based on long-term ground measurements; (2) The moderate-resolution imaging spectroradiometer (MODIS) AOD product was introduced to support the image selection and background aerosol correction in the establishment of seasonal surface reflectance database for GF-1
Summary
As an important component of atmosphere, aerosols play a vital role in climate change, earth radiation budget and air quality [1,2,3]. The DT method shows a good performance over dark surfaces (e.g., dense vegetation), but tends to overestimate AOD over bright surfaces (especially urbanized areas) [22,23]. Another widely adopted technique is called the Deep. Valid and high resolution AOD retrieval covering all land surface types (including urban areas) is very important for regional air quality monitoring in China. High spatial resolution sensors such as the Landsat series usually have a long re-visiting period (16 days), which makes it more difficult in terms of algorithm design. The performance and limitations of the algorithm were discussed
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