Four Wide-Field-Viewing（WFV） cameras are taken onboard the GF-1 satellite

which is a newly launched earth-observing satellite from China. The satellite is employed to monitor land use

environmental parameters

and agriculture

among others. However

a highaccuracy Atmospheric Correction（AC） algorithm is imperative to process the GF-1 WFV data for quantitative applications. The key problems in the AC of WFV cameras include the following: large amount of data

lack of auxiliary data

and aerosol and molecular variations. In the paper

an AC algorithm for GF-1 WFV data is introduced. Based on radiance transfer theory

the fast AC algorithm for WFV data was established as follows:（1） The radiometric calibration was completed in four seasons by cross-calibration method using Landsat 8 data. The apparent reflectance in all four bands of the WFV camera was received at the solar zenith angle

and the solar irradiance was obtained at the top of atmosphere.（2） The sun and viewing zenith angles were calculated at 1km resolution with the use of the auxiliary WFV data

including projection information

satellite passed time

view zenith angle at nadir

and pixel position.（3） Rayleigh scattering was corrected for each pixel in an image with the use of altitude data and the second simulation of the satellite signal in the solar spectrum（6S） in the same view geometry.（4） Aerosol Optical Depth（AOD） was derived from the apparent reflectance in the blue band by the deep blue algorithm at 10 km resolution with the use of MODIS 8-day surface reflectance product.（5） In every 10 km ×10 km block of WFV image

the retrieved AOD was inputted into the6 S

and the three atmospheric parameters were determined. Then

from the apparent reflectance in the four bands

the surface reflectance in the four bands was retrieved using the atmospheric parameters in every block. After all the blocks were processed

the AC of the WFV image was completed. The AC module for GF-1 WFV data was developed using interactive data language and our AC algorithm. Three GF-1 WFV images over North China Plain acquired on September 27

2013

March 13

2015 and May 25

2015 were selected to conduct the experiments that will verify the performance of our AC algorithm and module. The results show that our algorithm has significantly removed the atmospheric influences

including molecular and aerosol scattering and absorption. However

if the aerosol layer is thick

the influence of the atmosphere cannot be completely removed. From these images

we select three typical surfaces for further study

including vegetation

soil

and urban.Then

the reflectance after AC is compared with that before AC. The reflectance after AC was close to the spectrum of these surfaces

and the corrected normalized difference vegetation index reflects the character of the typical surface. In this paper

a new AC algorithm based on the aerosol retrieved from the deep blue algorithm was built for GF-1 WFV data. The scattering and absorption of molecules and aerosols in the GF-1 WFV data were well corrected using the proposed algorithm

which also allowed for the rapid acquisition of surface reflectance. However

our algorithm may still be improved in terms of robustness against high-concentration aerosol

such as haze

and against adjacency effect over non uniform surface.