High Temperal Resolution AOD Retrieval of Northern China in 2014 Winter Based on Geostationary Satellite Remote Sensing Data

Abstract:

Observations from satellite can provide large region, fast and dynamic monitoring of aerosol properties. Polar Satellites provide once a day of observations at most, which is difficult to monitor aerosol temporal variabilities clearly. Only geostationary orbit satellites have the ability to provide both high temporal and spatial resolution observations.

The Korea Geostationary Ocean Color Imager (GOCI) onboard COMs-1 (Communication、Ocean & Meteorological Satellite-1) mainly designed for ocean observation, but it has a good potential for land monitoring. Cross calibration between GOCI and the US Moderate Resolution Imaging Spectrometer (MODIS) can improve the land radiation characteristics of GOCI, which can expand its ability in land observation.Cross calibration results show that the simulated TOA (Top Of Atmosphere) radiance from MODIS and GOCI measured TOA radiance agrees well.

The geostationary orbit satellite observing characteristics of the nearly constant view geometry and the high temporal resolution were used in aerosol retrieval algorithm. For images of two adjacent time points, the difference of TOA radiance mostly comes from the change caused by aerosol. AOD retrievals were accomplished using a Look-Up Table (LUT) strategy with assumptions of quickly varied aerosol and slowly varied surface with time. The AOD retrieval algorithm calculates AOD by minimizing the surface reflectance variations of series observations in a short period of time, e.g. several days. GOCI data from January 1, 2014 to April 1, 2014 were used to retrieve AOD, when the haze was very heavy. The monitoring of hourly AOD variations were implemented during this period and the retrieved AOD agrees well with AREONET (AErosol RObotic NETwork) ground-based measurements. The result was also compared with MODIS AOD products.

In conclusion, GOCI was calibrated using MODIS data firstly in order to improve the radiation characteristics of land; then, the AOD retrieval algorithm was developed based on time series GOCI data; third, the AOD retrieval algorithm was developed to retrieve AOD synchronously.In this paper, the GOCI data are used to retrieve AOD with higher temporal resolution, which is important to atmospheric environmental monitoring.