Abstract
The Medium-Resolution Spectral Imager (MERSI) is one of the major payloads of China’s second-generation polar-orbiting meteorological satellite, FengYun-3 (FY-3), and it is similar to the Moderate-Resolution Imaging Spectroradiometer (MODIS). The MERSI data are suitable for mapping terrestrial, atmospheric and oceanographic variables at continental to global scales. This study presents a direct-estimation method to retrieve surface shortwave net radiation (SSNR) data from MERSI top-of-atmosphere (TOA) reflectance and cloud mask products. This study is the first attempt to use the MERSI to retrieve SSNR data. Several critical issues concerning remote sensing of SSNR were investigated, including scale effects in validating SSNR data, impacts of the MERSI calibration update on the estimation of SSNR and the dependency of the retrieval accuracy of SSNR data on view geometry. We also incorporated data from twin MODIS sensors to assess how time and the number of satellite overpasses affect the retrieval of SSNR data. Validation against one-year data over seven Surface Radiation Budget Network (SURFRAD) stations showed that the presented algorithm estimated daily SSNR at the original resolution of the MERSI with a root mean square error (RMSE) of 41.9 W/m2 and a bias of −1.6 W/m2. Aggregated to a spatial resolution of 161 km, the RMSE of MERSI retrievals can be reduced by approximately 10 W/m2. Combined with MODIS data, the RMSE of daily SSNR estimation can be further reduced to 22.2 W/m2. Compared with that of daily SSNR, estimation of monthly SSNR is less affected by the number of satellite overpasses per day. The RMSE of monthly SSNR from a single MERSI sensor is as small as 13.5 W/m2.
Highlights
Surface shortwave net radiation (SSNR), calculated as the difference between surface incident shortwave radiation and the amount of radiation reflected back into the atmosphere by the surface, is a function of both atmospheric and surface properties, in addition to solar elevation angle [1,2]
Several critical issues on remote sensing of SSNR were investigated in the study, including scale effects in validating SSNR, the impacts of Medium-Resolution Spectral Imager (MERSI)’s calibration update on estimating high level products and the dependency of the retrieval accuracy of SSNR on view geometry
41.9 W/m2 (30.0%) and a small negative bias of −1.6 W/m2 from the MERSI data (Figure 2). This accuracy is comparable to the result of an early study that used Moderate-Resolution Imaging Spectroradiometer (MODIS) (41.0 W/m2; [17]), where the atmospheric absorption band was not used and the effects of water vapor absorption were corrected with external data of water vapor concentration
Summary
Surface shortwave net radiation (SSNR), calculated as the difference between surface incident shortwave radiation and the amount of radiation reflected back into the atmosphere by the surface, is a function of both atmospheric and surface properties, in addition to solar elevation angle [1,2]. The major drawback of this method is that it fails to consider the intra-daily variations of atmospheric conditions, such as aerosol loading, cloud coverage and water vapor concentration. They further refined the algorithm by considering variations in view angles and improving simulations of radiative transfer with more representative aerosol and cloud types [8]. Similar to NASA’s MODIS, the Medium-Resolution Spectral Imager (MERSI) is a moderate-resolution multispectral radiometer operated by the China Meteorological Administration [9] It is one of the major payloads of the FengYun (FY)-3 series, China’s polar-orbiting meteorological satellites. We adopted the direct-estimation method for MODIS data to retrieve daily SSNR from MERSI observations.
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