Net Radiation Estimated from the FY-2D Data over the Source Region of the Yellow River

Numerous studies have developed algorithms for estimating the net radiation by satellite remote sensing data obtained under clear sky conditions using polar orbiting meteorological satellite. However, estimating net radiation under cloudy sky conditions using geostationary meteorological satellites with remote sensing sensors remains a significant challenge. In this paper, we developed algorithms to estimate net radiation through the day under all cloud covered conditions using the data from the visible and infrared spin scan radiometer, which is onboard the Chinese geostationary meteorological satellite. The geostationary sensor can be utilized to regularly generate temporally consistent top-of-atmosphere (TOA) and the temperature of brightness blackbody, both at hourly scales because of its frequent temporal sampling (at 1 hour interval). Under the clear sky condition, FengYun-2D (FY2D) data are used to derive the hourly net radiation. For cloudy case, the transmission coefficient is calculated using TOA reflectance and the attenuation of solar radiation in the atmosphere. Then, amounts of solar radiation under different atmospheric and cloud covered conditions are recorded. The methodology is applied over the source of the Yellow River on September 2009. Compared with groundbased measurements, the root mean square errors of the net radiation estimated under clear and cloudy conditions using the FY-2D data are 27.0W·m-2 and 38.0W·m-2, respectively. The proposed methodology can rely exclusively on remote sensing data in the absence of ancillary ground observations; thus, it can potentially estimate the surface energy budget regionally.