Abstract
Changes in surface net radiation Rn control the earth’s climate, the hydrological cycle, and plant photosynthesis. However, Rn is not readily available. This study develops a method to estimate surface daytime Rn from solar shortwave radiation measurements as well as conventional meteorological observations (or satellite retrievals) including daily minimum temperature, daily temperature range, and relative humidity, and vegetation indices from satellite data. Measurements collected at 22 U.S. and 2 Tibetan Plateau, China, sites from 2000 to 2006 are used to develop and validate the method. Land cover types include desert, semidesert, croplands, grasslands, and forest. Site elevations range from 98 to 4700 m. The results show that the method estimates Rn under clear and cloudy conditions accurately over a range of land cover types, elevations, and climates without requiring local calibration. The results show that the method estimates Rn accurately. The bias varies from −7.8 to 9.7 W m−2 (±3% in relative value) for different sites, and the root-mean-square error ranges from 12.8 to 21 W m−2 (from +5% to +9% in relative value) for different sites, with an average of 16.9 W m−2 (+6% relative) for all sites. The correlation coefficient for all sites is about 0.99. The correlation coefficient between the measured and predicted annual anomaly (year average subtracted from multiyear average) in daytime Rn is as high as 0.91, demonstrating that the method accurately estimates long-term variation in Rn.
Corresponding author address: Kaicun Wang, Department of Geography, University of Maryland, College Park, College Park, MD 20742. Email: kcwang@umd.edu
