Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1339-1345, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1339-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1339-1345, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1339-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

QUANTIFYING THE CONTRIBUTIONS OF ENVIRONMENTAL PARAMETERS TO CERES SURFACE NET RADIATION ERROR IN CHINA

X. Pan1, Y. Yang1, Y. Liu2, X. Fan2, L. Shan1, and X. Zhang1 X. Pan et al.
  • 1School of Earth Science and Engineering, Hohai University, Nanjing 211100, China
  • 2Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China

Keywords: CERES, surface net radiation, China, environmental parameters, error contributions

Abstract. Error source analyses are critical for the satellite-retrieved surface net radiation (Rn) products. In this study, we evaluate the Rn error sources in the Clouds and the Earth’s Radiant Energy System (CERES) project at 43 sites from July in 2007 to December in 2007 in China. The results show that cloud fraction (CF), land surface temperature (LST), atmospheric temperature (AT) and algorithm error dominate the Rn error, with error contributions of ~-20, ~15, ~10 and ~10 W/m2 (net shortwave (NSW)/longwave (NLW) radiation), respectively. For NSW, the dominant error source is algorithm error (more than 10 W/m2), particularly in spring and summer with abundant cloud. For NLW, due to the high sensitivity of algorithm and large LST/CF error, LST and CF are the largest error sources, especially in northern China. The AT influences the NLW error large in southern China because of the large AT error in there. The total precipitable water has weak influence on Rn error even with the high sensitivity of algorithm. In order to improve Rn quality, CF and LST (AT) error in northern (southern) China should be decreased.