Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 703-710, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-703-2018
© 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, 703-710, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-703-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

APPLICABILITY OF VARIOUS INTERPOLATION APPROACHES FOR HIGH RESOLUTION SPATIAL MAPPING OF CLIMATE DATA IN KOREA

A. Jo1, J. Ryu2, H. Chung3, Y. Choi3, and S. Jeon3 A. Jo et al.
  • 1Dept. of Lifescience, Korea University, Seoul, Korea
  • 2Environmental GIS/RS Center, Korea University, Seoul, Korea
  • 3Dept. of Environmental Science & Ecological Engineering, Korea University, Seoul, Korea

Keywords: Climate Change, Interpolation, IDW, Cokriging, Kriging, Precipitation, Temperature

Abstract. The purpose of this study is to create a new dataset of spatially interpolated monthly climate data for South Korea at high spatial resolution (approximately 30m) by performing various spatio-statistical interpolation and comparing with forecast LDAPS gridded climate data provided from Korea Meterological Administration (KMA). Automatic Weather System (AWS) and Automated Synoptic Observing System (ASOS) data in 2017 obtained from KMA were included for the spatial mapping of temperature and rainfall; instantaneous temperature and 1-hour accumulated precipitation at 09:00 am on 31th March, 21th June, 23th September, and 24th December. Among observation data, 80 percent of the total point (478) and remaining 120 points were used for interpolations and for quantification, respectively. With the training data and digital elevation model (DEM) with 30 m resolution, inverse distance weighting (IDW), co-kriging, and kriging were performed by using ArcGIS10.3.1 software and Python 3.6.4. Bias and root mean square were computed to compare prediction performance quantitatively. When statistical analysis was performed for each cluster using 20 % validation data, co kriging was more suitable for spatialization of instantaneous temperature than other interpolation method. On the other hand, IDW technique was appropriate for spatialization of precipitation.