Sciences in Cold and Arid Regions ›› 2016, Vol. 8 ›› Issue (5): 441-449.doi: 10.3724/SP.J.1226.2016.00441

Previous Articles    

The efficacy of Kriging spatial interpolation for filling temporal gaps in daily air temperature data: A case study in northeast China

YanLin Zhang1, XiaoLi Chang1,2, Ji Liang1, DongLiang Luo2, RuiXia He2   

  1. 1. National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China;
    2. State Key Laboratory of Frozen Soils Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
  • Received:2016-04-11 Revised:2016-06-21 Published:2018-11-23
  • Contact: YanLin Zhang, lecturer of Hunan University of Science and Technology. No. 02, Taoyuan Road, Xiangtan, Hunan 411201, China.
  • Supported by:
    The authors are grateful to the anonymous reviewers for their comments and suggestions on the manuscript. This research was funded by the Chinese National Fund Projects (Nos. 41401028, 41201066) and by the State Key Laboratory of Frozen Soils Engineering (Project No. SKLFSE201201).

Abstract: Quality-controlled and serially complete daily air temperature data are essential to evaluating and modelling the influences of climate change on the permafrost in cold regions. Due to malfunctions and location changes of observing stations, temporal gaps (i.e., missing data) are common in collected datasets. The objective of this study was to assess the efficacy of Kriging spatial interpolation for estimating missing data to fill the temporal gaps in daily air temperature data in northeast China. A cross-validation experiment was conducted. Daily air temperature series from 1960 to 2012 at each station were estimated by using the universal Kriging (UK) and Kriging with an external drift (KED), as appropriate, as if all the observations at a given station were completely missing. The temporal and spatial variation patterns of estimation uncertainties were also checked. Results showed that Kriging spatial interpolation was generally desirable for estimating missing data in daily air temperature, and in this study KED performed slightly better than UK. At most stations the correlation coefficients (R2) between the observed and estimated daily series were >0.98, and root mean square errors (RMSEs) of the estimated daily mean (Tmean), maximum (Tmax), and minimum (Tmin) of air temperature were <3℃. However, the estimation quality was strongly affected by seasonality and had spatial variation. In general, estimation uncertainties were small in summer and large in winter. On average, the RMSE in winter was approximately 1℃ higher than that in summer. In addition, estimation uncertainties in mountainous areas with complex terrain were significantly larger than those in plain areas.

Key words: daily air temperature, gap filling, Kriging spatial interpolation, northeast China

Addink EA, 1999. A comparison of conventional and geostatistical methods to replace clouded pixels in noaa-avhrr images. International Journal of Remote Sensing, 20(5):961-977. DOI:10.1080/014311699213028.
Beck I, Ludwig R, Bernier M, et al., 2015. Assessing permafrost degradation and land cover changes (1986-2009) using remote sensing data over Umiujaq, sub-arctic Québec. Permafrost and Periglacial Processes, 26(2):129-141. DOI:10.1002/ppp.1839.
Bockheim JG, 2015. Global distribution of cryosols with mountain permafrost:An overview. Permafrost and Periglacial Processes, 26(1):1-12. DOI:10.1002/ppp.1830.
Chang XL, Jin HJ, He RX, et al., 2013. Review of permafrost monitoring in the northern Da Hinggan Mountains, Northeast China. Journal of Glaciology and Geocryology, 35(1):93-100.
Christensen N, Wood A, Voisin N, et al., 2004. The effects of climate change on the hydrology and water resources of the Colorado River Basin. Climatic Change, 62(1-3):337-363. DOI:10.1023/B:CLIM.0000013684.13621.1f.
Cressie N, 1990. The origins of kriging. Mathematical Geology, 22(3):239-252. DOI:10.1007/bf00889887.
Deutsch CV, Journel AG, 1998. GSLIB:Geostatistical Software and User's Guide, 2nd Edition. New York:Oxford University Press.
Diggle PJ, Ribeiro PJ, 2007. Model-based Geostatistics. New York:Springer.
Eischeid JK, Pasteris PA, Diaz HF, et al., 2000. Creating a serially complete, national daily time series of temperature and precipitation for the western United States. Journal of Applied Meteorology, 39(9):1580-1591. DOI:10.1175/1520-0450(2000)039<1580:CASCND>2.0.CO;2
Goovaerts P, 1997. Geostatistics for Natural Resources Evaluation (Applied Geostatistics). New York:Oxford University Press.
Guttman NB, 1991. January singularities in the northeast from a statistical viewpoint. Journal of Applied Meteorology, 30(3):358-367. DOI:10.1175/1520-0450(1991)030<0358:JSITNF>2.0.CO;2.
Hengl T, 2007. A Practical Guide to Geostatistical Mapping of Environmental Variables. Italy:European Communities.
Hiemstra PH, Pebesma EJ, Twenhofel CJW, et al., 2009. Real-time automatic interpolation of ambient gamma dose rates from the Dutch Radioactivity Monitoring Network. Computers & Geosciences, 35(8):1711-1721. DOI:10.1016/j.cageo.2008.10.011.
Jin H, Hao J, Chang X, et al., 2010. Zonation and assessment of frozen-ground conditions for engineering geology along the China-Russia crude oil pipeline route from Mo'he to Daqing, northeastern China. Cold Regions Science and Technology, 64(3):213-225. DOI:
Jin H, Luo D, Wang S, et al., 2011. Spatiotemporal variability of permafrost degradation on the Qinghai-Tibet Plateau. Sciences in Cold and Arid Regions, 3(4):281-305.
Jin H, Yu Q, Lü L, et al., 2007. Degradation of permafrost in the Xing'anling mountains, northeastern China. Permafrost and Periglacial Processes, 18(3):245-258. DOI:10.1002/ppp.589.
Kandasamy S, Baret F, Verger A, et al., 2012. A comparison of methods for smoothing and gap filling time series of remote sensing observations:Application to modis lai products. Biogeosciences Discuss, 9(12):17053-17097. DOI:10.5194/bgd-9-17053-2012.
Luo D, Jin HJ, Marchenko S, et al., 2014a. Distribution and changes of active layer thickness (alt) and soil temperature (ttop) in the source area of the Yellow River using the Gipl model. Science China Earth Sciences, 57(8):1834-1845. DOI:10.1007/s11430-014-4852-1.
Luo D, Jin H, Jin R, et al., 2014b. Spatiotemporal variations of climate warming in northern northeast China as indicated by freezing and thawing indices. Quaternary International, 349:187-195. DOI: 06.064.
Meng Q, Liu Z, Borders BE, 2013. Assessment of regression kriging for spatial interpolation-comparisons of seven GIS interpolation methods. Cartography and Geographic Information Science, 40(1):28-39. DOI:10.1080/15230406.2013.762138.
Rossi RE, Dungan JL, Beck LR, 1994. Kriging in the shadows:Geostatistical interpolation for remote sensing. Remote Sensing of Environment, 49(1):32-40. DOI:
Sannel ABK, Hugelius G, Jansson P, et al., 2016. Permafrost warming in a subarctic peatland-which meteorological controls are most important? Permafrost and Periglacial Processes, 27(2):177-188. DOI:10.1002/ppp.1862.
Serreze MC, Walsh JE, Chapin FS, et al., 2000. Observational evidence of recent change in the northern high-latitude environment. Climatic Change, 46(1):159-207. DOI:10.1023/a:1005504031923.
Stooksbury DE, Idso CD, Hubbard KG, 1999. The effects of data gaps on the calculated monthly mean maximum and minimum temperatures in the continental United States:A spatial and temporal study. Journal of Climate, 12(5):1524-1533. DOI:10.1175/1520-0442(1999)012<1524:TEODGO>2.0.CO;2.
Wackernagel H, 2003. Multivariate Geostatistics:An Introduction with Applications, 2nd Edition. Springer-Verlag.
Wei Z, Jin H, Zhang J, et al., 2011. Prediction of permafrost changes in northeastern China under a changing climate. Science China Earth Sciences, 54(6):924-935. DOI:10.1007/s11430-010-4109-6
Weiss DJ, Atkinson PM, Bhatt S, et al., 2014. An effective approach for gap-filling continental scale remotely sensed time-series. Isprs Journal of Photogrammetry and Remote Sensing, 98:106-118. DOI:
No related articles found!
Full text



No Suggested Reading articles found!