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기계학습 모형과 잔차 크리깅을 이용한 저해상도 위성기반강수 자료의 공간 상세화

Spatial Disaggregation of Coarse Scale Satellite-based Precipitation Data using Machine Learning Model and Residual Kriging

박노욱|김예슬
기후연구소 (KU Climate Research Institute)|2016.06|기후연구 (Journal of Climate Research)|제11권 제2호|pp. 183-195|DOI http://dx.doi.org/10.14383/cri.2016.11.2.183

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참고문헌

키워드

기계학습상세화machine learning크리깅krigingprecipitation강수disaggregation

초록

Abstract

A novel disaggregation model that combines a machine learning model and kriging of residuals is presented to map precipitation at a fine scale from coarse scale precipitation data. Random forest (RF) and fine scale auxiliary variables are used to estimate trend components at a fine scale. Residual components are then estimated by area-to-point residual kriging. A case study of spatial disaggregation of TRMM monthly precipitation data acquired over the Korean peninsula is carried out to illustrate the potential of the presented disaggregation method. From the evaluation results, the presented method outperformed the RF-based disaggregation method that only considers trend components and ignores residual components, in terms of accuracy statistics and the ability of coherent predictions. This case study indicates that accounting for residual components by applying a proper spatial prediction method such as area-to-point kriging is very important in spatial disaggregation of coarse scale spatial data, even though advanced regression models such as RF could have high goodness of fit for the quantification of relationships between a target attribute and auxiliary variables.

참고문헌 (31)

  1. Agam, N., "A vegetation index based technique for spatial sharpening of thermal imagery", Remote Sensing of Environment, 107(4), pp.545~558 (2007)
  2. Atkinson, P. M., "Downscaling in remote sensing", International Journal of Applied Earth Observation and Geoinformation, 22, pp.106~114 (2013)
  3. Atzberger, C., "Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs", Remote Sensing, 5(2), pp.949~981 (2013)
  4. Badas, M. G., "Orographic influences in rainfall downscaling", Advances in Geosciences, 2, pp.285~292 (2005)
  5. 백승균, "고해상도 기온 및 강수 분포도 작성을 위한 공동크리깅 적용성 평가", 기후연구, 6(3), pp.242~253 (2011), 기후연구소
  6. Bechtel, B., "Downscaling land surface temperature in an urban area: a case study for Hamburg", Germany, Remote Sensing, 4(10), pp.3184~3200 (2012)
  7. Breiman, L., "Random forests", Machine Learning, 45(1), pp.5~32 (2001)
  8. Brown, M., "Global phenological response to climate change in crop areas using satellite remote sensing of vegetation, humidity and temperature over 26years", Remote Sensing of Environment, 126, pp.174~183 (2012)
  9. Chen, F., "Spatial downscaling of TRMM 3B43 precipitation considering spatial heterogeneity", International Journal of Remote Sensing, 35(9), pp.3074~3093 (2014)
  10. Fang, J., "Spatial downscaling of TRMM precipitation data based on the orographical effect and meteorological conditions in a mountainous area", Advances in Water Resources, 61, pp.42~50 (2013)
  11. Goovaerts, P., "Geostatistics for Natural Resources Evaluation" (1997), Oxford University Press
  12. Goovaerts, P., "Kriging and semivariogram deconvolution in the presence of irregular geographical units", Mathematical Geosciences, 40(1), pp.101~128 (2008)
  13. Guan, H., "A clusteroptimizing regression-based approach for precipitation spatial downscaling in mountainous terrain", Journal of Hydrology, 375(3), pp.578~588 (2009)
  14. Huffman, G. J., "The TRMM multisatellite precipitation analysis (TMPA): quasiglobal, multiyear, combined-sensor precipitation estimates at fine scales", Journal of Hydrometeorology, 8(1), pp.38~55 (2007)
  15. Hutengs, C., "Downscaling land surface temperatures at regional scales with random forest regression", Remote Sensing of Environment, 178, pp.127~141 (2016)
  16. Iguchi, T., "Rain-profiling algorithm for the TRMM precipitation radar", Journal of Applied Meteorology, 39(12), pp.2038~2052 (2000)
  17. 장동호, "가변적 지역평균 단순 크리깅을 이용한 남한 지역 고해상도 기온 및 강수 분포도 작성 및 평가", 기후연구, 10(2), pp.165~182 (2015), 기후연구소[DOI]
  18. Jia, S., "A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China", Remote Sensing of Environment, 115(2), pp.3069~3079 (2011)
  19. Kummerow, C. D., "The tropical rainfall measuring mission(TRMM) sensor package", Journal of Atmospheric Ocean Technology, 15(3), pp.809~817 (1998)
  20. Kyriakidis, P. C., "A geostatistical framework for area to point spatial interpolation", Geographical Analysis, 36(3), pp.259~289 (2004)
  21. Latifovic, R., "Analysis of climate change impacts on lake ice phenology in Canada using the historical satellite data record", Remote Sensing of Environment, 106(4), pp.492~507 (2007)
  22. Najafi, M. R., "Statistical downscaling of precipitation using machine learning with optimal predictor selection", Journal of Hydrologic Engineering, 16(8), pp.650~664 (2011)
  23. Onema, J. M. K., "NDVIrainfall relationship in the Semliki watershed of the equatorial Nile", Physics and Chemistry of the Earth, Parts A, C, pp.2009, 34(13)[DOI]
  24. Park, N. -W., "Spatial downscaling of TRMM precipitation using geostatistics and fine scale environmental variables", Advances in Meteorology, pp.1~9 (2013)
  25. 박노욱, "지형자료를 이용한 월별 기온분포의 지구통계학적 분석: 지리산 일대 사례연구", 기후연구, 8(3), pp.217~235 (2013), 기후연구소[DOI]
  26. Rahman, S., "Investigating spatial downscaling of satellite rainfall data for streamflow simulation in a mediumsized basin", Journal of Hydrometeorology, 10(4), pp.1063~1079 (2009)
  27. Rodriguez-Galiano, V. F., "Incorporating the downscaled Landsat TM thermal band in land-cover classification using random forest", Photogrammetric Engineering and Remote Sensing, 78(2), pp.129~137 (2012)
  28. Shi, Y., "Mapping annual precipitation across mainland China in the period 2001-2010 from TRMM3B43 product using spatial downscaling approach", Remote Sensing, 7(5), pp.5849~5878 (2015)
  29. Srivastava, P. K., "Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application", Water Resources Management, 27(8), pp.3127~3144 (2013)
  30. Yang, J., "The role of satellite remote sensing in climate change studies", Nature climate change, 3(10), pp.875~883 (2013)
  31. Yuan, H., "Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling", Remote Sensing of Environment, 115(5), pp.1171~1187 (2011)

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