Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

Impact of Trend Estimates on Predictive Performance in Model Evaluation for Spatial Downscaling of Satellite-based Precipitation Data

  • Kim, Yeseul (Department of Geoinformatic Engineering, Inha University) ;
  • Park, No-Wook (Department of Geoinformatic Engineering, Inha University)
  • Received : 2016.12.13
  • Accepted : 2017.02.07
  • Published : 2017.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

Keywords

References

  1. Atkinson, P.M., 2013. Downscaling in remote sensing, International Journal of Applied Earth Observation and Geoinformation, 22: 106-114. https://doi.org/10.1016/j.jag.2012.04.012
  2. Chen, C., S. Zhao, Z. Duan, and Z. Qin, 2015. An improved spatial downscaling procedure for TRMM 3B43 precipitation product using geographically weighted regression, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(9): 4592-4604. https://doi.org/10.1109/JSTARS.2015.2441734
  3. Chen, F., Y. Liu, Q. Liu, and X. Liu, 2014. Spatial downscaling of TRMM 3B43 precipitation considering spatial heterogeneity, International Journal of Remote Sensing, 35(9): 3074-3093. https://doi.org/10.1080/01431161.2014.902550
  4. Foody, G.M., 2003. Geographical weighting as a further refinement to regression modeling: an example focused on the NDVI-rainfall relationship, Remote Sensing of Environment, 88(3): 283-293. https://doi.org/10.1016/j.rse.2003.08.004
  5. Fotheringham, A.S., C. Brunsdon, and M. Charlton, 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, John Wiley & Sons, Chichester, UK.
  6. Goovaerts, P., 2008. Kriging and semivariogrm deconvolution in the presence of irregular geographical units, Mathematical Geosciences, 40(1): 101-128. https://doi.org/10.1007/s11004-007-9129-1
  7. Hastie, T., R. Tibshirani, and J. Friedman, 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, USA.
  8. Huffman, G.J., R.F. Alder, D.T. Bolvin, G. Gu, E.J. Nelkin, K.P. Bowman, Y. Hong, E.F. Stocker, and D.B. Wolff, 2007. The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scale, Journal of Hydrometeorology, 8(1): 38-55. https://doi.org/10.1175/JHM560.1
  9. Immerzeel, W., M. Rutten, and P. Droogers, 2009. Spatial downscaling of TRMM precipitation using vegetative response on the Iberian Peninsula, Remote Sensing of Environment, 113(2): 362-370. https://doi.org/10.1016/j.rse.2008.10.004
  10. Jia, S., W. Zhu, A. Lu, and T. Yan, 2011. A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China, Remote Sensing of Environment, 115(12): 3069-3079. https://doi.org/10.1016/j.rse.2011.06.009
  11. Jing, W., Y. Yang, X. Yue, and X. Zhao, 2016. A spatial downscaling algorithm for satellite-based precipitation over the Tibetan Plateau based on NDVI, DEM, and land surface temperature, Remote Sensing, 8(8): 655. https://doi.org/10.3390/rs8080655
  12. Kim, J.P., K.-W. Park, I.-W. Jung, K.-S. Han, and G. Kim, 2013. Application of high resolution multi-satellite precipitation products and a distributed hydrological modeling for daily runoff simulation, Korean Journal of Remote Sensing, 29(2): 263-274 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2013.29.2.10
  13. Kyriakidis, P.C., 2004. A geostatistical framework for area-to-point spatial interpolation, Geographical Analysis, 36(3): 259-289. https://doi.org/10.1111/j.1538-4632.2004.tb01135.x
  14. Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and J. Simpson, 1998. The Tropical Rainfall Measuring Mission (TRMM) sensor package, Journal of Atmospheric and Oceanic Technology, 15(3): 809-817. https://doi.org/10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2
  15. Lee, J., J. Im, and J. Kim, 2014. Hydrological drought assessment and monitoring based on remote sensing for ungauged areas, Korean Journal of Remote Sensing, 30(4): 525-536 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2014.30.4.10
  16. Myers, R.H., 1990. Classical and Modern Regression with Applications (Duxbury Classic), Duxbury Press, Boston, U.S.A.
  17. Park, N.-W., 2013. Spatial downscaling of TRMM precipitation using geostatistics and fine scale environmental variables, Advances in Meteorology, 2013, Article ID 237126, doi:10.1155/2013/237126.
  18. Park, N.-W., S. Hong, P.C. Kyriakidis, W. Lee, and S.-J. Lyu, 2016. Geostatistical downscaling of AMSR2 precipitation, International Journal of Remote Sensing, 37(16): 3858-3869. https://doi.org/10.1080/01431161.2016.1204031
  19. Ryu, G.-H., B.J. Sohn, C.D. Kummerow, E.-K. Seo, and G.J. Tripoli, 2012. Rain-rate characteristics over the Korean Peninsula and improvement of the Goddard Profiling (GPROF) database for TMI rainfall retrievals, Journal of Applied Meteorology and Climatology, 51(4): 786-798. https://doi.org/10.1175/JAMC-D-11-094.1
  20. Shmueli, G., 2010. To explain or to predict? Statistical Science, 25(3): 289-310. https://doi.org/10.1214/10-STS330
  21. van Hoek, M., L. Jia, J. Zhou, C. Zheng, and M. Menenti, 2016. Early drought detection by spectral analysis of satellite time series of precipitation and Normalized Difference Vegetation Index (NDVI), Remote Sensing, 8(5), 422, doi: 10.3390/rs8050422.
  22. Xu, G., X. Xu, M. Liu, A.Y. Sun, and K. Wang, 2015. Spatial downscaling of TRMM precipitation product using a combined multifractal and regression approach: demonstration for South China, Water, 7(6): 3083-3102. https://doi.org/10.3390/w7063083

Cited by

  1. Assessing the Impacts of Errors in Coarse Scale Data on the Performance of Spatial Downscaling: An Experiment with Synthetic Satellite Precipitation Products vol.33, pp.4, 2017, https://doi.org/10.7780/kjrs.2017.33.4.10
  2. Spatial Downscaling of TRMM Precipitation Using an Optimal Regression Model with NDVI in Inner Mongolia, China vol.47, pp.6, 2020, https://doi.org/10.1134/s0097807820060123