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http://dx.doi.org/10.7780/kjrs.2019.35.4.8

An Overview of Theoretical and Practical Issues in Spatial Downscaling of Coarse Resolution Satellite-derived Products  

Park, No-Wook (Department of Geoinformatic Engineering, Inha University)
Kim, Yeseul (Department of Geoinformatic Engineering, Inha University)
Kwak, Geun-Ho (Department of Geoinformatic Engineering, Inha University)
Publication Information
Korean Journal of Remote Sensing / v.35, no.4, 2019 , pp. 589-607 More about this Journal
Abstract
This paper presents a comprehensive overview of recent model developments and practical issues in spatial downscaling of coarse resolution satellite-derived products. First, theoretical aspects of spatial downscaling models that have been applied when auxiliary variables are available at a finer spatial resolution are outlined and discussed. Based on a thorough literature survey, the spatial downscaling models are classified into two categories, including regression-based and component decomposition-based approaches, and their characteristics and limitations are then discussed. Second, open issues that have not been fully taken into account and future research directions, including quantification of uncertainty, trend component estimation across spatial scales, and an extension to a spatiotemporal downscaling framework, are discussed. If methodological developments pertaining to these issues are done in the near future, spatial downscaling is expected to play an important role in providing rich thematic information at the target spatial resolution.
Keywords
Downscaling; Regression; Satellite-derived products; Geostatistics;
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Times Cited By KSCI : 6  (Citation Analysis)
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1 Park, K.-A., E.-Y. Lee, E. Chang, and S. Hong, 2015. Spatial and temporal variability of sea surface temperature and warming trends in the Yellow Sea, Journal of Marine Systems, 143: 24-38.   DOI
2 Park, N.-W., 2013. Spatial downscaling of TRMM precipitation using geostatistics and fine scale environmental variables, Advances in Meteorology, 2013: 237126.
3 Park, N.-W. and P.C. Kyriakidis, 2019. A geostatistical approach to spatial quality assessment of coarse spatial resolution remote sensing products, Journal of Sensors, 2019: 7297593.
4 Park, N.-W., S. Hong, P.C. Kyriakidis, W. Lee, and S.-J. Lyu, 2016. Geostatistical downscaling of AMSR2 precipitation with COMS infrared observations, International Journal of Remote Sensing, 37(16): 3858-3869.   DOI
5 Park, N.-W., Y. Kim, G.-H. Kwak, M.-G. Park, S. Park, and S. Chae, 2018. Downscaling of coarse resolution remote sensing data using geostatistical simulation, Proc. of the Korean Society of Remote Sensing Fall Conference 2018, Muju, Korea, Oct. 24-26, p. 193 (in Korean).
6 Pawlowsky-Glahn, V. and A. Buccianti, 2011. Compositional Data Analysis: Theory and Applications, Wiley, New York, NY, USA.
7 Pereira, O., A.J. Melfi, C.R. Montes, and Y. Lucas, 2018. Downscaling of ASTER thermal images based on geographically weighted regression kriging, Remote Sensing, 10(4): 633.   DOI
8 Sanchez-Ruiz, S., M. Piles, N. Sanchez, J. Martinez-Fernandez, M. Vall-Ilossera, and A. Camps, 2014. Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates, Journal of Hydrology, 516: 273-283.   DOI
9 Sharifi, E., B. Saghafian, and R. Steinacker, 2019. Downscaling satellite precipitation estimates with multiple linear regression, artificial neural networks, and spline interpolation techniques, Journal of Geophysical Research: Atmospheres, 124(2): 789-805.   DOI
10 Amazirh, A., O. Merlin, and S. Er-Raki, 2019. Including Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data, ISPRS Journal of Photogrammetry and Remote Sensing, 150: 11-26.   DOI
11 Atkinson, P.M., 2013. Downscaling in remote sensing, International Journal of Applied Earth Observation and Geoinformation, 22: 106-114.   DOI
12 Chen, F., Y. Liu, and Q. Liu, 2014. Spatial downscaling of TRMM 3B43 precipitation considering spatial heterogeneity, International Journal of Remote Sensing, 35(9): 3074-3093.   DOI
13 Bartkowiak, P., M. Castelli, and C. Notarnicola, 2019. Downscaling land surface temperature from MODIS dataset with random forest approach over Alpine vegetated areas, Remote Sensing, 11(11): 1319.   DOI
14 Boucher, A. and P.C. Kyriakidis, 2006. Super-resolution land cover mapping with indicator geostatistics, Remote Sensing of Environment, 104(3): 264-282.   DOI
15 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.   DOI
16 Chiles, J.-P. and P. Delfiner, 1999. Geostatistics: Modeling Spatial Uncertainty, Wiley, Hoboken, NJ, USA.
17 Cho, A.-R. and M.-S. Suh, 2013. Evaluation of land surface temperature operationally retrieved from Korean geostationary satellite (COMS) data, Remote Sensing, 5(8): 3951-3970.   DOI
18 Tanaka, Y., T. Iwata, T. Tanaka, T. Kurashima, M. Okawa, and H. Toda, 2018. Refining coarse-grained spatial data using auxiliary spatial data sets with various granularities, arXiv:1809.07952.
19 Skofronick-Jackson, G., W.A. Petersen, W. Berg, C. Kidd, E.F. Stocker, D.B. Kirschbaum, R. Kakar, S.A. Braun, G.J. Huffman, T. Iguchi, P.E. Kirstetter, C. Kummerow, R. Meneghini, R. Oki, W.S. Olson, Y.N. Takayabu, K. Furukawa, and T. Wilheit, 2017. The Global Precipitation Measurement (GPM) Mission for science and society, Bulletin of the American Meteorological Society, 98(8): 1679-1695.   DOI
20 Son, S. and J. Kim, 2019. Land cover classification map of Northeast Asia using GOCI data, Korean Journal of Remote Sensing, 35(1): 83-92.   DOI
21 Waller, L.A. and C.A. Gotway, 2004. Applied Spatial Statistics for Public Health Data, Wiley, Hoboken, NJ, USA.
22 Wang, Q., W. Shi, P.M. Atkinson, and Y. Zhao, 2015. Downscaling MODIS images with area-to-point regression kriging, Remote Sensing of Environment, 166: 191-204.   DOI
23 Wei, Z., Y. Meng, W. Zhang, J. Peng, and L. Meng, 2019. Downscaling SMAP soil moisture estimation with gradient boosting decision tree regression over the Tibetan Plateau, Remote Sensing of Environment, 225: 30-44.   DOI
24 Cressie, N., 1993. Statistics for Spatial Data, Wiley, Hoboken, NJ, USA.
25 Choe, Y.-J. and J.-H. Yom, 2017. Downscaling of MODIS land surface temperature to LANDSAT scale using multi-layer perceptron, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 35(4): 313-318.   DOI
26 Choi, M. and Y. Hur, 2012. A microwave-optical/infrared disaggregation for improving spatial representation of soil moisture using AMSR-E and MODIS products, Remote Sensing of Environment, 124: 259-269.   DOI
27 Xue, J. and B. Su, 2017. Significant remote sensing vegetation indices: A review of development and applications, Journal of Sensors, 2017: 1353691.
28 Yang, Y., C. Cao, X. Pan, X. Li, and X. Zhu, 2017. Downscaling land surface temperature in an arid area by using multiple remote sensing indices with random forest regression, Remote Sensing, 9(8): 789.   DOI
29 Yoo, C., J. Im, S. Park, and D. Cho, 2017. Thermal characteristics of Daegu using land cover data and satellite-derived surface temperature downscaled based on machine learning, Korean Journal of Remote Sensing, 33(6-2): 1101-1118 (in Korean with English abstract).   DOI
30 Christensen, W.F., 2011. Filtered kriging for spatial data with heterogeneous measurement error variances, Biometrics, 67(3): 947-957.   DOI
31 Crow, W. and E.F. Wood, 2002. The value of coarsescale soil moisture observations for regional surface energy balance modeling, Journal of Hydrometeorology, 3(4): 467-482.   DOI
32 Duan, S.B. and Z. Li, 2016. Spatial downscaling of MODIS land surface temperatures using geographically weighted regression: Case study in northern China, IEEE Transactions on Geoscience and Remote Sensing, 54(11): 6458-6469.   DOI
33 Gao, F., J. Masek, M. Schwaller, and F. Hall, 2006. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance, IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2207-2218.   DOI
34 Goovaerts, P., 1997. Geostatistics for Natural Resources Evaluation, Oxford University Press, New York, NY, USA.
35 Goovaerts, P., 2006. Geostatistical analysis of disease data: Accounting for spatial support and population density in the isopleth mapping of cancer mortality risk using area-to-point Poisson kriging, International Journal of Health Geographics, 5: 52.   DOI
36 Zaksek, K. and K. Ostir, 2012. Downscaling land surface temperature for urban heat island diurnal cycle analysis, Remote Sensing of Environment, 117: 114-124.   DOI
37 Goovaerts, P., 2008. Kriging and semivariogram deconvolution in the presence of irregular geographical units, Mathematical Geosciences, 40(1): 101-128.   DOI
38 Hong, Y., R.F. Adler, F. Hossain, S. Curtis, and G.J. Huffman, 2007. A first approach to global runoff simulation using satellite rainfall estimation, Water Resources Research, 43: W08502.   DOI
39 Hutengs, C. and M. Vohland, 2016. Downscaling land surface temperatures at regional scales with random forest regression, Remote Sensing of Environment, 178: 127-141.   DOI
40 Yoo, E.-H. and P.C. Kyriakidis, 2006. Area-to-point kriging with inequality-type data, Journal of Geographical Systems, 8(4): 357-390.   DOI
41 Zhang, J., P. Atkinson, and M. Goodchild, 2014. Scale in Spatial Information and Analysis, CRC Press, Boca Raton, FL, USA.
42 Zhang, Y., Y. Li, X. Ji, X. Luo, and X. Li, 2018. Fine-resolution precipitation mapping in a mountainous watershed: Geostatistical downscaling of TRMM products based on environmental variables, Remote Sensing, 10(1): 119.   DOI
43 Zhao, W., N. Sanchez, H. Lu, and A. Li, 2018. A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression, Journal of Hydrology, 563: 1009-1024.   DOI
44 Zheng, X. and J. Zhu, 2015. A methodological approach for spatial downscaling of TRMM precipitation data in North China, International Journal of Remote Sensing, 36(1): 144-169.   DOI
45 Jin, Y., Y. Ge, J. Wang, G.B.M. Heuvelink, and L. Wang, 2018a. Geographically weighted area-to-point regression kriging for spatial downscaling in remote sensing, Remote Sensing, 10(4): 579.   DOI
46 Im, J., S. Park, J. Rhee, J. Baik, and M. Choi, 2016. Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches, Environmental Earth Sciences, 75(15): 1120.   DOI
47 Immerzeel, W.W., M.M. Rutten, and P. Droogers, 2009. Spatial downscaling of TRMM precipitation using vegetation response on the Iberian Peninsula, Remote Sensing of Environment, 113(2): 362-370.   DOI
48 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.   DOI
49 Jin, Y., Y. Ge, J. Wang, Y. Chen, G.B.M. Heuvelink, and P.M. Atkinson, 2018b. Downscaling AMSR-2 soil moisture data with geographically weighted area-to-area regression kriging, IEEE Transactions on Geoscience and Remote Sensing, 56(4): 2362-2376.   DOI
50 Keil, P., J. Belmaker, A.M. Wilson, P. Unitt, and W. Jetz, 2013. Downscaling of species distribution models: A hierarchical approach, Methods in Ecology and Evolution, 4(1): 82-94.   DOI
51 Kim, N., K.-J. Ha, N.-W. Park, J. Cho, S. Hong, and Y.-W. Lee, 2019a. A comparison between major artificial intelligence models for crop yield prediction: Case study of the Midwestern United States, 2006-2015, ISPRS International Journal of Geo-Information, 8(5): 240.   DOI
52 Zhu, X., J. Chen, F. Gao, X. Chen, and J.G. Masek, 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions, Remote Sensing of Environment, 114(11): 2610-2623.   DOI
53 Zhu, X., E.H. Helmer, F. Gao, D. Liu, J. Chen, and M.A. Lefsky, 2016. A flexible spatiotemporal method for fusing satellite images with different resolutions, Remote Sensing of Environment, 172: 165-177.   DOI
54 Zou, B., Y. Luo, N. Wan, Z. Zheng, T. Sternberg, and Y. Liao, 2015. Performance comparison of LUR and OK in $PM_{2.5}$ concentration mapping: A multidimensional perspective, Scientific Reports, 5: 8698.   DOI
55 Kim, D., H. Moon, H. Kim, J. Im, and M. Choi, 2018. Inter-comparison of downscaling techniques for satellite soil moisture products, Advances in Meteorology, 2018: 4832423.
56 Kim, D., N.-W. Park, N. Kim, K. Kim, S. Lee, Y. Kim, J. Kim, D. Shin, Y. Cho, and Y. Lee, 2017. Downscaling Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture data using regression-kriging, Journal of the Korean Cartographic Association, 17(2): 99-110 (in Korean with English abstract).   DOI
57 Kim, Y. and N.-W. Park, 2016. Spatial disaggregation of coarse scale satellite-based precipitation data using machine learning model and residual kriging, Journal of Climate Research, 11(2): 183-195 (in Korean with English abstract).   DOI
58 Kim, Y. and N.-W. Park, 2017a. Impact of trend estimates on predictive performance in model evaluation for spatial downscaling of satellite-based precipitation data, Korean Journal of Remote Sensing, 33(1): 25-35.   DOI
59 Kim, Y., G.-H. Kwak, and N.-W. Park, 2019b. Area-to-area filtered kriging for error correction of satellite-based products, Proc. of International Symposium on Remote Sensing 2019, Taipei, Taiwan, Apr. 17-19.
60 Kim, Y. and N.-W. Park, 2017b. Assessing the impacts of errors in coarse scale data on the performance of spatial downscaling: An experiment with synthetic satellite precipitation products, Korean Journal of Remote Sensing, 33(4): 445-454.   DOI
61 Kim, Y., M.-L. Ou, S.-B. Ryoo, Y. Chun, E.-H. Lee, and S. Hong, 2013. Soil moisture retrieved from microwave satellite data and its relationship with the Asian Dust (Hwangsa) frequency in East Asia during the period from 2003 to 2010, Asia-Pacific Journal of Atmospheric Sciences, 49(4): 527-534.   DOI
62 Kyriakidis, P.C., N.L. Miller, and J. Kim, 2004. A spatial time series framework for simulating daily precipitation at regional scales, Journal of Hydrology, 297(1-4): 236-255.   DOI
63 Kwak, G.-H., N.-W. Park, and P.C. Kyriakidis, 2018. Development of an R-based spatial downscaling tool to predict fine scale information from coarse scale satellite products, Korean Journal of Remote Sensing, 34(1): 89-99.   DOI
64 Kyriakidis, P.C., 2004. A geostatistical framework for area-to-point spatial interpolation, Geographical Analysis, 36(3): 259-289.   DOI
65 Kyriakidis, P.C. and E.-H. Yoo, 2005. Geostatistical prediction and simulation of point values from areal data, Geographical Analysis, 37(2): 124-151.   DOI
66 Oh, H.-J., N.-W. Park, S.-S. Lee, and S. Lee, 2012. Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping, International Journal of Remote Sensing, 33(10): 3211-3231.   DOI
67 Liu, Y., Y. Yang, W. Jing, and X. Yue, 2018. Comparison of different machine learning approaches for monthly satellite-based soil moisture downscaling over Northeast China, Remote Sensing, 10(1): 31.
68 Ma, Z., Z. Shi, Y. Zhou, J. Xu, W. Yu, and Y. Yang, 2017. A spatial data mining algorithm for downscaling TMPA 3B43 V7 data over the Qinghai-Tibet Plateau with the effects of systematic anomalies removed, Remote Sensing of Environment, 200: 378-395.   DOI
69 Ma, Z., K. He, X. Tan, J. Xu, W. Fang, Y. He, and Y. Hong, 2018. Comparisons of spatially downscaling TMPA and IMERG over the Tibetan Plateau, Remote Sensing, 10(12): 1883.   DOI
70 Pardo-Iguzquiza, E. and P.M. Atkinson, 2007. Modelling the semivariograms and cross-semivariograms required in downscaling cokriging by numerical convolution-deconvolution, Computers & Geosciences, 33(10): 1273-1284.   DOI