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Estimating Air Temperature over Mountainous Terrain by Combining Hypertemporal Satellite LST Data and Multivariate Geostatistical Methods  

Park, Sun-Yurp (Department of Geography and Environmental Studies, University of Hawaii-Hilo)
Publication Information
Journal of the Korean Geographical Society / v.44, no.2, 2009 , pp. 105-121 More about this Journal
Abstract
The accurate official map of air temperature does not exist for the Hawaiian Islands due to the limited number of weather stations on the rugged volcanic landscape. To alleviate the major problem of temperature mapping, satellite-measured land surface temperature (LST) data were used as an additional source of sample points. The Moderate Resolution Imaging Spectroradiometer (MODIS) system provides hypertemperal LST data, and LST pixel values that were frequently observed (${\ge}$14 days during a 32-day composite period) had a strong, consistent correlation with air temperature. Systematic grid points with a spacing of 5km, 10km, and 20km were generated, and LST-derived air temperature estimates were extracted for each of the grid points and used as input to inverse distance weighted (IDW) and cokriging methods. Combining temperature data and digital elevation model (DEM), cokriging significantly improved interpolation accuracy compared to IDW. Although a cokriging method is useful when a primary variable is cross-correlated with elevation, interpolation accuracy was sensitively influenced by the seasonal variations of weather conditions. Since the spatial variations of local air temperature are more variable in the wet season than in the dry season, prediction errors were larger during the wet season than the dry season.
Keywords
Hawaiian Islands; MODIS LST; IDW; cokriging; interpolation;
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1 Barnes, W. L., Pagano, T. S., and Salomonson, V. V., 1998, Prelaunch characteristics of the Moderate Resolution Imaging Spectroradiometer (MODIS) in EOS-AM1, IEEE Transactions on Geoscience and Remote Sensing, 36, 1088-1100   DOI   ScienceOn
2 Barry, R. G. and Chorley, R. J., 1987, Atmosphere, Weather and Climate, Routledge, London
3 Daly, C., Nelson, R. P., and Phillips, D. L., 1994, A statistical-topographic model for mapping climatological precipitation over mountainous terrain, Journal of Applied Meteorology, 33, 140- 158   DOI   ScienceOn
4 Daly, C., Helmer, E. H., and Quiñones, M., 2003, Mapping the climate of Puerto Rico, Vieques and Culebra, International Journal of Climatology, 23, 1359-1381   DOI   ScienceOn
5 Declercq, F. A. N., 1996, Interpolation methods for scattered sample data: accuracy, spatial patterns, processing time, Cartography and Geographic Information Systems, 23, 128-144   DOI
6 Goovaerts, P., 2000, Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall, Journal of Hydrology, 228, 113-129   DOI   ScienceOn
7 Martinez-Cob, A. and Cuenca, R. H., 1992, Influence of elevation on regional evapotranspiration using multivariate geostatistics for various climatic regimes in Oregon, Journal of Hydrology, 136, 353-380   DOI   ScienceOn
8 Nullet, D., Juvik, J. O., and Wall, A., 1995, A Hawaiian mountain climate cross-section, Climate Research, 5, 131-137   DOI
9 Nullet, D. and Sanderson, M., 1995, Radiation and energy balances and air temperature, in Sanderson, M. (eds.), Prevailing Trade Winds- Weather and Climate in Hawaii, University of Hawaii Press, Honolulu, 37-55
10 Zimmerman, D., Pavlik, C., Ruggles, A., and Armstrong, M. P., 1999, An experimental comparison of ordinary and universal kriging and inverse distance weighting, Mathematical Geology, 31, 375-390   DOI   ScienceOn
11 Vitousek, P. M., Aplet, G., and Turner, D., 1992, The Mauna Loa environmental matrix: foliar and soil nutrients, Oecologia, 89, 372-382   DOI
12 Webster, R. and Oliver, M. A., 2001, Geostatistics for Environmental Sciences, Wiley, Chichester
13 Havesi, A. J., Flint, A. L., and Istok, J. D., 1992, Precipitation estimation in mountainous terrain using multivariate geostatistics. Part II: isohyetal maps, Journal of Applied Meteorology, 31, 677- 688   DOI
14 Jarvis, C. H., Stuart, N., and Cooper, W., 2003, Informetric and statistical diagnostics to provide artificially-intelligent support for spatial analysis: the example of interpolation, International Journal of Geographical Information Science, 17, 495-516   DOI   ScienceOn
15 Slocum, T. A., McMaster, R. B., Kessler, F. C., and Howard, H. H., 2009, Thematic Cartography and Geovisualization, Prentice Hall, Upper Saddle River, 286-287
16 Martinez-Cob, A., 1996, Multivariate geostatistical analysis of evaportranspiration and precipitation in mountainous terrain, Journal of Hydrology, 174, 19-35   DOI   ScienceOn
17 Phillips, D. L., Dolph, J., and Marks, D., 1992, A comparison of geostatistical procedures for spatial analysis of precipitation in mountainous terrain, Agricultural and Forest Meteorology, 58, 119-141   DOI   ScienceOn
18 Schroeder, T., 1995, Climate controls, in Sanderson, M. (ed.), Prevailing Trade Winds-Weather and Climate in Hawaii, University of Hawaii Press, Honolulu, 12-36
19 Ishida, T. and Kawashima, S., 1993, Use of cokriging to eastimate surface air temperature from elevation, Theoretical and Applied Climatology, 47, 147- 157   DOI   ScienceOn
20 Park, N. and Jang, D., 2008, Mapping of temperature and rainfall using DEM and multivariate kriging, Journal of the Korean Geographical Society, 43, 1002-1015
21 Giambelluca, T. W. and Nullet, D., 1991, Influence of the trade-wind inversion on the climate of a leeward mountain slope in Hawaii, Climate Research, 1, 207-216   DOI
22 Issaks, E. H. and Srivastava, R. M., 1989, An Introduction to Applied Geostatistics, Oxford University Press, Oxford
23 Kaufman, Y. J., Herring, D. D., Ranson K. J., and Collatz, G. J., 1998, Earth observing system AM1 mission to earth, IEEE Transactions on Geoscience and Remote Sensing, 36, 1045-1055   DOI   ScienceOn
24 Lull, H. W. and Ellison, L., 1950, Precipitation in relation to altitude in central Utah, Ecology, 31, 479-484   DOI   ScienceOn
25 Justice, D. H., Salomonsonm, V., Privette, J., Riggs, G., Strahler, A., Lucht, R., Myneni, R., Knjazihhin, Y., Running, S., Nemani, R., Vermte, E., Townsend, J., Dfries, R., Roy, D., Wan, Z., Huete, A., Leeuwen van, R., Wolfe, R., Giglio, L., Muller, J. P., Lewis, P., and Barnsley, M., 1998, The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research, IEEE Transactions on Geoscience and Remote Sensing, 36, 1228-1249   DOI   ScienceOn