Abstract
Geographic Information Systems (GIS) and modeling are becoming powerful tools in agricultural research and natural resource management. This study proposes an empirical methodology for modeling and mapping of the monthly and annual air temperature using remote sensing and GIS techniques. The study area is Gangetic West Bengal and its neighborhood in the eastern India, where a number of weather systems occur throughout the year. Gangetic West Bengal is a region of strong heterogeneous surface with several weather disturbances. This paper also examines statistical approaches for interpolating climatic data over large regions, providing different interpolation techniques for climate variables' use in agricultural research. Three interpolation approaches, like inverse distance weighted averaging, thin-plate smoothing splines, and co-kriging are evaluated for 4° × 4° area, covering the eastern part of India. The land use/land cover, soil texture, and digital elevation model are used as the independent variables for temperature modeling. Multiple regression analysis with standard method is used to add dependent variables into regression equation. Prediction of mean temperature for monsoon season is better than winter season. Finally standard deviation errors are evaluated after comparing the predicted temperature and observed temperature of the area. For better improvement, distance from the coastline and seasonal wind pattern are stressed to be included as independent variables.
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References
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford
Bouman BAM, van Keulen H, Rabbinge R (1996) The ‘School of de Wit’ crop growth simulation models: a pedigree and historical overview. Agr Syst 52(2–3):171–198
Burrough, McDonnell (1998) Principles of geographical information systems. Oxford University Press, New York
Collins FC, Bolstad PV (1996) A comparison of spatial interpolation techniques in temperature estimation. In: Proceedings of the Third International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, New Mexico, January 21–25. National Center for Geographic Information Analysis (NCGIA), Santa Barbara, California
Coltelli M, Fornaro G, Franceschetti G, Lanari R, Migiaccio M, Moreira JR, Papathanassaou KP, Puglisi G, Riccio D, Schwabisch M (1996) SIR-C/X-SAR multifrequency multipass interferometry: a new tool for geological interpretation. J Geophys Res 101:23127–23148
Daly C, Gibson WP, Taylor GH, Johnson GL, Pateris P (2002) A knowledge-based approach to the statistical mapping of climate. Clim Res 22(2):99–113
Dowding S, Kuuskivi T, LI X (2004) Void fill of SRTM elevation data—principles, processes and performance, In: images to decisions: remote sensing foundations for GIS applications, ASPRS, Fall Conference, September 12–16, Kansas City, MO, USA
Eckstein BA (1989) Evaluation of spline and weighted average interpolation algorithms. Comput Geosci 15:79–94
Eliasson, I., Svensson, M. K. (2006) Spatial air temperature variations and urban land use - a statistical approach, Meteorological Applications, 10 (2), pp. 135-149.
Hartkamp AD, Beurs K De, Stein A, White JW (1999) Interpolation techniques for climate variables, NRG-GIS Series 99–01. CIMMYT, Mexico, D.F
Hutchinson MF, Gessler PE (1994) Splines-more than just a smooth interpolator. Geoderma 62:45–67
Jarvis CH, Stuart N (2001) A comparison between strategies for interpolating maximum and minimum daily air temperatures. b. The interaction between guiding variable and interpolation method. J Appl Meteorol 40:1075–1084
Lohar D, Pal B (1995) The effect of irrigation on pre-monsoon season precipitation over south West Bengal, India. J Clim 8:2567–2570
Mallawaarachchi T, Walker PA, Young MD, Smyth RE, Lynch HS, Dudgeon G (1996) GIS based integrated modelling systems for natural resource management. Agr Syst 50(2):169–189
Matheron, G. (1970), The theory of regionalized variables and its applications, Issue 5, Les Cahiers du Centre de Morphologie Mathématique de Fontainebleau, Paris: École Nationale Supérieure des Mine, pp. 212
Mitchell TD, Jones PD (2005) An improved method of constructiong a database of monthly climate observation and associated high-resolution grid. Int J Climatol 25:693–712
Parton, W. J. (1984), Predicting soil temperatures in a short grass steppe, Soil SCI, 138, pp. 93-101
Rigol JP, Jarvis CH, Stuart N (2001) Artificial neural networks as a tool for spatial interpolation. Int J Geogr Inform Sci 15(4):323–343
Watson DF, Philip GM (1985) A refinement of inverse distance weighted interpolation. Geo-Processing 2:315–327
Acknowledgments
One of the authors (SS) expresses sincere gratitude to the Department of Surveying and Land Studies, Papua New Guinea University of Technology for providing digital image interpretation laboratory facility to carry out the research work. The authors are also grateful to the anonymous referees for their valuable comments and suggestions.
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Samanta, S., Pal, D.K., Lohar, D. et al. Interpolation of climate variables and temperature modeling. Theor Appl Climatol 107, 35–45 (2012). https://doi.org/10.1007/s00704-011-0455-3
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DOI: https://doi.org/10.1007/s00704-011-0455-3