Spatiotemporal modeling of monthly soil temperature using artificial neural networks

Theoretical and Applied Climatology

Volumn 113, Issue 3-4, 2013, Pages 481-494

  Wu, Wei ^a   Tang, Xiao Ping ^b   Guo, Nai Jia ^a   Yang, Chao ^a   Liu, Hong Bin ^a   Shang, Yue Feng ^c  

^a SOUTHWEST UNIVERSITY   (China)

^b Chongqing EHV Bureau   (China)

^c Chongqing Agricultural Technology Extension Station   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; COMPLEX TERRAIN; DIGITAL ELEVATION MODEL; MODELING; REMOTE SENSING; SOIL TEMPERATURE; SPATIOTEMPORAL ANALYSIS;

EID: 84880763583     PISSN: 0177798X     EISSN: 14344483     Source Type: Journal    
DOI: 10.1007/s00704-012-0807-7     Document Type: Article

References (38)

1. Beven K, Kirkby M (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24: 43-69.
2. Beven KJ, Wood EF (1983) Catchment geomorphology and the dynamics of runoff contributing areas. J Hydrol 65: 139-158.
3. Beven KJ, Wood EF, Sivapalan M (1988) On hydrological heterogeneity-catchment morphology and catchment response. J Hydrol 100: 353-375.
4. Bilgili M (2011) The use of artificial neural networks for forecasting the monthly mean soil temperatures in Adana Turkey. Turk J Agric For 35: 83-93.
5. Burrough PA (1986) Principles of geographical information systems for land resources assessment. Clarendon, Oxford.
6. Chen et al (2002) Interpolation of wind-induced pressure time series with an artificial neural network. J Wind Eng Ind Aerodyn 90: 589-615.
7. Chiles JP, Delfiner P (1999) Geostatistics: modeling spatial uncertainty. Wiley, New York.
8. Cooperative Research Group (2001). Chinese soil taxonomy. Science Press, Beijing-New York.
9. Daly C (2006) Guidelines for assessing the suitability of spatial climate data sets. Int J Climatol 26: 707-721.
10. George (2001) Prediction of soil temperature by using artificial neural networks algorithms. Nonlinear Anal 47: 1737-1748.
11. Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228: 113-129.
12. Hagan MT, Menhaj M (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5: 989-993.
13. Hu Q, Feng S (2003) A daily soil temperature dataset and soil temperature climatology of the contiguous United States. J Appl Meteorol 42: 1139-1156.
14. Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New York.
15. Karnieli A, Bayasgalan M, Bayarjargal Y, Agam N, Khudulmur S, Tucker CJ (2006) Comments on the use of the vegetation health index over Mongolia. Int J Remote Sens 27: 2017-2024.
16. Karnieli A, Agam N, Pinker RT, Anderson MC, Mark L, Imhoff ML, Gutman GG, Panov N, Goldberg A (2009) Use of NDVI and land surface temperature for drought assessment: merits and limitations. J Climate 23: 618-633.
17. Kermani BG, Schiffman SS, Nagle HT (2005) Performance of the Levenberg-Marquardt neural network training method in electronic nose applications. Sensor Actuator B 110: 13-22.
18. Lam S (1983) Spatial interpolation methods: a review. Am Cartographer 10: 129-149.
19. Liu HB, Xie DT, Wu W (2008) Soil water content forecasting by ANN and SVM hybrid architecture. Environ Monit Assess 143: 187-193.
20. Ayres MP, MacLean SF Jr (1985) Estimation of soil temperature from climatic variables at Barrow, Alaska, U. S. A. Arctic Alpine Res 17: 425-432.
21. Meyers DE (1994) Spatial interpolation: an overview. Geoderma 62: 17-28.
22. Mihalakakou G (2002) On estimating soil surface temperature profiles. Energ Buildings 34: 251-259.
23. Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling: a review of hydrological, geomorphologic and biological applications. Hydrol Process 5: 3-30.
24. Moore ID, Gessler PE, Nielsen GA, Peterson GA (1993) Terrain analysis for soil-specific crop management. In: Robert PC, Rust RC, Larson WE (eds) Proceedings of First Workshop, Soil Specific Crop Management. SSSA, Madison, pp 27-55.
25. Ozturk M, Salman O, Koc M (2011) Artificial neural network model for estimating the soil temperature. Can J Soil Sci 91: 551-562.
26. Pan HL, Mahrt L (1987) Interaction between soil hydrology and boundary-layer development. Bound-Lay Meteorol 38: 185-202.
27. Picton P (2000) Neural networks, 2nd edn. Palgrave, New York.
28. Quinn P, Beven K, Chevallier P, Planchon O (1991) The prediction of hillslope flow paths for distributed hydrological modeling using digital terrain models. Hydrol Process 5: 59-79.
29. Raquel B, Fernando M, Agustín R, Koldo O (2007) Geostatistical modelling of air temperature in a mountainous region of Northern Spain. Agric Forest Meteorol 146: 173-188.
30. Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great Plains with ERTS-1. In: Proceedings of 3rd earth resources technology satellite symposium, goddard space flight center, Washington, DC, 10-14 December 1973, Vol. 1 pp 309-317.
31. Seibert J, Bishop KH, Nyberg L (1997) A test of TOPMODEL's ability to predict spatially distributed groundwater levels. Hydrol Process 11: 1131-1144.
32. Stumer et al (2010) Spatial interpolation of in situ data by self-organizing map algorithms (neural networks) for the assessment of carbon stocks in European forests. Forest Ecol Manag 260: 287-293.
33. Tabari H, Sabziparvar AA, Ahmadi M (2011) Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region. Meteorol Atmos Phys 110: 135-142.
34. Webster R, Oliver A (1992) Sample adequately to estimate variograms of soil properties. J Soil Sci 43: 177-192.
35. Wu W, Fan Y, Wang ZY, Liu HB (2008) Assessing effects of digital elevation model resolutions on soil-landscape correlations in a hilly area. Agric Ecosyst Environ 126: 209-216.
36. Xu WX, Gu S, Zhao XQ, Xiao JS, Tang YH, Fang JY, Zhang J, Jiang S (2011) High positive correlation between soil temperature and NDVI from 1982 to 2006 in alpine meadow of the Three-River Source Region on the Qinghai-Tibetan Plateau. Int J Appl Earth Obs 13: 528-535.
37. Yonaba H, Anctil F, Fortin V (2010) Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting. J Hydrol Eng 15: 275-283.
38. Zheng DL, Hunt ER Jr, Running SW (1993) A daily soil temperature model based on air temperature and precipitation for continental applications. Clim Res 2: 183-191.