Mapping soil organic matter with limited sample data using geographically weighted regression

Journal of Spatial Science

Volumn 59, Issue 1, 2014, Pages 91-106

  Wang, K ^a   Zhang, C R ^b   Li, W D ^b   Lin, J ^b   Zhang, D X ^b  

^a MINJIANG UNIVERSITY   (China)

^b UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

environmental modeling; geographically weighted regression; mapping; ordinary least squares; soil organic matter

Indexed keywords

ACCURACY ASSESSMENT; DATA INTERPRETATION; LEAST SQUARES METHOD; MAPPING; PRECISION AGRICULTURE; SOIL ORGANIC MATTER; SPATIAL DISTRIBUTION; SPATIAL VARIATION;

EID: 84896712166     PISSN: 14498596     EISSN: None     Source Type: Journal    
DOI: 10.1080/14498596.2013.812024     Document Type: Review

References (43)

1. Alejandro, M.D.A., & Kenji, O. (2007) Estimation of vegetation parameter for modeling soil erosion using linear Spectral Mixture Analysis of Landsat ETM data. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 62, pp. 309-324.
2. Bai, J.H., Ouyang, H., Deng, W., Zhu, Y.M., Zhang, X.L., & Wang, Q.G. (2005) Spatial distribution characteristics of organic matter and total nitrogen of marsh soils in river marginal wetlands. Geoderma, vol. 124, pp. 181-192.
3. Baxter, S.J., & Oliver, M.A. (2005) The spatial prediction of soil mineral N and potentially available N using elevation. Geoderma, vol. 128, pp. 325-339.
4. Bickford, S., & Laffan, S. (2006) Multi-extent analysis of the relationship between pteridophyte species richness and climate. Global Ecology and Biogeography, vol. 15, pp. 588-601.
5. Bishop, T.F.A., & Lark, R.M. (2006) The geostatistical analysis of experiments at the landscape-scale. Geoderma, vol. 133, pp. 87-106.
6. Bregt, A.K., Gesing, H.J., & Alkasuma, M. (1992) Mapping the conditional probability of soil variables. Geoderma, vol. 53, pp. 15-29.
7. Brunsdon, C., Fotheringham, S., & Charlton, M. (1998a) Geographically weighted regression - modelling spatial non-stationarity. The Statistician, vol. 47, pp. 431-443.
8. Brunsdon, C., Fotheringham, S., & Charlton, M. (1998b) Spatial nonstationarity and autoregressive models. Environment and Planning A, vol. 30, pp. 957-973.
9. Brunsdon, C., Fotheringham, S., & Charlton, M. (2002) Geographically weighted summary statistics: a framework for localized exploratory data analysis. Computers, Environment and Urban Systems, vol. 26, pp. 501-524.
10. Chen, X.X., Vierling, L., Rowell, E., & DeFelice, T. (2004) Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest. Remote Sensing of Environment, vol. 91, pp. 14-26.
11. Dobson, A.J., & Barnett, A.G. (2008) An Introduction to Generalized Linear Models, Chapman and Hall, London.
12. Dungan, J. (1998) Spatial prediction of vegetation quantities using ground and image data. International Journal of Remote Sensing, vol. 19, pp. 267-285.
13. 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, vol. 88, pp. 283-293.
14. Fotheringham, S., Charlton, M., & Brundson, C. (1997) Measuring spatial variations in relationships with geographically weighted regression. In: Fischer, M.M., & Getis, A., eds. Recent Developments in Spatial Analysis, Springer-Verlag, Berlin, pp. 60-82.
15. Fotheringham, S., Charlton, M., & Brundson, C. (1998) Geographically weighted regression: a natural evolution of the expansion method for spatial data. Environment and Planning A, vol. 30, pp. 1905-1927.
16. Fotheringham, S., Brunsdo, C.A., & Remote Charlton, M. (2002) Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, John Wiley & Sons, New York.
17. Gessler, P.E., Moore, I.D., McKenzie, N.J., & Ryan, P.J. (1995) Soil-landscape modelling and spatial prediction of soil attributes. International Journal of Geographical Information Systems, vol. 9, pp. 421-432.
18. Huang, B., Sun, W.X., Zhao, Y.C., Zhu, J., Yang, R.Q., Zou, Z., Ding, F., & Su, J.P. (2007) Temporal and spatial variability of soil organic matter and total nitrogen in an agricultural ecosystem as affected by farming practices. Geoderma, vol. 139, pp. 336-345.
19. Hughson, L., Huntley, D., & Razack, M. (1996) Cokriging limited transmissivity data using widely sampled specific capacity from pump tests in an alluvial aquifer. Ground Water, vol. 34, pp. 12-18.
20. Hurvich, C.M., Simonoff, J.S., & Tsai, C.L. (1998) Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. Journal of Royal Statistical Society Series B, vol. 60, pp. 271-293.
21. Jaimes, N.B.P.J., Sendra, J.B., Delgado, M.G., & Plata, R.F. (2010) Exploring the driving forces behind deforestation in the state of Mexico (Mexico) using geographically weighted regression. Applied Geography, vol. 30, pp. 576-591.
22. Jiang, J.R., Zhong, S.L., Yuan, Z.P., Xiao, R.L., & Zhang, Y.Z. (1987) Effect of different cropping system and underwater level on content of soil organic matter and ferric oxide in paddy soil. Journal of Hunan Agricultural University (Natural Sciences), vol. 3, pp. 25-30.
23. Lark, R.M. (2000) Regression analysis with spatially autocorrelated error: simulation studies and application to mapping of soil organic matter. International Journal of Geographical Information Science, vol. 14, pp. 247-264.
24. Lesch, S.M., Strauss, D.J., & Rhoades, J.D. (1995) Spatial prediction of soil-salinity using electromagnetic induction techniques. I. Statistical prediction models - a comparison of multiple linear regression and cokriging. Water Resources Research, vol. 31, pp. 373-386.
25. Leung, Y., Mei, C.L., & Zhang, W.X. (2000) Statistical tests for spatial nonstationarity based on the geographically weighted regression model. Environment and Planning A, vol. 32, pp. 9-32.
26. Lian, G., Guo, X.D., Fu, B.J., & Hu, C.X. (2006) Spatial variability and prediction of soil organic matter at county scale on the Loess Plateau (in Chinese). Progress in Geography, vol. 25, pp. 112-123.
27. Loader, C. (1999) Local Regression and Likelihood, Springer, New York. 290 pp.
28. Mabit, L., Bernard, C., Makhlouf, M., & Laverdière, M.R. (2008) Spatial variability of erosion and soil organic matter content estimated from 137Cs measurements and geostatistics. Geoderma, vol. 145, pp. 245-251.
29. McKenzie, N.J., & Austin, M.P. (1993) A quantitative Australian approach to medium and small scale surveys based on soil stratigraphy and environmental correlation. Geoderma, vol. 57, pp. 329-355.
30. Mishra, U., Lal, R., Liu, D.S., & Marc, V.M. (2010) Predicting the spatial variation of the soil organic carbon pool at a regional scale. Soil Science Society of America Journal, vol. 74, pp. 906-914.
31. Moore, I.D., Gessler, P.E., Nielsen, G.A., & Peterson, G.A. (1993) Soil attribute prediction using terrain analysis. Soil Science Society of America Journal, vol. 57, pp. 443-452.
32. Nerini, D., Monestiez, P., & Manté, C. (2010) Cokriging for spatial functional data. Journal of Multivariate Analysis, vol. 101, pp. 409-418.
33. Pachepsky, Y.A., Timlin, D.J., & Rawls, W.J. (2001) Soil water retention as related to topographic variables. Soil Science Society of America Journal, vol. 65, pp. 1787-1795.
34. Propastin, P.A. (2009) Spatial non-stationarity and scale-dependency of prediction accuracy in the remote estimation of LAI over a tropical rainforest in Sulawesi, Indonesia. Remote Sensing of Environment, vol. 113, pp. 2234-2242.
35. Quinino, R.C., Reis, E.A., & Bessegato, L.F. (2012) Using the coefficient of determination R2 to test the significance of multiple linear regression. Teaching Statistics, vol. 35, pp. 84-88.
36. Robinson, C., & Schumacker, R.E. (2009) Interaction effects: centering, variance inflation factor, and interpretation issues. Multiple Linear Regression Viewpoints, vol. 35, pp. 6-11.
37. Stein, A., & Corsten, L.C.A. (1991) Universal kriging and cokriging as a regression procedure. Biometrics, vol. 47, pp. 575-587.
38. Sumfleth, K., & Duttmann, R. (2008) Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators. Landscape Ecology, vol. 8, pp. 485-501.
39. Tu, J., & Xia, Z.G. (2008) Examining spatially varying relationships between land use and water quality using geographically weighted regression I: model design and evaluation. The Science of the Total Environment, vol. 407, pp. 358-378.
40. Viaud, V., Angers, D.A., Parnaudeau, V., Morvan, T., & Menasseri, A.S. (2011) Response of organic matter to reduced tillage and animal manure in a temperate loamy soil. Soil Use and Management, vol. 27, pp. 84-93.
41. Wang, K., Wang, H.J., Shi, X.Z., Weindorf, D.C., Yu, D.S., Liang, Y., & Shi, D.M. (2009) Landscape analysis of dynamic soil erosion in Subtropical China: a case study in Xingguo County, Jiangxi Province. Soil and Tillage Research, vol. 105, pp. 313-321.
42. Young, L.J., Gotway, C.A., Yang, J., Kearney, G., & DuClos, C. (2009) Linking health and environmental data in geographical analysis: it's so much more than centroids. Spatial and Spatio-temporal Epidemiology, vol. 1, pp. 73-84.
43. Zech, W., Senesi, N., Guggenberger, G., Kaiser, K., Lehmann, J., Miano, T.M., Miltner, A., & Schroth, G. (1997) Factors controlling humification and mineralization of soil organic matter in the tropics. Geoderma, vol. 79, pp. 117-161.