Soil Salinity Sampling Strategy Using Spatial Modeling Techniques, Remote Sensing, and Field Data
Publication: Journal of Irrigation and Drainage Engineering
Volume 134, Issue 6
Abstract
The aim of this study was to develop a methodology for collecting soil salinity samples. The objectives of this paper are to: (1) estimate the number of soil salinity samples needed to capture the variability in the soil salinity data with high accuracy; and (2) compare two types of satellite images with different resolutions: Ikonos with resolution and Landsat 5 with resolution. To achieve these objectives, two satellite images were acquired (one for Ikonos and one for Landsat 5) to evaluate the correlation between the measured soil salinity and remote sensing data. From the observed data, three subsets were randomly extracted with each subset containing: 75, 50, and 25% of the data for each field. These three subsets were then used in the modeling process. Ordinary least squares (OLS) (i.e., multiple regression) was used to explore the coarse-scale variability in soil salinity as a function of the Ikonos and Landsat 5 bands. A stepwise procedure was used to identify the best subset of satellite bands to include in the regression models that minimized the Akakie information criteria (AIC). Then, the spatial structure of the residuals from the OLS models were described using sample variogram models. The variogram model with the smallest AIC was selected to describe the spatial dependencies in the soil salinity data. If the residuals were spatially correlated, ordinary kriging was used to model the spatial distribution of soil salinity in the fields. A tenfold cross validation was used to estimate the prediction error for soil salinity. To evaluate the effectiveness of the models, various measures of the prediction error were computed. This study provides an accurate methodology that can be used by researchers in reducing the number of soil samples that need to be collected. This is especially valuable in projects that last several years. The results of this study suggest that the number of soil samples that need to be collected and therefore their cost can be significantly reduced and soil salinity estimation can be significantly improved by using kriging. The results also show that the Ikonos image performed better than the Landsat 5 image.
Get full access to this article
View all available purchase options and get full access to this article.
References
Agterberg, F. P. (1984). “Trend surface analysis.” Spatial statistics and models, G. L. Gaile, and C. J. Willmott, eds., Reidel, Dordrecht, The Netherlands, 147–171.
Akaike, H. (1997). “On entropy maximization principal.” Application of statistics, P. R. Krishnaiah, ed., North-Holland, Amsterdam, The Netherlands, 27–41.
Brockwell, P. J., and Davis, R. A. (1991). “Time series.” Theory and methods, Springer, New York.
Burgess, T. M., and Webster, R. (1980a). “Optimal interpolation and isarithmic mapping of soil properties. I: The semi-variogram and punctual kriging.” Eur. J. Soil. Sci., 31(2), 315–331.
Burgess, T. M., and Webster, R. (1980b). “Optimal interpolation and isarithmic mapping of soil properties. II: Block kriging.” Eur. J. Soil. Sci., 31(2), 333–341.
Chung, C. K., Chong, S. K., and Varsa, E. C. (1995). “Sampling strategies for fertility on a Stoy silt loam soil.” Commun. Soil Sci. Plant Anal., 26(5/6), 741–763.
Cliff, A. D., and Ord, J. K. (1981). Spatial processes, models and applications, Pion Ltd., London, 21–45.
Di, H. J., Trangmar, B. B., and Kemp, R. A. (1989). “Use of geostatistics in designing sampling strategies for soil survey.” Soil Sci. Soc. Am. J., 53(4), 1163–1167.
Dwivedi, R. S., and Rao, B. R. M. (1992). “The selection of the best possible Landsat TM band combination for delineating salt-affected soils.” Int. J. Remote Sens., 13(11), 2051–2058.
Efron, B., and Tibshirani, R. J. (1993). An introduction to the bootstrap, Chapman and Hall, New York.
ERDAS Imagine. (2006). ERDAS Inc., Leica Geosystems, Atlanta.
Geisser, S. (1975). “The predictive sample reuse method with applications.” J. Am. Stat. Assoc., 70, 320–328.
Ghassemi, F., Jackeman, A. J., and Nix, H. A. (1995). Salinization of land and water resources: Human causes, extent, management and case studies, CAB International, Wallingford Oxon, U.K.
Golovina, N. N., Minskiy, D. Ye, Pankova, I., and Solov’yev, D. A. (1992). “Automated air photo interpretation in the mapping of soil salinization in cotton-growing zones.” Mapping Sciences and Remote Sensing, 29(3), 262–268.
Gotway, C. A., Ferguson, R. B., Hergert, G. W., and Peterson, T. A. (1996). “Comparison of kriging and inverse-distance methods for mapping soil parameters.” Soil Sci. Soc. Am. J., 60, 1237–1247.
Guisan, A., and Zimmermann, N. E. (2000). “Predictive habitat distribution models in ecology.” Ecol. Modell., 135, 47–186.
Hevesi, J. A., Istok, J. D., and Flint, A. L. (1992). “Precipitation estimation in mountainous terrain using multivariate geostatistics. Part I: Structural analysis.” J. Appl. Meteorol., 31, 661–676.
Hill, M. J., and Donald, G. E. (2003). “Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series.” Remote Sens. Environ., 84(3), 367–384.
Hillel, D. (2000). Salinity management for sustainable irrigation: Integrating science, environment, and economics, The World Bank, Washington, D.C.
Isaaks, E. H., and Srivastava, R. M. (1989). An introduction to applied geostatistics, Oxford University Press, New York, 249–266, 278–322.
Journel, A. G., and Huijbregts, Ch. J. (1978). Mining geostatistics, Academic, London.
Kravchenko, A., and Bullock, D. G. (1999). “A comparative study of interpolation methods for mapping soil properties.” Agron. J., 91, 393–400.
Matheron, G. (1963). “Principles of geostatistics.” Econ. Geol., 58, 1246–1266.
McBratney, A. B., and Webster, R. (1983). “How many observations are needed for regional estimation of soil properties?” Soil Sci., 135(3), 177–183.
McNeill, J. D. (1980). “Electromagnetic terrain conductivity measurement at low induction numbers.” Technical Note TN6, Geonics Ltd., Mississauga, Ont., Canada.
Meirvenne, M. V., and Hofman, G. (1989). “Spatial variability of soil nitrate-nitrogen after potatoes and its change during winter.” Plant Soil, 120, 103–110.
Norman, C. P. (1990). “Training manual on the use of the EM38 for soil salinity appraisal.” Technical Report Series No., 181, Department of Agriculture and Rural Affairs, Victoria, Canada.
Paz, A., Taboada, M. T., and Gomez, M. J. (1996). “Spatial variability in topsoil micronutrients in a one-hectare cropland plot.” Int. J. Comput. Fluid Dyn. 27(3/4), 479–503.
Phillips, J. D. (1985). “Measuring complexity of environmental gradients.” Vegetatio, Dr. W. Junk Publisher, Dordrecht, The Netherlands, 64, 95–102.
Postel, S. (1999). Pillar of sand: Can the irrigation miracle last? W. W. Norton and Co., New York.
Pozdnyakova, L., and Zhang, R. (1999). “Geostatistical analyses of soil salinity in a large field.” Precision Agriculture, Springer, The Netherlands, 1, 153–165.
Reich, R. M., Aguierre-Bravo, C., Kalkhan, M. A., and Bravo, V. A. (1999). “Spatially based forest inventory for Ejido El Largo, Chihuahua, Mexico.” Proc., North America Science Symp. Toward a Unified Framework for Inventorying and Monitoring Forest Ecosystem Resources, USDA Forest Service, Rocky Mountain Research Station, Washington, D.C., 31–41.
Richards, L. A. (1954). “Diagnosis and improvement of saline and alkali soils.” U.S. Salinity Laboratory staff agriculture handbook No. 60, USDA, Washington, D.C.
Robbins, C. W., and Wiegand, C. L. (1990). “Field and laboratory measurements.” Agricultural salinity assessment and management, ASCE, New York.
Robertson, G. P. (1987). “Geostatistics in ecology: Interpolating with known variance.” Ecology, 68 (3), 744–748.
Schloeder, C. A., Zimmermann, N. E., and Jacobs, M. J. (2001). “Comparison of methods for interpolating soil properties using limited data.” Soil Sci. Soc. Am. J., 65, 470–479.
Shanahan, J. F., et al. (2001). “Use of remote-sensing imagery to estimate corn grain yield.” Agron. J., 93(3), 583–589.
Spies, B., and Woodgate, P. (2004). “Salinity mapping in the Australian context.” Technical Rep., Land and Water, Australia.
Stone, M. (1974). “Cross-validation choice and assessment of statistical predictions.” J. R. Stat. Soc. Ser. B (Methodol.), 36, 111–133.
Szabolcs, I. (1989). Salt-affected soils, CRC, Boca Raton, Fla.
Utset, A., Ruiz, M. E., Herrera, J., and Ponce de Leon, D. (1998). “A geostatistical method for soil salinity sample site spacing.” Geoderma, 86, 143–151.
Wiegand, C. L., Rhoades, J. D., Escobar, D. E., and Everitt, J. H. (1994). “Photographic and videographic observations for determining and mapping the response of cotton to soil salinity.” Remote Sens. Environ., 49, 212–223.
Wilford, J. R., Dent, D. L., Dowling, T., and Braaten R. (2001). “Rapid mapping of soils and salt stores using airborne radiometrics and digital elevation models.” AGSO Res. News., May, 33–40.
Wittler, J. M., Cardon, G. E., Gates, T. K., Cooper, C. A., and Sutherland, P. L. (2006). “Calibration of electromagnetic induction for regional assessment of soil water salinity in an irrigated valley.” J. Irrig. Drain. Eng., 132(5), 436–444.
Information & Authors
Information
Published In
Journal of Irrigation and Drainage Engineering
Volume 134 • Issue 6 • December 2008
Pages: 768 - 777
Copyright
© 2008 ASCE.
History
Received: May 29, 2007
Accepted: Apr 10, 2008
Published online: Dec 1, 2008
Published in print: Dec 2008
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.