The Annals of Applied Statistics

Multivariate spatial mapping of soil water holding capacity with spatially varying cross-correlations

Rachel M. Messick, Matthew J. Heaton, and Neil Hansen

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Abstract

Irrigation in agriculture mitigates the adverse effects of drought and improves crop production and yield. Still, water scarcity remains a persistent issue and water resources need to be used responsibly. To improve water use efficiency, precision irrigation is emerging as an approach where farmers can vary the application of water according to within field variation in soil and topographic conditions. As a precursor, methods to characterize spatial variation of soil hydraulic properties are needed. One such property is soil water holding capacity (WHC). This analysis develops a multivariate spatial model for predicting WHC across a field at various soil depths using sparse WHC observations and covariates such as soil electrical conductivity. To capture spatially varying cross-correlations in an efficient manner, we propose to extend the conditional specification of a multivariate Gaussian process by using spatially varying coefficients. Because data is already sparse, our analysis fully utilizes incomplete observations by imputing missing values that we treat as not missing at random. Additionally, due to the high cost of measuring WHC, we use a multivariate integrated mean square error criterion to choose a new observation location that, after sampling, will result in the least predictive uncertainty across the entire field.

Article information

Source
Ann. Appl. Stat. Volume 11, Number 1 (2017), 69-92.

Dates
Received: April 2016
Revised: September 2016
First available in Project Euclid: 8 April 2017

Permanent link to this document
http://projecteuclid.org/euclid.aoas/1491616872

Digital Object Identifier
doi:10.1214/16-AOAS991

Keywords
Multivariate spatial processes conditional specification spatial design Gaussian process not missing at random

Citation

Messick, Rachel M.; Heaton, Matthew J.; Hansen, Neil. Multivariate spatial mapping of soil water holding capacity with spatially varying cross-correlations. Ann. Appl. Stat. 11 (2017), no. 1, 69--92. doi:10.1214/16-AOAS991. http://projecteuclid.org/euclid.aoas/1491616872.


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References

  • Allen, R. G., Pereira, L. S., Raes, D., Smith, M. et al. (1998). Crop evapotranspiration—guidelines for computing crop water requirements—FAO irrigation and drainage paper 56. FAO, Rome 300 D05109.
  • Apanasovich, T. and Genton, M. G. (2010). Cross-covariance functions for multivariate random fields based on latent dimensions. Biometrika 97 15–30.
  • Apanasovich, T. V., Genton, M. G. and Sun, Y. (2012). A valid Matérn class of cross-covariance functions for multivariate random fields with any number of components. J. Amer. Statist. Assoc. 107 180–193.
  • Banerjee, S., Gelfand, A. E., Finley, A. O. and Sang, H. (2008). Gaussian predictive process models for large spatial data sets. J. R. Stat. Soc. Ser. B. Stat. Methodol. 70 825–848.
  • Bruce, R. R. and Luxmoore, R. J. (1986). Water retention: Field methods. In Methods of Soil Analysis: Part 1—Physical and Mineralogical Methods, 2nd ed. (A. Klute, ed.). 663–686. Soil Science Society of America, American Society of Agronomy, Madison, WI.
  • Cressie, N. and Johannesson, G. (2008). Fixed rank kriging for very large spatial data sets. J. R. Stat. Soc. Ser. B. Stat. Methodol. 70 209–226.
  • Cressie, N. and Zammit-Mangion, A. (2015). Multivariate spatial covariance models: A conditional approach. Available at arXiv:1504.01865v1.
  • Currin, C., Mitchell, T., Morris, M. and Ylvisaker, D. (1991). Bayesian prediction of deterministic functions, with applications to the design and analysis of computer experiments. J. Amer. Statist. Assoc. 86 953–963.
  • Diggle, P. J. and Ribeiro, P. J. (2002). Bayesian inference in Gaussian model-based geostatistics. Geogr. Environ. Model. 6 129–146.
  • Finley, A. O., Sang, H., Banerjee, S. and Gelfand, A. E. (2009). Improving the performance of predictive process modeling for large datasets. Comput. Statist. Data Anal. 53 2873–2884.
  • Fuentes, M. and Reich, B. (2013). Multivariate spatial nonparametric modelling via kernel processes mixing. Statist. Sinica 23 75–97.
  • Gelfand, A. E. and Banerjee, S. (2010). Multivariate spatial process models. In Handbook of Spatial Statistics 495–515. CRC Press, Boca Raton, FL.
  • Gelfand, A. E., Kim, H.-J., Sirmans, C. F. and Banerjee, S. (2003). Spatial modeling with spatially varying coefficient processes. J. Amer. Statist. Assoc. 98 387–396.
  • Gelfand, A. E., Schmidt, A. M., Banerjee, S. and Sirmans, C. F. (2004). Nonstationary multivariate process modeling through spatially varying coregionalization. TEST 13 263–312. With discussion by Montserrat Fuentes, Dave Higdon and Bruno Sansó and a rejoinder by the authors.
  • Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statist. Sci. 7 457–472.
  • Genton, M. G. and Kleiber, W. (2015). Cross-covariance functions for multivariate geostatistics. Statist. Sci. 30 147–163.
  • Gneiting, T., Kleiber, W. and Schlather, M. (2010). Matérn cross-covariance functions for multivariate random fields. J. Amer. Statist. Assoc. 105 1167–1177.
  • Gneiting, T. and Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. J. Amer. Statist. Assoc. 102 359–378.
  • Guhaniyogi, R., Finley, A. O., Banerjee, S. and Kobe, R. K. (2013). Modeling complex spatial dependencies: Low-rank spatially varying cross-covariances with application to soil nutrient data. J. Agric. Biol. Environ. Stat. 18 274–298.
  • Heaton, M. J., Christensen, W. F. and Terres, M. A. (2017). Nonstationary Gaussian process models using spatial hierarchical clustering from finite differences. Technometrics 59 93–101.
  • Higdon, D. (2002). Space and space–time modeling using process convolutions. In Quantitative Methods for Current Environmental Issues (C. Anderson, V. Barnett, P. C. Chatwin and A. H. El-Shaarawi, eds.) 37–56. Springer-Verlag, London.
  • Johnson, M. E., Moore, L. M. and Ylvisaker, D. (1990). Minimax and maximin distance designs. J. Statist. Plann. Inference 26 131–148.
  • Jones, G. L., Haran, M., Caffo, B. S. and Neath, R. (2006). Fixed-width output analysis for Markov chain Monte Carlo. J. Amer. Statist. Assoc. 101 1537–1547.
  • Kang, E. L. and Cressie, N. (2011). Bayesian inference for the spatial random effects model. J. Amer. Statist. Assoc. 106 972–983.
  • Kitchen, N. R., Drummond, S. T., Lund, E. D., Sudduth, K. A. and Buchleiter, G. W. (2003). Soil electrical conductivity and topography related to yield for three contrasting soil–crop systems. Agron. J. 95 483–495.
  • Kleiber, W. and Genton, M. G. (2013). Spatially varying cross-correlation coefficients in the presence of nugget effects. Biometrika 100 213–220.
  • Kleiber, W., Sain, S. R., Heaton, M. J., Wiltberger, M., Reese, C. S. and Bingham, D. (2013). Parameter tuning for a multi-fidelity dynamical model of the magnetosphere. Ann. Appl. Stat. 7 1286–1310.
  • Klute, A. (1986). Water retention: Laboratory methods. In Methods of Soil Analysis: Part 1—Physical and Mineralogical Methods, 2nd ed. (A. Klute, ed.) 635–662. Soil Science Society of America, American Society of Agronomy, Madison, WI.
  • Krause, A., Singh, A. and Guestrin, C. (2008). Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies. J. Mach. Learn. Res. 9 235–284.
  • Lemos, R. T. and Sansó, B. (2009). A spatio-temporal model for mean, anomaly, and trend fields of North Atlantic sea surface temperature. J. Amer. Statist. Assoc. 104 5–18.
  • Little, R. J. A. and Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd ed. Wiley-Interscience [John Wiley & Sons], Hoboken, NJ.
  • Longchamps, L., Khosla, R., Reich, R. and Gui, D. W. (2015). Spatial and temporal variability of soil water content in leveled fields. Soil Sci. Soc. Amer. J. 79 1446–1454.
  • Majumdar, A., Paul, D. and Bautista, D. (2010). A generalized convolution model for multivariate nonstationary spatial processes. Statist. Sinica 20 675–695.
  • Mzuku, M., Khosla, R., Reich, R., Inman, D., Smith, F. and MacDonald, L. (2005). Spatial variability of measured soil properties across site-specific management zones. Soil Sci. Soc. Amer. J. 69 1572–1579.
  • Natural Resources Conservation Service (1997). National Engineering Handbook: Irrigation guide. U.S. Department of Agriculture.
  • Natural Resources Conservation Service (2016). National Soil Survey Handbook. U.S. Department of Agriculture.
  • Nychka, D., Bandyopadhyay, S., Hammerling, D., Lindgren, F. and Sain, S. (2015). A multiresolution Gaussian process model for the analysis of large spatial datasets. J. Comput. Graph. Statist. 24 579–599.
  • Plaster, E. (2013). Soil Science and Management. Cengage Learning, Independence, KY.
  • Ranjan, P., Lu, W., Bingham, D., Reese, S., Williams, B. J., Chou, C.-C., Doss, F., Grosskopf, M. and Holloway, J. P. (2011). Follow-up experimental designs for computer models and physical processes. J. Stat. Theory Pract. 5 119–136.
  • Royle, J. A. and Berliner, L. M. (1999). A hierarchical approach to multivariate spatial modeling and prediction. J. Agric. Biol. Environ. Stat. 4 29–56.
  • Sacks, J., Schiller, S. B. and Welch, W. J. (1989). Designs for computer experiments. Technometrics 31 41–47.
  • Sadler, E. J., Evans, R., Stone, K. C. and Camp, C. R. (2005). Opportunities for conservation with precision irrigation. J. Soil Water Conserv. 60 371–378.
  • Sang, H., Jun, M. and Huang, J. Z. (2011). Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors. Ann. Appl. Stat. 5 2519–2548.
  • Santner, T. J., Williams, B. J. and Notz, W. I. (2003). The Design and Analysis of Computer Experiments. Springer, New York.
  • Seager, R., Hoerling, M., Schubert, S., Wang, H., Lyon, B., Kumar, A., Nakamura, J. and Henderson, N. (2014). Causes and predictability of the 2011–14 California drought. Assessment report, National Oceanic and Atmospheric Administration, Silver Spring, MD.
  • Zhang, H. (2004). Inconsistent estimation and asymptotically equal interpolations in model-based geostatistics. J. Amer. Statist. Assoc. 99 250–261.