Skip to main content
SpringerLink
Log in

Integration of electromagnetic induction sensor data in soil sampling scheme optimization using simulated annealing

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Soil survey is generally time-consuming, labor-intensive, and costly. Optimization of sampling scheme allows one to reduce the number of sampling points without decreasing or even increasing the accuracy of investigated attribute. Maps of bulk soil electrical conductivity (EC a ) recorded with electromagnetic induction (EMI) sensors could be effectively used to direct soil sampling design for assessing spatial variability of soil moisture. A protocol, using a field-scale bulk EC a survey, has been applied in an agricultural field in Apulia region (southeastern Italy). Spatial simulated annealing was used as a method to optimize spatial soil sampling scheme taking into account sampling constraints, field boundaries, and preliminary observations. Three optimization criteria were used. the first criterion (minimization of mean of the shortest distances, MMSD) optimizes the spreading of the point observations over the entire field by minimizing the expectation of the distance between an arbitrarily chosen point and its nearest observation; the second criterion (minimization of weighted mean of the shortest distances, MWMSD) is a weighted version of the MMSD, which uses the digital gradient of the grid EC a data as weighting function; and the third criterion (mean of average ordinary kriging variance, MAOKV) minimizes mean kriging estimation variance of the target variable. The last criterion utilizes the variogram model of soil water content estimated in a previous trial. The procedures, or a combination of them, were tested and compared in a real case. Simulated annealing was implemented by the software MSANOS able to define or redesign any sampling scheme by increasing or decreasing the original sampling locations. The output consists of the computed sampling scheme, the convergence time, and the cooling law, which can be an invaluable support to the process of sampling design. The proposed approach has found the optimal solution in a reasonable computation time. The use of bulk EC a gradient as an exhaustive variable, known at any node of an interpolation grid, has allowed the optimization of the sampling scheme, distinguishing among areas with different priority levels.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Adamchuk, V. I., & Viscarra Rossel, R. A. (2010). Development of on-the-go proximal soil sensor systems. In R. A. Viscarra Rossel, A. B. McBratney, & B. Minasny (Eds.), Proximal soil sensing (pp. 15–28). New York: Springer.

    Chapter  Google Scholar 

  • Adamchuk, V. I., Viscarra Rossel, R. A., Sudduth, K. A., & Schulze Lammers, P. (2011). Sensor fusion for precision agriculture. In C. Thomas (Ed.), Sensor fusion - foundation and applications (pp. 27–40). Croatia: In-Tech.

    Google Scholar 

  • Barca, E., Passarella, G., Vurro, M., & Morea, A. (2011). A software for optimal information based downsizing/upsizing of existing monitoring networks. In Spatial2 Conference: Spatial Data Methods for Environmental and Ecological Processes, Foggia (IT), 1-2 September 2011.

  • Barca, E., Passarella, G., Vurro, M., & Morea, A. (2015). MSANOS: Data-driven, multi-approach software for optimal redesign of environmental monitoring networks. Water Resources Management, 29(2), 619–644.

  • Benson, R. R., Glaccum, A., & Noel, M. R. (1988). Geophysical techniques for sensing buried wastes and waste migration. Dublin: National Water Well Association.

    Google Scholar 

  • Campbell, J. (2002). Introduction to remote sensing (3rd ed.). New York: The Guilford Press.

    Google Scholar 

  • Castrignanò A., Morari F., Fiorentino C., Pagliarin C., Brenna S. (2008) Constrained optimization of spatial sampling in skeletal soils using EMI data and continuous simulated annealing. In Proceeding of 1st Global Workshop on High Resolution Digital Soil Sensing & Mapping. 5–8 February 2008, Sydney – Australia (CD-ROM).

  • Cochran, W. G. (1977). Sampling techniques (3rd ed.). New York: Wiley.

    Google Scholar 

  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46.

    Article  Google Scholar 

  • Corwin, D. L., & Lesch, S. M. (2005). Characterizing soil spatial variability with apparent soil electrical conductivity. I. Survey protocols. Computers and Electronics in Agriculture, 46, 103–133.

    Article  Google Scholar 

  • Cunha, M. C., & Sousa, J. (1999). Water distribution network design optimization: Simulated annealing approach. Journal of Water Resources Planning and Management, 125, 215–221.

  • De Benedetto, D., Castrignanò, A., Sollitto, D., Modugno, F., Buttafuoco, G., & Lo Papa, G. (2012). Integrating geophysical and geostatistical techniques to map the spatial variation of clay. Geoderma, 171–172, 53–63.

  • De Benedetto, D., Castrignanò, A., Rinaldi, M., Ruggieri, S., Santoro, F., Figorito, B., Gualano, S., Diacono, M., & Tamborrino, R. (2013). An approach for delineating homogeneous zones by using multi-sensor data. Geoderma, 199, 117–127.

    Article  Google Scholar 

  • de Gruijter, J., Brus, D., Bierkens, F. P., & Knotters, M. (2006). Sampling for natural resource monitoring. Berlin: Springer.

    Book  Google Scholar 

  • Drosou M., Pitoura E. (2009). Comparing diversity heuristics. Technical Report 2009–05. Computer Science Department, University of Ioannina.

  • Fleiss, J. L. (1981). Statistical methods for rates and proportions. New York: Wiley.

    Google Scholar 

  • Geovariances (2014). Isatis Technical Ref., ver. 2014, Geovariances & Ecole Des Mines De Paris. Avon Cedex, France, www.geovariances.fr.

  • Kerry, R., Oliver, M. A., & Frogbrook, Z. L. (2010). Sampling in precision agriculture. In M. A. Oliver (Ed.), Geostatistical applications for precision agriculture (pp 35–64). Springer.

  • Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671–680.

    Article  CAS  Google Scholar 

  • Lobato, F. S., Assis, E. G., Steffen, Jr V., & da Silva Neto, A. J. (2012). Design and identification problems of rotor bearing systems using the simulated annealing algorithm. In M. Sales Guerra Tsuzuki (Ed.), Simulated annealing - single and multiple objective problems (pp 197–216). Croatia: In-Tech.

  • Marchant, B. P., & Lark, R. M. (2010) Sampling in precision agriculture, optimal designs from uncertain models. In M. A. Oliver (Ed.), Geostatistical applications for precision agriculture (pp 65–87). Springer.

  • McBratney, A. B., & Webster, R. (1983). Optimal interpolation and isarithmic mapping of soil properties. Co-regionalization and multiple sampling strategies. Journal of Soil Science, 34, 137–162.

    Article  Google Scholar 

  • McNeill, J. D. (1980). Electromagnetic terrain conductivity measurement at low induction numbers. Geonics Limited, Technical Note TN 6, Geonics Ltd. Mississauga, Ontario, Canada.

  • Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21, 1087–1092.

    Article  CAS  Google Scholar 

  • Müller, W. G. (1998). Collecting spatial data-optimum design of experiments for random fields. Heidelberg: Physical-Verlag.

    Google Scholar 

  • Nourani, Y., & Andresen, B. (1998). A comparison of simulated annealing cooling strategies. Journal of Physics A: Mathematical and General, 31, 8373–8385.

    Article  Google Scholar 

  • Pierce, F. J., & Nowak, P. (1999). Aspects of precision agriculture. Advances in Agronomy, 67, 1–85.

    Article  Google Scholar 

  • Soil Survey Staff. (2010). Keys to soil taxonomy (11th ed.). Washington: USDA-Natural Resources Conservation Service.

    Google Scholar 

  • Stein, A., & Ettema, C. (2003). An overview of spatial sampling procedures and experimental design of spatial studies for ecosystem comparisons. Agriculture, Ecosystems & Environment, 94, 31–47.

    Article  Google Scholar 

  • Triki, E., Collette, Y., & Siarry, P. (2005). A theoretical study on the behavior of simulated annealing leading to a new cooling schedule. European Journal of Operational Research, 166, 77–92.

    Article  Google Scholar 

  • Van Groenigen, J. W., & Stein, A. (1998). Spatial simulated annealing for constrained optimization of spatial sampling schemes. Journal of Environmental Quality, 27, 1078–1086.

    Article  Google Scholar 

  • Van Groenigen, J. W., Siderius, W., & Stein, A. (1999). Constrained optimisation of soil sampling for minimisation of the kriging variance. Geoderma, 87(3), 239–259.

  • Van Laarhoven, P. J., & Aarts, E. H. (1987). Simulated annealing. Netherlands: Springer.

    Book  Google Scholar 

  • Viscarra Rossel, R. A., & Adamchuk, V. I. (2013). Proximal soil sensing. In M. A. Oliver, T. F. A. Bishop, & B. P. Marchant (Eds.), Precision agriculture for sustainability and environmental protection (pp. 99–118). Abingdon: Routledge.

    Google Scholar 

  • Wackernagel, H. (2003). Multivariate geostatistics: an introduction with applications. Berlin: Springer.

    Book  Google Scholar 

  • Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists. John Wiley & Sons, New York.

  • Webster, R., & Lark, R. M. (2013). Field sampling for environmental science and management. Abingdon: Routledge.

Download references

Author information

Authors and Affiliations

  1. Water Research Institute (IRSA)–National Research Council (CNR), Bari, Italy

    E. Barca & G. Passarella

  2. Research Unit for Cropping Systems in Dry Environments, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Bari, Italy

    A. Castrignanò & D. De Benedetto

  3. Institute for Agricultural and Forest Systems in the Mediterranean, National Research Council of Italy, Rende, CS, Italy

    G. Buttafuoco

Authors
  1. E. Barca
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Castrignanò
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. Buttafuoco
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. D. De Benedetto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. G. Passarella
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. Barca.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barca, E., Castrignanò, A., Buttafuoco, G. et al. Integration of electromagnetic induction sensor data in soil sampling scheme optimization using simulated annealing. Environ Monit Assess 187, 422 (2015). https://doi.org/10.1007/s10661-015-4570-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-015-4570-y

Keywords

Search

Navigation