Abstract
When doing sensing for high-resolution soil mapping, one has to decide on the disposition of the sensor, which is a special case of spatial sampling. To optimise the pattern of measurements, a cost model and a quality model are proposed. The quality model reflects the coverage of the geographic space, and this is illustrated with some practical experiments. Optimisation of sensing patterns is worked out for two different types of sensing equipment. If the sensor variable differs from the target (management or decision) variable, then a model is needed to predict the target variable from the ancillary data. So in that case, one also has to decide how and where to sample for calibration data. This ‘calibration sampling’ differs from ‘sensor sampling’, as now coverage of the predictor space rather than the geographic space is important. In addition, the handling of extremes is an issue here. Existing methods for calibration sampling are reviewed and a suggestion is made for a new approach, based on fuzzy cluster analysis, which might avoid some of the shortcomings of existing methods.
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References
Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York, NY
Brus DJ, de Gruijter JJ, van Groenigen JW (2003) Designing spatial coverage designs by the k-means clustering algorithm. Proceedings of 8th international FZK/TNO conference on contaminated soil (ConSoil 2003), Gent, Belgium, pp 504–509
Brus DJ, Heuvelink GBM (2007) Optimization of sample patterns for universal kriging of environmental variables. Geoderma 138:86–95
de Gruijter JJ, Brus DJ, Bierkens MFP, Knotters, M (2006) Sampling for natural resource monitoring. Springer, Heidelberg
Fitzgerald GJ, Lesch SM, Barnes EM, Luckett WE (2006) Directed sampling using remote sensing with a response surface sampling design for site-specific agriculture. Comput Electron Agric 53:98–112
McBratney AB, de Gruijter JJ (1992) A continuum approach to soil classification by modified fuzzy k-means with extragrades. J Soil Sci 43:159–175
McKay MD, Beckman RJ, Conover WJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21:239–245
Minasny B, McBratney AB (2006) A conditioned Latin hypercube method for sampling in the presence of ancillary information. Comput Geosci 2:1378–1388
Myers RH, Montgomery DC (2002) Response surface methodology: process and product optimization using designed experiments, 2nd edn. Wiley, New York
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de Gruijter, J., McBratney, A., Taylor, J. (2010). Sampling for High-Resolution Soil Mapping. In: Viscarra Rossel, R., McBratney, A., Minasny, B. (eds) Proximal Soil Sensing. Progress in Soil Science. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8859-8_1
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DOI: https://doi.org/10.1007/978-90-481-8859-8_1
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