Abstract
The special problem of surface approximation from noisy traverse data was examined using three differentclasses of algorithms: global function minimization, triangulation, and weighted sum approximations. Tests utilized bothan analytical function and field data collected from a real-time, ground-based organic matter sensor. Thin plate splinesand natural neighbor approximation were shown to generally outperform Shepards method in the generation of surfacesfrom noisy traverse data. Thin plate splines were recommended for surface generation of data from ground-based soilproperties sensors.
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