In monitoring and modeling landscape soil processes, the sampling andmodeling scales should, ideally, be commensuratewith the scales of the soil characteristics of interest. Unfortunately, this is usually not possible, both because the true covariance structure of the variable of interest is unknown a priori and because of logistical constraints. We examine the biases and random errors in variogram parameters that result from the choice of a sample scale triplet (spacing, extent, and support… CONTINUE READING
Fig. 7. (Top) Effect of spacing LS* and extent LE* on the estimated nugget ĉ0*, by the weighted least squares (WLS, green) and maximum likelihood (ML, blue) methods. (Middle) Effect of spacing LS* and extent LE* on the estimated sill ĉs* (WLS green, ML blue) and the sample variance s2* (red). (Bottom) Effect of spacing LS* and extent LE* on the estimated correlation length l̂* (WLS green, ML blue) and the integral scale Ĵ* (red). Gridded sampling (median with 25 and 75% quantiles as error bars). All are for the three-parameter model.