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Processing of spatial information for mapping of soil organic carbon
Precise and accurate estimates of soil carbon stock (CS) at various scales are key to understanding the potential for terrestrial sequestration of atmospheric CO2. Soil CS exhibits significant field-scale variability due to spatially-varying topography and parent material or past differences in vegetation and management history that affect soil carbon cycling. This limits the accuracy of classical sampling and estimation. We hypothesized that correlated secondary information can aid in the spatial sampling and mapping of soil CS in cases where no prior information on the spatial variation of CS is available. The objectives of this study were to: (a) evaluate different geostatistical methods for incorporating secondary information into the spatial estimation of field-scale CS; (b) identify suitable secondary information for increasing the precision of CS maps; (c) formulate a strategy for utilizing prior and mixed secondary information for spatial classification, and (d) develop an approach for optimizing spatial sampling schemes based on secondary information. Simple kriging with varying local means (or regression kriging) was the most robust method for incorporating secondary information in the spatial estimation of CS. On-the-go sensed apparent electrical conductivity was the most useful secondary information evaluated, providing the greatest contribution to increasing map accuracy and precision. A novel algorithm for spatial clustering utilizing mixed categorical and continuous secondary information was developed. The spatial clusters served as stratification for optimizing spatial sampling utilizing the simulated annealing algorithm. Optimization resulted in even spatial coverage of the field and provided a robust variogram estimate. Results indicate that sampling demand can be reduced if correlated secondary information is used for field stratification and spatial interpolation at unsampled locations. Future efforts should concentrate on the: (a) application of regression kriging techniques to CS mapping at different spatial scales, particularly with regard to availability and effectiveness of secondary information, (b) determination of the optimum sample sizes for a particular accuracy, precision and cost of sampling, and (c) use of secondary information for improving sampling designs for regional estimations of whole-field mean CS. ^
Agriculture, Soil Science|Environmental Sciences
Simbahan, Gregorio C, "Processing of spatial information for mapping of soil organic carbon" (2004). ETD collection for University of Nebraska - Lincoln. AAI3142102.