BUILDING PREDICTIVE MODELS OF SOIL PARTICLE-SIZE DISTRIBUTION

作者：Samuel Rosa Alessandro^*; Diniz Dalmolin Ricardo Simao; Miguel Pablo

来源：Revista Brasileira de Ciencia do Solo, 2013, 37(2): 422-430.

DOI：10.1590/S0100-06832013000200013

• digital soil mapping
• terrain attributes
• multiple linear regression
• cross-validation
• additive log-ratio

Is it possible to build predictive models (PMs) of soil particle-size distribution (psd) in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index). The PMs explained more than half of the data variance. This performance is similar to (or even better than) that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed in geologically more complex areas. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, PMs built on terrain attributes are efficient in predicting the particle-size distribution (psd) of soils in regions of complex geology.

• 出版日期2013-4