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Including Topography and Vegetation Attributes for Developing Pedotransfer Functions
Author(s) -
Sharma Sanjay K.,
Mohanty Binayak P.,
Zhu Jianting
Publication year - 2006
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2005.0087
Subject(s) - pedotransfer function , normalized difference vegetation index , environmental science , vegetation (pathology) , digital elevation model , soil map , watershed , hydrology (agriculture) , digital soil mapping , soil science , correlation coefficient , remote sensing , soil water , geology , mathematics , hydraulic conductivity , statistics , computer science , climate change , medicine , geotechnical engineering , pathology , machine learning , oceanography
With the advent of advanced geographical informational systems (GIS) and remote sensing technologies in recent years, topographic (elevation, slope, aspect, and flow accumulation) and vegetation attributes are routinely available from digital elevation models (DEMs) and normalized difference vegetation index (NDVI) at different spatial (remote sensor footprint, watershed, regional) scales. Based on the correlation of soil distribution and vegetation growth patterns across a topographically heterogeneous landscape, this study explores the use of topographic and vegetation attributes in addition to pedologic attributes to develop pedotransfer functions (PTFs) for estimating soil hydraulic properties in the Southern Great Plains of the USA. The extensive Southern Great Plains 1997 (SGP97) hydrology experiment database was used to derive these functions by using artificial neural networks. Eighteen models combining bootstrapping technique with artificial neural networks were developed in a hierarchical manner to predict the soil water contents at eight different soil water potentials (θ at 5, 10, 333, 500, 1000, 3000, 8000, and 15 000 cm) and the van Genuchten hydraulic parameters (θ r , θ s , α, n ). The performance of the neural network models was evaluated using the Spearman correlation coefficient between the observed and the predicted values and root mean square error (RMSE). Although variability exists within bootstrapped replications, improvements (of different levels of statistical significance) were achieved with certain input combinations of basic soil properties, topography and vegetation information compared with using only the basic soil properties as inputs. Topography (DEM) and vegetation (NDVI) attributes at finer scales were useful to capture the variations within the soil mapping units for the SGP97 region dominated by perennial grass cover.