Quantifying the Aspect Effect: An Application of Solar Radiation Modeling for Soil Survey [Erratum: 2009 Sept-Oct, v. 73, no. 5, p. 1755.]

• Published in: Soil Science Society of America journal Vol. 73; no. 4; pp. 1345 - 1352
• Main Authors: Beaudette, D.E, O'Geen, A.T
• Format: Journal Article
• Language: English
• Published: Madison Soil Science Society 01.07.2009; Soil Science Society of America; American Society of Agronomy
• Subjects: Agronomy. Soil science and plant productions; aspect; aspect angle; Biological and medical sciences; Earth sciences; Earth, ocean, space; equations; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; Geographic information systems; Geologic mapping; geomorphology; light intensity; mathematical models; Microclimate; Mollisols; Regression analysis; Remote sensing; Soil formation; Soil science; Soil surveys; Soil surveys, classification and mapping; Soil surveys, classification and mapping, soil genesis; Soils; Solar radiation; Spatial distribution; statistical models; Surficial geology; terrain; topography; upland soils; models; soil sciences; solar radiation; Soil science; Earth science; quantitative analysis; Soil investigation; Modeling
• ISSN: 0361-5995; 1435-0661
• DOI: 10.2136/sssaj2008.0229

Aspect angle is commonly used by soil scientists and ecologists as a qualitative proxy for landscape-scale variation in microclimate and microclimate-induced phenomena. This property of the landscape is simple to measure in the field and has a direct interpretation. Aspect angle is not easily integrated into more quantitative, physically based measures of landscape-scale phenomena, however, because (i) solar geometry, not aspect angle, drives near-surface processes, and (ii) there are numerical difficulties associated with periodic variables. We used the European Solar Radiation Atlas solar radiation model to generate an annual radiation load surface for subsequent modeling of microclimate-coupled near-surface processes at Pinnacles National Monument in California. Estimated annual solar radiation load values coupled with a local geologic map were used as predictor variables in a logistic regression model constructed to predict the spatial distribution of upland Mollisols. A total of 185 field observations were used to build the model, which had an 83 percent correctly classified (PCC) rate and a receiver operating characteristic (ROC) area of 0.78 to 0.96. A 50-fold cross-validation (repeated refitting of the model with a subset of observations) procedure indicated a mean classification error rate of 22%. Solar radiation modeling offers an exciting new approach for linking geographic information system and field observation in regions where microclimate-coupled soil-forming factors dominate pedogenesis. In addition, the quantification of a previously qualitatively described phenomena (the aspect effect) leads to a continuous description of soil taxonomic features at a much finer scale than is currently possible in soil survey.