Development of Reflectance Spectral Libraries for Characterization of Soil Properties

土壤水分 土工试验 土壤科学 多元统计 环境科学 离群值 反射率 土壤肥力 矿物学 化学 数学 统计 物理 光学
作者
Keith Shepherd,Markus Walsh
出处
期刊:Soil Science Society of America Journal [Wiley]
卷期号:66 (3): 988-998 被引量:942
标识
DOI:10.2136/sssaj2002.9880
摘要

Methods for rapid estimation of soil properties are needed for quantitative assessments of land management problems. We developed a scheme for development and use of soil spectral libraries for rapid nondestructive estimation of soil properties based on analysis of diffuse reflectance spectroscopy. A diverse library of over 1000 archived topsoils from eastern and southern Africa was used to test the approach. Air‐dried soils were scanned using a portable spectrometer (0.35–2.5 μm) with an artificial light source. Soil properties were calibrated to soil reflectance using multivariate adaptive regression splines (MARS), and screening tests were developed for various soil fertility constraints using classification trees. A random sample of one‐third of the soils was withheld for validation purposes. Validation r 2 values for regressions were: exchangeable Ca, 0.88; effective cation‐exchange capacity (ECEC), 0.88; exchangeable Mg, 0.81; organic C concentration, 0.80; clay content, 0.80; sand content, 0.76; and soil pH, 0.70. Validation likelihood ratios for diagnostic screening tests were: ECEC <4.0 cmol c kg −1 , 10.8; pH <5.5, 5.6; potential N mineralization >4.1 mg kg −1 d −1 , 2.9; extractable P <7 mg kg −1 , 2.9; exchangeable K <0.2 cmol c kg −1 , 2.6. We show the response of prediction accuracy to sample size and demonstrate how the predictive value of spectral libraries can be iteratively increased through detection of spectral outliers among new samples. The spectral library approach opens up new possibilities for modeling, assessment and management of risk in soil evaluations in agricultural, environmental, and engineering applications. Further research should test the use of soil reflectance in pedotransfer functions for prediction of soil functional attributes.
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