TEST OF SPECTRAL LIBRARY APPROACH

We tested the overall spectral library approach for the prediction of several important soil properties and soil fertility tests using a spectral library of over 1000 African topsoils. We tested basic relationships between soil properties and soil reflectance, and investigated the response of prediction error to (i) variation in calibration sample size, and (ii) screening for library outliers when predicting new samples (corresponding to the decision nodes in the above scheme).

These are our conclusion...

Reflectance spectroscopy used in the laboratory can provide rapid and simultaneous prediction of several fundamental soil physical and chemical properties. Calibrations and screening tests for various soil properties and soil fertility constraints can be developed, based on a limited number of samples selected from soil spectral libraries, to an accuracy level that is typically acceptable for large-area applications. Even for site-specific management, the method would allow large numbers of samples to be taken from a field, which may give a better overall estimate of a given soil property than more accurate measurements of the property at a lower density sampling. The number of calibration samples required depends on the strength of the calibration with soil reflectance for a given soil attribute and the required level of accuracy. When predicting new soil samples, detection of spectral outliers allows the population of soils for which predictions are applicable to be systematically increased, thereby iteratively increasing the value of the spectral library. Computer programs could be developed for routine use of spectral libraries as an integral part of the spectrometer software.

A spectral library approach provides a tool for generalizing results of soil assessments that are conducted at a limited number of sites, and thereby increases the efficiency of expensive and time-consuming soil-related studies. The rapid nature of the measurement allows soil variability to be more adequately sampled than with conventional approaches and thereby facilitates risk-based approaches to soil assessments. For example, knowledge of the uncertainty in prediction of soil functional attributes, taking into account soil variability, allows users to make informed decisions about the trade off between the cost of the measurement and the risk (or potential for regret) associated with using the prediction.

Further investigations should test reflectance spectroscopy for direct prediction of a wide range of soil functional attributes for agricultural, environmental and engineering applications, both in the laboratory and field, and develop operational schemes for its use in risk-based soil assessments. Because soil reflectance provides an integrated measure of number of fundamental soil properties, such calibrations could perform better, and would certainly be more rapid, than pedotransfer functions based on conventional measurements of soil properties. Soil functional attributes that are often predicted from basic soil properties tested in this study include net primary productivity, plant growth response to soil constraints and ameliorants, soil erodibility, soil compressibility and shrinkage, water retention and conductivity, and capacity to adsorb wastes and pollutants.

 The spectral library approach provides a coherent framework for linking soil information with remote sensing information for improved spatial prediction of soil functional capacity. Remote sensing of soil properties directly from space platforms is hampered by problems such as atmospheric interference, shade and shadow effects, mixtures of materials within pixels, and variation in soil moisture content. Studies on the effect of soil moisture content on calibrations between soil functional attributes and soil reflectance would help to evaluate the potential of reflectance spectroscopy in the field. Future studies should explore approaches that combine soil spectral libraries, and other geo-referenced information, such as from digital terrain models and field observations, with information from multi- and hyper-spectral remote sensing imagery (e.g. Shepherd and Walsh, 2002.)

 

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