Journal
EUROPEAN JOURNAL OF SOIL SCIENCE
Volume 66, Issue 4, Pages 661-669Publisher
WILEY
DOI: 10.1111/ejss.12265
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Funding
- National Science and Engineering Research of Canada (NSERC) Discovery
- Commonwealth Scientific and Industrial Research Organisation (CSIRO) Land and Water Flagship
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Recent advances in semiconductor technologies have given rise to the development of mid-infrared (mid-IR) spectrometers that are compact, relatively inexpensive, robust and suitable for in situ proximal soil sensing. The objectives of this research were to evaluate a prototype portable mid-IR spectrometer for direct measurements of soil reflectance and to model the spectra to predict sand, clay and soil organic matter (SOM) contents under a range of field soil water conditions. Soil samples were collected from 23 locations at different depths in four agricultural fields to represent a range of soil textures, from sands to clay loams. The particle size distribution and SOM content of 48 soil samples were measured in the laboratory by conventional analytical methods. In addition to air-dry soil, each sample was wetted with two different amounts of water before the spectroscopic measurements were made. The prototype spectrometer was used to measure reflectance (R) in the range between 1811 and 898cm(-1) (approximately 5522 to 11136nm). The spectroscopic measurements were recorded randomly and in triplicate, resulting in a total of 432 reflectance spectra (48 samplesxthree soil water contentsxthree replicates). The spectra were transformed to log(10) (1/R) and mean centred for the multivariate statistical analyses. The 48 samples were split randomly into a calibration set (70%) and a validation set (30%). A partial least squares regression (PLSR) was used to develop spectroscopic calibrations to predict sand, clay and SOM contents. Results show that the portable spectrometer can be used with PLSR to predict clay and sand contents of either wet or dry soil samples with a root mean square error (RMSE) of around 10%. Predictions of SOM content resulted in RMSE values that ranged between 0.76 and 2.24%.
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