Diffuse reflectance mid infra-red spectroscopy combined with machine learning algorithms can differentiate spectral signatures in shallow and deeper soils for the prediction of pH and organic matter content
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CitationFelipe Bachion de Santana, Eric C. Grunsky, Mairéad M. Fitzsimons, Vincent Gallagher, Karen Daly, Diffuse reflectance mid infra-red spectroscopy combined with machine learning algorithms can differentiate spectral signatures in shallow and deeper soils for the prediction of pH and organic matter content, CATENA, Volume 218, 2022, 106552, ISSN 0341-8162, https://doi.org/10.1016/j.catena.2022.106552.
AbstractPrecision and sustainable agriculture requires information about soil pH and organic matter (OM) content at higher spatial and temporal scales than current agronomic sampling and analytical methods allow. This study examined the accuracy of spectral models using high throughput screening (HTS) in diffuse reflectance mode in mid Infra-red (MIR)/DRIFT combined with machine learning algorithms to predict soil pH(CaCl2) and %OM in shallow and deeper topsoils compared to laboratory methods. Models were developed from an archive of samples taken on a 4 km2 grid from the northern half of Ireland (Terra Soil project), which includes 18,859 samples (9,396 shallow + 9,463 deeper). The application of Cubist models showed that for different depths there are minor different spectral group associations with pH and %OM values. These differences resulted in a loss of accuracy in the extrapolation of the topsoil model to predict values from deeper topsoils or vice versa. Therefore we recommend the use of samples from both depths to build a calibration model.The proposed methodology was able to determine %OM and pH using a unique multivariate regression model for both depths, with RMSEP values of 1.12 and 0.89 %; RPIQ values of 42.34 and 38.48; R2val of 0.9989 and 0.9993 for %OM determinations in shallow and deeper topsoils, respectively. For pH determinations the RMSEP values obtained were 0.25 and 0.34; RPIQ values of 6.04 and 4.94; R2val 0.9385 and 0.8954. Both regression models are classified as excellent predictions models, yielding RPIQ values >4.05 for shallow and deeper topsoils. The results demonstrated the high potential of HTS-DRIFT combined with machine learning algorithms as a rapid, accurate, and cost-effective method to build large soil spectral libraries, displaying predicted results similar to two separate soil laboratory methods (pH and LOI).
FunderTeagasc; Geological Survey Ireland
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