Estimating Soil Properties and Classes from Spectra

[1]  R. Brereton,et al.  Partial least squares discriminant analysis: taking the magic away , 2014 .

[2]  Budiman Minasny,et al.  From pedotransfer functions to soil inference systems , 2002 .

[3]  C. Hurburgh,et al.  Near-Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties , 2001 .

[4]  Ron Wehrens,et al.  The pls Package: Principal Component and Partial Least Squares Regression in R , 2007 .

[5]  A. McBratney,et al.  Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy , 2010 .

[6]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[7]  Kim H. Esbensen,et al.  The RPD Myth… , 2014 .

[8]  Budiman Minasny,et al.  Optimizing wavelength selection by using informative vectors for parsimonious infrared spectra modelling , 2019, Comput. Electron. Agric..

[9]  P. Heuberger,et al.  Calibration of process-oriented models , 1995 .

[10]  Alex B. McBratney,et al.  Using a legacy soil sample to develop a mid-IR spectral library , 2008 .

[11]  Y. Pachepsky,et al.  Development of pedotransfer functions in soil hydrology , 2004 .

[12]  Keith H. Northcote,et al.  A factual key for the recognition of Australian soils , 1971 .

[13]  G. Heuvelink,et al.  Accounting for non-stationary variance in geostatistical mapping of soil properties , 2018, Geoderma.

[14]  R. V. Rossel,et al.  Spectral soil analysis and inference systems : A powerful combination for solving the soil data crisis , 2006 .

[15]  T. Næs,et al.  Ensemble methods and partial least squares regression , 2004 .

[16]  Thomas Scholten,et al.  The spectrum-based learner: A new local approach for modeling soil vis–NIR spectra of complex datasets , 2013 .

[17]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[18]  Madlene Nussbaum,et al.  Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models , 2017 .

[19]  Liu Xianming,et al.  A Time Petri Net Extended with Price Information , 2007 .

[20]  Johan Bouma,et al.  Using Soil Survey Data for Quantitative Land Evaluation , 1989 .

[21]  B. Wilson,et al.  Soil charcoal prediction using attenuated total reflectance mid-infrared spectroscopy , 2017 .

[22]  Budiman Minasny,et al.  Estimating Pedotransfer Function Prediction Limits Using Fuzzy k-Means with Extragrades , 2010 .

[23]  M. Imhof,et al.  Modelling and prediction of soil water contents at field capacity and permanent wilting point of dryland cropping soils , 2011 .

[24]  G. Batten.  Plant analysis using near infrared reflectance spectroscopy : the potential and the limitations , 1998 .

[25]  J. Walker,et al.  Australian Soil and Land Survey Field Handbook , 1984 .

[26]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[27]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[28]  M. Barker,et al.  Partial least squares for discrimination , 2003 .

[29]  Marthijn P.W. Sonneveld,et al.  Mapping soil clay contents in Dutch marine districts using gamma‐ray spectrometry , 2011 .

[30]  Tereza Zádorová,et al.  Uncertainty propagation in VNIR reflectance spectroscopy soil organic carbon mapping , 2013 .

[31]  B. Minasny,et al.  Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy , 2008 .

[32]  L. Lin,et al.  A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.

[33]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[34]  Ronei Jesus Poppi,et al.  Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters. , 2018, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[35]  Gerard B. M. Heuvelink,et al.  Sampling for validation of digital soil maps , 2011 .