Schmidt, M. W. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).
Google Scholar
Drobnik, T., Greiner, L., Keller, A. & Grêt-Regamey, A. Soil quality indicators-from soil functions to ecosystem services. Ecol. Ind. 94, 151–169 (2018).
Google Scholar
Bradford, M. A. et al. Managing uncertainty in soil carbon feedbacks to climate change. Nat. Clim. Change 6, 751–758 (2016).
Google Scholar
Viscarra Rossel, R. et al. A global spectral library to characterize the world’s soil. Earth Sci. Rev. 155, 198–230 (2016).
Amundson, R. & Biardeau, L. Opinion: Soil carbon sequestration is an elusive climate mitigation tool. Proc. Natl. Acad. Sci. 115, 11652–11656 (2018).
Google Scholar
Smith, P. et al. How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal. Glob. Change Biol. 26, 219–241 (2020).
Google Scholar
Zhang, S., Yu, Z., Lin, J. & Zhu, B. Responses of soil carbon decomposition to drying-rewetting cycles: A meta-analysis. Geoderma 361, 114069 (2020).
Google Scholar
Bot, A. & Benites, J. The Importance of Soil Organic Matter: Key to Drought-Resistant Soil and Sustained Food Production. 80 (Food & Agriculture Org., 2005).
Rawles, W. J. & Brakensiek, D. Estimating soil water retention from soil properties. J. Irrig. Drain. Div. 108, 166–171 (1982).
Google Scholar
Zhao, D., Zhao, X., Khongnawang, T., Arshad, M. & Triantafilis, J. A Vis–NIR spectral library to predict clay in Australian cotton growing soil. Soil Sci. Soc. Am. J. 82, 1347–1357 (2018).
Google Scholar
Demattê, J. A., Campos, R. C., Alves, M. C., Fiorio, P. R. & Nanni, M. R. Visible–NIR reflectance: A new approach on soil evaluation. Geoderma 121, 95–112 (2004).
Google Scholar
Viscarra Rossel, R. Fine-resolution multiscale mapping of clay minerals in Australian soils measured with near infrared spectra. J. Geophys. Res. Earth Surf. 116 (2011).
Viscarra Rossel, R., Walvoort, D., McBratney, A., Janik, L. J. & Skjemstad, J. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131, 59–75 (2006).
Viscarra Rossel, R. & Lark, R. Improved analysis and modelling of soil diffuse reflectance spectra using wavelets. Eur. J. Soil Sci. 60, 453–464 (2009).
Abdi, H. & Williams, L. J. Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2, 433–459 (2010).
Google Scholar
Næs, T. & Martens, H. Principal component regression in NIR analysis: Viewpoints, background details and selection of components. J. Chemom. 2, 155–167 (1988).
Google Scholar
Geladi, P. & Kowalski, B. R. Partial least-squares regression: A tutorial. Anal. Chim. Acta 185, 1–17 (1986).
Google Scholar
Rossel, R. V. Robust modelling of soil diffuse reflectance spectra by “bagging-partial least squares regression”. J. Near Infrared Spectrosc. 15, 39–47 (2007).
Viscarra Rossel, R. & Behrens, T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158, 46–54 (2010).
Tsakiridis, N. L., Keramaris, K. D., Theocharis, J. B. & Zalidis, G. C. Simultaneous prediction of soil properties from VNIR–SWIR spectra using a localized multi-channel 1-d convolutional neural network. Geoderma 367, 114208 (2020).
Google Scholar
Yang, J., Wang, X., Wang, R. & Wang, H. Combination of convolutional neural networks and recurrent neural networks for predicting soil properties using Vis–NIR spectroscopy. Geoderma 380, 114616 (2020).
Google Scholar
Shen, Z. & Viscarra Rossel, R. A. Automated spectroscopic modelling with optimised convolutional neural networks. Sci. Rep. 11, 208. https://doi.org/10.1038/s41598-020-80486-9 (2021).
Google Scholar
Li, F., Wang, L., Liu, J., Wang, Y. & Chang, Q. Evaluation of leaf n concentration in winter wheat based on discrete wavelet transform analysis. Remote Sens. 11, 1331 (2019).
Google Scholar
Meng, X. et al. Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data. Int. J. Appl. Earth Obs. Geoinf. 89, 102111 (2020).
Google Scholar
Jiang, B. Geospatial analysis requires a different way of thinking: The problem of spatial heterogeneity. GeoJournal 80, 1–13 (2015).
Google Scholar
Song, Y., Wang, J., Ge, Y. & Xu, C. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data. GISci. Remote Sens. 57, 593–610 (2020).
Google Scholar
Yang, Z. et al. The effect of environmental heterogeneity on species richness depends on community position along the environmental gradient. Sci. Rep. 5, 1–7 (2015).
Jenny, H. Factors of Soil Formation (McGraw-Hill, 1941).
Ye, H. et al. Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon. Spat. Stat. 20, 76–91 (2017).
Google Scholar
Viscarra Rossel, R. & Webster, R. Predicting soil properties from the Australian soil visible-near infrared spectroscopic database. Eur. J. Soil Sci. 63. https://doi.org/10.1111/j.1365-2389.2012.01495.x (2012).
Sila, A., Pokhariyal, G. & Shepherd, K. Evaluating regression-kriging for mid-infrared spectroscopy prediction of soil properties in western Kenya. Geoderma Reg. 10, 39–47 (2017).
Google Scholar
Fotheringham, A. S., Brunsdon, C. & Charlton, M. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (Wiley, 2003).
Brunsdon, C., Fotheringham, A. S. & Charlton, M. E. Geographically weighted regression: A method for exploring spatial nonstationarity. Geogr. Anal. 28, 281–298 (1996).
Google Scholar
Bidanset, P. E. & Lombard, J. R. The effect of kernel and bandwidth specification in geographically weighted regression models on the accuracy and uniformity of mass real estate appraisal. J. Prop. Tax Assess. Admin. 11, 5–14 (2014).
Brunsdon, C., Fotheringham, A. & Charlton, M. Geographically weighted summary statistics? A framework for localised exploratory data analysis. Comput. Environ. Urban Syst. 26, 501–524 (2002).
Google Scholar
Comber, A. et al. The GWR route map: A guide to the informed application of geographically weighted regression. arXiv preprint arXiv:2004.06070 (2020).
Fotheringham, A. S., Yang, W. & Kang, W. Multiscale geographically weighted regression (MGWR). Ann. Am. Assoc. Geogr. 107, 1247–1265 (2017).
Yu, H. et al. Inference in multiscale geographically weighted regression. Geogr. Anal. 52, 87–106 (2020).
Google Scholar
Wheeler, D. & Tiefelsdorf, M. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J. Geogr. Syst. 7, 161–187 (2005).
Google Scholar
Harris, P., Fotheringham, A. S. & Juggins, S. Robust geographically weighted regression: A technique for quantifying spatial relationships between freshwater acidification critical loads and catchment attributes. Ann. Assoc. Am. Geogr. 100, 286–306 (2010).
Google Scholar
Harris, P., Brunsdon, C., Lu, B., Nakaya, T. & Charlton, M. Introducing bootstrap methods to investigate coefficient non-stationarity in spatial regression models. Spat. Stat. 21, 241–261 (2017).
Google Scholar
Cho, S.-H., Lambert, D. M. & Chen, Z. Geographically weighted regression bandwidth selection and spatial autocorrelation: An empirical example using Chinese agriculture data. Appl. Econ. Lett. 17, 767–772 (2010).
Google Scholar
Lu, B., Yang, W., Ge, Y. & Harris, P. Improvements to the calibration of a geographically weighted regression with parameter-specific distance metrics and bandwidths. Comput. Environ. Urban Syst. 71, 41–57 (2018).
Google Scholar
Arabameri, A., Pradhan, B. & Rezaei, K. Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in gis. J. Environ. Manag. 232, 928–942 (2019).
Google Scholar
Li, X. et al. Mapping soil organic carbon and total nitrogen in croplands of the corn belt of northeast china based on geographically weighted regression kriging model. Comput. Geosci. 135, 104392 (2020).
Google Scholar
Cao, K., Diao, M. & Wu, B. A big data-based geographically weighted regression model for public housing prices: A case study in Singapore. Ann. Am. Assoc. Geogr. 109, 173–186 (2019).
Ge, Y. et al. Geographically weighted regression-based determinants of malaria incidences in northern China. Trans. GIS 21, 934–953 (2017).
Google Scholar
Viscarra Rossel, R. A. & Hicks, W. S. Soil organic carbon and its fractions estimated by visible–near infrared transfer functions. Eur. J. Soil Sci. 66(3), 438–450 (2015).
Google Scholar
Wight, J. P., Ashworth, A. J. & Allen, F. L. Organic substrate, clay type, texture, and water influence on NIR carbon measurements. Geoderma 261, 36–43 (2016).
Google Scholar
Costa, L. R., Tonoli, G. H. D., Milagres, F. R. & Hein, P. R. G. Artificial neural network and partial least square regressions for rapid estimation of cellulose pulp dryness based on near infrared spectroscopic data. Carbohyd. Polym. 224, 115186 (2019).
Google Scholar
Murphy, R. J., Schneider, S., Taylor, Z. & Nieto, J. Mapping clay minerals in an open-pit mine using hyperspectral imagery and automated feature extraction. In Vertical Geology, From Remote Sensing to 3D Geological Modelling. Proceedings of the first Vertical Geology Conference, Lausanne, Switzerland, 5–7 (2014).
Todorova, M. H. & Atanassova, S. L. Near infrared spectra and soft independent modelling of class analogy for discrimination of chernozems, luvisols and vertisols. J. Near Infrared Spectrosc. 24, 271–280 (2016).
Google Scholar
Stenberg, B., Viscarra Rossel, R., Mouazen, A. & Wetterlind, J. Visible and Near Infrared Spectroscopy in Soil Science, vol. 107 (Academic Press, 2010).
Harris, P., Fotheringham, A., Crespo, R. & Charlton, M. The use of geographically weighted regression for spatial prediction: An evaluation of models using simulated data sets. Math. Geosci. 42, 657–680 (2010).
Google Scholar
Department of Primary Industries and Regional Development, Western Australia. South West Agricultural Region (dpird-008) (2020).
Australian Bureau of Statistics. Value of Agricultural Commodities Produced, Australia (2020).
Department of Primary Industries and Regional Development, Western Australia. Western Australian Wheat Industry (2019).
Rayment, G. E. & Lyons, D. J. Soil Chemical Methods—Australasia (CSIRO Publishing, 2010).
Dolui, S. et al. Structural correlation-based outlier rejection (score) algorithm for arterial spin labeling time series. J. Magn. Reson. Imaging 45, 1786–1797 (2017).
Google Scholar
Pollet, T. V. & van der Meij, L. To remove or not to remove: The impact of outlier handling on significance testing in testosterone data. Adapt. Hum. Behav. Physiol. 3, 43–60 (2017).
Google Scholar
Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49, 764–766 (2013).
Google Scholar
Mallat, S. G. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989).
Google Scholar
Whitcher, B. waveslim: Basic Wavelet Routines for One-, Two-, and Three-Dimensional Signal Processing (2020).
O’brien, R. M. A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 41, 673–690 (2007).
Akinwande, M. O. et al. Variance inflation factor: As a condition for the inclusion of suppressor variable (s) in regression analysis. Open J. Stat. 5, 754 (2015).
Google Scholar
Webster, R. & Oliver, M. A. Sample adequately to estimate variograms of soil properties. J. Soil Sci. 43, 177–192 (1992).
Google Scholar
Atteia, O., Dubois, J.-P. & Webster, R. Geostatistical analysis of soil contamination in the Swiss Jura. Environ. Pollut. 86, 315–327 (1994).
Google Scholar
Brunsdon, C., Fotheringham, S. & Charlton, M. Geographically weighted regression-modelling spatial non-stationarity. J. R. Stat. Soc. Ser. D (The Statistician) 47, 431–443 (1998).
Google Scholar
Gollini, I., Lu, B., Charlton, M., Brunsdon, C. & Harris, P. Gwmodel: An r package for exploring spatial heterogeneity using geographically weighted models. arXiv preprint arXiv:1306.0413 (2013).
Lu, B., Harris, P., Charlton, M. & Brunsdon, C. The GWmodel R package: Further topics for exploring spatial heterogeneity using geographically weighted models. Geo Spat. Inf. Sci. 17, 85–101 (2014).
Google Scholar
Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Science & Business Media, 2009).
Burnham, K. P. & Anderson, D. R. A practical information-theoretic approach. Model selection and multimodel inference 2 (2002).
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, Austria 2020).
Mevik, B.-H., Wehrens, R., Liland, K. H. & Hiemstra, P. pls: Partial Least Squares and Principal Component Regression (2020).
Bivand, R., Yu, D., Nakaya, T. & Garcia-Lopez, M. spgwr: Geographically weighted regression. R Package Version 0.6-34. http://cran.r-project.org/web/packages/spgwr/. Accessed August 30th 2020 (2020).
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