Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001.
a,
b
Clark, R. N. and Roush, T. L.: Reflectance spectroscopy: Quantitative analysis
techniques for remote sensing applications, J. Geophys. Res.,
89, 6329–6340, 1984. a
Davinic, M., Fultz, L. M., Acosta-Martinez, V., Calderón, F. J., Cox,
S. B., Dowd, S. E., Allen, V. G., Zak, J. C., and Moore-Kucera, J.:
Pyrosequencing and mid-infrared spectroscopy reveal distinct aggregate
stratification of soil bacterial communities and organic matter composition,
Soil Biol. Biochem., 46, 63–72, 2012. a
Delgadobaquerizo, M., Maestre, F. T., Reich, P. B., Jeffries, T. C., Gaitan,
J. J., Encinar, D., Berdugo, M., Campbell, C. D., and Singh, B. K.: Microbial
diversity drives multifunctionality in terrestrial ecosystems, Nat.
Commun., 7, 10541–10541, 2016. a
Delgadobaquerizo, M., Oliverio, A. M., Brewer, T. E., Benaventgonzalez, A.,
Eldridge, D. J., Bardgett, R. D., Maestre, F. T., Singh, B. K., and Fierer,
N.: A global atlas of the dominant bacteria found in soil, Science, 359,
320–325, 2018a. a
Delgadobaquerizo, M., Reith, F., Dennis, P. G., Hamonts, K., Powell, J. R.,
Young, A. G., Singh, B. K., and Bissett, A.: Ecological drivers of soil
microbial diversity and soil biological networks in the Southern Hemisphere,
Ecology, 99, 583–596, 2018b.
a,
b
Donohue, R. J., McVicar, T., and Roderick, M. L.: Climate-related trends in
Australian vegetation cover as inferred from satellite observations,
1981–2006, Glob. Change Biol., 15, 1025–1039, 2009. a
Duan, Y. B., Xie, N. D., Song, Z. Q., Ward, C. S., Yung, C. M., Hunt, D. E.,
Johnson, Z. I., and Wang, G. Y.: A high-resolution time series reveals
distinct seasonal patterns of planktonic fungi at a temperate coastal ocean
site (Beaufort, North Carolina, USA), Appl. Environ. Microbiol.,
84, e00967-18,
https://doi.org/10.1128/AEM.00967-18, 2018.
a
Fisher, A., Rudin, C., and Dominici, F.: All Models are Wrong, but Many are
Useful: Learning a Variable's Importance by Studying an Entire Class of
Prediction Models Simultaneously, J. Mach. Learn. Res., 20,
1–81, 2019. a
Friedman, J. H.: Greedy function approximation: A gradient boosting machine,
Ann. Stat., 29, 1189–1232, 2001. a
Gai, J., Christie, P., Feng, G., and Li, X. L.: Twenty years of research on
community composition and species distribution of arbuscular mycorrhizal
fungi in China: a review, Mycorrhiza, 16, 229–239, 2006. a
Gallant, J., Wilson, N., Dowling, T., Read, A., and Inskeep, C.: SRTM-derived 1
second digital elevation models version 1.0, Geoscience Australia: Canberra,
ACT, 2011. a
Griffiths, R. I., Thomson, B. C., James, P., Bell, T., Bailey, M., and
Whiteley, A. S.: The bacterial biogeography of British soils, Environ.
Microbiol., 13, 1642–1654, 2011. a
Griffiths, R. I., Thomson, B. C., Plassart, P., Gweon, H. S., D., S., Creamer,
R. E., Lemanceau, P., and Bailey, M. J.: Mapping and validating predictions
of soil bacterial biodiversity using European and national scale datasets,
Appl. Soil Ecol., 97, 61–68, 2016. a
Hart, M. M., Cross, A. T., D'Agui, H. M., Dixon, K. W., Van der Heyde, M.,
Mickan, B., Horst, C., Grez, B. M., Valliere, J. M., Viscarra Rossel, R. A.,
Whiteley, A., Wong, W. S., Zhong, H., and Nevill, P.: Examining assumptions
of soil microbial ecology in the monitoring of ecological restoration,
Ecol. Solut. Evid., 1, e12031,
https://doi.org/10.1002/2688-8319.12031, 2020.
a,
b
Haverd, V., Raupach, M. R., Briggs, P. R., Canadell, J. G., Isaac, P., Pickett-Heaps, C., Roxburgh, S. H., van Gorsel, E., Viscarra Rossel, R. A., and Wang, Z.: Multiple observation types reduce uncertainty in Australia's terrestrial carbon and water cycles, Biogeosciences, 10, 2011–2040,
https://doi.org/10.5194/bg-10-2011-2013, 2013.
a
Horrigue, W., Dequiedt, S., Chemidlin Prévost-Bouré, N., Jolivet, C., Saby,
N. P., Arrouays, D., Bispo, A., Maron, P., and Ranjard, L.: Predictive model
of soil molecular microbial biomass, Ecol. Ind., 64, 203–211,
2016. a
Jenny, H.: Factors of Soil Formation: A System of Quantitative Pedology,
Courier Corporation, New York, ISBN 978-0-48668-128-3, 1994. a
Kivlin, S. N. and Hawkes, C. V.: Tree species, spatial heterogeneity, and
seasonality drive soil fungal abundance, richness, and composition in
Neotropical rainforests, Environ. Microbiol., 18, 4662–4673, 2016.
a,
b
Lecun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444,
https://doi.org/10.1038/nature14539, 2015.
a
Li, J., Delgadobaquerizo, M., Wang, J., Hu, H., Cai, Z., Zhu, Y., and Singh,
B. K.: Fungal richness contributes to multifunctionality in boreal forest
soil, Soil Biol. Biochem., 136, 107526,
https://doi.org/10.1016/j.soilbio.2019.107526, 2019.
a
Liu, L., Ji, M., and Buchroithner, M.: Transfer learning for soil spectroscopy
based on convolutional neural networks and its application in soil clay
content mapping using hyperspectral imagery, Sensors (Switzerland), 18, 9,
https://doi.org/10.3390/s18093169, 2018.
a
Minty, B., Franklin, R., Milligan, P., Richardson, M., and Wilford, J.: The
radiometric map of Australia, Explor. Geophys., 40, 325–333, 2009. a
Morellos, A., Pantazi, X., Moshou, D., Alexandridis, T., Whetton, R.,
Tziotzios, G., Wiebensohn, J., Bill, R., and Mouazen, A. M.: Machine learning
based prediction of soil total nitrogen, organic carbon and moisture content
by using VIS-NIR spectroscopy, Biosyst. Eng., 152, 104–116, 2016. a
Müller, B.: Experimental interactions between clay minerals and bacteria: a
review, Pedosphere, 25, 799–810, 2015.
a,
b
Ng, W., Minasny, B., Montazerolghaem, M., Padarian, J., Ferguson, R., Bailey,
S., and McBratney, A. B.: Convolutional neural network for simultaneous
prediction of several soil properties using visible/near-infrared,
mid-infrared, and their combined spectra, Geoderma, 352, 251–267,
https://doi.org/10.1016/j.geoderma.2019.06.016, 2019.
a
Nicolas, C., Martinbertelsen, T., Floudas, D., Bentzer, J., Smits, M. M.,
Johansson, T., Troein, C., Persson, P., and Tunlid, A.: The soil organic
matter decomposition mechanisms in ectomycorrhizal fungi are tuned for
liberating soil organic nitrogen, ISME J., 13, 977–988, 2019. a
Nilsson, R. H., Larsson, K.-H., Taylor, A. F., Bengtsson-Palme, J., Jeppesen,
T. S., Schigel, D., Kennedy, P., Picard, K., Glöckner, F. O., Tedersoo, L.,
Saar, I., Kõljalg, U., and Abarenkov, K.: The UNITE database for molecular
identification of fungi: handling dark taxa and parallel taxonomic
classifications, Nucl. Acids Res., 47, D259–D264,
https://doi.org/10.1093/nar/gky1022, 2018.
a
Prescott, J. A.: A climatic index for the leaching factor in soil formation,
J. Soil Sci., 1, 9–19, 1950. a
Quinlan, J. R.: Learning with continuous classes, in: 5th Australian joint
conference on artificial intelligence, vol. 92, pp. 343–348, Singapore,
1992. a
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria,
https://www.R-project.org (last access: 22 March 2022), 2014. a
Rousk, J., Brookes, P. C., and Baath, E.: Contrasting Soil pH Effects on Fungal
and Bacterial Growth Suggest Functional Redundancy in Carbon Mineralization,
Appl. Environ. Microbiol., 75, 1589–1596, 2009. a
Schubler, A., Schwarzott, D., and Walker, C.: A new fungal phylum, the
Glomeromycota: phylogeny and evolution, Fung. Biol., 105, 1413–1421,
2001. a
Shi, Z., Ji, W., Viscarra Rossel, R. A., Chen, S., and Zhou, Y.: Prediction of
soil organic matter using a spatially constrained local partial least squares
regression and the Chinese vis–NIR spectral library, Eur. J.
Soil Sci., 66, 679–687, 2015. a
Stenberg, B., Viscarra Rossel, R. A., Mouazen, A. M., and Wetterlind, J.:
Visible and Near Infrared Spectroscopy in Soil Science, Adv. Agron.,
107, 163–215, 2010.
a,
b,
c,
d
Suykens, J. A. K., Gestel, T. V., Brabanter, J. D., Moor, B. D., and
Vandewalle, J.: Least Squares Support Vector Machines, 29–70,
https://doi.org/10.1142/5089,
World Scientific Collaborate, Hackensack, USA 2002.
a
Talley, S. M., Coley, P. D., and Kursar, T. A.: The effects of weather on
fungal abundance and richness among 25 communities in the Intermountain West,
BMC Ecol., 2, 7,
https://doi.org/10.1186/1472-6785-2-7, 2002.
a
Tsakiridis, N. L., Keramaris, K. D., Theocharis, J. B., and 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,
https://doi.org/10.1016/j.geoderma.2020.114208, 2020.
a,
b
Vetrovsky, T., Kohout, P., Kopecky, M., Machac, A., Man, M., Bahnmann, B. D.,
Brabcova, V., Choi, J., Meszarosova, L., Human, Z. R., et al.: A
meta-analysis of global fungal distribution reveals climate-driven patterns,
Nat. Commun., 10, 5142,
https://doi.org/10.1038/s41467-019-13164-8, 2019.
a,
b
Viscarra Rossel, R., Bui, E., Caritat, P., and McKenzie, N. J.: Mapping iron oxides
and the color of Australian soil usingvisible–near‐infrared reflectance
spectra, J. Geophys. Res., 115, F04031,
https://doi.org/10.1029/2009JF001645, 2010.
a,
b
Viscarra Rossel, R., Adamchuk, V., Sudduth, K., McKenzie, N., and Lobsey, C.:
Proximal Soil Sensing. An Effective Approach for Soil Measurements in Space
and Time, Adv. Agron., 113, 243–291,
https://doi.org/10.1016/B978-0-12-386473-4.00010-5, 2011.
a
Viscarra Rossel, R. A.: Fine-resolution multiscale mapping of clay minerals
in Australian soils measured with near infrared spectra, J.
Geophys. Res.-Ea. Surf., 116, F04023,
https://doi.org/10.1029/2011JF001977, 2011.
a
Viscarra Rossel, R. A. and Behrens, T.: Using data mining to model and
interpret soil diffuse reflectance spectra, Geoderma, 158, 46–54, 2010.
a,
b
Viscarra Rossel, R. A. and Hicks, W. S.: Soil organic carbon and its
fractions estimated by visible-near infrared transfer functions, Eur.
J. Soil Sci., 66, 438–450,
https://doi.org/10.1111/ejss.12237, 2015.
a
Viscarra Rossel, R. A. and McBratney, A. B.: Soil chemical analytical accuracy
and costs: implications from precision agriculture, Aust. J.
Exp. Agr., 38, 765–775, 1998.
a,
b
Viscarra Rossel, R. A., Chen, C., Grundy, M., Searle, R., Clifford, D., and
Campbell, P.: The Australian three-dimensional soil grid: Australia's
contribution to the GlobalSoilMap project, Soil Res., 53, 845–864, 2015. a
Viscarra Rossel, R. A., Behrens, T., Ben-Dor, E., Brown, D., Demattê,
J., Shepherd, K., Shi, Z., Stenberg, B., Stevens, A., Adamchuk, V., Aichi,
H., Barthes, B., Bartholomeus, H., Bayer, A., Bernoux, M., Bottcher, K.,
Brodsky, L., Du, C., Chappell, A., Fouad, Y., Genot, V., Gomez, C., Grunwald,
S., Gubler, A., Guerrero, C., Hedley, C., Knadel, M., Morras, H., Nocita, M.,
Ramírez López, L., Roudier, P., Campos, E., Sanborn, P.,
Sellitto, V., Sudduth, K., Rawlins, B., Walter, C., Winowiecki, L., Hong, S.,
and Ji, W.: A global spectral library to characterize the world's soil,
Earth-Sci. Rev., 155, 198–230,
https://doi.org/10.1016/j.earscirev.2016.01.012,
2016.
a,
b
Xu, T. and Hutchinson, M.: ANUCLIM version 6.1 user guide, The Australian
National University, Fenner School of Environment and Society, Canberra,
2011. a
Yang, Y., Viscarra Rossel, R., Li, S., Bissett, A., Lee, J., Shi, Z., Behrens,
T., and Court, L. N.: Soil bacterial abundance and diversity better explained
and predicted with spectro-transfer functions, Soil Biol. Biochem.,
129, 29–38, 2019.
a,
b,
c
