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Open Soil Spectral Library

8 References

Aitkenhead, M. J., & Black, H. I. (2018). Exploring the impact of different input data types on soil variable estimation using the ICRAF-ISRIC global soil spectral database. Applied Spectroscopy, 72(2), 188–198. doi:10.1177/0003702817739013
Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989). Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy, 43(5), 772–777. doi:10.1366/0003702894202201
Benedetti, F., & van Egmond, F. (2021). Global Soil Spectroscopy Assessment. Spectral soil data — Needs and capacities (p. 42). Rome, Italy: FAO. doi:10.4060/cb6265en
Chang, C.-W., Laird, D., Mausbach, M. J., & Hurburgh Jr, C. R. (2001). Near-infrared reflectance spectroscopy–principal components regression analyses of soil properties. Soil Science Society of America Journal, 65(2), 480. doi:10.2136/sssaj2001.652480x
Cortés-Ciriano, I., Westen, G. J. P. van, Bouvier, G., Nilges, M., Overington, J. P., Bender, A., & Malliavin, T. E. (2015). Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel. Bioinformatics, 32(1), 85–95. doi:10.1093/bioinformatics/btv529
Dangal, S., Sanderman, J., Wills, S., & Ramirez-Lopez, L. (2019). Accurate and precise prediction of soil properties from a large mid-infrared spectral library. Soil Systems, 3(1), 11. doi:10.3390/soilsystems3010011
Garrett, L. G., Sanderman, J., Palmer, D. J., Dean, F., Patel, S., Bridson, J. H., & Carlin, T. (2022). Mid-infrared spectroscopy for planted forest soil and foliage nutrition predictions, new zealand case study. Trees, Forests and People, 8, 100280. doi:10.1016/j.tfp.2022.100280
Garrity, D., & Bindraban, P. (2004). A globally distributed soil spectral library visible near infrared diffuse reflectance spectra. Nairobi, Kenya: ICRAF (World Agroforestry Centre) / ISRIC (World Soil Information) Spectral Library. Retrieved from https://doi.org/10.34725/DVN/MFHA9C
Hengl, T., Miller, M. A., Križan, J., Shepherd, K. D., Sila, A., Kilibarda, M., et al.others. (2021). African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Scientific Reports, 11(1), 1–18. doi:10.1038/s41598-021-85639-y
Jackson, J. E., & Mudholkar, G. S. (1979). Control procedures for residuals associated with principal component analysis. Technometrics, 21(3), 341–349. doi:10.1080/00401706.1979.10489779
Jović, B., Ćirić, V., Kovačević, M., Šeremešić, S., & Kordić, B. (2019). Empirical equation for preliminary assessment of soil texture. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 206, 506–511. doi:10.1016/j.saa.2018.08.039
Lang, M., Binder, M., Richter, J., Schratz, P., Pfisterer, F., Coors, S., … Bischl, B. (2019). mlr3: A modern object-oriented machine learning framework in R. Journal of Open Source Software. doi:10.21105/joss.01903
Norinder, U., Carlsson, L., Boyer, S., & Eklund, M. (2014). Introducing conformal prediction in predictive modeling. A transparent and flexible alternative to applicability domain determination. Journal of Chemical Information and Modeling, 54(6), 1596–1603. doi:10.1021/ci5001168
Orgiazzi, A., Ballabio, C., Panagos, P., Jones, A., & Fernández-Ugalde, O. (2018). LUCAS Soil, the largest expandable soil dataset for Europe: a review. European Journal of Soil Science, 69(1), 140–153. doi:10.1111/ejss.12499
Quinlan, J. (1992). Learning with continuous classes. In Proc. 5th australian joint conference on artificial intelligence, tasmania, 1992 (pp. 343–348).
Quinlan, J. (1993). Combining instance-based and model-based learning. In Proc. Tenth int. Conference on machine learning (pp. 236–243).
Sanderman, J., Savage, K., & Dangal, S. R. (2020). Mid-infrared spectroscopy for prediction of soil health indicators in the United States. Soil Science Society of America Journal, 84(1), 251–261. doi:10.1002/saj2.20009
Santana, F. B. de, Hall, Rebecca. L., Lowe, V., Browne, M. A., Grunsky, E. C., Fitzsimons, M. M., … Daly, K. (2023). A systematic approach to predicting and mapping soil particle size distribution from unknown samples using large mid-infrared spectral libraries covering large-scale heterogeneous areas. Geoderma, 434, 116491. doi:10.1016/j.geoderma.2023.116491
Schiedung, M., Bellè, S.-L., Malhotra, A., & Abiven, S. (2022). Organic carbon stocks, quality and prediction in permafrost-affected forest soils in north canada. CATENA, 213, 106194. doi:10.1016/j.catena.2022.106194
Summerauer, L., Baumann, P., Ramirez-Lopez, L., Barthel, M., Bauters, M., Bukombe, B., … Six, J. (2021). The central african soil spectral library: A new soil infrared repository and a geographical prediction analysis. SOIL, 7(2), 693–715. doi:10.5194/soil-7-693-2021
Vagen, T.-G., Winowiecki, L. A., Desta, L., Tondoh, E. J., Weullow, E., Shepherd, K., & Sila, A. (2020). Mid-Infrared Spectra (MIRS) from ICRAF Soil and Plant Spectroscopy Laboratory: Africa Soil Information Service (AfSIS) Phase I 2009-2013. World Agroforestry - Research Data Repository. doi:10.34725/DVN/QXCWP1
Wadoux, A. M. J. C., Malone, B., McBratney, A. B., Fajardo, M., & Minasny, B. (2021). Soil Spectral Inference with R: Analysing Digital Soil Spectra Using the R Programming Environment. Springer International Publishing.
Wijewardane, Nuwan K., Ge, Y., Wills, S., & Libohova, Z. (2018). Predicting physical and chemical properties of US soils with a mid-infrared reflectance spectral library. Soil Science Society of America Journal, 82(3), 722–731. doi:10.2136/sssaj2017.10.0361
Wijewardane, Nuwan K., Ge, Y., Wills, S., & Loecke, T. (2016). Prediction of soil carbon in the conterminous united states: Visible and near infrared reflectance spectroscopy analysis of the rapid carbon assessment project. Soil Science Society of America Journal, 80(4), 973–982. doi:10.2136/sssaj2016.02.0052
Wills, S., Loecke, T., Sequeira, C., Teachman, G., Grunwald, S., & West, L. T. (2014). Overview of the u.s. Rapid carbon assessment project: Sampling design, initial summary and uncertainty estimates. In Soil carbon (pp. 95–104). Springer International Publishing. doi:10.1007/978-3-319-04084-4_10
Yang, L., & Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415, 295–316. doi:10.1016/j.neucom.2020.07.061