References
1.
Safanelli JL, Hengl T, Parente LL, Minarik R,
Bloom DE, Todd-Brown K, et al. Open soil spectral library
(OSSL): Building reproducible soil calibration models
through open development and community engagement. PLoS One. 2025;20:
e0296545. doi:10.1371/journal.pone.0296545
2.
Safanelli JL, Sanderman J, Bloom D, Todd-Brown
K, Parente LL, Hengl T, et al. An interlaboratory comparison of
mid-infrared spectra acquisition: Instruments and procedures matter.
Geoderma. 2023;440: 116724. doi:10.1016/j.geoderma.2023.116724
3.
Partida C, Safanelli JL, Mitu SM, Murad MOF, Ge
Y, Ferguson R, et al. Building a near-infrared (NIR) soil
spectral dataset and predictive machine learning models using a handheld
NIR spectrophotometer. Data Brief. 2025;58: 111229. doi:10.1016/j.dib.2024.111229
4.
Mitu
SM, Smith C, Sanderman J, Ferguson RR, Shepherd K, Ge Y. Evaluating
consistency across multiple NeoSpectra (compact fourier
transform near‐infrared) spectrometers for estimating common soil
properties. Soil Sci Soc Am J. 2024;88: 1324–1339. doi:10.1002/saj2.20678
5.
Lang
M, Binder M, Richter J, Schratz P, Pfisterer F, Coors S, et al. mlr3: A modern object-oriented machine learning
framework in R. Journal of Open Source Software. 2019.
doi:10.21105/joss.01903
6.
Quinlan J. Learning with continuous classes.
Proc 5th australian joint conference on artificial intelligence,
tasmania, 1992. 1992. pp. 343–348.
7.
Quinlan J. Combining instance-based and
model-based learning. Proc Tenth int Conference on machine learning.
1993. pp. 236–243.
8.
Yang
L, Shami A. On hyperparameter optimization of machine learning
algorithms: Theory and practice. Neurocomputing. 2020;415: 295–316.
doi:10.1016/j.neucom.2020.07.061
9.
Barnes RJ, Dhanoa MS, Lister SJ. Standard
normal variate transformation and de-trending of near-infrared diffuse
reflectance spectra. Applied Spectroscopy. 1989;43: 772–777. doi:10.1366/0003702894202201
10.
Norinder U, Carlsson L, Boyer S, Eklund M.
Introducing conformal prediction in predictive modeling. A transparent
and flexible alternative to applicability domain determination. Journal
of Chemical Information and Modeling. 2014;54: 1596–1603. doi:10.1021/ci5001168
11.
Cortés-Ciriano I, Westen GJP van, Bouvier G,
Nilges M, Overington JP, Bender A, et al. Improved large-scale
prediction of growth inhibition patterns using the NCI60
cancer cell line panel. Bioinformatics. 2015;32: 85–95. doi:10.1093/bioinformatics/btv529
12.
Dangal S, Sanderman J, Wills S, Ramirez-Lopez
L. Accurate and precise prediction of soil properties from a large
mid-infrared spectral library. Soil Systems. 2019;3: 11. doi:10.3390/soilsystems3010011
13.
Jović B, Ćirić V, Kovačević M, Šeremešić S,
Kordić B. Empirical equation for preliminary assessment of soil texture.
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy.
2019;206: 506–511. doi:10.1016/j.saa.2018.08.039
14.
Garrett LG, Sanderman J, Palmer DJ, Dean F,
Patel S, Bridson JH, et al. Mid-infrared spectroscopy for planted forest
soil and foliage nutrition predictions, new zealand case study. Trees,
Forests and People. 2022;8: 100280. doi:10.1016/j.tfp.2022.100280
15.
Schiedung M, Bellè S-L, Malhotra A, Abiven S.
Organic carbon stocks, quality and prediction in permafrost-affected
forest soils in north canada. CATENA. 2022;213: 106194.
doi:10.1016/j.catena.2022.106194
16.
Wijewardane NK, Ge Y, Wills S, Loecke T.
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. 2016;80:
973–982. doi:10.2136/sssaj2016.02.0052
17.
Summerauer L, Baumann P, Ramirez-Lopez L,
Barthel M, Bauters M, Bukombe B, et al. The central african soil
spectral library: A new soil infrared repository and a geographical
prediction analysis. SOIL. 2021;7: 693–715. doi:10.5194/soil-7-693-2021
18.
Chang C-W, Laird D, Mausbach MJ, Hurburgh Jr
CR. Near-infrared reflectance spectroscopy–principal components
regression analyses of soil properties. Soil Science Society of America
Journal. 2001;65: 480. doi:10.2136/sssaj2001.652480x
19.
Jackson JE, Mudholkar GS. Control procedures
for residuals associated with principal component analysis.
Technometrics. 1979;21: 341–349. doi:10.1080/00401706.1979.10489779
20.
Santana FB de, Hall RebeccaL, Lowe V, Browne
MA, Grunsky EC, Fitzsimons MM, et al. 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. 2023;434: 116491. doi:10.1016/j.geoderma.2023.116491
21.
Wijewardane NK, Ge Y, Wills S, Libohova Z.
Predicting physical and chemical properties of US
soils with a mid-infrared reflectance spectral library. Soil
Science Society of America Journal. 2018;82: 722–731. doi:10.2136/sssaj2017.10.0361
22.
Orgiazzi A, Ballabio C, Panagos P, Jones A,
Fernández-Ugalde O. LUCAS Soil, the largest
expandable soil dataset for Europe: a review. European Journal of
Soil Science. 2018;69: 140–153. doi:10.1111/ejss.12499
23.
Vagen T-G, Winowiecki LA, Desta L, Tondoh EJ,
Weullow E, Shepherd K, et al. 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; 2020. doi:10.34725/DVN/QXCWP1
24.
Aitkenhead MJ, Black HI. Exploring the impact of different input data types on
soil variable estimation using the ICRAF-ISRIC global soil spectral
database. Applied spectroscopy. 2018;72: 188–198. doi:10.1177/0003702817739013
25.
Wadoux AMJC, Malone B, McBratney AB, Fajardo M,
Minasny B. Soil Spectral Inference with R:
Analysing Digital Soil Spectra Using the R Programming
Environment. Springer International Publishing; 2021.
26.
Benedetti F, van Egmond F. Global Soil Spectroscopy Assessment. Spectral soil data —
Needs and capacities. Rome, Italy: FAO; 2021. p. 42. doi:10.4060/cb6265en