3 Soil spectroscopy tools and users

3.1 Soil Spectroscopy Instruments

Most frequently used MIR / VISNIR instruments based on @BenedettiEgmond2021.

Figure 1.3: Most frequently used MIR / VISNIR instruments based on Benedetti and van Egmond (2021).

3.1.1 Bruker Alpha 1_FT-MIR_Zn Se

  • πŸ†” Bruker_Alpha1_FT.MIR.Zn.Se
  • 🏭 Producer: Bruker
  • πŸ”— Documentation: https://www.bruker.com/
  • πŸ“‚ Model type: MIR (4000-400 cm-1)
  • πŸ“… Production year: 2012

3.1.2 Bruker Alpha 1_FT-MIR_KBr

  • πŸ†” Bruker_Alpha1_FT.MIR.KBr
  • 🏭 Producer: Bruker
  • πŸ”— Documentation: https://www.bruker.com/
  • πŸ“‚ Model type: MIR (4000-400 cm-1)
  • πŸ“… Production year: 2012

3.1.3 Bruker Vertex 70 with HTS-XT accessory

3.1.4 ASD Labspec 2500 with Muglight accessory

3.1.5 ASD FieldSpec FR

3.1.6 XDS Rapid Content Analyzer

3.2 Registered soil spectral libraries

3.2.1 KSSL.SSL

Description: MIR and NIR spectral library and associated soil characterization database, which now includes >50,000 MIR spectra collected on soils primarily from the United States. Freely available by request (see below).

KSSL VisNIR spectral signatures for different soil texture fractions.

Figure 3.1: KSSL VisNIR spectral signatures for different soil texture fractions.

3.2.2 ICRAF.ISRIC

Description: A Globally Distributed Soil Spectral Library Mid Infrared Diffuse Reflectance Spectra. MIR scans for some 785 profiles from the ISRIC World Soil Reference Collection. The samples are from 58 countries spanning Africa, Asia, Europe, North America, and South America. Data available under the CC-BY 4.0 license.

ICRAF-ISRIC VisNIR spectral signatures for different soil texture fractions.

Figure 3.2: ICRAF-ISRIC VisNIR spectral signatures for different soil texture fractions.

3.2.3 LUCAS.SSL

Description: LUCAS 2009, 2015 top-soil data. VisNIR scans of some 19,860 in 2009 (2012) and 21,859 points in 2015. Data available publicly under condition that β€œGraphical representation of individual units on a map is permitted as far as the geographical location of the soil samples is not detectable”. Additional 600 samples have been scanned by Woodwell Climate Research using an MIR instrument.

3.2.4 AFSIS1.SSL

Description: Africa Soil Information Service (AfSIS) Soil Chemistry Phase I. MIR and VisNIR scans of 1903 georeferenced soil samples collected from 19 countries in Sub-Saharan Africa including a suite of wet chemistry data. Data available publicly under Open Data Commons Open Database License (β€œODbL”) version 1.0, with attribution to AfSIS. Data retrieved from: https://registry.opendata.aws/afsis/ and https://doi.org/10.34725/DVN/QXCWP1.

3.2.5 AFSIS2.SSL

Description: Africa Soil Information Service (AfSIS) Soil Chemistry Phase II. The three datasets for Tanzania (https://doi.org/10.34725/DVN/XUDGJY), Ghana (https://doi.org/10.34725/DVN/SPRSFN) and Nigeria (https://doi.org/10.34725/DVN/WLAKR2). Documentation of the datasets is available in Hengl et al. (2021). Data has been analyzed at the ICRAF Soil-Plant Spectral Diagnostics Laboratory, Nairobi, and the Rothamsted Research. From the 31,269 soil scans, only 820 (2.6%) have reference data atteched to it.

  • πŸ“• Hengl, T., Miller, M.A.E., KriΕΎan, J., Shepherd, K.D., Sila, A., Kilibarda, M., Antonijevi, O., GluΕ‘ica, L., Dobermann, A., Haefele, S.M., McGrath, S.P., Acquah, G.E., Collinson, J., Parente, L., Sheykhmousa, M., Saito, K., Johnson, J-M., Chamberlin, J., Silatsa, F.B.T., Yemefack, M., Wendt, J., MacMillan, R.A., Wheeler I. and Crouch, J. (2021) African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Scientific Reports, 11, 6130. https://www.nature.com/articles/s41598-021-85639-y.
  • πŸ”— Project website: https://data.worldagroforestry.org/dataverse/icraf_soils
  • ©️ Data license: Creative Commons β€” CC0 1.0 Universal
  • πŸ“ Unique locations: 367
  • πŸ“‹ Unique complete scans: 820
  • πŸ“ Import steps: AFSIS2

3.2.6 CAF.SSL

Description: The Central African Soil Spectral Library. MIR scans of some collected based on 1800 samples in Central Africa.

3.2.7 NEON.SSL

Description: The National Ecological Observatory Network (NEON) Soil Spectral Library. Soil samples were sent to KSSL and scanned using standard procedures.

3.3 Soil spetroscopy organizations

National and regional soil spectral laboratories actively processing soil samples and providing soil spectroscopy services, and other organizations and companies producing or using soil spectroscopy data.

See also: The Global Soil Laboratory Network (GLOSOLAN)

3.3.1 KSSL

3.4 Soil Spectroscopy software

Open source and commercial software used to process soil spectral scans. See also this detailed review of software used for soil spectroscopy. Even more in-depth review of R packages used for soil spectroscopy can be found in Wadoux et al. (2021).

3.4.1 asdreader

  • πŸ“› Name: asdreader
  • πŸ’Ό Specialty: Reading ASD Binary Files in R
  • πŸ’» Programming language: R project
  • πŸ”— Homepage: https://github.com/pierreroudier/asdreader
  • πŸ“• Roudier, P. (2020). asdreader: reading ASD binary files in R. R package version 0.1-3 CRAN.
  • ©️ License: GPL
  • πŸ“§ Maintainer: Pierre Roudier

3.4.2 prospectr

  • πŸ“› Name: prospectr
  • πŸ’Ό Specialty: Signal processing, Resampling
  • πŸ’» Programming language: R project
  • πŸ”— Homepage: https://github.com/l-ramirez-lopez/prospectr
  • πŸ“• Stevens, A., & Ramirez-Lopez, L. (2014). An introduction to the prospectr package. R Package Vignette, Report No.: R Package Version 0.1, 3.
  • ©️ License: MIT + file LICENSE
  • πŸ“§ Maintainer: Leornardo Ramirez-Lopez

3.4.3 simplerspec

References

Benedetti, F., and F. van Egmond. 2021. Global Soil Spectroscopy Assessment. Spectral soil data β€” Needs and capacities. Rome, Italy: FAO. https://doi.org/10.4060/cb6265en.
Hengl, Tomislav, Matthew AE Miller, Josip KriΕΎan, Keith D Shepherd, Andrew Sila, Milan Kilibarda, Ognjen AntonijeviΔ‡, et al. 2021. β€œAfrican Soil Properties and Nutrients Mapped at 30 m Spatial Resolution Using Two-Scale Ensemble Machine Learning.” Scientific Reports 11 (1): 1–18. https://doi.org/10.1038/s41598-021-85639-y.
Wadoux, A. M. J. C., B. Malone, A. B. McBratney, M. Fajardo, and B. Minasny. 2021. Soil Spectral Inference with R: Analysing Digital Soil Spectra Using the R Programming Environment. Progress in Soil Science. Springer International Publishing.