LabOptimisation

Sample optimisation

Soil spectral inference estimates a soil property of interest, e.g. soil organic carbon (SOC) content from the soil’s spectral signature. This involves building a predictive calibration model for this soil property of interest. To build the calibration model, a soil spectral library is needed, i.e. of soil spectral measurements and corresponding analytical values (e.g. SOC content) measured through conventional laboratory analysis. It is important that the soil spectral library and calibration model built from it are representative of the spectral diversity of the soil samples analysed.

The C4S platform allows users to representatively select samples from a set of soil spectral measurements for conventional laboratory analysis, taking into account also, if desired, the existing national soil spectral library resources. Through selection of samples that represent the greatest spectral diversity, users can improve the predictive capacity and generalizability of their spectral libraries.

References

Moloney JP, Searle R, Malone BP, Dino A, Karunaratne S, Benn D, Stockmann U (2026) The Soil Spectral Selection System: A Practical Platform to operationalise Soil Spectroscopy. Computers and Electronics in Agriculture. Submitted.

Stockmann U, Moloney J, Searle R, Dino A, Malone B, Karunaratne S, Benn D, Glover M, Zhao T (2025) Australia’s national soil spectral library empowering rapid Soil Organic Carbon measurement. End of Project Outcomes Summary Report. Department of Climate Change, Energy, the Environment and Water (DCCEEW) National Soil Carbon Innovation Challenge (NSCIC) Development and Demonstration Grant Round 2 Project SCICDDII000042. CSIRO, Australia.