Internal soil standard as a tool to assess and correct spectral variability between laboratories for a practical quantitative utilization

Soil spectroscopy is increasingly used to provide accurate and affordable prediction of various soil properties. However, variability in instrument characteristics and operational protocols still hinders the integration and harmonization of Visible-Near InfraRed- Short Wave InfraRed (VNIR–SWIR) Soil Spectral Libraries (SSLs). This study examines the use of 99% pure silica sand, Lucky Bay (LB), as an Internal Soil Standard (ISS), to reduce spectral variability and correct the systematic errors across laboratories. A ring trial test using 60 samples across 11 laboratories was conducted to assess the effect of the ISS correction on spectral consistency and model performance for Soil Organic Carbon (SOC) and clay content predictions. Spectral correction using the ISS reduced the Standard Deviation (SD) in reflectance by 13–70%, effectively reducing dissimilarity across instruments. Partial Least Squares Regression (PLSR) modeling using mean spectra of all instruments showed that a standard protocol resulted in prediction accuracy (Ratio of Performance to InterQuartile range (RPIQ) = 1.50) comparable to a frequently used reference instrument (Foss XDS, RPIQ = 1.57). While models built on individual datasets performed well, combining non-corrected spectra reduced prediction performance (RPIQ) by 11%, which was decreased to 8% after ISS correction. Therefore, we concluded that this approach is particularly useful for increasing interoperability of soil spectral datasets acquired across different instruments and laboratory conditions, which is key in the development of large-scale SSLs. Furthermore, reflectance variation at 1700 nm of the ISS was found as a practical Quality Assurance and Quality Control (QA/QC) indicator, offering a real-time baseline for evaluating instrument performance.