A LUCAS‐based mid‐infrared soil spectral library: Its usefulness for soil survey and precision agriculture

• Published in: Journal of plant nutrition and soil science Vol. 185; no. 3; pp. 370 - 383
• Main Authors: Leenen, Matthias, Pätzold, Stefan, Tóth, Gergely, Welp, Gerhard
• Format: Journal Article
• Language: English
• Published: Weinheim Wiley Subscription Services, Inc 01.06.2022
• Subjects: Agriculture; Arable land; Calibration; Clay; diffuse reflectance spectroscopy; Infrared spectroscopy; Land cover; Land use; Least squares method; Nutrient availability; Nutrients; Organic carbon; Organic soils; parent material; partial least squares regression; precision agriculture; Precision farming; Prediction models; Regional analysis; Soil properties; soil sensing; Soil surveys; Soil testing; Spectrum analysis; Topsoil; within‐field soil heterogeneity; Germany
• ISSN: 1436-8730; 1522-2624
• DOI: 10.1002/jpln.202100031

Background Mid‐infrared spectroscopy (MIRS) is commonly recognized as a rapid and high throughput measurement technology for numerous soil properties, given that appropriate prediction models are calibrated. Soil spectral libraries (SSL) may reduce effort and costs for MIRS practical application. Aims To calibrate MIR‐SSL‐based prediction models for soil properties and to test their applicability to independent sample sets at regional scale (e.g., for soil survey) and at field scale (i.e., for precision agriculture, PA). Methods Spectra of 1013 arable topsoil samples of the European Land Use/Land Cover Area Frame Survey 2009 (LUCAS) from Belgium, the Netherlands, Luxembourg, and Germany formed the basis for the MIR‐SSL. Leave‐one‐out cross‐validation (LOOCV) via partial least squares regression served to calibrate (1) generic prediction models including all samples, and (2) stratified models for different parent materials. Test‐set validation (TSV) was conducted on samples from independent campaigns at (1) regional scale with a sample set from Schleswig‐Holstein (Germany; n = 385) and (2) field scale for four individual fields in Germany (n = 513). Results Generic LOOCV models successfully predicted soil organic carbon, total nitrogen, sand, silt, clay, carbonate, and pH. Calibration for available nutrients failed. The TSV was successful for the regional sample set for all variables (2.5 ≤ RPIQ ≤ 5.9), except for carbonate (RPIQ = 0). At field scale, the validation was highly variable for different sites and parameters. Stratified models using soil parent material as auxiliary variable improved only occasionally the applicability at field scale, that is, on single fields and only for clay and carbonate. Conclusions Although the MIR‐SSL in its present state cannot be recommended for nutrient management, it provides valuable support for soil survey and PA.