Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring

Abstract

Abstract. Information on soils' composition and physical, chemical and biological properties is paramount to elucidate agroecosystem functioning in space and over time. For this purpose, we developed a national Swiss soil spectral library (SSL; n=4374) in the mid-infrared (mid-IR), calibrating 16 properties from legacy measurements on soils from the Swiss Biodiversity Monitoring program (BDM; n=3778; 1094 sites) and the Swiss long-term Soil Monitoring Network (NABO; n=596; 71 sites). General models were trained with the interpretable rule-based learner CUBIST, testing combinations of {5,10,20,50, and 100} ensembles of rules (committees) and {2, 5, 7, and 9} nearest neighbors used for local averaging with repeated 10-fold cross-validation grouped by location. To evaluate the information in spectra to facilitate long-term soil monitoring at a plot level, we conducted 71 model transfers for the NABO sites to induce locally relevant information from the SSL, using the data-driven sample selection method RS-LOCAL. In total, 10 soil properties were estimated with discrimination capacity suitable for screening (R2≥0.72; ratio of performance to interquartile distance (RPIQ) ≥ 2.0), out of which total carbon (C), organic C (OC), total nitrogen (N), pH and clay showed accuracy eligible for accurate diagnostics (R2>0.8; RPIQ ≥ 3.0). CUBIST and the spectra estimated total C accurately with the root mean square error (RMSE) = 8.4 g kg−1 and the RPIQ = 4.3, while the measured range was 1–583 g kg−1 and OC with RMSE = 9.3 g kg−1 and RPIQ = 3.4 (measured range 0–583 g kg−1). Compared to the general statistical learning approach, the local transfer approach – using two respective training samples – on average reduced the RMSE of total C per site fourfold. We found that the selected SSL subsets were highly dissimilar compared to validation samples, in terms of both their spectral input space and the measured values. This suggests that data-driven selection with RS-LOCAL leverages chemical diversity in composition rather than similarity. Our results suggest that mid-IR soil estimates were sufficiently accurate to support many soil applications that require a large volume of input data, such as precision agriculture, soil C accounting and monitoring and digital soil mapping. This SSL can be updated continuously, for example, with samples from deeper profiles and organic soils, so that the measurement of key soil properties becomes even more accurate and efficient in the near future.

Predicting Soil Properties for Agricultural Land in the Caucasus Mountains Using Mid-Infrared Spectroscopy

“…The results regarding the MIR and Vis-NIR predictions, particularly for Ca, pH, CaCO 3 , and Fe, were in line with other studies and highlighted the capabilities of non-destructive FTIR spectroscopy as a time-saving and alternate approach for predicting a range of key soil properties [4,9,18,23,24,29]. Seybold et al [29] effectively predicted different chemical and physical soil properties using MIR spectra coupled with PLSR for A and B horizons in Mollisols from central USA using an MIR spectral library.…”

“…Contrary to other nutrients with moderate variations (20-40%), the macro-nutrient elements K and P were largely variable (~50%), which is presumably related to N fertilization practices in cultivated fields as opposed to non-fertilized shrublands or pastures. The large variation in macro-and micro-nutrient contents could be related to differences in the elevation, as well as their sensitivity to the natural and land management processes and mechanisms of bonding to soil particles and organic matter [19,23,29,41].…”

The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication

“…Many researchers have demonstrated the efficiency of soil spectroscopy and robust predictive capabilities for multiple soil properties [28], summarized in [7]. In addition, the MIR spectral range has been proven to provide superior prediction compared to vis-NIR-SWIR spectra [29][30][31]. The SSLs, thus, are important research initiatives [10] but the data are only available through scientific journal publications.…”

Waldböden – intakt und funktional

“…Dies betrifft u. a. die langjährige Nutzung, Unterschiede im Bewuchs der Pflanzen oder Auswertungen zur Farbe der Oberfläche aller offenen Ackerflächen (Bare Soil Map). Weiterhin ermöglichen es spektroskopische Bestimmungsmethoden im Labor und Feld, bestimmte Bodeneigenschaften effizient zu erfassen (Baumann et al 2021, Viscarra Rossel et al 2022. Hierbei kommen spektroskopische Techniken im nahen und mittleren Infrarotbereich zum Einsatz (NIR und MIR).…”