Preprints
https://doi.org/10.5194/soil-2020-105
https://doi.org/10.5194/soil-2020-105

  22 Feb 2021

22 Feb 2021

Review status: this preprint is currently under review for the journal SOIL.

Developing the Swiss soil spectral library for local estimation and monitoring

Philipp Baumann1,a, Anatol Helfenstein1,2, Andreas Gubler3, Armin Keller4, Reto Giulio Meuli3, Daniel Wächter3, Juhwan Lee5, Raphael Viscarra Rossel6, and Johan Six1 Philipp Baumann et al.
  • 1Institute of Agricultural Sciences, Department of Environmental Systems Science (D-USYS), ETH Zürich, Switzerland
  • 2Soil Geography and Landscape Group, Wageningen University, PO Box 47, 6700 AA Wageningen, The Netherlands
  • 3Swiss Soil Monitoring Network (NABO), Agroscope, Reckenholzstrasse 191, 8046 Zürich, Switzerland
  • 4Swiss Competence Center for Soils (KOBO), School of Agricultural, Forest and Food Sciences HAFL, Bern University of Applied Sciences BFH, Bern, Switzerland
  • 5Department of Agronomy and Medicinal Plant Resources, Gyeongnam National University of Science and Technology, Jinju, Republic of Korea
  • 6Soil and Landscape Science, School of Molecular and Life Sciences, Curtin University, GPO Box U1987, Perth WA 6845, Australia
  • acurrent address: Swiss Competence Center for Soils (KOBO), School of Agricultural, Forest and Food Sciences HAFL, Bern University of Applied Sciences BFH, Bern, Switzerland

Abstract. Information on soils' composition and physical, chemical and biological properties is paramount to elucidate agroecosystem functioning in space and over time. For this purposes we developed a national Swiss soil spectral library (SSL; n = 4374) in the mid-infrared (mid-IR), calibrating 17 properties from legacy measurements on soils from the Swiss biodiversity monitoring program (n = 3778; 1094 sites) and the Swiss long-term monitoring network (n = 596; 71 sites). General models were trained with the interpretable rule-based learner CUBIST, testing combinations of {5, 10, 20, 50, 100} committees of rules and {2, 5, 7, 9} neighbors to localize predictions with repeated by location grouped ten-fold cross-validation. 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. Eleven soil properties were estimated with discrimination capacity suitable for screening (R2 > 0.6), out of which total carbon (C), organic C (OC), total N, organic matter content, pH, and clay showed accuracy eligible for accurate diagnostics (R2 > 0.8). Cubist and the spectra estimated total C accurately with RMSE = 0.84 % while the measured range was 0.1–⁠58.3 %, and OC with RMSE = 1.20 % (measured range 0.0–⁠27.3 %). Compared to general estimates of properties from Cubist, local modeling on average reduced the root mean square error of total C per site fourfold. We found that the selected SSL subsets were highly dissimilar 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.

Philipp Baumann et al.

Status: open (until 09 Apr 2021)

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Philipp Baumann et al.

Philipp Baumann et al.

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Short summary
We developed the Swiss mid-infrared spectral library and a statistical model collection across 4374 soil samples with reference measurements of 17 properties. Our library incorporates soil from 1094 grid locations and 71 long-term monitoring sites. This work confirms once again that nationwide spectral libraries with diverse soils can reliably feed information to fast chemical diagnosis. Our way of data-driven reduction of the library has accuracy potential for monitoring carbon at plot scale.