08 Jan 2021

08 Jan 2021

Review status: a revised version of this preprint is currently under review for the journal SOIL.

Predicting soil carbon by efficiently using variation in a mid-IR soil spectral library

Anatol Helfenstein1,2, Philipp Baumann1, Raphael Viscarra Rossel3, Andreas Gubler4, Stefan Oechslin5, and Johan Six1 Anatol Helfenstein et al.
  • 1Department of Environmental Systems Science, Swiss Federal Institute of Technology, ETH-Zurich, Universitätsstrasse 2,8092 Zürich, Switzerland
  • 2Soil Geography and Landscape Group, Wageningen University, PO Box 47, 6700 AA Wageningen, the Netherlands
  • 3School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, Perth, Western Australia, Australia
  • 4Swiss Soil Monitoring Network (NABO), Agroscope, Reckenholzstrasse 191, 8046 Zürich, Switzerland
  • 5School of Agricultural, Forest and Food Sciences HAFL, Bern University of Applied Sciences BFH, Bern, Switzerland

Abstract. Traditional laboratory methods of acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modelling can link and massively scale up these methods for many soil characteristics in a cost-effective and timely manner. In Switzerland, only 10 % to 15 % of agricultural soils have been mapped sufficiently to serve spatial decision support systems, presenting an urgent need for rapid quantitative soil characterization. The current Swiss soil spectral library (SSL; n = 4374) in the mid-infrared range includes soil samples from the Biodiversity Monitoring Program (BDM), arranged in a regularly spaced grid across Switzerland, and temporally-resolved data from the Swiss Soil Monitoring Network (NABO). Given the relatively low representation of organic soils and their organo-mineral diversity in the SSL, we aimed to develop both an efficient calibration sampling scheme and accurate modelling strategy to estimate soil carbon (SC) contents of heterogeneous samples between 0 m to 2 m depth from 26 locations within two drained peatland regions (HAFL dataset; n = 116). The focus was on minimizing the need for new reference analyses by efficiently mining the spectral information of SSL instances and their target-feature representations.

We used partial least square regressions (PLSR) together with a 5 times repeated, grouped by location, 10-fold cross validation (CV) to predict SC ranging from 1 % to 52 % in the local HAFL dataset. We compared the validation performance of different calibration schemes involving local models (1), models using the entire SSL spiked with local samples (2) and 15 subsets of local and SSL samples using the RS-LOCAL algorithm (3). Using local and RS-LOCAL calibrations with at least 5 local samples, we achieved similar validation results for predictions of SC up to 52 % (R2 = 0.94–0.96, bias = −0.6–1.5, RMSE = 2.6 % to 3.5 % total carbon). However, calibrations of representative SSL and local samples using RS-LOCAL only required 5 local samples for very accurate models (RMSE = 2.9 % total carbon), while local calibrations required 50 samples for similarly accurate results (RMSE < 3 % total carbon). Of the three approaches, the entire SSL spiked with local samples for model calibration led to validations with the lowest performance in terms of R2, bias and RMSE. Hence, we show that a simple and comprehensible modelling approach using RS-LOCAL together with a SSL is an efficient and accurate strategy when using infrared spectroscopy. It decreases field and laboratory work, the bias of SSL-spiking approaches and the uncertainty of local models. If adequately mined, the information in a SSL is sufficient to predict SC in new and independent study regions, even if the local soil characteristics are very different from the ones in the SSL. This will help to efficiently scale up the acquisition of quantitative soil information over space and time.

Anatol Helfenstein et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on soil-2020-93', Jonathan Sanderman, 11 Feb 2021
    • AC1: 'Reply on RC1', Anatol Helfenstein, 20 Mar 2021
  • RC2: 'Comment on soil-2020-93', Anonymous Referee #2, 22 Feb 2021
    • AC2: 'Reply on RC2', Anatol Helfenstein, 20 Mar 2021
  • EC1: 'Comment on soil-2020-93', Bas van Wesemael, 22 Mar 2021

Anatol Helfenstein et al.

Anatol Helfenstein et al.


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Short summary
In this study, we show that the variation in a soil spectral library (SSL) can be used to predict soil carbon at new and very different locations. The importance of this finding is that it requires less time-consuming lab work than calibrating a new model for every local application while still remaining similarly accurate as local models. Furthermore, we show that this method even works for predicting (drained) peat soils using an SSL with mostly mineral soils containing much less total carbon.