Abstract
Abstract. Traditional laboratory methods for acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modeling 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 that less than 2 % of the samples in the SSL originate from organic soils, we aimed to develop both an efficient calibration sampling scheme and accurate modeling strategy to estimate the soil carbon (SC) contents of heterogeneous samples between 0 and 2 m depth from 26 locations within two drained peatland regions (School of Agricultural, Forest and Food Sciences (HAFL) data set; n = 116). The focus was on minimizing the need for new reference analyses by efficiently mining the spectral information of the SSL. We used partial least square regressions (PLSRs), together with five repetitions of a location-grouped, 10-fold cross-validation, to predict SC ranging from 1 % to 52 % in the local HAFL data set. We compared the validation performance of different calibration schemes involving local models (1), models using the entire SSL combined with local samples (2), commonly referred to as spiking, and subsets of local and SSL samples optimized for the peatland target sites using the resampling local (RS-LOCAL) algorithm (3). Using local and RS-LOCAL calibrations with at least five local samples, we achieved similar validation results for predictions of SC up to 52 % (R2 = 0.93 to 0.97; bias = -0.07 to 1.65; root mean square error (RMSE) = 2.71 % to 3.89 % total carbon; ratio of performance to deviation (RPD) = 3.38 to 4.86; and ratio of performance to interquartile range (RPIQ) = 4.93 to 7.09). However, calibrations using RS-LOCAL only required five or 10 local samples for very accurate models (RMSE = 3.16 % and 2.71 % total carbon, respectively), while purely 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, RMSE, RPD and RPIQ. Hence, we show that a simple and comprehensible modeling 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 the 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.
Highlights
Soil, the “skin” of the Earth, is a vital part of the natural environment and essential for global ecosystem services, including food and fiber production, water filtration, climate regulation and carbon sequestration (Schmidt et al, 2011; Tiessen et al, 1994)
The lowest root mean square error (RMSE) was achieved in the cross-validated calibration when using a SG filter with a first derivative and second-order polynomial (Savitzky and Golay, 1964) in combination with a window size of 35 points (70 cm−1) and selecting only every eighth variable
This resulted in 209 variables between 634 and 3962 cm−1, which formed the predictors for subsequent modeling
Summary
The “skin” of the Earth, is a vital part of the natural environment and essential for global ecosystem services, including food and fiber production, water filtration, climate regulation and carbon sequestration (Schmidt et al, 2011; Tiessen et al, 1994). We gained our scientific understanding of soil through long and strenuous soil surveys complemented by careful chemical, physical, mineralogical and biological laboratory analysis. These conventional methodologies continue to be important for understanding complex soil processes, especially at specific locations. Accurate predictions can be made due to underlying relations between measured spectral patterns and absorbance features of soil characteristics, such as color and both mineral and organic constituents (Nocita et al, 2015). As well as soil texture, are commonly accurately predicted using spectroscopic modeling approaches (Viscarra Rossel et al, 2006; Wijewardane et al, 2018; Clairotte et al, 2016; Dangal et al, 2019). Model predictions for cation exchange capacity (CEC), exchangeable Ca2+ and Mg2+, soil pH, and several others have shown promising results (Guillou et al, 2015; Reeves and Smith, 2009; Madari et al, 2006; Viscarra Rossel et al, 2008)
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