Supplement of SOIL, 4, 1–22, 2018 https://doi.org/10.5194/soil-4-1-2018-supplement © Author(s) 2018. This work is distributed under SOIL the Creative Commons Attribution 3.0 License. Supplement of Evaluation of digital soil mapping approaches with large sets of environ- mental covariates Madlene Nussbaum et al. Correspondence to: Madlene Nussbaum ([email protected]) The copyright of individual parts of the supplement might differ from the CC BY 3.0 License. Supplementary Material: Evaluation statistical approaches for DSM 1 Supplementary Material List of Figures S1 Overview of sites topsoil clay { Berne..................2 S2 Overview of sites topsoil clay { Greifensee...............2 S3 Overview of sites topsoil ECEC { ZH forest..............3 List of Tables S1 Details on soil analysis and data harmonization............4 S2 Details on geodata and covariates....................5 S3 Descriptive statistics of soil properties { Berne.............6 S4 Descriptive statistics of soil properties { Berne (cont.).........7 S5 Descriptive statistics of soil properties { Greifensee..........8 S6 Descriptive statistics of soil properties { Greifensee (cont.)......9 S7 Descriptive statistics of soil properties { ZH forest........... 10 S8 Cross-validation statistics........................ 11 S9 Ratio of bias2 to MSE.......................... 12 S10 Optimal model parameters { Berne................... 13 S11 Optimal model parameters { Greifensee, ZH forest........... 14 Supplementary Material: Evaluation statistical approaches for DSM 2 lake validation sites (198) agricultural area calibration sites (750) LLyssyss Data sources: Soil sampling locations © 2013 LANAT Canton of Berne Lakes: swissTLM3D © 2013 swisstopo / Relief: DHM25 © 2012 swisstopo 0 5 10 km Reproduced with the authorisation of swisstopo (JA100120 / JD100042) Figure S1: Location of sites in the Berne study region shown as an example for topsoil clay content (0{10 cm). Black dots are locations used for model calibration, locations with red triangles were used for model validation. validation sites (194) calibration sites (902) lake agricultural area Zurich 0 5 10 km Data sources: Soil sampling locations © 2013 FABO Canton of Zurich (TID 22742) Lakes: swissTLM3D © 2013 swisstopo Relief: DHM25 © 2012 swisstopo Reproduced with the authorisation of swisstopo (JA100120 / JD100042) Figure S2: Location of sites in the Greifensee study region shown as an example for topsoil clay content (0{10 cm). Black dots are locations used for model calibration, locations with red triangles were used for model validation. Supplementary Material: Evaluation statistical approaches for DSM 3 calibration sites (1055) validation sites (293) lake forested area Winterthur Zurich Uster 0 5 10 15 km Data sources: Soil sampling locations © 2013 FABO Canton of Zurich (TID 22742) Lakes: swissTLM3D © 2013 swisstopo Relief: DHM25 © 2012 swisstopo Reproduced with the authorisation of swisstopo (JA100120 / JD100042) Figure S3: Location of sites in the ZH forest study region shown as an example for topsoil effective cation exchange capacity (ECEC, 0{20 cm). Black dots are locations used for model calibration, locations with red triangles were used for model validation. Supplementary Material: Evaluation statistical approaches for DSM Table S1: Details on soil analysis methods and remarks on soil data harmonization for modelled soil properties, more information can be found in Walthert et al. (2016, F: percentage of field estimates, PTF: percentage of observations calculated by pedotransfer functions [PTF], a: study region Be: Berne, Gr: Greifensee, Zf: ZH forest, SOM: soil organic matter, SD: soil depth, BD: bulk density of fine soil fraction ≤ 2 mm). property r soil analysis method soil data harmonization F [%] PTF [%] unit texture Be, Gr sedimentation (ELF, 1996; Ribi, 2014), Accordance of field estimates and pipette measurements: RMSE 7.1{9.8 % for clay Be: 78 wt.% field estimates (ordinal data, Brunner and 8.2{13.2 % for silt depending on source/survey (Walthert et al., 2016) Gr: 48 et al., 1997; J¨aggliet al., 1998) gravel Be, Gr volumetric content of coarse fragments > Be: 100 vol.% 2 mm estimated in the field (ordinal data, Gr: 100 Brunner et al., 1997; J¨aggliet al., 1998) pH Be, Gr, Zf potentiometric measurements in H2O or robust linear regression to transfer H2O measurements to CaCl2, calibrated on 227 Be: 72 Be: 9 { CaCl2, partly field-moist samples (Zf), samples (60 sites): pHCaCl2 = 1:00∗pHH2O−0:46, RMSE 0.195, SSmse 0.945 computed Zf: 30 (field- Gr: 10 field estimates (ordinal data by pH indi- on 55 samples (12 sites) not used for model calibration. Accordance of field estimates moist) cator solution, ELF, 1996; J¨aggliet al., and pHCaCl2 : RMSE 0.521, SSmse 0.726. 1998; Ribi, 2008; Walthert et al., 2004) SOM Be, Gr oxidation with K2Cr2O7 /H2SO4 (ELF, accordance of field estimates with measurements: RMSE 6.822 %, SSmse 0.745 for Be, Be: 77 wt.% 1996), field estimates (ordinal data, RMSE 3.51 %, SSmse 0.786 for Gr. Gr: 33 Brunner et al., 1997; J¨aggliet al., 1998) −1 ECEC Zf 1 M ammonium chloride extraction robust linear regression to estimate ECEC from Ca, Mg, K and Al measured by ammo- Zf: 9 mmolc kg (FAC, 1989; Walthert et al., 2004, 2013), nium acetate/EDTA extraction with 604 samples (198 sites), SSmse 0.939, RMSE 2.650 0.5 M ammonium acetate and 0.02 EDTA log mmolc, computed on 142 samples (49 sites) not used for calibration (Nussbaum extraction at pH 4.65 (Lakanen and and Papritz, 2015). Ervi¨o, 1971; ELF, 1996) SD Be, Gr Plant exploitable (effective) soil depth SD derived from horizon thickness di, presence of waterlogging horizon qualifiers (g: Gr: 100 Be: 100 cm (SD) estimated in field (ordinal data, wi = 0:33, gg: wi = 0:33, r: wi = 0:1) and C horizon (parent material, wi = 0) by Pn J¨aggliet al., 1998) SD = i=1 di (1 − si) wi where wi = 1 in case of absence of mentioned qualifiers and si volumetric gravel content [%] of horizon i. BD Zf1 bulk density of fine soil faction (diame- robust linear regression with samples from Be, Gr and Swiss forests (Nussbaum et al., Zf: 98 Mg m−3 ter ≤ 2 mm) sampled by volumetric cores 2016) with SOM, soil depth and land use as covariates, calibrated on 757 samples −3 (Brunner et al., 1997; Walthert et al., (245 sites) with RMSE 0.253 Mg m , SSmse 0.508 computed on 279 samples (81 2004) sites) not used for calibration. Where SOM was missing PTF with sampling depth and land use only, calibrated on 1040 samples (308 sites) with RMSE 0.333 Mg m−3, SSmse 0.147 computed on same 279 samples (latter PTF used only to transfer samples to comparable depth intervals). 1 BD was used in Be, Gr and Zf to convert data related to soil horizons to fixed-depth intervals (see Sect. 2.2). 4 Table S2: Details on geodata sets and derived covariates (r: pixel size for raster datasets or scale for vector datasets, a: limited to study region Be: Berne, Gr: Supplementary Material: Evaluation statistical approaches for DSM Greifensee or Zf: ZH forest, n: number of covariates per dataset). geodata set r a n description and details on covariate derivations Soil adapted 30 physiographic units of soil overview map (Nussbaum et al., 2014), evidence on drained wetlands Soil overview map (FSO, 2000a) 1:200 000 8 from historic maps, presence of drainage networks on agricultural land, evidence on anthropogenic interventions Wetlands Wild maps (ALN, 2002) 1:50 000 Gr 1 in soil (e.g. soil ameliorations), potential for agricultural production for different crops. Wetlands Siegfried maps (W¨ust-Galleyet al., 2015) 1:25 000 Gr 1 Agricultural suitability (LANAT, 2015) 1:25 000 Be 1 Anthropogenic soil interventions (AWEL, 2012) 1:5 000 Gr 1 Drainage networks (ALN, 2014b) 1:5 000 Gr 2 Parent material geological/geotechnical map units (partly aggregated a priori), sheets of geological map (Swisstopo, 2016) Geological overview map (Swisstopo, 2005) 1:500 000 Be 4 coarsely harmonized through legend matching and aggregation, CaCO3 index based on geotechnical map Map of last glacial maximum (Swisstopo, 2009) 1:500 000 1 (BAFU and GRID-Europe, 2010), closest distance between soil sampling point and geological line or polygon Geotechnical map (BFS, 2001) 1:200 000 2 object on Zurich geological map (ALN, 2014a, drumlins, moraines of different glacial stages), approximate ice Geological map (ALN, 2014a) 1:50 000 7 level during last glaciation, presence of aquifer, aquifer covered by impermeable layer, areas suitable for gravel Geological maps (Swisstopo, 2016) 1:25 000 Be 1 Groundwater map (AWEL, 2014; AWA, 2014b) 1:25 000 Gr 2 exploitation. Hydrogeological infiltration zones (AWA, 2014a) 1:25 000 Be 2 Mineral raw materials (AGR, 2015) 1:25 000 Be 1 Climate mean annual/monthly temperature and precipitation, cloud cover, sunshine duration, radiation, degree days, MeteoSwiss 1961{1990 (Zimmermann and Kienast, 1999) 25/100 m 33 continentality index (Gams, 1935), temperature variation, ratio of actual to potential evapotranspiration and MeteoTest 1975{2010 (Remund et al., 2011) 250 m 38 site water balance (Grier and Running, 1977; Gurtz et al., 1999), NH3 concentration in air, NO2 immissions Air pollutants (BAFU, 2011) 500 m Zf 2 of years 2000, 2005, 2010. NO2 immissions (AWEL, 2015) 100 m Gr 3 Vegetation vegetation map units (aggregated a priori with ecograms), maximal potential to grow deciduous trees Landsat7 scene (USGS EROS, 2013) 30 m 9 (vegetation map), percentage of coniferous trees derived from spectral imagery (FSO, 2000b) and species DMC mosaic (DMC, 2015) 22 m 4 composition data of National Forest Inventory