The data set was used to predict soil organic carbon (%), clay (<2 µm), silt (2-50 µm), and sand (50-2000 µm) content (%) and pH on samples that were not analysed with wet chemistry. Subsequently, cation exchange capacity (cmol kg⁻¹; doi:10.1016/j.catena.2017.07.002) and water content at field capacity (cm³ cm⁻³; doi:10.1111/ejss.12192) were calculated with the referenced pedo-transfer functions. The soil samples were taken in an area of 1000 km² around Lora del Rio, Andalusia, Spain, in the Sierra Morena mountain range (Palaeozoic granite, gneiss, and slate), at the Guadalquivir river flood plain (Pleistocene marl, calcarenite, coarse sand, and Holocene sands and loams), and southern tertiary terraces (coarse gravel and cobble with sands and loams). Present soil types according to USDA Soil Taxonomy are Alfisols, Entisols, Inceptisols, and Vertisols.The samples were taken at 130 stratified randomly located soil profiles in two weeks of October 2018. At each location up to fife samples were taken with an auger depending on the soil thickness and bulked from 3 replicates. The sampled increments were 0-10, 10-20, 20-30, 40-60, and 70-100 cm. The samples were dried at 40 °C for 24 h, root fragments were removed. Subsequently, the samples were sieved (<2 mm) and ground. The soil spectra of all 506 samples were measured with a Tensor II (Bruker Optics, Ettlingen, Germany) for NIR (833–3500 nm, i.e. 2860–12000 cm⁻¹ with a resolution of 4 cm⁻¹) and a GladiATR (Pike Technologies, Madison, WI, USA) for MIR (2270–25000 nm, i.e. 400–4400 cm⁻¹ with a resolution of 2 cm⁻¹). Due to the overlapping spectra, we cut the spectra at 2500 nm and kept the spectra from 833–2500 nm and from 2500–25000 nm.A geographically representative subset of 97 samples was analysed for soil organic carbon with dry combustion in a Vario EL III (Elementar, Hanau, Germany), for pH in potassium chloride solution with a with a ProfiLine pH 3310 and a SenTix 81 electrode (WTW, Weilheim, Germany), and for grain size fractions clay (<2 µm), silt (2-50 µm) and sand (50-2000 µm) with a SediGraph III (Micromeritics, Norcross, GA, USA).The 97 samples were used to train a partial least squares regression model and make predictions to the remaining samples. Due to no effect, pre-processing of the raw spectra was omitted. The root mean squared errors of these models were 0.5 % for SOC content, 0.4 for pH, 4 % for clay, 5 % for silt, and 5 % for sand content. Finally, a simple soil quality index was calculated by averaging the classified cation exchange capacity, pH, and water content at field capacity (ISBN 978-0-64309-225-9 and doi:10.1016/j.ecolind.2016.11.016). The selected soil quality indicators and the soil quality index were used to investigate the relevant range of scales with multi-scale contextual spatial mapping (doi:10.1038/s41598-019-51395-3 and “Soil quality data and terrain data for 3D multi-scale contextual spatial modelling in Lora del Rio, Andalusia, Spain” Pangaea DOI).The data is provided relational data tables with the lab ID as unique identifier.

Supplement to: Rentschler, Tobias; Bartelheim, Martin; Behrens, Thorsten; Díaz-Zorita Bonilla, Marta; Teuber, Sandra; Scholten, Thomas; Schmidt, Karsten (2022): Contextual spatial modelling in the horizontal and vertical domains. Scientific Reports, 12(1)