Soil information and soil property maps for the Kurdistan region, Dohuk governorate (Iraq)

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Abstract
We present the first detailed soil property maps at multiple depths for the northwestern autonomous Kurdistan region of Iraq (Dohuk). A total of 532 soil samples from 122 sites were collected at five depth increments (0–10, 10–30, 30–50, 50–70, and 70–100 cm), and their mid-infrared (MIR) spectra were measured. A subset of 108 samples, selected via Kennard–Stone sampling, was analysed in a laboratory on ten soil properties. A Cubist model was trained and used from these measured values to predict all samples’ soil properties from their MIR spectra. Digital soil mapping was conducted using various machine learning regression techniques (ensemble learning, linear classifier, nearest neighbour classifier, decision trees), trained on the predicted soil properties and using a total of 85 covariates at 25 m pixel resolution, resulting in 50 prediction maps in total. Results were compared with the SoilGrids 2.0 product and a regional texture model. Soil depth was also mapped using a quantile random forest with 26 covariates. Our regional model outperformed global SoilGrids 2.0 predictions in resolution and accuracy, with texture RMSEs (sand: ∑RMSE = 9.35; silt: ∑RMSE = 6.8; clay: ∑RMSE = 10.28) comparable to local models. Quantile random forest achieved the best performance in 51 % of the models, and key predictors included Sentinel 2 SWIR, EVI, NDVI, and SAVI. Spatial patterns reflected the contrast between the flat areas of the Simele and Zakho plains, as opposed to the shallower and steeper Little Khabur Valley and anticline formations. Furthermore, the soil depth prediction model (R2 = 0.57; RMSE = 2.59 cm-0.5) showed strong correlation with slope and a similar pattern distribution with deeper soils in the flat areas of the Simele and Zakho plains, while shallow soils are visible in the anticline and strongly erodible areas. Our comprehensive dataset (Bellat et al., 2024a, b, c, d, 2025) offers substantial insights for soil knowledge in the region, as well as for aridic and semi-aridic areas.
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  • Deutsche Forschungsgemeinschaft (215859406)

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