Articles | Volume 18, issue 4
https://doi.org/10.5194/essd-18-2507-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-2507-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
09 Apr 2026
Data description article |
| 09 Apr 2026
| 09 Apr 2026
Soil information and soil property maps for the Kurdistan region, Dohuk governorate (Iraq)
Mathias Bellat
CORRESPONDING AUTHOR
CRC 1070 “ResourceCultures”, University of Tübingen, Tübingen, 72070, Germany
Department of Geosciences, Working group of Soil Science and Geomorphology, University of Tübingen, Tübingen, 72070, Germany
Mjahid Zebari
Ludwig-Maximilians-Universität München, Müchen, 80634, Germany
Nawroz University, Duhok, Iraq
Benjamin Glissmann
CRC 1070 “ResourceCultures”, University of Tübingen, Tübingen, 72070, Germany
Institute for Ancient Near Eastern Studies (IANES), University of Tübingen, Tübingen, 72070, Germany
Tobias Rentschler
CRC 1070 “ResourceCultures”, University of Tübingen, Tübingen, 72070, Germany
Department of Geosciences, Working group of Soil Science and Geomorphology, University of Tübingen, Tübingen, 72070, Germany
Digital Humanities Center, University of Tübingen, 72074, Germany
Paola Sconzo
Department of Culture and Society, University of Palermo, Palermo, 90133, Italy
Nafiseh Kakhani
CRC 1070 “ResourceCultures”, University of Tübingen, Tübingen, 72070, Germany
Department of Geosciences, Working group of Soil Science and Geomorphology, University of Tübingen, Tübingen, 72070, Germany
Ruhollah Taghizadeh-Mehrjardi
CRC 1070 “ResourceCultures”, University of Tübingen, Tübingen, 72070, Germany
Department of Geosciences, Working group of Soil Science and Geomorphology, University of Tübingen, Tübingen, 72070, Germany
Faculty of Agriculture and Natural resources, Ardakan University, Ardakan, Iran
Pegah Kohsravani
Department of Geosciences, Working group of Soil Science and Geomorphology, University of Tübingen, Tübingen, 72070, Germany
College of Agriculture, Shiraz University, Shiraz, Iran
Bekas Brifkany
Dohuk Directorate of Antiquities and Heritages, Dohuk, Iraq
Peter Pfälzner
CRC 1070 “ResourceCultures”, University of Tübingen, Tübingen, 72070, Germany
Institute for Ancient Near Eastern Studies (IANES), University of Tübingen, Tübingen, 72070, Germany
Thomas Scholten
CRC 1070 “ResourceCultures”, University of Tübingen, Tübingen, 72070, Germany
Department of Geosciences, Working group of Soil Science and Geomorphology, University of Tübingen, Tübingen, 72070, Germany
DFG Cluster of Excellence “Machine Learning: New Perspectives for Science”, University of Tübingen, Tübingen, 72076, Germany
Related authors
No articles found.
Rabindra Adhikari, Jan Huber, Amrit Maharjan, Madhavi Parajuli, Corinna Gall, Steffen Seitz, Chandra Kanta Subedi, Niels Schwab, Ram Prasad Chaudhary, Udo Schickhoff, Jürgen Böhner, Yvonne Oelmann, and Thomas Scholten
EGUsphere, https://doi.org/10.5194/egusphere-2026-3815, https://doi.org/10.5194/egusphere-2026-3815, 2026
This preprint is open for discussion and under review for SOIL (SOIL).
Short summary
Short summary
Mountain treeline ecosystems are highly sensitive to climate change, yet the role of soil microbes remains poorly understood. We investigated microbial biomass and soil nutrients across a near-natural Himalayan treeline ecotone in Nepal. Our results show that soil horizon development and altitudinal-vegetation zonation are stronger controls on microbial biomass than bulk soil nutrient concentrations, providing new insights into nutrient cycling in mountain soils.
Madhavi Parajuli, Rabindra Adhikari, Xiaoxia Yang, Luisa Brosch, Nadja Fürst, Thomas Scholten, Steffen Seitz, and Corinna Gall
EGUsphere, https://doi.org/10.5194/egusphere-2026-4029, https://doi.org/10.5194/egusphere-2026-4029, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
We studied how moss growing on forest soils influences long-term carbon storage in two temperate forests in Germany. By separating soil into different organic matter components, we found that moss can increase the amount of carbon held in the most stable form, especially in coniferous forests. These findings show that moss plays an important role in helping forests store carbon and should be considered in forest carbon assessments and climate change research.
Kay D. Seufferheld, Pedro V. G. Batista, Hadi Shokati, Thomas Scholten, and Peter Fiener
SOIL, 12, 301–319, https://doi.org/10.5194/soil-12-301-2026, https://doi.org/10.5194/soil-12-301-2026, 2026
Short summary
Short summary
Soil erosion threatens global food security, yet modeling soil conservation remains challenging. We evaluated WaTEM/SEDEM (Water and Tillage Erosion Model/Sediment Delivery Model) in six highly instrumented micro-scale watersheds optimised for soil conservation using a GLUE (Generalized Likelihood Uncertainty Estimation) framework. The model captured the magnitude of very low sediment yields but showed limited accuracy for annual steps. However, it performed well over eight-year timeframes and larger spatial scales, demonstrating its suitability for strategic, long-term soil conservation planning.
Hadi Shokati, Kay D. Seufferheld, Peter Fiener, and Thomas Scholten
Hydrol. Earth Syst. Sci., 30, 743–756, https://doi.org/10.5194/hess-30-743-2026, https://doi.org/10.5194/hess-30-743-2026, 2026
Short summary
Short summary
Floods threaten lives and property and require rapid mapping. We compared two artificial intelligence approaches on aerial imagery: a fine‑tuned Segment Anything Model (SAM) guided by point or bounding box prompts, and a U‑Net network with ResNet‑50 and ResNet‑101 backbones. The point‑based SAM was the most accurate with precise boundaries. Faster and more reliable flood maps help rescue teams, insurers, and planners to act quickly.
Fedor Scholz, Manuel Traub, Christiane Zarfl, Thomas Scholten, and Martin V. Butz
Hydrol. Earth Syst. Sci., 29, 6257–6283, https://doi.org/10.5194/hess-29-6257-2025, https://doi.org/10.5194/hess-29-6257-2025, 2025
Short summary
Short summary
We present a neural network model that estimates river discharge based on gridded elevation, precipitation, and solar radiation. Some instances of our model produce more accurate forecasts than the European Flood Awareness System (EFAS) when simulating discharge with lead times of 50 days on the Neckar river network in Germany. It consists of multiple components that are designed to model distinct sub-processes. We show that this makes the model behave in a more physically realistic way.
Kerstin Rau, Katharina Eggensperger, Frank Schneider, Michael Blaschek, Philipp Hennig, and Thomas Scholten
SOIL, 11, 833–847, https://doi.org/10.5194/soil-11-833-2025, https://doi.org/10.5194/soil-11-833-2025, 2025
Short summary
Short summary
We developed an uncertainty method to show where machine learning (ML) models predicting soil units are most reliable, especially for transfer tasks. The model was able to correctly predict soil patterns, especially along rivers, in a new but similar region without retraining. It was too confident about common soil types, showing the need for balanced data. This helps improve soil maps and guides better planning for future data collection, saving time and resources while showing uncertainty.
Corinna Gall, Silvana Oldenburg, Martin Nebel, Thomas Scholten, and Steffen Seitz
SOIL, 11, 199–212, https://doi.org/10.5194/soil-11-199-2025, https://doi.org/10.5194/soil-11-199-2025, 2025
Short summary
Short summary
Soil erosion is a major issue in vineyards due to often steep slopes and fallow interlines. While cover crops are typically used for erosion control, moss restoration has not yet been explored. In this study, moss restoration reduced surface runoff by 71.4 % and sediment discharge by 75.8 % compared with bare soil, similar to cover crops. Mosses could serve as ground cover where mowing is impractical, potentially reducing herbicide use in viticulture, although further research is needed.
Wanjun Zhang, Thomas Scholten, Steffen Seitz, Qianmei Zhang, Guowei Chu, Linhua Wang, Xin Xiong, and Juxiu Liu
Hydrol. Earth Syst. Sci., 28, 3837–3854, https://doi.org/10.5194/hess-28-3837-2024, https://doi.org/10.5194/hess-28-3837-2024, 2024
Short summary
Short summary
Rainfall input generally controls soil water and plant growth. We focus on rainfall redistribution in succession sequence forests over 22 years. Some changes in rainwater volume and chemistry in the throughfall and stemflow and drivers were investigated. Results show that shifted open rainfall over time and forest factors induced remarkable variability in throughfall and stemflow, which potentially makes forecasting future changes in water resources in the forest ecosystems more difficult.
Nicolás Riveras-Muñoz, Steffen Seitz, Kristina Witzgall, Victoria Rodríguez, Peter Kühn, Carsten W. Mueller, Rómulo Oses, Oscar Seguel, Dirk Wagner, and Thomas Scholten
SOIL, 8, 717–731, https://doi.org/10.5194/soil-8-717-2022, https://doi.org/10.5194/soil-8-717-2022, 2022
Short summary
Short summary
Biological soil crusts (biocrusts) stabilize the soil surface mainly in arid regions but are also present in Mediterranean and humid climates. We studied this stabilizing effect through wet and dry sieving along a large climatic gradient in Chile and found that the stabilization of soil aggregates persists in all climates, but their role is masked and reserved for a limited number of size fractions under humid conditions by higher vegetation and organic matter contents in the topsoil.
Corinna Gall, Martin Nebel, Dietmar Quandt, Thomas Scholten, and Steffen Seitz
Biogeosciences, 19, 3225–3245, https://doi.org/10.5194/bg-19-3225-2022, https://doi.org/10.5194/bg-19-3225-2022, 2022
Short summary
Short summary
Soil erosion is one of the most serious environmental challenges of our time, which also applies to forests when forest soil is disturbed. Biological soil crusts (biocrusts) can play a key role as erosion control. In this study, we combined soil erosion measurements with vegetation surveys in disturbed forest areas. We found that soil erosion was reduced primarily by pioneer bryophyte-dominated biocrusts and that bryophytes contributed more to soil erosion mitigation than vascular plants.
Cited articles
Abdi, B., Kolo, K., and Shahabi, H.: Soil erosion and degradation assessment integrating multi-parametric methods of RUSLE model, RS, and GIS in the Shaqlawa agricultural area, Kurdistan Region, Iraq, Environ. Monit. Assess., 195, 1149, https://doi.org/10.1007/s10661-023-11796-4, 2023. a
Abdulrahman, H. D., Mohammed, A., and Doski, J. A. H.: Formation And Development of Vertisols in Selivany Plain at Duhok Governorate, Kurdistan Region, Iraq, The Journal Of Duhok University, 23, 246–258, https://doi.org/10.26682/ajuod.2020.23.2.28, 2020. a, b, c
Abdulsalam, A. and Schlaich, F.: A VII 1 Vorderer Orient. Naturräumliche Gliederung, Dr. Ludwig Reichert Verlag, ISBN 9783882269246, https://reichert-verlag.de/en/tavo-maps/part-a/a_vii/9783882269246_a_vii_1_vorderer_orient_naturraeumliche_gliederung-detail (last access: 27 March 2026), 1988. a
Abuelgasim, A. and Ammad, R.: Mapping soil salinity in arid and semi-arid regions using Landsat 8 OLI satellite data, Remote Sensing Applications: Society and Environment, 13, 415–425, https://doi.org/10.1016/j.rsase.2018.12.010, 2019. a, b
Adhikari, K. and Hartemink, A. E.: Linking soils to ecosystem services – A global review, Geoderma, 262, 101–111, https://doi.org/10.1016/j.geoderma.2015.08.009, 2016. a
Agard, P., Omrani, J., Jolivet, L., Whitechurch, H., Vrielynck, B., Spakman, W., Monié, P., Meyer, B., and Wortel, R.: Zagros orogeny: a subduction-dominated process, Geol. Mag., 148, 692–725, https://doi.org/10.1017/S001675681100046X, 2011. a, b, c
Aitchison, J.: The statistical analysis of compositional data, Monographs on statistics and applied probability, Chapman and Hall, London, 1st edn., ISBN 978-0-412-28060-3, 1986. a
Al-Dabbagh, T. H. and Al-Naqib, S. Q.: Tigris river terrace mapping in northern Iraq and the geotechnical properties of the youngest stage near Dao Al-Qamar Village, Geological Society, London, Engineering Geology Special Publications, 7, 603–609, https://doi.org/10.1144/GSL.ENG.1991.007.01.59, 1991. a
Alavi, M.: Structures of the Zagros fold-thrust belt in Iran, Am. J. Sci., 307, 1064–1095, https://doi.org/10.2475/09.2007.02, 2007. a
Allik, J., Lauk, K., and Realo, A.: Factors Predicting the Scientific Wealth of Nations, Cross-Cult. Res., 54, 364–397, https://doi.org/10.1177/1069397120910982, 2020. a
Alwan, I. A., Karim, H. H., and Aziz, N. A.: Agro-Climatic Zones (ACZ) Using Climate Satellite Data in Iraq Republic, IOP Conf. Ser.-Mat. Sci., 518, 022034, https://doi.org/10.1088/1757-899X/518/2/022034, 2019. a, b
Anas, M., Liao, F., Verma, K. K., Sarwar, M. A., Mahmood, A., Chen, Z.-L., Li, Q., Zeng, X.-P., Liu, Y., and Li, Y.-R.: Fate of nitrogen in agriculture and environment: agronomic, eco-physiological and molecular approaches to improve nitrogen use efficiency, Biol. Res., 53, 47, https://doi.org/10.1186/s40659-020-00312-4, 2020. a
Arrouays, D., Grundy, M. G., Hartemink, A. E., Hempel, J. W., Heuvelink, G. B., Hong, S. Y., Lagacherie, P., Lelyk, G., McBratney, A. B., McKenzie, N. J., Mendonca-Santos, M. d., Minasny, B., Montanarella, L., Odeh, I. O., Sanchez, P. A., Thompson, J. A., and Zhang, G.-L.: GlobalSoilMap, in: Advances in Agronomy, vol. 125, 93–134, Elsevier, ISBN 978-0-12-800137-0, https://doi.org/10.1016/B978-0-12-800137-0.00003-0, 2014. a
Aselin, P.: A Python package for fitting Quinlan's Cubist regression model, pypi [code], https://pypi.org/project/cubist/ (last access: 27 March 2026), 2024. a
Azeez, S. N. and Rahimi, I.: Distribution of Gypsiferous Soil Using Geoinformatics Techniques for Some Aridisols in Garmian, Kurdistan Region-Iraq, Kurdistan Journal of Applied Research, 2, 57–64, https://doi.org/10.24017/science.2017.1.9, 2017. a, b, c
Bailo, D., Paciello, R., Sbarra, M., Rabissoni, R., Vinciarelli, V., and Cocco, M.: Perspectives on the Implementation of FAIR Principles in Solid Earth Research Infrastructures, Front. Earth Sci., 8, https://doi.org/10.3389/feart.2020.00003, 2020. a
Batjes, N. H., Ribeiro, E., and van Oostrum, A.: Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019), Earth Syst. Sci. Data, 12, 299–320, https://doi.org/10.5194/essd-12-299-2020, 2020. a, b, c
Baumann, P.: philipp-baumann/simplerspec: simplerspec_v0.2.1 (simplerspec_v0.2.1), Zenodo [code], https://doi.org/10.5281/zenodo.10075988, 2023. a
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and Wood, E. F.: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Scientific Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214, 2018. a
Behrens, T. and Scholten, T.: Digital soil mapping in Germany – a review, J. Plant Nutr. Soil Sc., 169, 434–443, https://doi.org/10.1002/jpln.200521962, 2006. a, b
Behrens, T., Viscarra Rossel, R. A., Ramirez-Lopez, L., and Baumann, P.: Soil spectroscopy with the Gaussian pyramid scale space, Geoderma, 426, 116095, https://doi.org/10.1016/j.geoderma.2022.116095, 2022. a
Beillouin, D., Corbeels, M., Demenois, J., Berre, D., Boyer, A., Fallot, A., Feder, F., and Cardinael, R.: A global meta-analysis of soil organic carbon in the Anthropocene, Nat. Commun., 14, 3700, https://doi.org/10.1038/s41467-023-39338-z, 2023. a
Bellat, M. and Kakhani, N.: DSM-Kurdistan, Zenodo [code], https://doi.org/10.5281/zenodo.19236909, 2026. a
Bellat, M., Glissmann, B., Rentschler, T., Sconzo, P., Pfälzner, P., Brifkany, B., and Scholten, T.: Soil properties predicted on mid-infrared (MIR) spectroscopy measurements in North-Western Kurdistan region, Iraq, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.973700, 2024a. a, b
Bellat, M., Glissmann, B., Rentschler, T., Sconzo, P., Pfälzner, P., Brifkany, B., and Scholten, T.: Soil properties in the North-Western Kurdistan region, Iraq, derived from laboratory measurements, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.973701, 2024b. a, b
Bellat, M., Glissmann, B., Rentschler, T., Sconzo, P., Pfälzner, P., Brifkany, B., and Scholten, T.: Soil bulk density and soil depth from on-site observations in the North-Western Kurdistan region, Iraq, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.973714, 2024c. a, b
Bellat, M., Glissmann, B., Rentschler, T., Sconzo, P., Pfälzner, P., Brifkany, B., and Scholten, T.: Digital soil mapping predicted on mid-infrared (MIR) spectroscopy measurements in North-Western Kurdistan region, Iraq (netCDF and GeoTIFF files) V3.0, figshare [data set], https://doi.org/10.6084/m9.figshare.31320958.v2, 2026a. a, b
Bellat, M., Zebari, M., Glissmann, B., Rentschler, T., Sconzo, P., Kakhani, N., Kohsravani, P., Taghizadeh-Mehrjardi, R., Brifkany, B., Pfälzner, P., and Scholten, T.: Material for: “Soil information in Kurdistan region, Dohuk governorate (Iraq)” (v.2.1.0), University of Tübingen [data set], https://doi.org/10.57754/FDAT.d5h1h-4x027, 2026. a, b
Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B., Roger, J.-M., and McBratney, A.: Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy, TRAC-Trend. Anal. Chem., 29, 1073–1081, https://doi.org/10.1016/j.trac.2010.05.006, 2010. a, b
Berberian, M.: Master “blind” thrust faults hidden under the Zagros folds: active basement tectonics and surface morphotectonics, Tectonophysics, 241, 193–224, https://doi.org/10.1016/0040-1951(94)00185-C, 1995. a, b
Blake, G. R. and Hartge, K. H.: Bulk Density, in: Methods of Soil Analysis, John Wiley & Sons, Ltd, 363–375, ISBN 978-0-89118-864-3, https://doi.org/10.2136/sssabookser5.1.2ed.c13, 1986. a
Blanc, E.-P., Allen, M., Inger, S., and Hassani, H.: Structural styles in the Zagros Simple Folded Zone, Iran, J. Geol. Soc. London, 160, 401–412, https://doi.org/10.1144/0016-764902-110, 2003. a
Boettinger, J., Ramsey, R., Bodily, J., Cole, N., Kienast-Brown, S., Nield, S., Saunders, A., and Stum, A.: Landsat Spectral Data for Digital Soil Mapping, in: Digital Soil Mapping with Limited Data, edited by: Hartemink, A. E., McBratney, A., and Mendonça-Santos, M. d. L., Springer Netherlands, Dordrecht, 193–202, ISBN 978-1-4020-8592-5, https://doi.org/10.1007/978-1-4020-8592-5_16, 2008. a, b
Bossio, D. A., Cook-Patton, S. C., Ellis, P. W., Fargione, J., Sanderman, J., Smith, P., Wood, S., Zomer, R. J., von Unger, M., Emmer, I. M., and Griscom, B. W.: The role of soil carbon in natural climate solutions, Nature Sustainability, 3, 391–398, https://doi.org/10.1038/s41893-020-0491-z, 2020. a, b
Bousbih, S., Zribi, M., Pelletier, C., Gorrab, A., Lili-Chabaane, Z., Baghdadi, N., Ben Aissa, N., and Mougenot, B.: Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2, Remote Sens., 11, 1520, https://doi.org/10.3390/rs11131520, 2019. a, b
Bouslihim, Y., John, K., Miftah, A., Azmi, R., Aboutayeb, R., Bouasria, A., Razouk, R., and Hssaini, L.: The effect of covariates on Soil Organic Matter and pH variability: a digital soil mapping approach using random forest model, Annals of GIS, 30, 215–232, https://doi.org/10.1080/19475683.2024.2309868, 2024. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Brevik, E. C., Fenton, T. E., and Lazari, A.: Soil electrical conductivity as a function of soil water content and implications for soil mapping, Precis. Agric., 7, 393–404, https://doi.org/10.1007/s11119-006-9021-x, 2006. a
Brevik, E. C., Calzolari, C., Miller, B. A., Pereira, P., Kabala, C., Baumgarten, A., and Jordán, A.: Soil mapping, classification, and pedologic modeling: History and future directions, Geoderma, 264, 256–274, https://doi.org/10.1016/j.geoderma.2015.05.017, 2016. a
Brus, D. J.: Sampling for digital soil mapping: A tutorial supported by R scripts, Geoderma, 338, 464–480, https://doi.org/10.1016/j.geoderma.2018.07.036, 2019. a
Brus, D. J.: Spatial Sampling with R, Chapman and Hall/CRC, Boca Raton, 1st edn., ISBN 978-1-003-25894-0, https://doi.org/10.1201/9781003258940, 2022. a, b
Chandrasekar, K., Sesha Sai, M. V. R., Roy, P. S., and Dwevedi, R. S.: Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product, Int. J. Remote Sens., 31, 3987–4005, https://doi.org/10.1080/01431160802575653, 2010. a, b
Cherubin, M. R., Pinheiro Junior, C. R., Souza, L. F. N., Canisares, L. P., Ferreira, T. O., Cerri, C. E. P., Minasny, B., and Smith, P.: Global blind spots in soil health research overlap with environmental vulnerability hotspots, Communications Earth & Environment, 6, 651, https://doi.org/10.1038/s43247-025-02663-w, 2025. a
Christen, E. and Saliem, K. A.: Managing Salinity in Iraq's Agriculture: Current State, Causes, and Impacts, Internal Report 1, International Center for Agricultural Research in the Dry Areas (ICARDA), Lebanon, https://hdl.handle.net/20.500.11766/9487 (last access: 27 March 2026), 2013. a
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015. a
Copernicus: ERA5-Land monthly averaged data from 1950 to present, Climate Data Store [data set], https://doi.org/10.24381/CDS.68D2BB30, 2019. a, b
Cossart, E., Fressard, M., and Chaize, B.: Spatial patterns of vineyard landscape evolution and their impacts on erosion susceptibility: RUSLE simulation applied in Mercurey (Burgundy, France) since the mid-20th century, Erdkunde, 74, 281–300, https://doi.org/10.3112/erdkunde.2020.04.04, 2020. a
Crawford, N. M. and Forde, B. G.: Molecular and Developmental Biology of Inorganic Nitrogen Nutrition, The Arabidopsis Book, 2002, https://doi.org/10.1199/tab.0011, 2002. a
Curran, P. J., West, S. G., and Finch, J. F.: The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis, Psychol. Methods, 1, 16–29, https://doi.org/10.1037/1082-989X.1.1.16, 1996. a
Desmet, P. and Govers, G.: A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units, J. Soil Water Conserv., 51, 427–433, https://doi.org/10.1080/00224561.1996.12457102, 1996. a
Didan, K.: MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, EarthData [data set], https://doi.org/10.5067/MODIS/MOD13Q1.061, 2021. a
Doski, J. A. H. and McClay, K.: Tectono-stratigraphic evolution, regional structure and fracture patterns of the Zagros fold-thrust belt in the Duhok region, Kurdistan, northern Iraq, Tectonophysics, 838, 229506, https://doi.org/10.1016/j.tecto.2022.229506, 2022. a, b, c, d
Dou, X., Zhang, J., Zhang, C., Ma, D., Chen, L., Zhou, G., Li, J., and Duan, Y.: Calcium carbonate regulates soil organic carbon accumulation by mediating microbial communities in northern China, CATENA, 231, 107327, https://doi.org/10.1016/j.catena.2023.107327, 2023. a
Duchaufour, P., Faivre, P., Poulenard, J., and Gury, M.: Introduction à la science du sol: sol, végétation, environnement, Dunod, Malakoff (Hauts-de-Seine), 7th edn., ISBN 978-2-10-081992-8, 2020. a
Eltaif, N. I. and Gharaibeh, M. A.: Soil Erosion Catastrophe in Iraq-Preview, Causes and Study Cases, in: Environmental Degradation in Asia: Land Degradation, Environmental Contamination, and Human Activities, edited by: Al-Quraishi, A. M. F., Mustafa, Y. T., and Negm, A. M., Springer International Publishing, Cham, 179–207, ISBN 978-3-031-12112-8, https://doi.org/10.1007/978-3-031-12112-8_9, 2022. a
Eltaif, N. I., Gharaibeh, M. A., and Fadhil Al-Quraishi, A. M.: Impact of Climate Change on Iraq: Severe Water Scarcity and Desertification, in: The Handbook of Environmental Chemistry, edited by: Barcelo, D. and Kostianoy, A. G., Springer, Berlin, Heidelberg, 25 pp., https://doi.org/10.1007/698_2024_1100, 2024. a
EROS: Landsat 4-5 Thematic Mapper Level-2, Collection 2, USGS [data set], https://doi.org/10.5066/P9IAXOVV, 2013. a, b, c, d
Ertan, N.: Turkey launches offensive against PKK targets in northern Iraq, Al-Monitor, https://www.al-monitor.com/originals/2022/04/turkey-launches-offensive-against-pkk-targets-northern-iraq (last access: 27 March 2026), 2022. a
European Soil Bureau Network and European Union: Soil atlas of Europe, Office for Official Publications of the European Communities, https://esdac.jrc.ec.europa.eu/content/soil-atlas-europe (last access: 27 March 2026), 2005. a
FAO and IIASA: Harmonized World Soil Database version 2.0, FAO; International Institute for Applied Systems Analysis (IIASA), ISBN 978-92-5-137499-3, https://doi.org/10.4060/cc3823en, 2023. a, b
Fernández-Buces, N., Siebe, C., Cram, S., and Palacio, J. L.: Mapping soil salinity using a combined spectral response index for bare soil and vegetation: A case study in the former lake Texcoco, Mexico, J. Arid Environ., 65, 644–667, https://doi.org/10.1016/j.jaridenv.2005.08.005, 2006. a, b
Forti, L., Perego, A., Brandolini, F., Mariani, G. S., Zebari, M., Nicoll, K., Regattieri, E., Barbaro, C. C., Bonacossi, D. M., Qasim, H. A., Cremaschi, M., and Zerboni, A.: Geomorphology of the northwestern Kurdistan Region of Iraq: landscapes of the Zagros Mountains drained by the Tigris and Great Zab Rivers, J. Maps, 17, 225–236, https://doi.org/10.1080/17445647.2021.1906339, 2021. a, b, c, d, e, f, g, h, i, j
Forti, L., Mariani, G. S., Brandolini, F., Pezzotta, A., and Zerboni, A.: Declassified intelligence satellite imagery as a tool to reconstruct past landforms and surface processes: The submerged riverscape of the Tigris River below the Mosul Dam Lake, Iraq, Earth Surf. Proc. Land., 47, https://doi.org/10.1002/esp.5389, 2022. a, b, c
Fouad, S. F.: Western Zagros Fold – Thrust belt, part I: the Low Folded Zone, Iraqi Bulltin of Geology and Mining, 6, 39–62, https://ibgm-iq.org/ibgm/index.php/ibgm/article/view/221 (last access: 27 March 2026), 2012. a
Fouad, S. F.: Western Zagros Fold – Thrust belt, part II: the High Folded Zone, Iraqi Bulltin of Geology and Mining, 6, 53–71, https://ibgm-iq.org/ibgm/index.php/ibgm/article/view/274 (last access: 27 March 2026), 2014. a
Friedman, S. P.: Soil properties influencing apparent electrical conductivity: a review, Comput. Electron. Agr., 46, 45–70, https://doi.org/10.1016/j.compag.2004.11.001, 2005. a
Gao, B.-C.: NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sens. Environ., 58, 257–266, https://doi.org/10.1016/S0034-4257(96)00067-3, 1996. a, b
Google: earthengine-api: Earth Engine Python API, pypi, http://code.google.com/p/earthengine-api (last access: 27 March 2026), 2025. a
Guan, Z.-H., Cao, Z., Li, X. G., Scholten, T., Kühn, P., Wang, L., Yu, R.-P., and He, J.-S.: Soil phosphorus availability mediates the effects of nitrogen addition on community- and species-level phosphorus-acquisition strategies in alpine grasslands, Sci. Total Environ., 906, 167630, https://doi.org/10.1016/j.scitotenv.2023.167630, 2024. a
Guyon, I., Weston, J., Barnhill, S., and Vapnik, V.: Gene Selection for Cancer Classification using Support Vector Machines, Mach. Learn., 46, 389–422, https://doi.org/10.1023/A:1012487302797, 2002. a
Hengl, T. and MacMillan, A. R.: Predictive Soil Mapping with R, OpenGeoHub foundation, Wageningen, the Netherlands, ISBN 978-0-359-30635-0, http://soilmapper.org (last access: 27 March 2026), 2019. a
Hijmans, R. J., Bivand, R., Chirico, M., Cordano, E., Dyba, K., Pebesma, E., Rowlingson, B., and Sumner, M. D.: terra: Spatial Data Analysis, CRAN, https://doi.org/10.32614/CRAN.package.terra, 2025. a, b
Hillel, D. and Hatfield, J. L. (Eds.): Encyclopedia of soils in the environment, Elsevier/Academic Press, Oxford, UK, Boston, 1st edn., ISBN 978-0-12-348530-4, 2005. a
Hobley, E. U. and Prater, I.: Estimating soil texture from vis-NIR spectra, Eur. J. Soil Sci., 70, 83–95, https://doi.org/10.1111/ejss.12733, 2019. a, b
Huete, A.: A soil-adjusted vegetation index (SAVI), Remote Sens. Environ., 25, 295–309, https://doi.org/10.1016/0034-4257(88)90106-X, 1988. a, b, c
Huete, A., Justice, C., and Liu, H.: Development of vegetation and soil indices for MODIS-EOS, Remote Sens. Environ., 49, 224–234, https://doi.org/10.1016/0034-4257(94)90018-3, 1994. a, b, c
Hulley, G. and Hook, S.: VIIRS/NPP Land Surface Temperature Daily L3 Global 1km SIN Grid Day V001, EarthData [data set], https://doi.org/10.5067/VIIRS/VNP21A1D.001, 2018. a, b, c, d
Isiker, A. K., Günay, Y., and Senel, M.: Geological map of Turkey Cizre, General Directorate of Mineral Research and Exploration, Ankara, https://mta.gov.tr/en/mta/mission (last access: 27 November 2023, no longer available online), 2002. a
Janik, L. J., Soriano-Disla, J. M., Forrester, S. T., and McLaughlin, M. J.: Effects of soil composition and preparation on the prediction of particle size distribution using mid-infrared spectroscopy and partial least-squares regression, Soil Res., 54, 889–904, https://doi.org/10.1071/SR16011, 2016. a
Kennard, R. W. and Stone, L. A.: Computer Aided Design of Experiments, Technometrics, 11, 137–148, https://doi.org/10.2307/1266770, 1969. a
Khadivi, S., Mouthereau, F., Barbarand, J., Adatte, T., and Lacombe, O.: Constraints on palaeodrainage evolution induced by uplift and exhumation on the southern flank of the Zagros–Iranian Plateau, J. Geol. Soc. London, 169, 83–97, https://doi.org/10.1144/0016-76492011-031, 2012. a
Khan, N. M., Rastoskuev, V. V., Shalina, E. V., and Sato, Y.: Mapping salt-affected soils using remote sensing indicators-a simple approach with the use of GIS IDRISI, in: 22nd Asian Conference on Remote Sensing, Asian Association on remote Sensing, Singapore, https://acrs-aars.org/proceeding/ACRS2001/Papers/AGS-05.pdf (last access: 27 March 2026), 2001. a, b, c
Koshnaw, R. I., Horton, B. K., Stockli, D. F., Barber, D. E., Tamar-Agha, M. Y., and Kendall, J. J.: Neogene shortening and exhumation of the Zagros fold-thrust belt and foreland basin in the Kurdistan region of northern Iraq, Tectonophysics, 694, 332–355, https://doi.org/10.1016/j.tecto.2016.11.016, 2017. a
Koshnaw, R. I., Horton, B. K., Stockli, D. F., Barber, D. E., and Tamar‐Agha, M. Y.: Sediment routing in the Zagros foreland basin: Drainage reorganization and a shift from axial to transverse sediment dispersal in the Kurdistan region of Iraq, Basin Res., 32, 688–715, https://doi.org/10.1111/bre.12391, 2020. a
Kouli, M., Soupios, P., and Vallianatos, F.: Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework, Chania, Northwestern Crete, Greece, Environ. Geol., 57, 483–497, https://doi.org/10.1007/s00254-008-1318-9, 2009. a
Kuhn, M. and Quinlan, J. R.: Cubist: Rule- And Instance-Based Regression Modeling, r package version 0.4.4.9000, CRAN [code], https://doi.org/10.32614/CRAN.package.Cubist, 2024. a
Kursa, M. B. and Rudnicki, W. R.: Feature Selection with the Boruta Package, J. Stat. Softw., 36, https://doi.org/10.18637/jss.v036.i11, 2010. a, b
Köppen, W. P.: Das geographische System der Klimate, Handbuch der Klimatologie, Gebrüder Borntraeger, Berlin, https://koeppen-geiger.vu-wien.ac.at/pdf/Koppen_1936.pdf (last access: 27 March 2026), 1936. a
Lacerda, M. P. C., Demattê, J. A. M., Sato, M. V., Fongaro, C. T., Gallo, B. C., and Souza, A. B.: Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification, Remote Sens., 8, 701, https://doi.org/10.3390/rs8090701, 2016. a
Lachmuth, S., Dönmez, C., Hoffmann, C., Specka, X., Svoboda, N., and Helming, K.: Facilitating Effective Reuse of Soil Research Data: The BonaRes Repository, Eur. J. Soil Sci., 76, e70103, https://doi.org/10.1111/ejss.70103, 2025. a
Lagacherie, P., McBratney, A. B., and Voltz, M.: Digital Soil Mapping, An Introductory Perspective, no. 31 in Developments in Soil Science, Elsevier, ISBN 978-0-444-52958-9, https://doi.org/10.1016/S0166-2481(06)31059-8, 2006. a
Lal, R., Monger, C., Nave, L., and Smith, P.: The role of soil in regulation of climate, Philos. T. R. Soc. B, 376, 20210084, https://doi.org/10.1098/rstb.2021.0084, 2021. a
Lilburne, L., Helfenstein, A., Heuvelink, G. B. M., and Eger, A.: Interpreting and evaluating digital soil mapping prediction uncertainty: A case study using texture from SoilGrids, Geoderma, 450, 117052, https://doi.org/10.1016/j.geoderma.2024.117052, 2024. a, b
Liu, F., Yang, F., Zhao, Y.-G., Zhang, G.-L., and Li, D.-C.: Predicting soil depth in a large and complex area using machine learning and environmental correlations, J. Integr. Agr., 21, 2422–2434, https://doi.org/10.1016/S2095-3119(21)63692-4, 2022. a, b, c, d
Ludwig, B., Greenberg, I., Vohland, M., and Michel, K.: Optimised use of data fusion and memory‐based learning with an Austrian soil library for predictions with infrared data, Eur. J. Soil Sci., 74, e13394, https://doi.org/10.1111/ejss.13394, 2023. a
Ma, T., Brus, D. J., Zhu, A.-X., Zhang, L., and Scholten, T.: Comparison of conditioned Latin hypercube and feature space coverage sampling for predicting soil classes using simulation from soil maps, Geoderma, 370, 114366, https://doi.org/10.1016/j.geoderma.2020.114366, 2020. a
Malone, B., Stockmann, U., Glover, M., McLachlan, G., Engelhardt, S., and Tuomi, S.: Digital soil survey and mapping underpinning inherent and dynamic soil attribute condition assessments, Soil Security, 6, 100048, https://doi.org/10.1016/j.soisec.2022.100048, 2022. a, b
Malone, B. P., McBratney, A. B., and Minasny, B.: Empirical estimates of uncertainty for mapping continuous depth functions of soil attributes, Geoderma, 160, 614–626, https://doi.org/10.1016/j.geoderma.2010.11.013, 2011. a
Malone, B. P., McBratney, A. B., and Minasny, B.: Using R for Digital Soil Mapping, Progress in Soil Science, Springer International Publishing: Imprint: Springer, Cham, 1st edn., ISBN 978-3-319-44327-0, https://doi.org/10.1007/978-3-319-44327-0, 2017. a
McBratney, A., Mendonça Santos, M., and Minasny, B.: On digital soil mapping, Geoderma, 117, 3–52, https://doi.org/10.1016/S0016-7061(03)00223-4, 2003. a, b, c, d
McFeeters, S. K.: The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, Int. J. Remote Sens., 17, 1425–1432, https://doi.org/10.1080/01431169608948714, 1996. a, b, c
Mehri, A., Karimi, H., Mustafa, Y. T., Al-Quraishi, A. M. F., and Galalizadeh, S.: Predicting Soil Erosion Using RUSLE Model in Duhok Governorate, Kurdistan Region of Iraq, in: Earth and Environmental Sciences Library, Springer International Publishing, Cham, 171–187, ISBN 978-3-031-58314-8 978-3-031-58315-5, https://doi.org/10.1007/978-3-031-58315-5_9, 2024. a, b
Meinshausen, N. and Michel, L.: quantregForest: Quantile Regression Forests, CRAN [code], https://doi.org/10.32614/CRAN.package.quantregForest, 2020. a, b
Meinshausen, N.: Quantile Regression Forests, J. Mach. Learn. Res., 7, 983–999, https://jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf (last access: 27 March 2026), 2006. a
Minasny, B. and McBratney, A. B.: A conditioned Latin hypercube method for sampling in the presence of ancillary information, Comput. Geosci., 32, 1378–1388, https://doi.org/10.1016/j.cageo.2005.12.009, 2006. a
Moeys, J., Shangguan, W., Petzold, R., Minasny, B., Rosca, B., Jelinski, N., Zelazny, W., Souza, R. M. S., Safanelli, J. L., and Caten, A. T.: soiltexture: Functions for Soil Texture Plot, Classification and Transformation, CRAN [code], https://doi.org/10.32614/CRAN.package.soiltexture, 2024. a
Moharana, P. C., Jena, R. K., Kumar, N., Singh, R. S., and Rao, S. S.: Assessment of soil organic and inorganic carbon stock at different soil depths after conversion of desert into arable land in the hot arid regions of India, Carbon Manag., 12, 153–165, https://doi.org/10.1080/17583004.2021.1893128, 2021. a
Molinaro, M., Leturmy, P., Guezou, J.-C., Frizon de Lamotte, D., and Eshraghi, S. A.: The structure and kinematics of the southeastern Zagros fold-thrust belt, Iran: From thin-skinned to thick-skinned tectonics, Tectonics, 24, https://doi.org/10.1029/2004TC001633, 2005. a
Morgan, R. P. C.: Soil erosion and conservation, Blackwell Pub, Malden, MA, 3rd edn., ISBN 978-1-4051-1781-4, 2005. a
Morgan, R. P. C., Morgan, D. D. V., and Finney, H. J.: A predictive model for the assessment of soil erosion risk, J. Agr. Eng. Res., 30, 245–253, https://doi.org/10.1016/S0021-8634(84)80025-6, 1984. a
Mouthereau, F., Lacombe, O., and Vergés, J.: Building the Zagros collisional orogen: Timing, strain distribution and the dynamics of Arabia/Eurasia plate convergence, Tectonophysics, 532–535, 27–60, https://doi.org/10.1016/j.tecto.2012.01.022, 2012. a, b, c
Muhaimeed, A., Aziz, S., and Amin, I.: Genesis of argilic and calcic horizons in some northern Iraqi soils, Journal of Agricultural and Biological Science, 8, 29–34, https://www.arpnjournals.com/jabs/research_papers/rp_2013/jabs_0113_511.pdf (last access: 27 March 2026) 2013. a
Muhaimeed, A., Saloom, A., Saliem, K. A., Alani, K., and Muklef, W. M.: Classification and distribution of Iraqi soils, International Journal of Agriculture Innovations and Research, 2, 997–1002, https://www.ijair.org/administrator/components/com_jresearch/files/publications/IJAIR_670_Final.pdf (last access: 27 March 2026), 2014. a
Muhaimeed, A. S.: Using Remote Sensing to Predict Soil Properties in Iraq, in: Environmental Remote Sensing and GIS in Iraq, edited by: Al-Quraishi, A. M. F. and Negm, A. M., Springer International Publishing, Cham, 41–59, ISBN 978-3-030-21343-5 978-3-030-21344-2, https://doi.org/10.1007/978-3-030-21344-2_3, 2020. a
Najmaldin, H. E.: Statistical Analysis of Rainfall Variations in Duhok City and Semel District, Kurdistan Region of Iraq, International Journal of Research in Environmental Science, 9, 31–38, https://doi.org/10.20431/2454-9444.0903004, 2023. a
Neina, D.: The Role of Soil pH in Plant Nutrition and Soil Remediation, Applied and Environmental Soil Science, 2019, 5794869, https://doi.org/10.1155/2019/5794869, 2019. a
Ng, W., Minasny, B., Malone, B., and Filippi, P.: In search of an optimum sampling algorithm for prediction of soil properties from infrared spectra, PeerJ, 6, e5722, https://doi.org/10.7717/peerj.5722, 2018. a, b
Ng, W., Malone, B., Minasny, B., and Jeon, S.: Near and mid infrared soil spectroscopy, in: Reference Module in Earth Systems and Environmental Sciences, Elsevier, ISBN 978-0-12-409548-9, https://doi.org/10.1016/B978-0-12-822974-3.00022-7, 2022a. a
Ng, W., Minasny, B., Jeon, S. H., and McBratney, A.: Mid-infrared spectroscopy for accurate measurement of an extensive set of soil properties for assessing soil functions, Soil Security, 6, 100043, https://doi.org/10.1016/j.soisec.2022.100043, 2022b. a, b
Nield, S. J., Boettinger, J. L., and Ramsey, R. D.: Digitally Mapping Gypsic and Natric Soil Areas Using Landsat ETM Data, Soil Sci. Soc. Am. J., 71, 245–252, https://doi.org/10.2136/sssaj2006-0049, 2007. a, b
Padarian, J., Minasny, B., and McBratney, A. B.: Machine learning and soil sciences: a review aided by machine learning tools, SOIL, 6, 35–52, https://doi.org/10.5194/soil-6-35-2020, 2020. a
Patton, N. R., Lohse, K. A., Godsey, S. E., Crosby, B. T., and Seyfried, M. S.: Predicting soil thickness on soil mantled hillslopes, Nat. Commun., 9, 3329, https://doi.org/10.1038/s41467-018-05743-y, 2018. a, b
Pfälzner, P. and Sconzo, P.: The Eastern Khabur Archaeological Survey in Iraqi Kurdistan. A Preliminary Report on the 2014 Season, in: Zeitschrift für Orient-Archäologie, no. 9 in Zeitschrift für Orient-Archäologie, Wasmuth, Tübingen, Berlin, 10–69, ISBN 9783803002235, https://wasmuth-verlag.de/shop/archaeologie-alte-kulturen/zeitschrift-fuer-orient-archaeologie-9-2016/ (last access: 27 March 2026), 2016. a
Ponikarov, V. and Mikhailov, I.: Geological map of Syria, General Establishement of Geology and Mineral Ressource, Damascus, https://searchworks.stanford.edu/view/2953240 (last access: 27 March 2026), 1986. a
Python Software Foundation: Python Language Reference, https://www.python.org/ (last access: 27 March 2026), 2022. a
Quinlan, J. R.: Learning with continuous classes, in: Proceedings AI’92, 5th Australian Conference on Artificial Intelligence, edited by: Adams, A. and Sterling, L., World Scientific, Hobart, Tasmania, 343–348, ISBN 978-981-02-1250-6 978-981-4536-27-1, https://doi.org/10.1142/9789814536271, 1992. a
Rabot, E., Wiesmeier, M., Schlüter, S., and Vogel, H. J.: Soil structure as an indicator of soil functions: A review, Geoderma, 314, 122–137, https://doi.org/10.1016/j.geoderma.2017.11.009, 2018. a
Ramirez-Lopez, L., Schmidt, K., Behrens, T., van Wesemael, B., Demattê, J. A., and Scholten, T.: Sampling optimal calibration sets in soil infrared spectroscopy, Geoderma, 226–227, 140–150, https://doi.org/10.1016/j.geoderma.2014.02.002, 2014. a
Renard, K. G., Foster, G. R., Weesies, G. A., and Porter, J. P.: RUSLE: Revised universal soil loss equation, J. Soil Water Conserv., 46, 30–33, https://www.jswconline.org/content/46/1/30 (last access: 27 March 2026), 1991. a
Roudier, P.: clhs: Conditioned Latin Hypercube Sampling, CRAN [code], https://doi.org/10.32614/CRAN.package.clhs, 2021. a
Safanelli, J. L., Hengl, T., Parente, L. L., Minarik, R., Bloom, D. E., Todd-Brown, K., Gholizadeh, A., de Sousa Mendes, W., and Sanderman, J.: Open Soil Spectral Library (OSSL): Building reproducible soil calibration models through open development and community engagement, PloS One, 20, e0296545, https://doi.org/10.1371/journal.pone.0296545, 2025. a, b
Salman, S. A., Shahid, S., Ismail, T., Ahmed, K., Chung, E.-S., and Wang, X.-J.: Characteristics of Annual and Seasonal Trends of Rainfall and Temperature in Iraq, Asia-Pac. J. Atmos. Sci., 55, 429–438, https://doi.org/10.1007/s13143-018-0073-4, 2019. a
Schaetzl, R. J. and Anderson, S.: Soils: genesis and geomorphology, Cambridge University Press, Cambridge, New York, ISBN 978-0-521-81201-6, 2005. a
Schmidinger, J. and Heuvelink, G. B. M.: Validation of uncertainty predictions in digital soil mapping, Geoderma, 437, 116585, https://doi.org/10.1016/j.geoderma.2023.116585, 2023. a, b
Scholten, T., Goebes, P., Kühn, P., Seitz, S., Assmann, T., Bauhus, J., Bruelheide, H., Buscot, F., Erfmeier, A., Fischer, M., Härdtle, W., He, J.-S., Ma, K., Niklaus, P. A., Scherer-Lorenzen, M., Schmid, B., Shi, X., Song, Z., von Oheimb, G., Wirth, C., Wubet, T., and Schmidt, K.: On the combined effect of soil fertility and topography on tree growth in subtropical forest ecosystems – a study from SE China, J. Plant Ecol., 10, 111–127, https://doi.org/10.1093/jpe/rtw065, 2017. a
Sembroni, A., Reitano, R., Faccenna, C., and Callieri, P.: The geologic configuration of the Zagros Fold and Thrust Belt: an overview, Mediterranean Geoscience Reviews, 6, 61–86, https://doi.org/10.1007/s42990-024-00118-6, 2024. a, b
Sharififar, A., Minasny, B., Arrouays, D., Boulonne, L., Chevallier, T., van Deventer, P., Field, D. J., Gomez, C., Jang, H.-J., Jeon, S.-H., Koch, J., McBratney, A. B., Malone, B. P., Marchant, B. P., Martin, M. P., Monger, C., Munera-Echeverri, J.-L., Padarian, J., Pfeiffer, M., Richer-de Forges, A. C., Saby, N. P. A., Singh, K., Song, X.-D., Zamanian, K., Zhang, G.-L., and van Zijl, G.: Chapter Four – Soil inorganic carbon, the other and equally important soil carbon pool: Distribution, controlling factors, and the impact of climate change, in: Advances in Agronomy, edited by: Sparks, D. L., vol. 178, Academic Press, 165–231, https://doi.org/10.1016/bs.agron.2022.11.005, 2023. a
Shi, G., Sun, W., Shangguan, W., Wei, Z., Yuan, H., Li, L., Sun, X., Zhang, Y., Liang, H., Li, D., Huang, F., Li, Q., and Dai, Y.: A China dataset of soil properties for land surface modelling (version 2, CSDLv2), Earth Syst. Sci. Data, 17, 517–543, https://doi.org/10.5194/essd-17-517-2025, 2025. a, b, c, d, e, f, g
Shrestha, D. L. and Solomatine, D. P.: Machine learning approaches for estimation of prediction interval for the model output, Neural Networks, 19, 225–235, https://doi.org/10.1016/j.neunet.2006.01.012, 2006. a
Sissakian, V. K. and Abdul Jab'bar, M. F.: Classification of the alluvial fan in Iraq, Iraqi Bulletin of Geology and Mining, 10, 43–67, https://iasj.rdd.edu.iq/journals/uploads/2025/08/24/9fbf65b6df78dc8f152f9f30511a6440.pdf (last access: 1 February 2024), 2014. a
Sissakian, V. K., Abdul Jab'bar, M. F., Al-Ansari, N., and Knutson, S.: Old Alluvial Fan Relics in North and Northeast Iraq, Journal of Earth Sciences and Geotechnical Engineering, 5, 45–62, https://www.diva-portal.org/smash/get/diva2:985298/FULLTEXT01.pdf (last access: 27 March 2026), 2015. a
Smith, R. and Robertson, V. C.: Soil and irrigation classification of shallow soils overlying gypsum bed, northern Iraq, J. Soil Sci., 13, 106–115, https://doi.org/10.1111/j.1365-2389.1962.tb00687.x, 1962. a, b
Sonnenwald, D. H.: Scientific collaboration, Annu. Rev. Inform. Sci., 41, 643–681, 2007. a
Stenberg, B., Viscarra Rossel, R. A., Mouazen, A. M., and Wetterlind, J.: Visible and Near Infrared Spectroscopy in Soil Science, in: Advances in Agronomy, vol. 107, Elsevier, 163–215, ISBN 978-0-12-381033-5, https://doi.org/10.1016/S0065-2113(10)07005-7, 2010. a
Stevens, A. and Ramirez-Lopez, L.: An Introduction to the prospectr package, CRAN, https://doi.org/10.32614/CRAN.package.prospectr, 2014. a
Stumpf, F., Schmidt, K., Behrens, T., Schönbrodt‐Stitt, S., Buzzo, G., Dumperth, C., Wadoux, A., Xiang, W., and Scholten, T.: Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping, J. Plant Nutr. Soil Sc., 179, 499–509, https://doi.org/10.1002/jpln.201500313, 2016. a
Suleymanov, A., Richer-de Forges, A. C., Saby, N. P. A., Arrouays, D., Martin, M. P., and Bispo, A.: National-scale digital soil mapping performances are related to covariates and sampling density: Lessons from France, Geoderma Regional, 37, e00801, https://doi.org/10.1016/j.geodrs.2024.e00801, 2024. a
Swarnkar, S., Malini, A., Tripathi, S., and Sinha, R.: Assessment of uncertainties in soil erosion and sediment yield estimates at ungauged basins: an application to the Garra River basin, India, Hydrol. Earth Syst. Sci., 22, 2471–2485, https://doi.org/10.5194/hess-22-2471-2018, 2018. a
Taghizadeh-Mehrjardi R., Sarmadian, F., Minasny, B., Triantafilis, J., and Omid, M.: Digital Mapping of Soil Classes Using Decision Tree and Auxiliary Data in the Ardakan Region, Iran, Arid Land Res. Manag., 28, 147–168, https://doi.org/10.1080/15324982.2013.828801, 2014. a
Taghizadeh-Mehrjardi, R., Schmidt, K., Amirian-Chakan, A., Rentschler, T., Zeraatpisheh, M., Sarmadian, F., Valavi, R., Davatgar, N., Behrens, T., and Scholten, T.: Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space, Remote Sens., 12, 1095, https://doi.org/10.3390/rs12071095, 2020. a
Telo da Gama, J.: The Role of Soils in Sustainability, Climate Change, and Ecosystem Services: Challenges and Opportunities, Ecologies, 4, 552–567, https://doi.org/10.3390/ecologies4030036, 2023. a
Thapa, P.: Spatial estimation of soil erosion using RUSLE modeling: a case study of Dolakha district, Nepal, Environmental Systems Research, 9, 15, https://doi.org/10.1186/s40068-020-00177-2, 2020. a
Thomas, G. W.: Soil pH and Soil Acidity, in: Methods of Soil Analysis, John Wiley & Sons, Ltd, 475–490, ISBN 978-0-89118-866-7, https://doi.org/10.2136/sssabookser5.3.c16, 1996. a
Trivedi, P., Singh, B. P., and Singh, B. K.: Chapter 1 – Soil Carbon: Introduction, Importance, Status, Threat, and Mitigation, in: Soil Carbon Storage, edited by: Singh, B. K., Academic Press, 28 pp., ISBN 978-0-12-812766-7, https://doi.org/10.1016/B978-0-12-812766-7.00001-9, 2018. a
Tsagris, M., Athineou, G., Alenazi, A., and Adam, C.: Compositional: Compositional Data Analysis, CRAN, https://doi.org/10.32614/CRAN.package.Compositional, 2025. a
Umer, M. I., Rajab, S. M., and Ismail, H. K.: Effect of CaCO3 Form on Soil Inherent Quality Properties of Calcareous Soils, Materials Sci. Forum, 1002, 459–467, https://doi.org/10.4028/www.scientific.net/MSF.1002.459, 2020. a
Vaysse, K. and Lagacherie, P.: Using quantile regression forest to estimate uncertainty of digital soil mapping products, Geoderma, 291, 55–64, https://doi.org/10.1016/j.geoderma.2016.12.017, 2017. a
Vermote, E.: MODIS/Terra Surface Reflectance 8-Day L3 Global 250m SIN Grid V061, EarthData [data set], https://doi.org/10.5067/MODIS/MOD09Q1.061, 2021. a, b
Viscarra Rossel, R., Behrens, T., Ben-Dor, E., Brown, D., Demattê, J., Shepherd, K., Shi, Z., Stenberg, B., Stevens, A., Adamchuk, V., Aïchi, H., Barthès, B., Bartholomeus, H., Bayer, A., Bernoux, M., Böttcher, K., Brodský, L., Du, C., Chappell, A., Fouad, Y., Genot, V., Gomez, C., Grunwald, S., Gubler, A., Guerrero, C., Hedley, C., Knadel, M., Morrás, H., Nocita, M., Ramirez-Lopez, L., Roudier, P., Campos, E. R., Sanborn, P., Sellitto, V., Sudduth, K., Rawlins, B., Walter, C., Winowiecki, L., Hong, S., and Ji, W.: A global spectral library to characterize the world's soil, Earth-Sci. Rev., 155, 198–230, https://doi.org/10.1016/j.earscirev.2016.01.012, 2016. a, b, c
Viscarra Rossel, R. A., Behrens, T., Ben‐Dor, E., Chabrillat, S., Demattê, J. A. M., Ge, Y., Gomez, C., Guerrero, C., Peng, Y., Ramirez‐Lopez, L., Shi, Z., Stenberg, B., Webster, R., Winowiecki, L., and Shen, Z.: Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century, Eur. J. Soil Sci., 73, https://doi.org/10.1111/ejss.13271, 2022. a
Wadoux, A. M.-C., Heuvelink, G. B., Lark, R. M., Lagacherie, P., Bouma, J., Mulder, V. L., Libohova, Z., Yang, L., and McBratney, A. B.: Ten challenges for the future of pedometrics, Geoderma, 401, 115155, https://doi.org/10.1016/j.geoderma.2021.115155, 2021a. a
Wadoux, A. M. J.-C., Malone, B., Minasny, B., Fajardo, M., and McBratney, A. B.: Soil spectral inference with R analysing digital soil spectra, Springer Cham, S.l., ISBN 978-3-030-64895-4, https://doi.org/10.1007/978-3-030-64896-1, 2021b. a
Wan, Z., Hook, S., and Hulley, G.: MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V061, EarthData [data set], https://doi.org/10.5067/MODIS/MOD11A2.061, 2021. a, b
Wang, Y. and Witten, I. H.: Induction of model trees for predicting continuous classes, Working Papers, https://hdl.handle.net/10289/1183 (last access: 27 March 2026), 1996. a
Webber, C. L., Bremer, U. F., Taghizadeh-Mehrjardi, R., Weber, B., Rosa, A., Scholten, T., and Seitz, S.: Biological soil crusts as a major ecosystem component in sandization areas of the Brazilian Pampa, Geoderma Regional, 34, e00682, https://doi.org/10.1016/j.geodrs.2023.e00682, 2023. a
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., ’t Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.: The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 3, 160018, https://doi.org/10.1038/sdata.2016.18, 2016. a
Wilkinson, T. J.: Soil Development and Early Land Use in the Jazira Region, Upper Mesopotamia, World Archaeol., 22, 87–103, https://www.jstor.org/stable/124805 (last access: 27 March 2026), 1990. a
Williams, P., Dardenne, P., and Flinn, P.: Tutorial: Items to be included in a report on a near infrared spectroscopy project, J. Near Infrared Spec., 25, 85–90, https://doi.org/10.1177/0967033517702395, 2017. a
Yacoub, S. Y., Othman, A. A., and Kadhim, T. H.: Geomorphology of the Low Folded Zone, Iraqi Bulltin of Geology and Mining, 5, 7–37, https://ibgm-iq.org/ibgm/index.php/ibgm/article/view/221 (last access: 27 March 2026), 2012. a
Yan, F., Shangguan, W., Zhang, J., and Hu, B.: Depth-to-Bedrock Map of China at a Spatial Resolution of 100 Meters, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2018-103, 2018. a
Yousif, B. S., Mustafa, Y. T., and Fayyadh, M. A.: Digital mapping of soil-texture classes in Batifa, Kurdistan Region of Iraq, using machine-learning models, Earth Sci. Inform., 16, 1687–1700, https://doi.org/10.1007/s12145-023-01005-8, 2023. a, b
Yu, X., Keitel, C., and Dijkstra, F. A.: Ameliorating soil acidity with calcium carbonate and calcium hydroxide: effects on carbon, nitrogen, and phosphorus dynamics, J. Soil Sci. Plant Nut., 23, 5270–5278, https://doi.org/10.1007/s42729-023-01400-6, 2023. a
Zamanian, K., Pustovoytov, K., and Kuzyakov, Y.: Pedogenic carbonates: Forms and formation processes, Earth-Sci. Rev., 157, 1–17, https://doi.org/10.1016/j.earscirev.2016.03.003, 2016. a, b
Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, L., Tsendbazar, N.-E., Ramoino, F., and Arino, O.: ESA WorldCover 10 m 2020 v100, Zenodo [data set], https://doi.org/10.5281/ZENODO.5571936, 2021. a
Zebari, M., Balling, P., Grützner, C., Navabpour, P., Witte, J., and Ustaszewski, K.: Structural style of the NW Zagros Mountains and the role of basement thrusting for its Mountain Front Flexure, Kurdistan Region of Iraq, J. Struct. Geol., 141, 104206, https://doi.org/10.1016/j.jsg.2020.104206, 2020. a, b
Zeraatpisheh, M., Bottega, E. L., Bakhshandeh, E., Owliaie, H. R., Taghizadeh-Mehrjardi, R., Kerry, R., Scholten, T., and Xu, M.: Spatial variability of soil quality within management zones: Homogeneity and purity of delineated zones, CATENA, 209, 105835, https://doi.org/10.1016/j.catena.2021.105835, 2022. a
Zhang, S., Liu, G., Chen, S., Rasmussen, C., and Liu, B.: Assessing soil thickness in a black soil watershed in northeast China using random forest and field observations, International Soil and Water Conservation Research, 9, 49–57, https://doi.org/10.1016/j.iswcr.2020.09.004, 2021. a
Zomer, R. and Trabucco, A.: Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3, figshare [data set], https://doi.org/10.6084/M9.FIGSHARE.7504448.V6, 2022. a
Zomer, R. J., Xu, J., and Trabucco, A.: Version 3 of the Global Aridity Index and Potential Evapotranspiration Database, Scientific Data, 9, 409, https://doi.org/10.1038/s41597-022-01493-1, 2022. a
Short summary
This dataset presents the first soil maps for the region produced using digital mapping techniques. It includes predictions for ten major physical and chemical soil properties at various depths, plus a map of total soil depth. For each property, we selected the most accurate models and key environmental drivers. In Southwestern Asia and many arid or semi-arid regions, detailed soil data are often missing. This dataset fills that gap, supporting agriculture, research, planning, and local policy.
This dataset presents the first soil maps for the region produced using digital mapping...
Altmetrics
Final-revised paper
Preprint