Please also find a list of publications at Google Scholar. Four most important publications are marked with two asterisk **.
Peer-reviewed articles
Baltensweiler, A., Walthert, L., Hanewinkel, M., Zimmermann, S., and Nussbaum, M.: Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland, Geoderma Regional, 27, e00 437, doi:10.1016/j.geodrs.2021.e00437, 2021.
Deluz, C., Nussbaum, M., Sauzet, O., Gondret, K., and Boivin, P.: Evaluation of the Potential for Soil Organic Carbon Content Monitoring With Farmers, Frontiers in Environmental Science, 8, doi:10.3389/fenvs.2020.00113, 2020.
Garosi, Y., Ayoubi, S., Nussbaum, M., and Sheklabadi, M.: Effects of different sources and spatial resolutions of environmental covariates on predicting soil organic carbon using machine learning in a semi-arid region of Iran, Geoderma Regional, 29, e00 513, doi:10.1016/j.geodrs.2022.e00513, 2022a.
Garosi, Y., Ayoubi, S., Nussbaum, M., Sheklabadi, M., Nael, M., and Kimiaee, I.: Use of the time series and multi-temporal features of Sentinel-1/2 satellite imagery to predict soil inorganic and organic carbon in a low-relief area with a semi-arid environment, International Journal of Remote Sensing, 43, 6856–6880, doi:10.1080/01431161.2022.2147037, 2022b.
Greiner, L., Nussbaum, M., Papritz, A., Fraefel, M., Zimmermann, S., Schwab, P., Grêt-Regamey, A., and Keller, A.: Assessment of soil multi-functionality to support the sustainable use of soil resources on the Swiss Plateau, Geoderma Regional, 14, e00 181, doi:10.1016/j.geodrs.2018.e00181, 2018a.
Greiner, L., Nussbaum, M., Papritz, A., Zimmermann, S., Gubler, A., Grêt-Regamey, A., and Keller, A.: Uncertainty indication in soil function maps – transparent and easy-to-use information to support sustainable use of soil resources, SOIL, 4, 123–139, doi:10.5194/soil-4-123-2018, 2018b.
** Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M., and Gräler, B.: Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables, PeerJ, 6, e5518, doi:10.7717/peerj.5518, 2018.
Herholz, C., Siegwart, J., Nussbaum, M., Studer, M. H.-P., Burgos, S.: Large temporal variations of functional properties of outdoor equestrian arena surfaces and a new concept of evaluating reactivity with light weight deflectometer settlement curves. Journal of Equine Veterinary Science, 104909, doi: 10.1016/j.jevs.2023.104909, 2023.
Nussbaum, M., Papritz, A., Baltensweiler, A., and Walthert, L.: Estimating soil organic carbon stocks of Swiss forest soils by robust external-drift kriging, Geoscientific Model Development, 7, 1197–1210, doi:10.5194/gmd-7-1197-2014, 2014.
Nussbaum, M., Papritz, A., Zimmerman, S., and Walthert, L.: Pedotransfer function to predict density of forest soils in Switzerland, Journal of Plant Nutrition and Soil Science, 179, 321–326, doi:10.1002/jpln.201500546, 2016.
Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A.: Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models, SOIL, 3, 191–210, doi:10.5194/soil-3-191-2017, 2017.
** Nussbaum, M., Zimmermann, S., Walthert, L., Baltensweiler, A.: Benefits of hierarchical predictions for digital soil mapping -- An approach to map bimodal soil pH, Geoderma,
Volume 437/116579, doi: 10.1016/j.geoderma.2023.116579, 2023.
** Nussbaum, M., Spiess, K., Baltensweiler, A., Grob, U., Keller, A., Greiner, L., Schaepman, M. E., and Papritz, A.: Evaluation of digital soil mapping approaches with large sets of environmental covariates, SOIL, 4, 1–22, doi:10.5194/soil-4-1-2018, 2018.
Petermann, E., Meyer, H., Nussbaum, M., and Bossew, P.: Mapping the geogenic radon potential for Germany by machine learning, Science of the Total Environment, 754, 142 291, doi:10.1016/j.scitotenv.2020.142291, 2021.
Theses and book chapters
Nussbaum, M.: Modelling of organic carbon content and stocks for forest soils in Switzerland (in German), Master Thesis, University of Zurich, Institute for Geography, Zürich, doi:10.3929/ethz-a-006785593, 2011.
Nussbaum, M.: Preliminary study to assess the impact of the soil promotion program of the Canton of Berne, by analysing the cantonal soil monitoring Berne (in German), diploma thesis for Diploma of Advanced Studies in Applied Statistics, Institute of Mathematical Statistics and Actuarial Science, University of Berne, 2014.
Nussbaum, M.: Digital Soil Mapping for Switzerland, Evaluation of Statistical Approaches and Mapping of Soil Properties, Ph.D. thesis, ETH Zurich, Zurich, Switzerland, doi:10.3929/ethz-b-000193154, 2017.
** Nussbaum, M.: Machine Learning and Processing of Large Data in Soil Science. Invited contribution, in: Encyclopedia of Soils in the Environment, Elsevier, Oxford, doi:10.1016/B978-0-12-822974-3.00065-3, 2022.
Applied research reports (selection)
Baltensweiler, A., Walthert, L., Zimmermann, S., and Nussbaum, M.: High resolution soil maps for Swiss forest soils (in German), Schweizerische Zeitschrift fur Forstwesen, 173, 288–291, doi:10.3188/szf.2022.0288, 2022.
Nussbaum, M. and Burgos, S.: Pedotransferfunctions to predict potential cation exchange capacity from laboratory methods FAC to AAE (in German), project report to the client Soil Protection Agency of Canton of Zurich, BFH, 2017.
Nussbaum, M. and Burgos, S.: Concept to survey detailed soil information for the complete Canton of Berne, technical report (in German), project report to the client Canton of Berne, BFH, 2019.
Nussbaum, M. and Papritz, A.: Validation of conventional soil maps with independent soil data – general approach and case study, unpublished article manuscript, 2017.
Nussbaum, M., Ettlin, L., Cöltekin, A., Suter, B., and Egli, M.: The Relevance of Scale in Soil Maps, Bulletin BGS, 32, 63–70, 2011.
Nussbaum, M., Burgos, S., Keller, A., Carrizoni, M., and Papritz, A.: Soil information systems and (digital) soil mapping in Europe. What can Switzerland learn from this? (in German), report, BFH and ETH Zurich, 2018.
Oechslin, S., Nussbaum, M., and Burgos, S.: Soil mapping St. Rheintal, final report to the client Verein St. Galler Rheintal (in German), project report to the Canton of St. Gallen, BFH, 2020.
Publications
2026
Scholarly publications
Nussbaum, M. (2026). Artificial intelligence research agents in soil science: the continuing importance of domain expertise. Frontiers in Science. [DOI]
Schmücker, N., Lehmann, P., Duddek, P., Kirchgessner, N., & Nussbaum, M. (2026). A Conceptual Framework for Assessing Soil Structural Attributes Across Contrasting Land-Use Types. European Journal of Soil Science, 77(3), Article e70343. [DOI] [Repository]
van der Kuil, R., Nooren, K., Nussbaum, M., Donders, T., & Bennink, E. (2026). Generation and validation of a high-resolution fossil diatom record from Saliña Bartol, Bonaire, based on convolutional neural networks. Applied Computing and Geosciences, 29, Article 100326. [DOI] [Repository]
Pomarol Moya, O., Nussbaum, M., Mehrkanoon, S., Kraaijenbrink, P. D. A., Gouttevin, I., Karssenberg, D., & Immerzeel, W. W. (2026). Improving snow water equivalent modelling: a comparative study of hybrid machine learning techniques. Cryosphere, 20(2), 1427-1444. [DOI] [Repository]
Hengl, T., Consoli, D., Tian, X., Nauman, T. W., Nussbaum, M., Isik, M. S., Parente, L., Ho, Y. F., Simoes, R., Gupta, S., Samuel-Rosa, A., Horst, T. Z., Safanelli, J. L., & Harris, N. (2026). OpenLandMap-soildb: global soil information at 30 m spatial resolution for 2000–2022+ based on spatiotemporal Machine Learning and harmonized legacy soil samples and observations. Earth System Science Data, 18(2), 989-1036. [DOI] [Repository]
Medina-Rubio, J., Nussbaum, M., van den Bremer, T. S., & van Sebille, E. (2026). Using surface drifters to characterise near-surface ocean dynamics in the southern North Sea: a data-driven approach. Ocean Science, 22(1), 49-74. [DOI] [Repository]
Medina-Rubio, J., Nussbaum, M., van den Bremer, T. S., & van Sebille, E. (2026). Using surface drifters to characterise near-surface ocean dynamics in the southern North Sea: a data-driven approach. Ocean Science, 22(1). [DOI] [Repository]
2025
Scholarly publications
Hengl, T., Consoli, D., Tian, X., Nauman, T. W., Nussbaum, M., Isik, M. S., Parente, L., Ho, Y.-F., Simoes, R., Gupta, S., Samuel-Rosa, A., Horst, T. Z., Safanelli, J. L., & Harris, N. (2025). OpenLandMap-soildb: global soil information at 30 m spatial resolution for 2000–2022+ based on spatiotemporal Machine Learning and harmonized legacy soil samples and observations. Copernicus Publications. [DOI] [Repository]
de Jong, K., Schmitz, O., Sutanudjaja, E., Nussbaum, M., Scheffer, P., & Karssenberg, D. (2025). The LUE modelling framework for scalable hydrological models. Abstract from EGU General Assembly 2025, Vienna. [DOI]
Moya, O. P., Karssenberg, D., Immerzeel, W. W., Kraaijenbrink, P., Nussbaum, M., Mehrkanoon, S., & Gouttevin, I. (2025). Bridging machine learning and physics-based models for improving snow water equivalent predictions in the northern hemisphere. Abstract from EGU General Assembly 2025, Vienna. [DOI]
Jeitany, J. E., Nussbaum, M., Pacetti, T., Schröder, B., & Caporali, E. (2025). Relevance of Landscape Metrics in Predicting Water-Related Ecosystem Services demonstrated on the Arno River Basin Italy. Abstract from EGU General Assembly 2024, Vienna, Austria. [DOI]
Kellermann, L., Tanner, S., Oechslin, S., Nussbaum, M., & Burgos, S. (2025). Useful digital soil mapping products for farmers . Abstract from EGU General Assembly 2024, Vienna, Austria. [DOI]
Tanner, S., Nussbaum, M., Oechslin, S., & Burgos, S. (2025). Multi-stage soil surveying complementing statistical sampling designs to provide high-resolution soil maps for policy. Abstract from EGU General Assembly 2024, Vienna, Austria. [DOI]
Moya, O. P., Nussbaum, M., Mehrkanoon, S., Kraaijenbrink, P. D. A., Gouttevin, I., Karssenberg, D., & Immerzeel, W. W. (2025). Improving forecasts of snow water equivalent with hybrid machine learning. EGUsphere [preprint]. [DOI] [Repository]
Nussbaum, M., Zimmermann, S., Walthert, L., & Baltensweiler, A. (2025). Hierarchical modelling in digital soil mapping: Advantages for mapping bimodal soil pH. Abstract from EGU General Assembly 2024, Vienna, Austria. [DOI]
Schoch, J., Nussbaum, M., Walthert, L., Carminati, A., & Lehmann, P. (2025). Does model complexity of a pedotransfer function for soil hydraulic properties hamper its transferability?. Abstract from EGU General Assembly 2024, Vienna, Austria. [DOI]
Schmücker, N., Lehmann, P., Duddek, P., Kirchgessner, N., Carminati, A., & Nussbaum, M. (2025). Bridging the Gap: A Multilevel Approach to Soil Health Assessment across Various Land Uses. Abstract from EGU General Assembly 2024, Vienna, Austria. [DOI]
Moya, O. P., Karssenberg, D., Immerzeel, W., Nussbaum, M., & Mehrkanoon, S. (2025). Understanding geoscientific system behaviour from machine learning surrogates. Abstract from EGU General Assembly 2024, Vienna, Austria. [DOI]
Campbell, G. A., Smith, P., Broothaerts, N., Panagos, P., Jones, A., Ballabio, C., De Rosa, D., de Jonge, L. W., Arthur, E., Gomes, L., Shokri, N., Afshar, M., Tóth, G., Lehmann, P., Borrelli, P., Alewell, C., Minarik, R., Hengl, T., Wheeler, I., ... Robinson, D. A. (2025). Continental Scale Soil Monitoring: A Proposed Multi-Scale Framing of Soil Quality. European Journal of Soil Science, 76(4), Article e70174. [DOI] [Repository]
Alblas, E., Edlinger, A., Helfenstein, J., Diaz Bernal, M. J., Stomph, D., Berendsen, B., Briones, M. J. I., Creamer, R., van Egmond, F., Grealish, G., Hacke, M., Jongedijk, E., Lawson, A., Lima, C., Moloney, J., Mommer, L., Nussbaum, M., Roberton, S., Ros, M., & Tempels, B. (2025). Bridging Law and Soil Science to Promote Soil Health. European Journal of Soil Science, 76(4), Article e70127. [DOI] [Repository]
Schoch, J., Nussbaum, M., Walthert, L., Carminati, A., & Lehmann, P. (2025). Transferability of pedotransfer functions for estimating soil hydraulic properties: An analysis of controlling factors for forest soils in Switzerland. Geoderma, 460, Article 117397. [DOI] [Repository]
Pomarol Moya, O., Mehrkanoon, S., Nussbaum, M., Immerzeel, W. W., & Karssenberg, D. (2025). Machine learning emulators of dynamical systems for understanding ecosystem behaviour. Ecological Modelling, 501, Article 110956. [DOI] [Repository]
2024
Scholarly publications
Birkinshaw, A., Sutter, M., Nussbaum, M., Kreuzer, M., & Reidy, B. (2024). Suitability of different machine learning algorithms for the classification of the proportion of grassland-based forages at the herd level using mid-infrared spectral information from routine milk control. Journal of Dairy Science, 107(12), 10724-10737. [DOI] [Repository]
van der Westhuizen, S., Heuvelink, G. B. M., Hofmeyr, D. P., Poggio, L., Nussbaum, M., & Brungard, C. (2024). Mapping soil thickness by accounting for right-censored data with survival probabilities and machine learning. European Journal of Soil Science, 75(5), Article e13589. [DOI] [Repository]
el Jeitany, J., Nussbaum, M., Pacetti, T., Schröder, B., & Caporali, E. (2024). Landscape metrics as predictors of water-related ecosystem services: Insights from hydrological modeling and data-based approaches applied on the Arno River Basin, Italy. Science of the Total Environment, 954, Article 176567. [DOI] [Repository]
Aebischer, P., Sutter, M., Birkinshaw, A., Nussbaum, M., & Reidy, B. (2024). Herbage biomass predictions from UAV data using a derived digital terrain model and machine learning. Grass and Forage Science, 79(4), 530-542. [DOI] [Repository]
2023
Scholarly publications
Nussbaum, M., Vogel, S., Oechslin, S., Tanner, S., & Burgos, S. (2023). Smoothed predicted distributions in digital soil mapping – a comprehensive comparative study to predict soil texture for irrigation. Abstract from EGU General Assembly 2023, Vienna, Austria. [DOI]
Burgos, S., Tanner, S., & Nussbaum, M. (2023). What is the accepted level of erroneous decision for the Swiss arable land inventory? Uncertainty perception of digital soil maps. Abstract from EGU General Assembly 2023, Vienna, Austria. [DOI]
Nussbaum, M., Zimmermann, S., Walthert, L., & Baltensweiler, A. (2023). Benefits of hierarchical predictions for digital soil mapping: An approach to map bimodal soil pH. Geoderma, 437, Article 116579. [DOI]
Nussbaum, M. (2023). Machine learning and processing of large data. In Encyclopedia of Soils in the Environment (2 ed., Vol. 4, pp. 509-520). Elsevier. [DOI]
2022
Scholarly publications
Garosi, Y., Ayoubi, S., Nussbaum, M., & Sheklabadi, M. (2022). Effects of different sources and spatial resolutions of environmental covariates on predicting soil organic carbon using machine learning in a semi-arid region of Iran. Geoderma Regional, 29, Article e00513. [DOI]
Garosi, Y., Ayoubi, S., Nussbaum, M., Sheklabadi, M., Nael, M., & Kimiaee, I. (2022). Use of the time series and multi-temporal features of Sentinel-1/2 satellite imagery to predict soil inorganic and organic carbon in a low-relief area with a semi-arid environment. International Journal of Remote Sensing, 43(18), 6856-6880. [DOI]
2021
Scholarly publications
Baltensweiler, A., Walthert, L., Hanewinkel, M., Zimmermann, S., & Nussbaum, M. (2021). Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland. Geoderma Regional, 27, Article e00437. [DOI]
Petermann, E., Meyer, H., Nussbaum, M., & Bossew, P. (2021). Mapping the geogenic radon potential for Germany by machine learning. Science of the Total Environment, 754, Article 142291. [DOI]
2020
Scholarly publications
Deluz, C., Nussbaum, M., Sauzet, O., Gondret, K., & Boivin, P. (2020). Evaluation of the Potential for Soil Organic Carbon Content Monitoring With Farmers. Frontiers in Environmental Science, 8. [DOI]
2018
Scholarly publications
Greiner, L., Nussbaum, M., Papritz, A., Zimmermann, S., Gubler, A., Grêt-Regamey, A., & Keller, A. (2018). Uncertainty indication in soil function maps – transparent and easy-to-use information to support sustainable use of soil resources. SOIL, 4(2), 123-139. [DOI]
Nussbaum, M., Spiess, K., Baltensweiler, A., Grob, U., Keller, A., Greiner, L., Schaepman, M. E., & Papritz, A. (2018). Evaluation of digital soil mapping approaches with large sets of environmental covariates. SOIL, 4(1), 1-22. [DOI]
Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M., & Gräler, B. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 6, Article e5518. [DOI]
Greiner, L., Nussbaum, M., Papritz, A., Fraefel, M., Zimmermann, S., Schwab, P., Grêt-Regamey, A., & Keller, A. (2018). Assessment of soil multi-functionality to support the sustainable use of soil resources on the Swiss Plateau. Geoderma Regional, 14, Article e00181. [DOI]
2017
Scholarly publications
Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., & Papritz, A. (2017). Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models. SOIL, 3(4), 191-210. [DOI]
2016
Scholarly publications
Nussbaum, M., Papritz, A., Zimmermann, S., & Walthert, L. (2016). Pedotransfer function to predict density of forest soils in Switzerland. Journal of Plant Nutrition and Soil Science, 179(3), 321-326. [DOI]