Madlene Nussbaum

1.9k total citations · 1 hit paper
25 papers, 1.2k citations indexed

About

Madlene Nussbaum is a scholar working on Environmental Engineering, Soil Science and Ecology. According to data from OpenAlex, Madlene Nussbaum has authored 25 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Environmental Engineering, 11 papers in Soil Science and 6 papers in Ecology. Recurrent topics in Madlene Nussbaum's work include Soil Geostatistics and Mapping (15 papers), Soil Carbon and Nitrogen Dynamics (9 papers) and Remote Sensing in Agriculture (4 papers). Madlene Nussbaum is often cited by papers focused on Soil Geostatistics and Mapping (15 papers), Soil Carbon and Nitrogen Dynamics (9 papers) and Remote Sensing in Agriculture (4 papers). Madlene Nussbaum collaborates with scholars based in Switzerland, Netherlands and Germany. Madlene Nussbaum's co-authors include Marvin N. Wright, Benedikt Gräler, G.B.M. Heuvelink, Tomislav Hengl, Andri Baltensweiler, Andreas Papritz, Lucie Greiner, Armin Keller, Michael E. Schaepman and Urs Grob and has published in prestigious journals such as The Science of The Total Environment, Journal of Dairy Science and Geoderma.

In The Last Decade

Madlene Nussbaum

22 papers receiving 1.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

2018 659 citations Tomislav Hengl, Madlene Nussbaum et al. PeerJ profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Madlene Nussbaum Switzerland	11	684	298	246	243	236	25	1.2k
Colby Brungard United States	16	857 1.3×	432 1.4×	268 1.1×	230 0.9×	337 1.4×	35	1.3k
Hanna Meyer Germany	19	716 1.0×	171 0.6×	172 0.7×	663 2.7×	707 3.0×	49	1.8k
Hamid Gholami Iran	25	398 0.6×	294 1.0×	121 0.5×	479 2.0×	195 0.8×	77	1.5k
Abdel-Aziz Belal Egypt	19	400 0.6×	196 0.7×	172 0.7×	394 1.6×	251 1.1×	50	1.1k
Anne C Richer-De-Forges France	25	1.3k 1.9×	853 2.9×	430 1.7×	119 0.5×	422 1.8×	43	1.7k
Tashpolat Tiyip China	20	737 1.1×	210 0.7×	136 0.6×	638 2.6×	316 1.3×	57	1.5k
Pierre Roudier New Zealand	19	1.1k 1.6×	704 2.4×	382 1.6×	262 1.1×	488 2.1×	45	2.0k
Norair Toomanian Iran	17	855 1.3×	378 1.3×	337 1.4×	150 0.6×	263 1.1×	38	1.2k
M. Knotters Netherlands	17	897 1.3×	285 1.0×	151 0.6×	296 1.2×	235 1.0×	55	1.4k
D.P. Shrestha Netherlands	15	259 0.4×	274 0.9×	89 0.4×	333 1.4×	214 0.9×	49	1.0k

Countries citing papers authored by Madlene Nussbaum

Since Specialization

Citations

This map shows the geographic impact of Madlene Nussbaum's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Madlene Nussbaum with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Madlene Nussbaum more than expected).

Fields of papers citing papers by Madlene Nussbaum

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Madlene Nussbaum. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Madlene Nussbaum. The network helps show where Madlene Nussbaum may publish in the future.

Co-authorship network of co-authors of Madlene Nussbaum

This figure shows the co-authorship network connecting the top 25 collaborators of Madlene Nussbaum. A scholar is included among the top collaborators of Madlene Nussbaum based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Madlene Nussbaum. Madlene Nussbaum is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Nauman, Travis, Madlene Nussbaum, Leandro Parente, et al.. (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.

Edlinger, Anna, Julian Helfenstein, B.J.A. Berendsen, et al.. (2025). Bridging Law and Soil Science to Promote Soil Health. European Journal of Soil Science. 76(4).

Nussbaum, Madlene, et al.. (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. The Science of The Total Environment. 954. 176567–176567. 4 indexed citations

Nussbaum, Madlene, et al.. (2024). Herbage biomass predictions from UAV data using a derived digital terrain model and machine learning. Grass and Forage Science. 79(4). 530–542. 3 indexed citations

Nussbaum, Madlene, et al.. (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. 2 indexed citations

Heuvelink, G.B.M., et al.. (2024). Mapping soil thickness by accounting for right‐censored data with survival probabilities and machine learning. European Journal of Soil Science. 75(5). 3 indexed citations

Nussbaum, Madlene, Stephan Zimmermann, Lorenz Walthert, & Andri Baltensweiler. (2023). Benefits of hierarchical predictions for digital soil mapping—An approach to map bimodal soil pH. Geoderma. 437. 116579–116579. 10 indexed citations

Herholz, C., et al.. (2023). 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. 129. 104909–104909. 1 indexed citations

Baltensweiler, Andri, Lorenz Walthert, Stephan Zimmermann, & Madlene Nussbaum. (2022). Hochauflösende Bodenkarten für den Schweizer Wald. Schweizerische Zeitschrift fur Forstwesen. 173(6). 288–291. 1 indexed citations

10.

Garosi, Younes, et al.. (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. 17 indexed citations

11.

Baltensweiler, Andri, Lorenz Walthert, Marc Hanewinkel, Stephan Zimmermann, & Madlene Nussbaum. (2021). Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland. Geoderma Regional. 27. e00437–e00437. 46 indexed citations

12.

Petermann, Eric, Hanna Meyer, Madlene Nussbaum, & Peter Bossew. (2020). Mapping the geogenic radon potential for Germany by machine learning. The Science of The Total Environment. 754. 142291–142291. 56 indexed citations

13.

Petermann, Eric, Hanna Meyer, Madlene Nussbaum, & Peter Bossew. (2020). Mapping the geogenic radon potential for Germany by machine learning. 2 indexed citations

14.

Nussbaum, Madlene, Andri Baltensweiler, & Lorenz Walthert. (2019). Soil property maps for Swiss forests by machine learning based model averaging. EGU General Assembly Conference Abstracts. 4981. 1 indexed citations

15.

Greiner, Lucie, Madlene Nussbaum, Andreas Papritz, et al.. (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. 20 indexed citations

16.

Nussbaum, Madlene, Andri Baltensweiler, Urs Grob, et al.. (2018). Evaluation of digital soil mapping approaches with large sets of environmental covariates. SOIL. 4(1). 1–22. 204 indexed citations

17.

Hengl, Tomislav, Madlene Nussbaum, Marvin N. Wright, G.B.M. Heuvelink, & Benedikt Gräler. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ. 6. e5518–e5518. 659 indexed citations breakdown →

18.

Greiner, Lucie, Madlene Nussbaum, Andreas Papritz, et al.. (2018). Assessment of soil multi-functionality to support the sustainable use of soil resources on the Swiss Plateau. Geoderma Regional. 14. e00181–e00181. 27 indexed citations

19.

Nussbaum, Madlene, et al.. (2017). Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models. SOIL. 3(4). 191–210. 20 indexed citations

20.

Nussbaum, Madlene, et al.. (2014). Estimating soil organic carbon stocks of Swiss forest soils by robust external-drift kriging. Geoscientific model development. 7(3). 1197–1210. 41 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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