Articles | Volume 4, issue 2
https://doi.org/10.5194/soil-4-123-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-4-123-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Uncertainty indication in soil function maps – transparent and easy-to-use information to support sustainable use of soil resources
Lucie Greiner
CORRESPONDING AUTHOR
Swiss Soil Monitoring Network (NABO), Agroscope, 8046 Zurich,
Switzerland
Madlene Nussbaum
School of Agricultural, Forest and Food Science (HAFL), Bern
University of Applied Sciences (BFH), 3052 Zollikofen, Switzerland
Andreas Papritz
Institute of Biogeochemistry and Pollutant Dynamics, Swiss Federal
Institute of Technology (ETH), 8092 Zurich, Switzerland
Stephan Zimmermann
Forest Soils and Biogeochemistry, Soil Functions and Soil Protection,
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), 8903
Birmensdorf, Switzerland
Andreas Gubler
Swiss Soil Monitoring Network (NABO), Agroscope, 8046 Zurich,
Switzerland
Adrienne Grêt-Regamey
Planning of Landscape and Urban Systems, Swiss Federal Institute of
Technology (ETH), 8093 Zurich, Switzerland
Armin Keller
Swiss Soil Monitoring Network (NABO), Agroscope, 8046 Zurich,
Switzerland
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The Cryosphere, 20, 1427–1444, https://doi.org/10.5194/tc-20-1427-2026, https://doi.org/10.5194/tc-20-1427-2026, 2026
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Two hybrid Machine Learning (ML) approaches predicting daily Snow Water Equivalent (SWE) were evaluated across ten Northern Hemisphere sites. By integrating meteorological data with Crocus snow model simulations, these hybrid models outperformed both standalone Crocus and traditional ML models. Notably, augmenting measured SWE data with Crocus simulations significantly improved performance at unseen locations, offering a promising new approach to long-term SWE prediction.
Tomislav Hengl, Davide Consoli, Xuemeng Tian, Travis W. Nauman, Madlene Nussbaum, Mustafa Serkan Isik, Leandro Parente, Yu-Feng Ho, Rolf Simoes, Surya Gupta, Alessandro Samuel-Rosa, Taciara Zborowski Horst, José L. Safanelli, and Nancy Harris
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We used satellite data and thousands of soil samples to create detailed global maps showing how soil changes over time. These maps reveal important patterns in soil health, such as a significant global loss of soil carbon in the past 25 years. Our results help track land degradation and support better land restoration efforts. This work provides a new global tool for understanding and protecting soil, a key resource for food, water, and climate.
Jimena Medina-Rubio, Madlene Nussbaum, Ton S. van den Bremer, and Erik van Sebille
Ocean Sci., 22, 49–74, https://doi.org/10.5194/os-22-49-2026, https://doi.org/10.5194/os-22-49-2026, 2026
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We study how tides, wind, and waves interact at the ocean surface by tracking ultra-thin drifters released in the southern North Sea for two months. Using model data together with data-driven machine learning models, we determine the relative contribution of each forcing mechanism in driving the drifters' velocity and improve the prediction of their trajectories. We also test the generalisability of this method by applying it to the same drifters in the Tyrrhenian Sea.
Christopher Chagumaira, Joseph G. Chimungu, Patson C. Nalivata, Martin R. Broadley, Madlene Nussbaum, Alice E. Milne, and R. Murray Lark
EGUsphere, https://doi.org/10.5194/egusphere-2022-583, https://doi.org/10.5194/egusphere-2022-583, 2022
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Our study examines different quantitative methods to predict concentrations of micronutrients in the soil from field samples. However, we emphasize the concerns of stakeholders, who use such information to make decisions, in this case in relation to the study and management of micronutrient deficiency risk in the human population. We propose a framework to think about these concerns then compare common approaches for digital soil mapping within this framework.
Philipp Baumann, Anatol Helfenstein, Andreas Gubler, Armin Keller, Reto Giulio Meuli, Daniel Wächter, Juhwan Lee, Raphael Viscarra Rossel, and Johan Six
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We developed the Swiss mid-infrared spectral library and a statistical model collection across 4374 soil samples with reference measurements of 16 properties. Our library incorporates soil from 1094 grid locations and 71 long-term monitoring sites. This work confirms once again that nationwide spectral libraries with diverse soils can reliably feed information to a fast chemical diagnosis. Our data-driven reduction of the library has the potential to accurately monitor carbon at the plot scale.
Anatol Helfenstein, Philipp Baumann, Raphael Viscarra Rossel, Andreas Gubler, Stefan Oechslin, and Johan Six
SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, https://doi.org/10.5194/soil-7-193-2021, 2021
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In this study, we show that a soil spectral library (SSL) can be used to predict soil carbon at new and very different locations. The importance of this finding is that it requires less time-consuming lab work than calibrating a new model for every local application, while still remaining similar to or more accurate than local models. Furthermore, we show that this method even works for predicting (drained) peat soils, using a SSL with mostly mineral soils containing much less soil carbon.
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Short summary
To maintain the soil resource, spatial information on soil multi-functionality is key. Soil function (SF) maps rate soils potentials to fulfill a certain function, e.g., nutrient regulation. We show how uncertainties in predictions of soil properties generated by digital soil mapping propagate into soil function maps, present possibilities to display this uncertainty information and show that otherwise comparable SF assessment methods differ in their behaviour in view of uncertainty propagation.
To maintain the soil resource, spatial information on soil multi-functionality is key. Soil...