Articles | Volume 11, issue 2
https://doi.org/10.5194/soil-11-655-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/soil-11-655-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
25 Sep 2025
Original research article |
| 25 Sep 2025
| 25 Sep 2025
Combining electromagnetic induction and satellite-based NDVI data for improved determination of management zones for sustainable crop production
Agrosphere Institute (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Cosimo Brogi
Agrosphere Institute (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Dave O'Leary
Hy-Res Research Group, School of Natural Sciences, Earth and Life, College of Science and Engineering, University of Galway, Galway, Ireland
Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Ireland
Ixchel M. Hernández-Ochoa
Institute of Crop Science & Resource Conservation (INRES), Crop Science Group, University of Bonn, 53115 Bonn, Germany
Marco Donat
Leibniz Centre for Agricultural Landscape Research, 15374 Müncheberg, Germany
Faculty of Landscape Management and Nature Conservation, University for Sustainable Development (HNEE), 16225 Eberswalde, Germany
Harry Vereecken
Agrosphere Institute (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Johan Alexander Huisman
Agrosphere Institute (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Related authors
No articles found.
Heye R. Bogena, Frank Herrmann, Andreas Lücke, Thomas Pütz, and Harry Vereecken
Earth Syst. Sci. Data, 17, 6965–6992, https://doi.org/10.5194/essd-17-6965-2025, https://doi.org/10.5194/essd-17-6965-2025, 2025
Short summary
Short summary
The Wüstebach catchment, which is part of the German TERENO (Terrestrial Environmental Observatories) network, was partially deforested in 2013 to promote natural forest regrowth. This data paper provides 16 years of hourly concentrations and fluxes of 11 solutes and runoff rates (2010–2024) from two runoff gauging stations, one affected by deforestation and one not, illustrating forest-management effects on solute transport processes at the catchment scale.
Alejandro Romero-Ruiz, Dave O'Leary, Dongxue Zhao, Yuxin Wu, and Sarah Garré
SOIL, 11, 1041–1051, https://doi.org/10.5194/soil-11-1041-2025, https://doi.org/10.5194/soil-11-1041-2025, 2025
Short summary
Short summary
Agrogeophysics harnesses geophysical methods for non-invasive, multiscale mapping and monitoring of soil properties and processes in the plant-soil-atmosphere continuum, which are responsible for climate regulation, food production and water security. Here, we present an overview of advances in Agrogeophysics in the last decade and discuss how the contributions to the Special Issue “Agrogeophysics: Illuminating soil’s hidden dimensions” help addressing agricultural and environmental challenges.
Jordan Steven Bates, Carsten Montzka, Rajina Bajracharya, Harry Vereecken, and François Jonard
EGUsphere, https://doi.org/10.5194/egusphere-2025-5336, https://doi.org/10.5194/egusphere-2025-5336, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
We used drone-based laser, multispectral, and thermal sensors to measure crop growth and health throughout the season. By combining these different data sources with an artificial intelligence model, we found that laser signal strength provides a powerful new way to estimate plant biomass. This method can improve how farmers and researchers monitor crop productivity and manage resources more sustainably.
Joschka Neumann, Nicolas Brüggemann, Patrick Chaumet, Normen Hermes, Jan Huwer, Peter Kirchner, Werner Lesmeister, Wilhelm August Mertens, Thomas Pütz, Jörg Wolters, Harry Vereecken, and Ghaleb Natour
Geosci. Instrum. Method. Data Syst., 14, 353–377, https://doi.org/10.5194/gi-14-353-2025, https://doi.org/10.5194/gi-14-353-2025, 2025
Short summary
Short summary
Climate change in combination with a steadily growing world population and a simultaneous decrease in agricultural land is one of the greatest global challenges facing mankind. In this context, Forschungszentrum Jülich established an "agricultural simulator" (AgraSim), which enables research into the effects of climate change on agricultural ecosystems and the optimization of agricultural cultivation and management strategies with the aid of combined experimental and numerical simulation.
François Rineau, Alexander H. Frank, Jannis Groh, Kristof Grosjean, Arnaud Legout, Daniil I. Kolokolov, Michel Mench, Maria Moreno-Druet, Benoît Pollier, Virmantas Povilaitis, Johanna Pausch, Thomas Puetz, Tjalling Rooks, Peter Schröder, Wieslaw Szulc, Beata Rutkowska, Xander Swinnen, Sofie Thijs, Harry Vereecken, Janna V. Veselovskaya, Mwahija Zubery, Renaldas Žydelis, and Evelin Loit
EGUsphere, https://doi.org/10.5194/egusphere-2025-4188, https://doi.org/10.5194/egusphere-2025-4188, 2025
Short summary
Short summary
Spreading crushed rock on farmland soil could help slow climate change by capturing CO2 from the atmosphere and convert it in carbonate ions. We found that this method not only captured carbon in soils but also stimulated natural biological processes that store even more carbon. These results suggest that enhanced weathering can act as a double benefit: removing carbon dioxide from the air while improving the health and resilience of agricultural soils.
Shiao Feng, Wenhong Wang, Yonggen Zhang, Zhongwang Wei, Jianzhi Dong, Lutz Weihermüller, and Harry Vereecken
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-410, https://doi.org/10.5194/essd-2025-410, 2025
Preprint under review for ESSD
Short summary
Short summary
Soil moisture is key for weather, farming, and ecosystems, but global datasets have gaps and biases. We compared three products against 1,615 stations with more than 1.9 million measured moisture, finding ERA5-Land highly correlated but biased high, and SMAP-L4 accurate but short-term. Fusing them created an enhanced dataset, improving correlation by 5%, reducing errors by 20%, and enhancing overall fit by 15%. This seamless resource aids drought tracking, water planning, and climate adaptation.
Jordan Bates, Carsten Montzka, Harry Vereecken, and François Jonard
EGUsphere, https://doi.org/10.5194/egusphere-2025-3919, https://doi.org/10.5194/egusphere-2025-3919, 2025
Short summary
Short summary
We used unmanned aerial vehicles (UAVs) with advanced cameras and laser scanning to measure crop water use and detect early signs of plant stress. By combining 3D views of crop structure with surface temperature and reflectance data, we improved estimates of water loss, especially in dense crops like wheat. This approach can help farmers use water more efficiently, respond quickly to stress, and support sustainable agriculture in a changing climate.
Solomon Ehosioke, Sarah Garré, Johan Alexander Huisman, Egon Zimmermann, Mathieu Javaux, and Frédéric Nguyen
Biogeosciences, 22, 2853–2869, https://doi.org/10.5194/bg-22-2853-2025, https://doi.org/10.5194/bg-22-2853-2025, 2025
Short summary
Short summary
Understanding the electromagnetic properties of plant roots is useful to quantify plant properties and monitor plant physiological responses to changing environmental factors. We investigated the electrical properties of the primary roots of Brachypodium and maize plants during the uptake of fresh and saline water using spectral induced polarization. Our results indicate that salinity tolerance varies with the species and that Maize is more tolerant to salinity than Brachypodium.
Dave O'Leary, Patrick Tuohy, Owen Fenton, Mark G. Healy, Hilary Pierce, Asaf Shnel, and Eve Daly
EGUsphere, https://doi.org/10.5194/egusphere-2025-1966, https://doi.org/10.5194/egusphere-2025-1966, 2025
Short summary
Short summary
We assess the impact of open drain damming to help restore drained peat soils. We measured how water levels and soil moisture changed over time and space using field sensors and geophysical mapping tools. Our results show that the impact of damming is limited to < 20 m on our site. This approach could support efforts to reduce carbon loss and improve the health of peatland landscapes in a practical, scalable way
Manuela S. Kaufmann, Anja Klotzsche, Jan van der Kruk, Anke Langen, Harry Vereecken, and Lutz Weihermüller
SOIL, 11, 267–285, https://doi.org/10.5194/soil-11-267-2025, https://doi.org/10.5194/soil-11-267-2025, 2025
Short summary
Short summary
To use fertilizers more effectively, non-invasive geophysical methods can be used to understand nutrient distributions in the soil. We utilize, in a long-term field study, geophysical techniques to study soil properties and conditions under different fertilizer treatments. We compared the geophysical response with soil samples and soil sensor data. In particular, electromagnetic induction and electrical resistivity tomography were effective in monitoring changes in nitrate levels over time.
Bamidele Oloruntoba, Stefan Kollet, Carsten Montzka, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 29, 1659–1683, https://doi.org/10.5194/hess-29-1659-2025, https://doi.org/10.5194/hess-29-1659-2025, 2025
Short summary
Short summary
We studied how soil and weather data affect land model simulations over Africa. By combining soil data processed in different ways with weather data of varying time intervals, we found that weather inputs had a greater impact on water processes than soil data type. However, the way soil data were processed became crucial when paired with high-frequency weather inputs, showing that detailed weather data can improve local and regional predictions of how water moves and interacts with the land.
Till Francke, Cosimo Brogi, Alby Duarte Rocha, Michael Förster, Maik Heistermann, Markus Köhli, Daniel Rasche, Marvin Reich, Paul Schattan, Lena Scheiffele, and Martin Schrön
Geosci. Model Dev., 18, 819–842, https://doi.org/10.5194/gmd-18-819-2025, https://doi.org/10.5194/gmd-18-819-2025, 2025
Short summary
Short summary
Multiple methods for measuring soil moisture beyond the point scale exist. Their validation is generally hindered by not knowing the truth. We propose a virtual framework in which this truth is fully known and the sensor observations for cosmic ray neutron sensing, remote sensing, and hydrogravimetry are simulated. This allows for the rigorous testing of these virtual sensors to understand their effectiveness and limitations.
Paolo Nasta, Günter Blöschl, Heye R. Bogena, Steffen Zacharias, Roland Baatz, Gabriëlle De Lannoy, Karsten H. Jensen, Salvatore Manfreda, Laurent Pfister, Ana M. Tarquis, Ilja van Meerveld, Marc Voltz, Yijian Zeng, William Kustas, Xin Li, Harry Vereecken, and Nunzio Romano
Hydrol. Earth Syst. Sci., 29, 465–483, https://doi.org/10.5194/hess-29-465-2025, https://doi.org/10.5194/hess-29-465-2025, 2025
Short summary
Short summary
The Unsolved Problems in Hydrology (UPH) initiative has emphasized the need to establish networks of multi-decadal hydrological observatories to tackle catchment-scale challenges on a global scale. This opinion paper provocatively discusses two endmembers of possible future hydrological observatory (HO) networks for a given hypothesized community budget: a comprehensive set of moderately instrumented observatories or, alternatively, a small number of highly instrumented supersites.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
Short summary
Short summary
Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
Hydrol. Earth Syst. Sci., 28, 4059–4063, https://doi.org/10.5194/hess-28-4059-2024, https://doi.org/10.5194/hess-28-4059-2024, 2024
Short summary
Short summary
Gao et al. (2023) question the importance of soil in hydrology, sparking debate. We acknowledge some valid points but critique their broad, unsubstantiated views on soil's role. Our response highlights three key areas: (1) the false divide between ecosystem-centric and soil-centric approaches, (2) the vital yet varied impact of soil properties, and (3) the call for a scale-aware framework. We aim to unify these perspectives, enhancing hydrology's comprehensive understanding.
Tobias Karl David Weber, Lutz Weihermüller, Attila Nemes, Michel Bechtold, Aurore Degré, Efstathios Diamantopoulos, Simone Fatichi, Vilim Filipović, Surya Gupta, Tobias L. Hohenbrink, Daniel R. Hirmas, Conrad Jackisch, Quirijn de Jong van Lier, John Koestel, Peter Lehmann, Toby R. Marthews, Budiman Minasny, Holger Pagel, Martine van der Ploeg, Shahab Aldin Shojaeezadeh, Simon Fiil Svane, Brigitta Szabó, Harry Vereecken, Anne Verhoef, Michael Young, Yijian Zeng, Yonggen Zhang, and Sara Bonetti
Hydrol. Earth Syst. Sci., 28, 3391–3433, https://doi.org/10.5194/hess-28-3391-2024, https://doi.org/10.5194/hess-28-3391-2024, 2024
Short summary
Short summary
Pedotransfer functions (PTFs) are used to predict parameters of models describing the hydraulic properties of soils. The appropriateness of these predictions critically relies on the nature of the datasets for training the PTFs and the physical comprehensiveness of the models. This roadmap paper is addressed to PTF developers and users and critically reflects the utility and future of PTFs. To this end, we present a manifesto aiming at a paradigm shift in PTF research.
Helen Scholz, Gunnar Lischeid, Lars Ribbe, Ixchel Hernandez Ochoa, and Kathrin Grahmann
Hydrol. Earth Syst. Sci., 28, 2401–2419, https://doi.org/10.5194/hess-28-2401-2024, https://doi.org/10.5194/hess-28-2401-2024, 2024
Short summary
Short summary
Sustainable management schemes in agriculture require knowledge of site-specific soil hydrological processes, especially the interplay between soil heterogeneities and crops. We disentangled such effects on soil moisture in a diversified arable field with different crops and management schemes by applying a principal component analysis. The main effects on soil moisture variability were quantified. Meteorological drivers, followed by different seasonal behaviour of crops, had the largest impact.
Lukas Strebel, Heye Bogena, Harry Vereecken, Mie Andreasen, Sergio Aranda-Barranco, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 28, 1001–1026, https://doi.org/10.5194/hess-28-1001-2024, https://doi.org/10.5194/hess-28-1001-2024, 2024
Short summary
Short summary
We present results from using soil water content measurements from 13 European forest sites in a state-of-the-art land surface model. We use data assimilation to perform a combination of observed and modeled soil water content and show the improvements in the representation of soil water content. However, we also look at the impact on evapotranspiration and see no corresponding improvements.
Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann
Geosci. Model Dev., 16, 7375–7409, https://doi.org/10.5194/gmd-16-7375-2023, https://doi.org/10.5194/gmd-16-7375-2023, 2023
Short summary
Short summary
In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
Theresa Boas, Heye Reemt Bogena, Dongryeol Ryu, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 27, 3143–3167, https://doi.org/10.5194/hess-27-3143-2023, https://doi.org/10.5194/hess-27-3143-2023, 2023
Short summary
Short summary
In our study, we tested the utility and skill of a state-of-the-art forecasting product for the prediction of regional crop productivity using a land surface model. Our results illustrate the potential value and skill of combining seasonal forecasts with modelling applications to generate variables of interest for stakeholders, such as annual crop yield for specific cash crops and regions. In addition, this study provides useful insights for future technical model evaluations and improvements.
Thomas Hermans, Pascal Goderniaux, Damien Jougnot, Jan H. Fleckenstein, Philip Brunner, Frédéric Nguyen, Niklas Linde, Johan Alexander Huisman, Olivier Bour, Jorge Lopez Alvis, Richard Hoffmann, Andrea Palacios, Anne-Karin Cooke, Álvaro Pardo-Álvarez, Lara Blazevic, Behzad Pouladi, Peleg Haruzi, Alejandro Fernandez Visentini, Guilherme E. H. Nogueira, Joel Tirado-Conde, Majken C. Looms, Meruyert Kenshilikova, Philippe Davy, and Tanguy Le Borgne
Hydrol. Earth Syst. Sci., 27, 255–287, https://doi.org/10.5194/hess-27-255-2023, https://doi.org/10.5194/hess-27-255-2023, 2023
Short summary
Short summary
Although invisible, groundwater plays an essential role for society as a source of drinking water or for ecosystems but is also facing important challenges in terms of contamination. Characterizing groundwater reservoirs with their spatial heterogeneity and their temporal evolution is therefore crucial for their sustainable management. In this paper, we review some important challenges and recent innovations in imaging and modeling the 4D nature of the hydrogeological systems.
Cosimo Brogi, Heye Reemt Bogena, Markus Köhli, Johan Alexander Huisman, Harrie-Jan Hendricks Franssen, and Olga Dombrowski
Geosci. Instrum. Method. Data Syst., 11, 451–469, https://doi.org/10.5194/gi-11-451-2022, https://doi.org/10.5194/gi-11-451-2022, 2022
Short summary
Short summary
Accurate monitoring of water in soil can improve irrigation efficiency, which is important considering climate change and the growing world population. Cosmic-ray neutrons sensors (CRNSs) are a promising tool in irrigation monitoring due to a larger sensed area and to lower maintenance than other ground-based sensors. Here, we analyse the feasibility of irrigation monitoring with CRNSs and the impact of the irrigated field dimensions, of the variations of water in soil, and of instrument design.
Maximilian Weigand, Egon Zimmermann, Valentin Michels, Johan Alexander Huisman, and Andreas Kemna
Geosci. Instrum. Method. Data Syst., 11, 413–433, https://doi.org/10.5194/gi-11-413-2022, https://doi.org/10.5194/gi-11-413-2022, 2022
Short summary
Short summary
The construction, operation and analysis of a spectral electrical
impedance tomography (sEIT) field monitoring setup with high spatial and temporal resolution are presented. Electromagnetic induction errors are corrected, allowing the recovery of images of in-phase conductivity and electrical polarisation of up to 1 kHz.
Olga Dombrowski, Cosimo Brogi, Harrie-Jan Hendricks Franssen, Damiano Zanotelli, and Heye Bogena
Geosci. Model Dev., 15, 5167–5193, https://doi.org/10.5194/gmd-15-5167-2022, https://doi.org/10.5194/gmd-15-5167-2022, 2022
Short summary
Short summary
Soil carbon storage and food production of fruit orchards will be influenced by climate change. However, they lack representation in models that study such processes. We developed and tested a new sub-model, CLM5-FruitTree, that describes growth, biomass distribution, and management practices in orchards. The model satisfactorily predicted yield and exchange of carbon, energy, and water in an apple orchard and can be used to study land surface processes in fruit orchards at different scales.
Jordan Bates, Francois Jonard, Rajina Bajracharya, Harry Vereecken, and Carsten Montzka
AGILE GIScience Ser., 3, 23, https://doi.org/10.5194/agile-giss-3-23-2022, https://doi.org/10.5194/agile-giss-3-23-2022, 2022
Wei Qu, Heye Bogena, Christoph Schüth, Harry Vereecken, Zongmei Li, and Stephan Schulz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-131, https://doi.org/10.5194/gmd-2022-131, 2022
Publication in GMD not foreseen
Short summary
Short summary
We applied the global sensitivity analysis LH-OAT to the integrated hydrology model ParFlow-CLM to investigate the sensitivity of the 12 parameters for different scenarios. And we found that the general patterns of the parameter sensitivities were consistent, however, for some parameters a significantly larger span of the sensitivities was observed, especially for the higher slope and in subarctic climatic scenarios.
Nicholas Jarvis, Jannis Groh, Elisabet Lewan, Katharina H. E. Meurer, Walter Durka, Cornelia Baessler, Thomas Pütz, Elvin Rufullayev, and Harry Vereecken
Hydrol. Earth Syst. Sci., 26, 2277–2299, https://doi.org/10.5194/hess-26-2277-2022, https://doi.org/10.5194/hess-26-2277-2022, 2022
Short summary
Short summary
We apply an eco-hydrological model to data on soil water balance and grassland growth obtained at two sites with contrasting climates. Our results show that the grassland in the drier climate had adapted by developing deeper roots, which maintained water supply to the plants in the face of severe drought. Our study emphasizes the importance of considering such plastic responses of plant traits to environmental stress in the modelling of soil water balance and plant growth under climate change.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
Short summary
Short summary
Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Lukas Strebel, Heye R. Bogena, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 15, 395–411, https://doi.org/10.5194/gmd-15-395-2022, https://doi.org/10.5194/gmd-15-395-2022, 2022
Short summary
Short summary
We present the technical coupling between a land surface model (CLM5) and the Parallel Data Assimilation Framework (PDAF). This coupling enables measurement data to update simulated model states and parameters in a statistically optimal way. We demonstrate the viability of the model framework using an application in a forested catchment where the inclusion of soil water measurements significantly improved the simulation quality.
Veronika Forstner, Jannis Groh, Matevz Vremec, Markus Herndl, Harry Vereecken, Horst H. Gerke, Steffen Birk, and Thomas Pütz
Hydrol. Earth Syst. Sci., 25, 6087–6106, https://doi.org/10.5194/hess-25-6087-2021, https://doi.org/10.5194/hess-25-6087-2021, 2021
Short summary
Short summary
Lysimeter-based manipulative and observational experiments were used to identify responses of water fluxes and aboveground biomass (AGB) to climatic change in permanent grassland. Under energy-limited conditions, elevated temperature actual evapotranspiration (ETa) increased, while seepage, dew, and AGB decreased. Elevated CO2 mitigated the effect on ETa. Under water limitation, elevated temperature resulted in reduced ETa, and AGB was negatively correlated with an increasing aridity.
Yafei Huang, Jonas Weis, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-569, https://doi.org/10.5194/hess-2021-569, 2021
Manuscript not accepted for further review
Short summary
Short summary
Trends in agricultural droughts cannot be easily deduced from measurements. Here trends in agricultural droughts over 31 German and Dutch sites were calculated with model simulations and long-term observed meteorological data as input. We found that agricultural droughts are increasing although precipitation hardly decreases. The increase is driven by increase in evapotranspiration. The year 2018 was for half of the sites the year with the most extreme agricultural drought in the last 55 years.
Bernd Schalge, Gabriele Baroni, Barbara Haese, Daniel Erdal, Gernot Geppert, Pablo Saavedra, Vincent Haefliger, Harry Vereecken, Sabine Attinger, Harald Kunstmann, Olaf A. Cirpka, Felix Ament, Stefan Kollet, Insa Neuweiler, Harrie-Jan Hendricks Franssen, and Clemens Simmer
Earth Syst. Sci. Data, 13, 4437–4464, https://doi.org/10.5194/essd-13-4437-2021, https://doi.org/10.5194/essd-13-4437-2021, 2021
Short summary
Short summary
In this study, a 9-year simulation of complete model output of a coupled atmosphere–land-surface–subsurface model on the catchment scale is discussed. We used the Neckar catchment in SW Germany as the basis of this simulation. Since the dataset includes the full model output, it is not only possible to investigate model behavior and interactions between the component models but also use it as a virtual truth for comparison of, for example, data assimilation experiments.
Jan Vanderborght, Valentin Couvreur, Felicien Meunier, Andrea Schnepf, Harry Vereecken, Martin Bouda, and Mathieu Javaux
Hydrol. Earth Syst. Sci., 25, 4835–4860, https://doi.org/10.5194/hess-25-4835-2021, https://doi.org/10.5194/hess-25-4835-2021, 2021
Short summary
Short summary
Root water uptake is an important process in the terrestrial water cycle. How this process depends on soil water content, root distributions, and root properties is a soil–root hydraulic problem. We compare different approaches to implementing root hydraulics in macroscopic soil water flow and land surface models.
Youri Rothfuss, Maria Quade, Nicolas Brüggemann, Alexander Graf, Harry Vereecken, and Maren Dubbert
Biogeosciences, 18, 3701–3732, https://doi.org/10.5194/bg-18-3701-2021, https://doi.org/10.5194/bg-18-3701-2021, 2021
Short summary
Short summary
The partitioning of evapotranspiration into evaporation from soil and transpiration from plants is crucial for a wide range of parties, from farmers to policymakers. In this work, we focus on a particular partitioning method, based on the stable isotopic analysis of water. In particular, we aim at highlighting the challenges that this method is currently facing and, in light of recent methodological developments, propose ways forward for the isotopic-partitioning community.
Cosimo Brogi, Johan A. Huisman, Lutz Weihermüller, Michael Herbst, and Harry Vereecken
SOIL, 7, 125–143, https://doi.org/10.5194/soil-7-125-2021, https://doi.org/10.5194/soil-7-125-2021, 2021
Short summary
Short summary
There is a need in agriculture for detailed soil maps that carry quantitative information. Geophysics-based soil maps have the potential to deliver such products, but their added value has not been fully investigated yet. In this study, we compare the use of a geophysics-based soil map with the use of two commonly available maps as input for crop growth simulations. The geophysics-based product results in better simulations, with improvements that depend on precipitation, soil, and crop type.
Theresa Boas, Heye Bogena, Thomas Grünwald, Bernard Heinesch, Dongryeol Ryu, Marius Schmidt, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 14, 573–601, https://doi.org/10.5194/gmd-14-573-2021, https://doi.org/10.5194/gmd-14-573-2021, 2021
Short summary
Short summary
In this study we were able to significantly improve CLM5 model performance for European cropland sites by adding a winter wheat representation, specific plant parameterizations for important cash crops, and a cover-cropping and crop rotation subroutine to its crop module. Our modifications should be applied in future studies of CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes.
Cited articles
Abdu, H., Robinson, D. A., Seyfried, M., and Jones, S. B.: Geophysical imaging of watershed subsurface patterns and prediction of soil texture and water holding capacity, Water Resour. Res., 44, 1–10, https://doi.org/10.1029/2008wr007043, 2008.
Adamchuk, V., Allred, B., Doolittle, J., Grote, K., and Viscarra Rossel, R. A.: Tools for proximal soil sensing, United States Dep. Agric., Soil Surv. Man. soil Sci. Div. Staff., Washington, DC, 355–356, 2017.
Adhikari, K., Smith, D. R., Collins, H., Hajda, C., Acharya, B. S., and Owens, P. R.: Mapping Within-Field Soil Health Variations Using Apparent Electrical Conductivity, Topography, and Machine Learning, Agronomy, 12, 1–16, https://doi.org/10.3390/agronomy12051019, 2022.
Ali, A., Rondelli, V., Martelli, R., Falsone, G., Lupia, F., and Barbanti, L.: Management Zones Delineation through Clustering Techniques Based on Soils Traits, NDVI Data, and Multiple Year Crop Yields, Agriculture, 12, https://doi.org/10.3390/agriculture12020231, 2022.
Altdorff, D., von Hebel, C., Borchard, N., van der Kruk, J., Bogena, H. R., Vereecken, H., and Huisman, J. A.: Potential of catchment-wide soil water content prediction using electromagnetic induction in a forest ecosystem, Environ. Earth Sci., 76, 1–11, https://doi.org/10.1007/s12665-016-6361-3, 2017.
Antle, J. M., Basso, B., Conant, R. T., Godfray, H. C. J., Jones, J. W., Herrero, M., Howitt, R. E., Keating, B. A., Munoz-Carpena, R., Rosenzweig, C., Tittonell, P., and Wheeler, T. R.: Towards a new generation of agricultural system data, models and knowledge products: Design and improvement, Agr. Syst., 155, 255–268, https://doi.org/10.1016/j.agsy.2016.10.002, 2017.
Arshad, M. A. C., Lowery, B., and Grossman, B.: Physical Tests for Monitoring Soil Quality, in: Methods for Assessing Soil Quality, John Wiley & Sons, Ltd, 123–141, https://doi.org/10.2136/sssaspecpub49.c7, 1997.
Becker, S. M., Franz, T. E., Abimbola, O., Steele, D. D., Flores, J. P., Jia, X., Scherer, T. F., Rudnick, D. R., and Neale, C. M. U.: Feasibility assessment on use of proximal geophysical sensors to support precision management, Vadose Zone J., 21, 1–18, https://doi.org/10.1002/vzj2.20228, 2022.
Bijeesh, T. V. and Narasimhamurthy, K. N.: Surface water detection and delineation using remote sensing images: a review of methods and algorithms, Sustain. Water Resour. Manag., 6, 1–23, https://doi.org/10.1007/s40899-020-00425-4, 2020.
Binley, A., Hubbard, S. S., Huisman, J., Revil, A., Robinson, D., Singha, K., and Slater, L. D.: The emergence of hydrogeophysics for improved understanding of subsurface processes over multiple scales, Water Resour. Res., 51, 3837–3866, https://doi.org/10.1002/2015WR017016, 2015.
Blanchy, G., McLachlan, P., Mary, B., Censini, M., Boaga, J., and Cassiani, G.: Comparison of multi-coil and multi-frequency frequency domain electromagnetic induction instruments, Front. Soil Sci., 4, 1–13, https://doi.org/10.3389/fsoil.2024.1239497, 2024.
Bongiovanni, R. and Lowenberg-Deboer, J.: Precision agriculture and sustainability, Precis. Agric., 5, 359–387, https://doi.org/10.1023/B:PRAG.0000040806.39604.aa, 2004.
Breunig, F. M., Galvão, L. S., Dalagnol, R., Dauve, C. E., Parraga, A., Santi, A. L., Della Flora, D. P., and Chen, S.: Delineation of management zones in agricultural fields using cover–crop biomass estimates from PlanetScope data, Int. J. Appl. Earth Obs., 85, https://doi.org/10.1016/j.jag.2019.102004, 2020.
Brogi, C., Huisman, J. A., Pätzold, S., von Hebel, C., Weihermüller, L., Kaufmann, M. S., van der Kruk, J., and Vereecken, H.: Large-scale soil mapping using multi-configuration EMI and supervised image classification, Geoderma, 335, 133–148, https://doi.org/10.1016/j.geoderma.2018.08.001, 2019.
Brogi, C., Huisman, J. A., Weihermüller, L., Herbst, M., and Vereecken, H.: Added value of geophysics-based soil mapping in agro-ecosystem simulations, SOIL, 7, 125–143, https://doi.org/10.5194/soil-7-125-2021, 2021.
Carfagna, E. and Gallego, F. J.: Using remote sensing for agricultural statistics, Int. Stat. Rev., 73, 389–404, https://doi.org/10.1111/j.1751-5823.2005.tb00155.x, 2005.
Castrignanò, A., Buttafuoco, G., Quarto, R., Parisi, D., Viscarra Rossel, R. A., Terribile, F., Langella, G., and Venezia, A.: A geostatistical sensor data fusion approach for delineating homogeneous management zones in Precision Agriculture, Catena, 167, 293–304, https://doi.org/10.1016/j.catena.2018.05.011, 2018.
Celebi, M. E., Kingravi, H. A., and Vela, P. A.: A comparative study of efficient initialization methods for the k-means clustering algorithm, Expert Syst. Appl., 40, 200–210, https://doi.org/10.1016/j.eswa.2012.07.021, 2013.
Chartzoulakis, K. and Bertaki, M.: Sustainable Water Management in Agriculture under Climate Change, Agric. Agric. Sci. Proc., 4, 88–98, https://doi.org/10.1016/j.aaspro.2015.03.011, 2015.
Chlingaryan, A., Sukkarieh, S., and Whelan, B.: Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review, Comput. Electron. Agr., 151, 61–69, https://doi.org/10.1016/j.compag.2018.05.012, 2018.
Ciampalini, A., André, F., Garfagnoli, F., Grandjean, G., Lambot, S., Chiarantini, L., and Moretti, S.: Improved estimation of soil clay content by the fusion of remote hyperspectral and proximal geophysical sensing, J. Appl. Geophys., 116, 135–145, https://doi.org/10.1016/j.jappgeo.2015.03.009, 2015.
Corwin, D. L. and Lesch, S. M.: Application of soil electrical conductivity to precision agriculture: Theory, principles, and guidelines, Agron. J., 95, 455–471, https://doi.org/10.2134/agronj2003.4550, 2003.
Corwin, D. L. and Lesch, S. M.: Apparent soil electrical conductivity measurements in agriculture, Comput. Electron. Agr., 46, 11–43, https://doi.org/10.1016/j.compag.2004.10.005, 2005.
Corwin, D. L. and Scudiero, E.: Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors, Adv. Agron., 158, 1–130, https://doi.org/10.1016/BS.AGRON.2019.07.001, 2019.
de Amorim, L. B. V., Cavalcanti, G. D. C., and Cruz, R. M. O.: The choice of scaling technique matters for classification performance, Appl. Soft Comput., 133, 1–37, https://doi.org/10.1016/j.asoc.2022.109924, 2023.
DIN ISO: 11277: 2002-08, Soil Qual. Part. size Distrib. Miner. soil Mater. method by sieving Sediment., DIN ISO, https://doi.org/10.31030/9283499, 2002.
Dobarco, M. R., McBratney, A., Minasny, B., and Malone, B.: A framework to assess changes in soil condition and capability over large areas, Soil Secur., 4, https://doi.org/10.1016/j.soisec.2021.100011, 2021.
Donat, M., Geistert, J., Grahmann, K., Bloch, R., and Bellingrath-Kimura, S. D.: Patch cropping- a new methodological approach to determine new field arrangements that increase the multifunctionality of agricultural landscapes, Comput. Electron. Agr., 197, 106894, https://doi.org/10.1016/j.compag.2022.106894, 2022.
DWD: Deutscher Wetterdienst (DWD) Climate Data Center (CDC): Monatssumme der Stationsmessungen der Niederschlagshöhe in mm für Deutschland, Version v21.3, Deutscher Wetterdienst, 2021.
ESRI: Esri, Maxar, Earthstar Geographics, and the GIS User Community, https://doc.arcgis.com/en/data-appliance/latest/maps/world-imagery.htm (last access: 21 February 2025), 2020.
Esteves, C., Fangueiro, D., Braga, R. P., Martins, M., Botelho, M., and Ribeiro, H.: Assessing the Contribution of ECa and NDVI in the Delineation of Management Zones in a Vineyard, Agronomy, 12, https://doi.org/10.3390/agronomy12061331, 2022.
Garré, S., Hyndman, D., Mary, B., and Werban, U.: Geophysics conquering new territories: The rise of “agrogeophysics,” Vadose Zone J., 20, 2–5, https://doi.org/10.1002/vzj2.20115, 2021.
Gebbers, R. and Adamchuk, V. I.: Precision Agriculture and Food Security, Science, 327, 828–831, https://doi.org/10.1126/science.1183899, 2010.
Geng, X., Mu, Y., Mao, S., Ye, J., and Zhu, L.: An Improved K-Means Algorithm Based on Fuzzy Metrics, IEEE Access, 8, 217416–217424, https://doi.org/10.1109/ACCESS.2020.3040745, 2020.
Geologischer Dienst NRW: Bodenkundliche Landesaufnahme, Geologischer Dienst Nordrhein-Westfalen, https://www.gd.nrw.de/, last access: 28 January 2025.
Georgi, C., Spengler, D., Itzerott, S., and Kleinschmit, B.: Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data, Precis. Agric., 19, 684–707, https://doi.org/10.1007/s11119-017-9549-y, 2018.
Grahmann, K., Reckling, M., Hernandez-Ochoa, I., and Ewert, F.: Intercropping for sustainability: Research developments and their application An agricultural diversification trial by patchy field arrangements at the landscape level: The landscape living lab “patchCROP,” Asp. Appl. Biol., 146, 2021, 2021.
Grahmann, K., Reckling, M., Hernández-Ochoa, I., Donat, M., Bellingrath-Kimura, S., and Ewert, F.: Co-designing a landscape experiment to investigate diversified cropping systems, Agr. Syst., 217, https://doi.org/10.1016/j.agsy.2024.103950, 2024.
Hamidov, A., Helming, K., Bellocchi, G., Bojar, W., Dalgaard, T., Ghaley, B. B., Hoffmann, C., Holman, I., Holzkämper, A., Krzeminska, D., Kv?`rn ̧, S. H., Lehtonen, H., Niedrist, G., Øygarden, L., Reidsma, P., Roggero, P. P., Rusu, T., Santos, C., Seddaiu, G., Skarb ̧vik, E., Ventrella, D., Ýarski, J., and Schönhart, M.: Impacts of climate change adaptation options on soil functions: A review of European case-studies, Land. Degrad. Dev., 29, 2378–2389, https://doi.org/10.1002/ldr.3006, 2018.
Hatfield, J. L. and Prueger, J. H.: Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices, Remote Sens.-Basel, 2, 562–578, https://doi.org/10.3390/rs2020562, 2010.
Hernández-Ochoa, I. M., Gaiser, T., Grahmann, K., Engels, A., Kersebaum, K. C., Seidel, S. J., and Ewert, F.: Cross model validation for a diversified cropping system, Eur. J. Agron., 157, https://doi.org/10.1016/j.eja.2024.127181, 2024.
Hou, D., Bolan, N. S., Tsang, D. C. W., Kirkham, M. B., and O'Connor, D.: Sustainable soil use and management: An interdisciplinary and systematic approach, Sci. Total Environ., 729, 138961, https://doi.org/10.1016/j.scitotenv.2020.138961, 2020.
Hunt, M. L., Blackburn, G. A., Carrasco, L., Redhead, J. W., and Rowland, C. S.: High resolution wheat yield mapping using Sentinel-2, Remote Sens. Environ., 233, 111410, https://doi.org/10.1016/j.rse.2019.111410, 2019.
IUSS Working Group: International soil classification system for naming soils and creating legends for soil maps, World Soil, 106, 166–168, 2015.
Jadoon, K. Z., Moghadas, D., Jadoon, A., Missimer, T. M., Al-Mashharawi, S. K., and McCabe, M. F.: Estimation of soil salinity in a drip irrigation system by using joint inversion of multicoil electromagnetic induction measurements, Water Resour. Res., 51, 3490–3504, https://doi.org/10.1002/2014WR016245, 2015.
James, I. T., Waine, T. W., Bradley, R. I., Taylor, J. C., and Godwin, R. J.: Determination of Soil Type Boundaries using Electromagnetic Induction Scanning Techniques, Biosyst. Eng., 86, 421–430, https://doi.org/10.1016/j.biosystemseng.2003.09.001, 2003.
Janrao, P., Mishra, D., and Bharadi, V.: Clustering Approaches for Management Zone Delineation in Precision Agriculture for Small Farms, SSRN Electron. J., 1347–1356, https://doi.org/10.2139/ssrn.3356457, 2019.
Jin, Z., Azzari, G., You, C., Di Tommaso, S., Aston, S., Burke, M., and Lobell, D. B.: Smallholder maize area and yield mapping at national scales with Google Earth Engine, Remote Sens. Environ., 228, 115–128, https://doi.org/10.1016/j.rse.2019.04.016, 2019.
Kaufmann, M. S., von Hebel, C., Weihermüller, L., Baumecker, M., Döring, T., Schweitzer, K., Hobley, E., Bauke, S. L., Amelung, W., Vereecken, H., and van der Kruk, J.: Effect of fertilizers and irrigation on multi-configuration electromagnetic induction measurements, Soil Use Manage., 36, 104–116, https://doi.org/10.1111/sum.12530, 2020.
Kaya, F., Ferhatoglu, C., and Baþayiðit, L.: Multi-Temporal Normalized Difference Vegetation Index Based on High Spatial Resolution Satellite Images Reveals Insight-Driven Edaphic Management Zones, AgriEngineering, 7, https://doi.org/10.3390/agriengineering7040092, 2025.
Keller, G. . and Frischknecht, F. .: Electrical Methods in Geophysical Propecting, Oxford, New York, Pergamon Press, 1966.
Khan, S., Tufail, M., Khan, M. T., Khan, Z. A., Iqbal, J., and Alam, M.: A novel semi-supervised framework for UAV based crop/weed classification, PLOS ONE, 16, https://doi.org/10.1371/journal.pone.0251008, 2021.
Kibblewhite, M. G., Ritz, K., and Swift, M. J.: Soil health in agricultural systems, Philos. T. R. Soc. B, 363, 685–701, https://doi.org/10.1098/rstb.2007.2178, 2008.
Koch, T., Deumlich, D., Chifflard, P., Panten, K., and Grahmann, K.: Using model simulation to evaluate soil loss potential in diversified agricultural landscapes, Eur. J. Soil Sci., 74, 1–14, https://doi.org/10.1111/ejss.13332, 2023.
Koganti, T., De Smedt, P., Farzamian, M., Knadel, M., Triantafilis, J., Christiansen, A. V., and Greve, M. H.: Editorial: Digital soil mapping using electromagnetic sensors, Front. Soil Sci., 4, 10–12, https://doi.org/10.3389/fsoil.2024.1536797, 2024.
Kohonen, T.: Essentials of the self-organizing map, Neural Networks, 37, 52–65, https://doi.org/10.1016/j.neunet.2012.09.018, 2013.
Kuang, B., Mahmood, H. S., Quraishi, M. Z., Hoogmoed, W. B., Mouazen, A. M., and van Henten, E. J.: Chapter four - Sensing Soil Properties in the Laboratory, In Situ, and On-Line: A Review, in: Advances in Agronomy, Academic Press, 114, 155–223, https://doi.org/10.1016/B978-0-12-394275-3.00003-1, 2012.
Lavoué, F., Van Der Kruk, J., Rings, J., André, F., Moghadas, D., Huisman, J. A., Lambot, S., LWeihermüller, Vanderborght, J., and Vereecken, H.: Electromagnetic induction calibration using apparent electrical conductivity modelling based on electrical resistivity tomography, Near Surf. Geophys., 8, 553–561, https://doi.org/10.3997/1873-0604.2010037, 2010.
Li, Y., Ni, Z., Jin, F., Li, J., and Li, F.: Research on Clustering Method of Improved Glowworm Algorithm Based on Good-Point Set, Math. Probl. Eng., 2018, https://doi.org/10.1155/2018/8724084, 2018.
Liaghat, S. and Balasundram, S. K.: A review: The role of remote sensing in precision agriculture, Am. J. Agric. Biol. Sci., 5, 50–55, https://doi.org/10.3844/ajabssp.2010.50.55, 2010.
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D.: Machine learning in agriculture: A review, Sensors (Switzerland), 18, 1–29, https://doi.org/10.3390/s18082674, 2018.
Liang, J., Zhao, X., Li, D., Cao, F., and Dang, C.: Determining the number of clusters using information entropy for mixed data, Pattern Recogn., 45, 2251–2265, https://doi.org/10.1016/j.patcog.2011.12.017, 2012.
Licker, R., Johnston, M., Foley, J. A., Barford, C., Kucharik, C. J., Monfreda, C., and Ramankutty, N.: Mind the gap: How do climate and agricultural management explain the “yield gap” of croplands around the world?, Global Ecol. Biogeogr., 19, 769–782, https://doi.org/10.1111/j.1466-8238.2010.00563.x, 2010.
López-Granados, F.: Weed detection for site-specific weed management: mapping and real-time approaches, Weed Res., 51, 1–11, https://doi.org/10.1111/j.1365-3180.2010.00829.x, 2011.
Lueck, E. and Ruehlmann, J.: Resistivity mapping with GEOPHILUS ELECTRICUS – Information about lateral and vertical soil heterogeneity, Geoderma, 199, 2–11, https://doi.org/10.1016/j.geoderma.2012.11.009, 2013.
McNeill, J. D.: Electromagnetic Terrain Conductivity Measurement at Low Induction Numbers, Geonics Ltd., Mississauga, ON, Canada, https://geonics.com/pdfs/technicalnotes/tn6.pdf (last access: 28 January 2025), 1980.
Meyer, S., Kling, C., Vogel, S., Schröter, I., Nagel, A., Kramer, E., Gebbers, R., Philipp, G., Lück, K., Gerlach, F., Scheibe, D., and Ruehlmann, J.: Creating soil texture maps for precision liming using electrical resistivity and gamma ray mapping, in: Precision Agriculture '19, Wageningen Academic Publishers, The Netherlands, https://doi.org/10.3920/978-90-8686-888-9_67, 539–546, 2019.
Mohammed, I., Marshall, M., de Bie, K., Estes, L., and Nelson, A.: A blended census and multiscale remote sensing approach to probabilistic cropland mapping in complex landscapes, ISPRS J. Photogramm., 161, 233–245, https://doi.org/10.1016/j.isprsjprs.2020.01.024, 2020.
Moshou, D., Bravo, C., Wahlen, S., West, J., McCartney, A., De Baerdemaeker, J., and Ramon, H.: Simultaneous identification of plant stresses and diseases in arable crops using proximal optical sensing and self-organising maps, Precis. Agric., 7, 149–164, https://doi.org/10.1007/s11119-006-9002-0, 2006.
O'Leary, D., Brown, C., Healy, M. G., Regan, S., and Daly, E.: Observations of intra-peatland variability using multiple spatially coincident remotely sensed data sources and machine learning, Geoderma, 430, 116348, https://doi.org/10.1016/j.geoderma.2023.116348, 2023.
O'Leary, D., Brogi, C., Brown, C., Tuohy, P., and Daly, E.: Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering, Front. Soil Sci., 4, 1–13, https://doi.org/10.3389/fsoil.2024.1346028, 2024.
Öttl, L. K., Wilken, F., Auerswald, K., Sommer, M., Wehrhan, M., and Fiener, P.: Tillage erosion as an important driver of in-field biomass patterns in an intensively used hummocky landscape, Land. Degrad. Dev., 32, 3077–3091, https://doi.org/10.1002/ldr.3968, 2021.
patchCROP: patchCROP Landscape Experiment, https://comm.zalf.de/sites/patchcrop/SitePages/Homepage.aspx (last access: 25 June 2025).
Patro, S. G. K. and sahu, K. K.: Normalization: A Preprocessing Stage, Iarjset, 20–22, https://doi.org/10.17148/iarjset.2015.2305, 2015.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, https://doi.org/10.48550/arXiv.1201.0490, 2011.
Pedrera-Parrilla, A., Brevik, E. C., Giráldez, J. V., and Vanderlinden, K.: Temporal stability of electrical conductivity in a sandy soil, Int. Agrophys., 30, 349–357, https://doi.org/10.1515/intag-2016-0005, 2016.
Pradipta, A., Soupios, P., Kourgialas, N., Doula, M., Dokou, Z., Makkawi, M., Alfarhan, M., Tawabini, B., and Kirmizakis, P.: Precision Agriculture – Part 1: Soil Applications, Water, 14, 1158, https://doi.org/10.3390/w14071158, 2022.
Robinet, J. and von Hebel, C., Govers, G., van der Kruk, J., Minella, J. P. G., Schlesner, A., Ameijeiras-Mariño, Y., and Vanderborght, J.: Spatial variability of soil water content and soil electrical conductivity across scales derived from Electromagnetic Induction and Time Domain Reflectometry, Geoderma, 314, 160–174, https://doi.org/10.1016/j.geoderma.2017.10.045, 2018.
Romero-Ruiz, A., O'Leary, D., Daly, E., Tuohy, P., Milne, A., Coleman, K., and Whitmore, A. P.: An agrogeophysical modelling framework for the detection of soil compaction spatial variability due to grazing using field-scale electromagnetic induction data, Soil Use Manage., 40, https://doi.org/10.1111/sum.13039, 2024.
Rudolph, S., van der Kruk, J., von Hebel, C., Ali, M., Herbst, M., Montzka, C., Pätzold, S., Robinson, D. A., Vereecken, H., and Weihermüller, L.: Linking satellite derived LAI patterns with subsoil heterogeneity using large-scale ground-based electromagnetic induction measurements, Geoderma, 241–242, 262–271, https://doi.org/10.1016/j.geoderma.2014.11.015, 2015.
Saifuzzaman, M., Adamchuk, V., Buelvas, R., Biswas, A., Prasher, S., Rabe, N., Aspinall, D., and Ji, W.: Clustering tools for integration of satellite remote sensing imagery and proximal soil sensing data, Remote Sens.-Basel, 11, 1–17, https://doi.org/10.3390/rs11091036, 2019.
Saputra, D. M., Saputra, D., and Oswari, L. D.: Effect of Distance Metrics in Determining K-Value in K-Means Clustering Using Elbow and Silhouette Method, Atlantis Press, 172, 341–346, https://doi.org/10.2991/aisr.k.200424.051, 2020.
Schmäck, J., Weihermüller, L., Klotzsche, A., von Hebel, C., Pätzold, S., Welp, G., and Vereecken, H.: Large-scale detection and quantification of harmful soil compaction in a post-mining landscape using multi-configuration electromagnetic induction, Soil Use Manage., 38, 212–228, https://doi.org/10.1111/sum.12763, 2022.
Schubert, E.: Stop using the elbow criterion for k-means and how to choose the number of clusters instead, ACM SIGKDD Explor. Newsl., 25, 36–42, https://doi.org/10.1145/3606274.3606278, 2023.
Scudiero, E., Teatini, P., Manoli, G., Braga, F., Skaggs, T. H., and Morari, F.: Workflow to establish time-specific zones in precision agriculture by spatiotemporal integration of plant and soil sensing data, Agronomy, 8, 1–21, https://doi.org/10.3390/agronomy8110253, 2018.
Simpson, D., Lehouck, A., Verdonck, L., Vermeersch, H., Van Meirvenne, M., Bourgeois, J., Thoen, E., and Docter, R.: Comparison between electromagnetic induction and fluxgate gradiometer measurements on the buried remains of a 17th century castle, J. Appl. Geophys., 68, 294–300, https://doi.org/10.1016/j.jappgeo.2009.03.006, 2009.
Sishodia, R. P., Ray, R. L., and Singh, S. K.: Applications of remote sensing in precision agriculture: A review, Remote Sens.-Basel, 12, 1–31, https://doi.org/10.3390/rs12193136, 2020.
Skakun, S., Kalecinski, N. I., Brown, M. G. L., Johnson, D. M., Vermote, E. F., Roger, J. C., and Franch, B.: Assessing within-field corn and soybean yield variability from worldview-3, planet, sentinel-2, and landsat 8 satellite imagery, Remote Sens.-Basel, 13, 1–18, https://doi.org/10.3390/rs13050872, 2021.
Stamford, J. D., Vialet-Chabrand, S., Cameron, I., and Lawson, T.: Development of an accurate low cost NDVI imaging system for assessing plant health, Plant Methods, 19, 1–19, https://doi.org/10.1186/s13007-023-00981-8, 2023.
Tagarakis, A., Liakos, V., Fountas, S., Koundouras, S., and Gemtos, T. A.: Management zones delineation using fuzzy clustering techniques in grapevines, Precis. Agric., 14, 18–39, https://doi.org/10.1007/s11119-012-9275-4, 2013.
Taşdemir, K., Milenov, P., and Tapsall, B.: A hybrid method combining SOM-based clustering and object-based analysis for identifying land in good agricultural condition, Comput. Electron. Agr., 83, 92–101, https://doi.org/10.1016/j.compag.2012.01.017, 2012.
Trivedi, M. B., Marshall, M., Estes, L., de Bie, C. A. J. M., Chang, L., and Nelson, A.: Cropland Mapping in Tropical Smallholder Systems with Seasonally Stratified Sentinel-1 and Sentinel-2 Spectral and Textural Features, Remote Sens.-Basel, 15, https://doi.org/10.3390/rs15123014, 2023.
UN: Do you know all 17 SDGs?, https://sdgs.un.org/goals (last access: 20 October 2024), 2021.
Usama, M., Qadir, J., Raza, A., Arif, H., Yau, K. L. A., Elkhatib, Y., Hussain, A., and Al-Fuqaha, A.: Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges, IEEE Access, 7, 65579–65615, https://doi.org/10.1109/ACCESS.2019.2916648, 2019.
Valentine, A. and Kalnins, L.: An introduction to learning algorithms and potential applications in geomorphometry and Earth surface dynamics, Earth Surf. Dynam., 4, 445–460, https://doi.org/10.5194/esurf-4-445-2016, 2016.
Vogel, S., Gebbers, R., Oertel, M., and Kramer, E.: Evaluating soil-borne causes of biomass variability in Grassland by remote and proximal sensing, Sensors (Switzerland), 19, 1–16, https://doi.org/10.3390/s19204593, 2019.
von Hebel, C., Rudolph, S., Mester, A., Huisman, J. A., Kumbhar, P., Vereecken, H., and van der Kruk, J.: Three-dimensional imaging of subsurface structural patterns using quantitative large-scale multiconfiguration electromagnetic induction data, Water Resour. Res., 50, 2732–2748, https://doi.org/10.1002/2013WR014864, 2014.
von Hebel, C., Matveeva, M., Verweij, E., Rademske, P., Kaufmann, M. S., Brogi, C., Vereecken, H., Rascher, U., and van der Kruk, J.: Understanding Soil and Plant Interaction by Combining Ground-Based Quantitative Electromagnetic Induction and Airborne Hyperspectral Data, Geophys. Res. Lett., 45, 7571–7579, https://doi.org/10.1029/2018GL078658, 2018.
von Hebel, C., Reynaert, S., Pauly, K., Janssens, P., Piccard, I., Vanderborght, J., van der Kruk, J., Vereecken, H., and Garré, S.: Toward high-resolution agronomic soil information and management zones delineated by ground-based electromagnetic induction and aerial drone data, Vadose Zone J., 20, 1–18, https://doi.org/10.1002/vzj2.20099, 2021.
Wang, F., Yang, S., Wei, Y., Shi, Q., and Ding, J.: Characterizing soil salinity at multiple depth using electromagnetic induction and remote sensing data with random forests: A case study in Tarim River Basin of southern Xinjiang, China, Sci. Total Environ., 754, 142030, https://doi.org/10.1016/j.scitotenv.2020.142030, 2021.
Wang, L., Duan, Y., Zhang, L., Rehman, T. U., Ma, D., and Jin, J.: Precise estimation of NDVI with a simple NIR sensitive RGB camera and machine learning methods for corn plants, Sensors (Switzerland), 20, 1–15, https://doi.org/10.3390/s20113208, 2020.
Ward, S. H. and Hohmann, G. W.: 4. Electromagnetic Theory for Geophysical Applications, in: Electromagnetic Methods in Applied Geophysics, Society of Exploration Geophysicists, 130–311, https://doi.org/10.1190/1.9781560802631.ch4, 1988.
Weiss, M., Jacob, F., and Duveiller, G.: Remote sensing for agricultural applications: A meta-review, Remote Sens. Environ., 236, 111402, https://doi.org/10.1016/j.rse.2019.111402, 2020.
Wilhelm, W. W., Ruwe, K., and Schlemmer, M. R.: Comparison of three leaf area index meters in a corn canopy, Crop Sci., 40, 1179–1183, https://doi.org/10.2135/cropsci2000.4041179x, 2000.
Xue, J. and Su, B.: Significant remote sensing vegetation indices: A review of developments and applications, J. Sensors, 2017, https://doi.org/10.1155/2017/1353691, 2017.
Ylagan, S., Brye, K. R., Ashworth, A. J., Owens, P. R., Smith, H., and Poncet, A. M.: Using Apparent Electrical Conductivity to Delineate Field Variation in an Agroforestry System in the Ozark Highlands, Remote Sens.-Basel, 14, 1–25, https://doi.org/10.3390/rs14225777, 2022.
Zhang, Y. and Wang, Y.: Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects, Food Chem. X, 19, 100860, https://doi.org/10.1016/j.fochx.2023.100860, 2023.
Zhu, Q., Lin, H., and Doolittle, J.: Repeated Electromagnetic Induction Surveys for Determining Subsurface Hydrologic Dynamics in an Agricultural Landscape, Soil Sci. Soc. Am. J., 74, 1750–1762, https://doi.org/10.2136/sssaj2010.0055, 2010.
Short summary
Farmers need precise information about their fields to use water, fertilizers, and other resources efficiently. This study combines underground soil data and satellite images to create detailed field maps using advanced machine learning. By testing different ways of processing data, we ensured a balanced and accurate approach. The results help farmers manage their land more effectively, leading to better harvests and more sustainable farming practices.
Farmers need precise information about their fields to use water, fertilizers, and other...
Special issue