Articles | Volume 23, issue 17
https://doi.org/10.5194/bg-23-6179-2026
© 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/bg-23-6179-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Sep 2026
Research article |
| 07 Sep 2026
| 07 Sep 2026
Wheat biomass estimation across crop development using UAV LiDAR structure–intensity fusion alongside multispectral and thermal data
Jordan Steven Bates
Earth Observation and Ecosystem Modeling (EOSystM) Laboratory, SPHERES Research Unit, University of Liege, Liege, Belgium
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
Rajina Bajracharya
Institute of Rural Studies, Thünen Institute, 38116 Braunschweig, Germany
Harry Vereecken
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
François Jonard
Earth Observation and Ecosystem Modeling (EOSystM) Laboratory, SPHERES Research Unit, University of Liege, Liege, Belgium
Related authors
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.
Matthias Cuntz, Stephan Thober, Anne Verhoef, Yijian Zeng, Aaron Boone, Agnès Ducharne, Sujan Koirala, David M. Lawrence, Philipp de Vrese, Carsten Montzka, Sonia I. Seneviratne, Harry Vereecken, Dani Or, Salma Tafasca, Naoki Mizukami, Rich Ellis, Patrick C. McGuire, and Lukas Gudmundsson
EGUsphere, https://doi.org/10.5194/egusphere-2026-4223, https://doi.org/10.5194/egusphere-2026-4223, 2026
Preprint archived
Short summary
Short summary
Land surface models (LSMs) are essential tools to analyze future climate change. Here we pushed state-of-the-art LSMs out of their comfort zone revealing hidden connections between processes encoded into the models. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of different water fluxes. We identified surface runoff and soil evaporation as the two main reasons behind model differences, which should be improved by using more mechanistic descriptions.
Francois 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
Biogeosciences, 23, 2261–2276, https://doi.org/10.5194/bg-23-2261-2026, https://doi.org/10.5194/bg-23-2261-2026, 2026
Short summary
Short summary
Enhanced weathering can significantly slow climate change by capturing CO2 and converting it into stable bicarbonate. In study, this method removed up to 1.5 tons of carbon per hectare in a single growing season. We had however very little evidence of bicarbonate formation. Our findings show that enhanced weathering facilitated instead carbon accrual via not only carbonate precipitation but also enhanced biogeochemical activities promoting additional carbon storage.
Youri Rothfuss, Samuel Le Gall, Nicolas Brüggemann, Sharmin Jahan, Mathieu Javaux, Julian Klaus, Harry Vereecken, and Dagmar van Dusschoten
EGUsphere, https://doi.org/10.5194/egusphere-2026-1518, https://doi.org/10.5194/egusphere-2026-1518, 2026
Short summary
Short summary
How plants cope with water stress is relevant when studying plant water sources in soil. We associated two techniques for measuring the content in water stable isotopes in the stem of sunflower plants and for locating where they take up water in a non-destructive manner. We highlight the role of stem water as a source of water to transpiration flux.
Wenhong Wang, Shiao Feng, Yonggen Zhang, Zhongwang Wei, Jianzhi Dong, Lutz Weihermüller, Cong-Qiang Liu, and Harry Vereecken
Earth Syst. Sci. Data, 18, 1061–1088, https://doi.org/10.5194/essd-18-1061-2026, https://doi.org/10.5194/essd-18-1061-2026, 2026
Short summary
Short summary
Current soil moisture data often suffers from gaps or errors. We combined the long-term coverage of ERA5-Land with the high accuracy of SMAP (Soil Moisture Active Passive) satellites to create a corrected global moisture dataset spanning 1950–2025. Validated against 3.8 million ground measurements, our product reduces errors by ~ 25 % in the modern period (2015–2020) and maintains ~ 20 % improvement historically (1960–2015). This reliable, daily 75-year record is essential for monitoring long-term climate trends and droughts.
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.
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.
Yanfei Li, Maud Henrion, Angus Moore, Sébastien Lambot, Sophie Opfergelt, Veerle Vanacker, François Jonard, and Kristof Van Oost
Biogeosciences, 22, 6369–6392, https://doi.org/10.5194/bg-22-6369-2025, https://doi.org/10.5194/bg-22-6369-2025, 2025
Short summary
Short summary
Combining Unmanned Aerial Vehicle (UAV) remote sensing with in-situ monitoring provides high spatial-temporal insights into CO2 fluxes from temperate peatlands. Dynamic factors (soil temperature and moisture) are the primary drivers contributing to 29 % of the spatial and 43 % of the seasonal variation. UAVs are effective tools for mapping daily soil respiration. CO2 fluxes from hot spots & moments contribute 20 % and 30 % of total CO2 fluxes, despite representing only 10 % of the area and time.
Maud Henrion, Sophie Opfergelt, Maëlle Villani, Philippe Roux, Djim Verleene, Eléonore du Bois d'Aische, Maxime Thomas, Gilles Denis, Edward A. G. Schuur, François Jonard, Veerle Vanacker, Kristof Van Oost, and Sébastien Lambot
EGUsphere, https://doi.org/10.5194/egusphere-2025-4667, https://doi.org/10.5194/egusphere-2025-4667, 2025
Preprint archived
Short summary
Short summary
Taliks play an important role in permafrost degradation. This study used Ground-penetrating radar to map taliks in Alaska, focusing on water tracks. The method successfully detected taliks and determined their upper depth. These were more frequent, shallower, and thicker under water tracks. This study showed that water tracks are hotspots for talik formation, with major implications for winter water flow in permafrost landscapes.
Thomas Dethinne, Nicolas Ghilain, Christoph Kittel, Benjamin Lecart, Xavier Fettweis, and François Jonard
EGUsphere, https://doi.org/10.5194/egusphere-2025-3907, https://doi.org/10.5194/egusphere-2025-3907, 2025
Short summary
Short summary
This study replace standard vegetation input of a regional climate model with a satellite-based vegetation dataset to assess how vegetation influences climate during extreme events and to test the sensitivity of the model. The results show a non-linear sensitivity to vegetation, and using an observation-based vegetation input allows for a better representation of the extreme events, highlight the need for an advanced representation of vegetation in climate model to improve climate predictions.
Salar Saeed Dogar, Cosimo Brogi, Dave O'Leary, Ixchel M. Hernández-Ochoa, Marco Donat, Harry Vereecken, and Johan Alexander Huisman
SOIL, 11, 655–679, https://doi.org/10.5194/soil-11-655-2025, https://doi.org/10.5194/soil-11-655-2025, 2025
Short summary
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.
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.
Maxime Thomas, Thomas Moenaert, Julien Radoux, Baptiste Delhez, Eléonore du Bois d'Aische, Maëlle Villani, Catherine Hirst, Erik Lundin, François Jonard, Sébastien Lambot, Kristof Van Oost, Veerle Vanacker, Matthias B. Siewert, Carl-Magnus Mörth, Michael W. Palace, Ruth K. Varner, Franklin B. Sullivan, Christina Herrick, and Sophie Opfergelt
EGUsphere, https://doi.org/10.5194/egusphere-2025-3788, https://doi.org/10.5194/egusphere-2025-3788, 2025
Short summary
Short summary
This study examines the rate of permafrost degradation, in the form of the transition from intact well-drained palsa to fully thawed and inundated fen at the Stordalen mire, Abisko, Sweden. Across the 14 hectares of the palsa mire, we demonstrate a 5-fold acceleration of the degradation in 2019–2021 compared to previous periods (1970–2014) which might lead to a pool of 12 metric tons of organic carbon exposed annually for the topsoil (23 cm depth), and an increase of ~1.3%/year of GHG emissions.
Anke Fluhrer, Martin J. Baur, María Piles, Bagher Bayat, Mehdi Rahmati, David Chaparro, Clémence Dubois, Florian M. Hellwig, Carsten Montzka, Angelika Kübert, Marlin M. Mueller, Isabel Augscheller, Francois Jonard, Konstantin Schellenberg, and Thomas Jagdhuber
Biogeosciences, 22, 3721–3746, https://doi.org/10.5194/bg-22-3721-2025, https://doi.org/10.5194/bg-22-3721-2025, 2025
Short summary
Short summary
This study compares established evapotranspiration products in central Europe and evaluates their multi-seasonal performance during wet and drought phases in 2017–2020 together with important soil–plant–atmosphere drivers. Results show that SEVIRI, ERA5-land, and GLEAM perform best compared to ICOS (Integrated Carbon Observation System) measurements. During moisture-limited drought years, ET (evapotranspiration) decreases due to decreasing soil moisture and increasing vapor pressure deficit, while in other years ET is mainly controlled by VPD (vapor pressure deficit).
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.
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.
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.
François Jonard, Andrew F. Feldman, Daniel J. Short Gianotti, and Dara Entekhabi
Biogeosciences, 19, 5575–5590, https://doi.org/10.5194/bg-19-5575-2022, https://doi.org/10.5194/bg-19-5575-2022, 2022
Short summary
Short summary
We investigate the spatial and temporal patterns of light and water limitation in plant function at the ecosystem scale. Using satellite observations, we characterize the nonlinear relationships between sun-induced chlorophyll fluorescence (SIF) and water and light availability. This study highlights that soil moisture limitations on SIF are found primarily in drier environments, while light limitations are found in intermediately wet regions.
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.
Cited articles
Abu Jabed, Md. A. and Murad, M. A. A.: Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability, Heliyon, 10, e40836, https://doi.org/10.1016/j.heliyon.2024.e40836, 2024.
Bates, J., Jonard, F., Bajracharya, R., Vereecken, H., and Montzka, C.: Machine Learning with UAS LiDAR for Winter Wheat Biomass Estimations, AGILE GIScience Ser., 3, 23, https://doi.org/10.5194/agile-giss-3-23-2022, 2022.
Bates, J. S., Montzka, C., Schmidt, M., and Jonard, F.: Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR, Remote Sens., 13, 710, https://doi.org/10.3390/rs13040710, 2021.
Bazrafkan, A., Delavarpour, N., Oduor, P. G., Bandillo, N., and Flores, P.: An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass, Remote Sens., 15, 3543, https://doi.org/10.3390/rs15143543, 2023.
Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., and Bareth, G.: Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging, Remote Sens., 6, 10395–10412, https://doi.org/10.3390/rs61110395, 2014.
Berni, J. A. J., Zarco-Tejada, P. J., Suarez, L., and Fereres, E.: Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle, IEEE T. Geosci. Remote, 47, 722–738, https://doi.org/10.1109/TGRS.2008.2010457, 2009.
Biswal, S., Pathak, N., Chatterjee, C., and Mailapalli, D. R.: Estimation of aboveground biomass from spectral and textural characteristics of paddy crop using UAV-multispectral images and machine learning techniques, Geocarto International, 39, 2364725, https://doi.org/10.1080/10106049.2024.2364725, 2024.
Cao, L., Liu, H., Fu, X., Zhang, Z., Shen, X., and Ruan, H.: Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests, Forests, 10, 145, https://doi.org/10.3390/f10020145, 2019.
Caturegli, L., Corniglia, M., Gaetani, M., Grossi, N., Magni, S., Migliazzi, M., Angelini, L., Mazzoncini, M., Silvestri, N., Fontanelli, M., Raffaelli, M., Peruzzi, A., and Volterrani, M.: Unmanned Aerial Vehicle to Estimate Nitrogen Status of Turfgrasses, PLoS One, 11, e0158268, https://doi.org/10.1371/journal.pone.0158268, 2016.
Dawidowicz, J. and Buczyński, R.: Comparison of the Effectiveness of Artificial Neural Networks and Elastic Net Regression in Surface Runoff Modeling, Water, 17, 405, https://doi.org/10.3390/w17030405, 2025.
Dreier, A., Lopez, G., Bajracharya, R., Kuhlmann, H., and Klingbeil, L.: Structural wheat trait estimation using UAV-based laser scanning data: Analysis of critical aspects and recommendations based on a case study, Precision Agriculture, 26, 18, https://doi.org/10.1007/s11119-024-10202-4, 2024.
Eitel, J. U. H., Magney, T. S., Vierling, L. A., Brown, T. T., and Huggins, D. R.: LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status, Field Crop. Res., 159, 21–32, https://doi.org/10.1016/j.fcr.2014.01.008, 2014.
Gitelson, A. A., Viña, A., Arkebauer, T. J., Rundquist, D. C., Keydan, G., and Leavitt, B.: Remote estimation of leaf area index and green leaf biomass in maize canopies, Geophys. Res. Lett., 30, 1248, https://doi.org/10.1029/2002GL016450, 2003.
Han, L., Yang, G., Dai, H., Xu, B., Yang, H., Feng, H., Li, Z., and Yang, X.: Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data, Plant Methods, 15, 10, https://doi.org/10.1186/s13007-019-0394-z, 2019.
Heiskanen, J., Korhonen, L., Hietanen, J., and Pellikka, P. K. E.: Use of airborne lidar for estimating canopy gap fraction and leaf area index of tropical montane forests, Int. J. Remote Sens., 36, 2569–2583, https://doi.org/10.1080/01431161.2015.1041177, 2015.
Huang, W., Li, W., Xu, J., Ma, X., Li, C., and Liu, C.: Hyperspectral Monitoring Driven by Machine Learning Methods for Grassland Above-Ground Biomass, Remote Sens., 14, 2086, https://doi.org/10.3390/rs14092086, 2022.
Hütt, C., Bolten, A., Hüging, H., and Bareth, G.: UAV LiDAR Metrics for Monitoring Crop Height, Biomass and Nitrogen Uptake: A Case Study on a Winter Wheat Field Trial, PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 91, 65–76, https://doi.org/10.1007/s41064-022-00228-6, 2022.
Jimenez-Berni, J. A., Deery, D. M., Rozas-Larraondo, P., Condon, A. G., Rebetzke, G. J., James, R. A., Bovill, W. D., Furbank, R. T., and Sirault, X. R. R.: High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR, Front. Plant Sci., 9, 237, https://doi.org/10.3389/fpls.2018.00237, 2018.
Johansen, K., Morton, M. J. L., Malbeteau, Y., Aragon, B., Al-Mashharawi, S., Ziliani, M. G., Angel, Y., Fiene, G., Negrão, S., Mousa, M. A. A., Tester, M. A., and McCabe, M. F.: Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest, Fr. Art. Int., 3, 28, https://doi.org/10.3389/frai.2020.00028, 2020.
Katimbo, A., Rudnick, D. R., Dejonge, K. C., Lo, T. H., Qiao, X., Franz, T. E., Nakabuye, H. N., and Duan, J.: Crop water stress index computation approaches and their sensitivity to soil water dynamics, Agr. Water Manage., 266, 107575, https://doi.org/10.1016/j.agwat.2022.107575, 2022.
Kidson, M., Nduku, L., Munghemezulu, C., Masiza, W., Nciizah, A., Adeleke, R., Rama, H., and Roopnarain, A.: Applying Artificial Neural Networks and Multivariable Linear Regression from Unmanned Aerial Vehicle Datasets to Estimate Durum and Bread Wheat Yield under Low Nitrogen Application Rates, Preprints [preprint], 2025021307, https://doi.org/10.20944/preprints202502.1307.v1, 2025.
Kim, S., McGaughey, R. J., Andersen, H.-E., and Schreuder, G.: Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data, Remote Sens. Environ., 113, 1575–1586, https://doi.org/10.1016/j.rse.2009.03.017, 2009.
Li, W., Niu, Z., Chen, H., Li, D., Wu, M., and Zhao, W.: Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system, Ecol. Indic., 67, 637–648, https://doi.org/10.1016/j.ecolind.2016.03.036, 2016.
Li, Y., Li, C., Cheng, Q., Duan, F., Zhai, W., Li, Z., Mao, B., Ding, F., Kuang, X., and Chen, Z.: Estimating Maize Crop Height and Aboveground Biomass Using Multi-Source Unmanned Aerial Vehicle Remote Sensing and Optuna-Optimized Ensemble Learning Algorithms, Remote Sens., 16, 3176, https://doi.org/10.3390/rs16173176, 2024.
Liao, J., Zhou, J., and Yang, W.: Comparing LiDAR and SfM digital surface models for three land cover types, Open Geosci., 13, 497–504, https://doi.org/10.1515/geo-2020-0257, 2021.
Lionel, B. M., Musabe, R., Gatera, O., and Twizere, C.: A comparative study of machine learning models in predicting crop yield, Discover Agriculture, 3, 151, https://doi.org/10.1007/s44279-025-00335-z, 2025.
Liu, S., Baret, F., Abichou, M., Boudon, F., Thomas, S., Zhao, K., Fournier, C., Andrieu, B., Irfan, K., Hemmerlé, M., and de Solan, B.: Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model, Agr. Forest Meteorol., 247, 12–20, https://doi.org/10.1016/j.agrformet.2017.07.007, 2017.
Lu, N., Zhou, J., Han, Z., Li, D., Cao, Q., Yao, X., Tian, Y., Zhu, Y., Cao, W., and Cheng, T.: Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system, Plant Methods, 15, 17, https://doi.org/10.1186/s13007-019-0402-3, 2019.
Ludovisi, R., Tauro, F., Salvati, R., Khoury, S., Mugnozza Scarascia, G., and Harfouche, A.: UAV-Based Thermal Imaging for High-Throughput Field Phenotyping of Black Poplar Response to Drought, Front. Plant Sci., 8, 1681, https://doi.org/10.3389/fpls.2017.01681, 2017.
Luo, S., Chen, J. M., Wang, C., Gonsamo, A., Xi, X., Lin, Y., Qian, M., Peng, D., Nie, S., and Qin, H.: Comparative Performances of Airborne LiDAR Height and Intensity Data for Leaf Area Index Estimation, IEEE J. Sel. Top. Appl. Earth Obs., 11, 300–310, https://doi.org/10.1109/JSTARS.2017.2765890, 2018.
Madec, S., Baret, F., de Solan, B., Thomas, S., Dutartre, D., Jézéquel, S., Hemmerlé, M., Colombeau, G., and Comar, A.: High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates, Front. Plant Sci., 8, 2002, https://doi.org/10.3389/fpls.2017.02002, 2017.
Maimaitijiang, M., Ghulam, A., Sidike, P., Hartling, S., Maimaitiyiming, M., Peterson, K., Shavers, E., Fishman, J., Peterson, J., Kadam, S., Burken, J., and Fritschi, F.: Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine, ISPRS J. Photogramm., 134, 43–58, https://doi.org/10.1016/j.isprsjprs.2017.10.011, 2017.
Mesas-Carrascosa, F. J., Castillejo-González, I. L., de la Orden, M. S., and Porras, A. G.-F.: Combining LiDAR intensity with aerial camera data to discriminate agricultural land uses, Comput. Electron. Agr., 84, 36–46, https://doi.org/10.1016/j.compag.2012.02.020, 2012.
Montzka, C., Donat, M., Raj, R., Welter, P., and Bates, J. S.: Sensitivity of LiDAR Parameters to Aboveground Biomass in Winter Spelt, Drones, 7, 121, https://doi.org/10.3390/drones7020121, 2023.
Morgan, G. R., Stevenson, L., Wang, C., and Avtar, R.: UAS Remote Sensing for Coastal Wetland Vegetation Biomass Estimation: A Destructive vs. Non-Destructive Sampling Experiment, Remote Sens., 17, 2335, https://doi.org/10.3390/rs17142335, 2025.
Neuville, R., Bates, J. S., and Jonard, F.: Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning, Remote Sens., 13, 352, https://doi.org/10.3390/rs13030352, 2021.
Pan, L., Liu, L., Condon, A. G., Estavillo, G. M., Coe, R. A., Bull, G., Stone, E. A., Petersson, L., and Rolland, V.: Biomass Prediction with 3D Point Clouds from LiDAR, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 1716–1726, https://doi.org/10.1109/WACV51458.2022.00178, 2022.
Richardson, J. J., Moskal, L. M., and Kim, S.-H.: Modeling approaches to estimate effective leaf area index from aerial discrete-return LIDAR, Agr. Forest Meteorol., 149, 1152–1160, https://doi.org/10.1016/j.agrformet.2009.02.007, 2009.
Ruwanpathirana, P. P., Sakai, K., Jayasinghe, G. Y., Nakandakari, T., Yuge, K., Wijekoon, W. M. C. J., Priyankara, A. C. P., Samaraweera, M. D. S., and Madushanka, P. L. A.: Evaluation of Sugarcane Crop Growth Monitoring Using Vegetation Indices Derived from RGB-Based UAV Images and Machine Learning Models, Agronomy, 14, 2059, https://doi.org/10.3390/agronomy14092059, 2024.
Sabol, J., Patočka, Z., and Mikita, T.: Usage of Lidar Data for Leaf Area Index Estimation, GeoScience Engineering, 60, 10-18, https://doi.org/10.2478/gse-2014-0013, 2014.
Sasaki, T., Imanishi, J., Ioki, K., Song, Y., and Morimoto, Y.: Estimation of leaf area index and gap fraction in two broad-leaved forests by using small-footprint airborne LiDAR, Landsc. Ecol. Eng., 12, 117–127, https://doi.org/10.1007/s11355-013-0222-y, 2016.
Scaioni, M., Höfle, B., Baungarten Kersting, A. P., Barazzetti, L., Previtali, M., and Wujanz, D.: Methods from Information Extraction from LiDAR Intensity Data and Multispectral LiDAR Technology, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII–3, 1503-1510, https://doi.org/10.5194/isprs-archives-XLII-3-1503-2018, 2018.
Sharma, P., Leigh, L., Chang, J., Maimaitijiang, M., and Caffé, M.: Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning, Sensors, 22, 601, https://doi.org/10.3390/s22020601, 2022.
Smigaj, M., Agarwal, A., Bartholomeus, H., Decuyper, M., Elsherif, A., de Jonge, A., and Kooistra, L.: Thermal Infrared Remote Sensing of Stress Responses in Forest Environments: A Review of Developments, Challenges, and Opportunities, Current Forestry Reports, 10, 56–76, https://doi.org/10.1007/s40725-023-00207-z, 2024.
Smith, D. T. L., Chen, Q., Massey-Reed, S. R., Potgieter, A. B., and Chapman, S. C.: Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location, Plant Methods, 20, 129, https://doi.org/10.1186/s13007-024-01236-w, 2024.
Solberg, S., Næsset, E., Hanssen, K. H., and Christiansen, E.: Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning, Remote Sens. Environ., 102, 364–376, https://doi.org/10.1016/j.rse.2006.03.001, 2006.
Takhtkeshha, N., Mandlburger, G., Remondino, F., and Hyyppä, J.: Multispectral Light Detection and Ranging Technology and Applications: A Review, Sensors, 24, 1669, https://doi.org/10.3390/s24051669, 2024.
Tian, W., Tang, L., Chen, Y., Li, Z., Zhu, J., Jiang, C., Hu, P., He, W., Wu, H., Pan, M., Lu, J., and Hyyppä, J.: Analysis and Radiometric Calibration for Backscatter Intensity of Hyperspectral LiDAR Caused by Incident Angle Effect, Sensors, 21, 2960, https://doi.org/10.3390/s21092960, 2021.
Tilly, N., Aasen, H., and Bareth, G.: Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass, Remote Sens., 7, 11449–11480, https://doi.org/10.3390/rs70911449, 2015.
Vahidi, M., Shafian, S., Thomas, S., and Maguire, R.: Pasture Biomass Estimation Using Ultra-High-Resolution RGB UAVs Images and Deep Learning, Remote Sens., 15, 5714, https://doi.org/10.3390/rs15245714, 2023.
Van Klompenburg, T., Kassahun, A., and Catal, C.: Crop yield prediction using machine learning: A systematic literature review, Comput. Electron. Agr., 177, 105709, https://doi.org/10.1016/j.compag.2020.105709, 2020.
Virtue, J., Turner, D., Williams, G., Zeliadt, S., McCabe, M., and Lucieer, A.: Thermal Sensor Calibration for Unmanned Aerial Systems Using an External Heated Shutter, Drones, 5, 119, https://doi.org/10.3390/drones5040119, 2021.
Wallace, L., Lucieer, A., Malenovský, Z., Turner, D., and Vopěnka, P.: Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds, Forests, 7, 62, https://doi.org/10.3390/f7030062, 2016.
Wang, D., Xin, X., Shao, Q., Brolly, M., Zhu, Z., and Chen, J.: Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar, Sensors, 17, 180, https://doi.org/10.3390/s17010180, 2017.
Wang, T., Liu, Y., Wang, M., Fan, Q., Tian, H., Qiao, X., and Li, Y.: Applications of UAS in Crop Biomass Monitoring: A Review, Front. Plant Sci., 12, 616689, https://doi.org/10.3389/fpls.2021.616689, 2021.
Wang, Y., Zhang, Q., Yu, F., Zhang, N., Zhang, X., Li, Y., Wang, M., and Zhang, J.: Progress in Research on Deep Learning-Based Crop Yield Prediction, Agronomy, 14, 2264, https://doi.org/10.3390/agronomy14102264, 2024.
Wu, Q., Zhong, R., Dong, P., Mo, Y., and Jin, Y.: Airborne LiDAR Intensity Correction Based on a New Method for Incidence Angle Correction for Improving Land-Cover Classification, Remote Sens., 13, 511, https://doi.org/10.3390/rs13030511, 2021.
Yang, Y., Qiu, J., Zhang, R., Huang, S., Chen, S., Wang, H., Luo, J., and Fan, Y.: Intercomparison of Three Two-Source Energy Balance Models for Partitioning Evaporation and Transpiration in Semiarid Climates, Remote Sens., 10, 1149, https://doi.org/10.3390/rs10071149, 2018.
You, H., Wang, T., Skidmore, A. K., and Xing, Y.: Quantifying the Effects of Normalisation of Airborne LiDAR Intensity on Coniferous Forest Leaf Area Index Estimations, Remote Sens., 9, 163, https://doi.org/10.3390/rs9020163, 2017.
Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., and Yan, G.: An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation, Remote Sens., 8, 501, https://doi.org/10.3390/rs8060501, 2016.
Short summary
This study compared drone-based laser, multispectral, and thermal sensors for estimating winter wheat biomass. Combining laser-derived crop structure and signal intensity improved predictions compared with crop height or multispectral data alone, while combining multispectral and thermal data with laser-derived features provided additional information during particular growth stages. The results highlight the potential of underutilized laser-derived features for crop biomass monitoring.
This study compared drone-based laser, multispectral, and thermal sensors for estimating winter...
Altmetrics
Final-revised paper
Preprint