242 results on '"Bogena, Heye"'
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2. Solute fluxes in headwater catchments with contrasting anthropogenic impact
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Płaczkowska, Eliza, Kijowska-Strugała, Małgorzata, Ketzler, Gunnar, Bogena, Heye Reemt, and Leuchner, Michael
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- 2024
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3. Repeating patterns in runoff time series: A basis for exploring hydrologic similarity of precipitation and catchment wetness conditions
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Hövel, Adriane, Stumpp, Christine, Bogena, Heye, Lücke, Andreas, Strauss, Peter, Blöschl, Günter, and Stockinger, Michael
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- 2024
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4. Global sensitivity analysis of APSIM-wheat yield predictions to model parameters and inputs
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Hao, Shirui, Ryu, Dongryeol, Western, Andrew W, Perry, Eileen, Bogena, Heye, and Franssen, Harrie Jan Hendricks
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- 2024
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5. Comment on ‘Examining the variation of soil moisture from cosmic-ray neutron probes footprint: experimental results from a COSMOS-UK site’ by Howells, O.D., Petropoulos, G.P., et al., Environ Earth Sci 82, 41 (2023)
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Scheiffele, Lena M., Schrön, Martin, Köhli, Markus, Dimitrova-Petrova, Katya, Altdorff, Daniel, Franz, Trenton, Rosolem, Rafael, Evans, Jonathan, Blake, James, Bogena, Heye, McJannet, David, Baroni, Gabriele, Desilets, Darin, and Oswald, Sascha E.
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- 2023
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6. Soil moisture observations and machine learning reveal preferential flow mechanisms in the Qilian Mountains
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Kang, Weiming, Tian, Jie, Reemt Bogena, Heye, Lai, Yao, Xue, Dongxiang, and He, Chansheng
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- 2023
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7. Soil hydrology in the Earth system
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Vereecken, Harry, Amelung, Wulf, Bauke, Sara L., Bogena, Heye, Brüggemann, Nicolas, Montzka, Carsten, Vanderborght, Jan, Bechtold, Michel, Blöschl, Günter, Carminati, Andrea, Javaux, Mathieu, Konings, Alexandra G., Kusche, Jürgen, Neuweiler, Insa, Or, Dani, Steele-Dunne, Susan, Verhoef, Anne, Young, Michael, and Zhang, Yonggen
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- 2022
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8. Multi‐Decadal Soil Moisture and Crop Yield Variability—A Case Study With the Community Land Model (CLM5).
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Boas, Theresa, Bogena, Heye, Ryu, Dongryeol, Western, Andrew, and Hendricks Franssen, Harrie‐Jan
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PLANT phenology , *CROP yields , *SOIL moisture , *FARMS , *AGRICULTURE , *ARABLE land - Abstract
While the impacts of climate change on global food security have been studied extensively, the capability of emerging tools that couple land surface processes and crop growth in reproducing inter‐annual yield variability at regional scale remains to be tested rigorously. In this study, we analyzed the effects of weather variations between years (1999–2019) on regional crop productivity for two agriculturally managed regions with contrasting climate and cropping conditions: the German state of North Rhine‐Westphalia (DE‐NRW) and the Australian state of Victoria (AUS‐VIC), using the latest version of the Community Land Model (CLM5) and the WFDE5 (WATCH Forcing Data methodology applied to ECMWF reanalysis version 5) reanalysis. Overall, the simulation results were able to reproduce the total annual crop yields of certain crops, while also capturing the differences in total yield magnitudes between the domains. However, the simulations showed limitations in correctly capturing inter‐annual differences of crop yield compared to official yield records, which resulted in relatively low correlation coefficients between 0.07 and 0.39 in AUS‐VIC and between 0.11 and 0.42 in DE‐NRW. The mean absolute deviation of simulated winter wheat yields was up to 4.6 times lower compared to state‐wide records from 1999 to 2019. Our results suggest the following limitations of CLM5: (a) limitations in simulating yield responses from plant hydraulic stress; (b) errors in simulating soil moisture contents compared to satellite‐derived data; and (c) errors in the representation of cropland in general, for example, crop parameterizations and human influences. Plain Language Summary: This study evaluates how year‐to‐year weather variations impact crop yield predictions for two regions, North Rhine‐Westphalia in Germany and Victoria in Australia changes. We use the community land model (CLM5) land surface model in combination with reanalysis weather data to investigate the model performance with respect to the representation of crop phenology, plant water stress, and soil moisture. Our results showcase the model's ability to predict total annual crop yield magnitudes for both regions, while also capturing the differences between the respective simulation domains. However, year‐to‐year changes in crop yield were lower in simulation results compared to official records, which indicated a lack of model sensitivity toward drought stress and general limitations in the representation of agricultural land. This research systematically assesses CLM5 model performance over arable land and provides useful insights into limitations of CLM5 that can help guide future empirical and technical model improvements. Key Points: Land surface models (LSMs) with integrated crop models can be used to quantify the impact of climate change on agro‐ecosystemsThe potential value of LSMs for agricultural purposes depends on their ability to adequately simulate inter‐annual variability of yieldThe representation of plant hydraulics and the soil moisture regime play key role in accurately simulating agro‐ecosystems [ABSTRACT FROM AUTHOR]
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- 2024
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9. Altered energy partitioning across terrestrial ecosystems in the European drought year 2018
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Graf, Alexander, Klosterhalfen, Anne, Arriga, Nicola, Bernhofer, Christian, Bogena, Heye, Bornet, Frédéric, Brüggemann, Nicolas, Brümmer, Christian, Buchmann, Nina, Chi, Jinshu, Chipeaux, Christophe, Cremonese, Edoardo, Cuntz, Matthias, Dušek, Jiří, El-Madany, Tarek S., Fares, Silvano, Fischer, Milan, Foltýnová, Lenka, Gharun, Mana, Ghiasi, Shiva, Gielen, Bert, Gottschalk, Pia, Grünwald, Thomas, Heinemann, Günther, Heinesch, Bernard, Heliasz, Michal, Holst, Jutta, Hörtnagl, Lukas, Ibrom, Andreas, Ingwersen, Joachim, Jurasinski, Gerald, Klatt, Janina, Knohl, Alexander, Koebsch, Franziska, Konopka, Jan, Korkiakoski, Mika, Kowalska, Natalia, Kremer, Pascal, Kruijt, Bart, Lafont, Sebastien, Léonard, Joël, De Ligne, Anne, Longdoz, Bernard, Loustau, Denis, Magliulo, Vincenzo, Mammarella, Ivan, Manca, Giovanni, Mauder, Matthias, Migliavacca, Mirco, Mölder, Meelis, Neirynck, Johan, Ney, Patrizia, Nilsson, Mats, Paul-Limoges, Eugénie, Peichl, Matthias, Pitacco, Andrea, Poyda, Arne, Rebmann, Corinna, Roland, Marilyn, Sachs, Torsten, Schmidt, Marius, Schrader, Frederik, Siebicke, Lukas, Šigut, Ladislav, Tuittila, Eeva-Stiina, Varlagin, Andrej, Vendrame, Nadia, Vincke, Caroline, Völksch, Ingo, Weber, Stephan, Wille, Christian, Wizemann, Hans-Dieter, Zeeman, Matthias, and Vereecken, Harry
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- 2020
10. Fifteen Years of Integrated Terrestrial Environmental Observatories (TERENO) in Germany: Functions, Services, and Lessons Learned.
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Zacharias, Steffen, Loescher, Henry W., Bogena, Heye, Kiese, Ralf, Schrön, Martin, Attinger, Sabine, Blume, Theresa, Borchardt, Dietrich, Borg, Erik, Bumberger, Jan, Chwala, Christian, Dietrich, Peter, Fersch, Benjamin, Frenzel, Mark, Gaillardet, Jérôme, Groh, Jannis, Hajnsek, Irena, Itzerott, Sibylle, Kunkel, Ralf, and Kunstmann, Harald
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TECHNOLOGICAL progress ,EARTH system science ,GLOBAL environmental change ,OBSERVATORIES ,ECOLOGICAL forecasting ,AGRICULTURAL innovations ,ATMOSPHERIC boundary layer ,WATER demand management - Abstract
The need to develop and provide integrated observation systems to better understand and manage global and regional environmental change is one of the major challenges facing Earth system science today. In 2008, the German Helmholtz Association took up this challenge and launched the German research infrastructure TERrestrial ENvironmental Observatories (TERENO). The aim of TERENO is the establishment and maintenance of a network of observatories as a basis for an interdisciplinary and long‐term research program to investigate the effects of global environmental change on terrestrial ecosystems and their socio‐economic consequences. State‐of‐the‐art methods from the field of environmental monitoring, geophysics, remote sensing, and modeling are used to record and analyze states and fluxes in different environmental disciplines from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere. Over the past 15 years we have collectively gained experience in operating a long‐term observing network, thereby overcoming unexpected operational and institutional challenges, exceeding expectations, and facilitating new research. Today, the TERENO network is a key pillar for environmental modeling and forecasting in Germany, an information hub for practitioners and policy stakeholders in agriculture, forestry, and water management at regional to national levels, a nucleus for international collaboration, academic training and scientific outreach, an important anchor for large‐scale experiments, and a trigger for methodological innovation and technological progress. This article describes TERENO's key services and functions, presents the main lessons learned from this 15‐year effort, and emphasizes the need to continue long‐term integrated environmental monitoring programmes in the future. Plain Language Summary: This paper discusses the importance of creating comprehensive environmental observation systems to better understand and address global and regional environmental changes. In 2008, a German research infrastructure named Terrestrial Environmental Observatories (TERENO) was established to build and maintain a network of observatories. The goal is to conduct interdisciplinary, long‐term research on the impacts of global environmental changes on terrestrial ecosystems and their socio‐economic effects. The TERENO network employs advanced methods from environmental monitoring, geophysics, remote sensing, and modeling to study various environmental aspects. Over the past 15 years, four observatories have been part of this network, contributing to valuable experience in overcoming challenges and exceeding expectations. Today, TERENO is a crucial component for environmental modeling and forecasting in Germany, serving as an information hub for practitioners and policymakers. It also fosters international collaboration, supports large‐scale experiments, and drives methodological and technological advancements. The article highlights key lessons learned from this 15‐year effort and emphasizes the importance of continuing such integrated environmental monitoring programs in the future. Key Points: Integrated observatories ensure a holistic Earth Systems perspective, offering data for current and future ecological challengesThe scientific and societal value of observatories is invaluable, but their design, construction and operation require considerable effortFor assured long‐term data collection, research infrastructure must have flexible design for adapting to changing research needs [ABSTRACT FROM AUTHOR]
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- 2024
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11. Soil Moisture Memory: State‐Of‐The‐Art and the Way Forward.
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Rahmati, Mehdi, Amelung, Wulf, Brogi, Cosimo, Dari, Jacopo, Flammini, Alessia, Bogena, Heye, Brocca, Luca, Chen, Hao, Groh, Jannis, Koster, Randal D., McColl, Kaighin A., Montzka, Carsten, Moradi, Shirin, Rahi, Arash, Sharghi S., Farnaz, and Vereecken, Harry
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DROUGHT management ,SOIL moisture ,EXTREME weather ,LAND management ,MEMORY ,WILDFIRES ,ECOLOGICAL disturbances ,ATMOSPHERE - Abstract
Soil moisture is an essential climate variable of the Earth system. Understanding its spatiotemporal dynamics is essential for predicting weather patterns and climate variability, monitoring and mitigating the effects and occurrence of droughts and floods, improving irrigation in agricultural areas, and sustainably managing water resources. Here we review in depth how soils can remember information on soil moisture anomalies over time, as embedded in the concept of soil moisture memory (SMM). We explain the mechanisms underlying SMM and explore its external and internal drivers; we also discuss the impacts of SMM on different land surface processes, focusing on soil‐plant‐atmosphere coupling. We explore the spatiotemporal variability, seasonality, locality, and depth‐dependence of SMM and provide insights into both improving its characterization in land surface models and using satellite observations to quantify it. Finally, we offer guidance for further research on SMM. Plain Language Summary: Our review paper takes an in‐depth look at soil moisture memory, which is how soil records its moisture history over time and space. Analogous to human psychology, which seeks to understand how a person's/society's memory influences his/her present and future behavior, understanding soil moisture memory encourages consideration of how such memory determines present state and might determine future behavior of soils exposed to environmental disturbances. Soil moisture memory can be affected by a variety of factors, both external (e.g., weather extremes) and internal (soil's unique properties). It affects everything from the air to the way our landscapes respond to disasters like droughts, wildfires, and floods. We also studied how this phenomenon affects the balance of water and energy in our environment, the health of our plants, and even how it communicates with the atmosphere. We show how it can change depending on where you are on the planet, the time of year, and how deep you dig into the soil. We offer scientists insights into how weather and land surface models can become more accurate by accounting for soil moisture memory. Its understanding not only helps us predict and manage our environment, but also provides opportunities for exciting scientific discoveries. Key Points: Atmospheric forcings, land use and management, and soil processes and mechanisms explain how and why soil moisture memory emerges in ecosystemsNonlocality of moisture memory, its spread across different regions, and its interaction with large‐scale climate phenomena are underexploredFurther advances in land surface models and closer integration of model simulations and observations are needed to better characterize moisture memory [ABSTRACT FROM AUTHOR]
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- 2024
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12. CO2 fluxes before and after partial deforestation of a Central European spruce forest
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Ney, Patrizia, Graf, Alexander, Bogena, Heye, Diekkrüger, Bernd, Drüe, Clemens, Esser, Odilia, Heinemann, Günther, Klosterhalfen, Anne, Pick, Katharina, Pütz, Thomas, Schmidt, Marius, Valler, Veronika, and Vereecken, Harry
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- 2019
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13. Dynamic response patterns of profile soil moisture wetting events under different land covers in the Mountainous area of the Heihe River Watershed, Northwest China
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Tian, Jie, Zhang, Baoqing, He, Chansheng, Han, Zhibo, Bogena, Heye Reemt, and Huisman, Johan Alexander
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- 2019
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14. Exploring the growth response of Norway spruce (Picea abies) along a small-scale gradient of soil water supply
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Rabbel, Inken, Neuwirth, Burkhard, Bogena, Heye, and Diekkrüger, Bernd
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- 2018
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15. Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content data.
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Strebel, Lukas, Bogena, Heye, Vereecken, Harry, Andreasen, Mie, Aranda-Barranco, Sergio, and Hendricks Franssen, Harrie-Jan
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SOIL moisture ,SOIL moisture measurement ,LEAF area index ,CLIMATIC zones ,KALMAN filtering ,EDDY flux - Abstract
Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 % to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters. [ABSTRACT FROM AUTHOR]
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- 2024
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16. Temperature-Corrected Calibration of GS3 and TEROS-12 Soil Water Content Sensors.
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Nasta, Paolo, Coccia, Francesca, Lazzaro, Ugo, Bogena, Heye R., Huisman, Johan A., Sica, Benedetto, Mazzitelli, Caterina, Vereecken, Harry, and Romano, Nunzio
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SOIL moisture ,SENSOR networks ,CALIBRATION ,LOAM soils ,COSMIC rays - Abstract
The continuous monitoring of soil water content is commonly carried out using low-frequency capacitance sensors that require a site-specific calibration to relate sensor readings to apparent dielectric bulk permittivity (K
b ) and soil water content (θ). In fine-textured soils, the conversion of Kb to θ is still challenging due to temperature effects on the bound water fraction associated with clay mineral surfaces, which is disregarded in factory calibrations. Here, a multi-point calibration approach accounts for temperature effects on two soils with medium to high clay content. A calibration strategy was developed using repacked soil samples in which the Kb -θ relationship was determined for temperature (T) steps from 10 to 40 °C. This approach was tested using the GS3 and TEROS-12 sensors (METER Group, Inc. Pullman, WA, USA; formerly Decagon Devices). Kb is influenced by T in both soils with contrasting T-Kb relationships. The measured data were fitted using a linear function θ = a K b + b with temperature-dependent coefficients a and b. The slope, a(T), and intercept, b(T), of the loam soil were different from the ones of the clay soil. The consideration of a temperature correction resulted in low RMSE values, ranging from 0.007 to 0.033 cm3 cm−3 , which were lower than the RMSE values obtained from factory calibration (0.046 to 0.11 cm3 cm−3 ). However, each experiment was replicated only twice using two different sensors. Sensor-to-sensor variability effects were thus ignored in this study and will be systematically investigated in a future study. Finally, the applicability of the proposed calibration method was tested at two experimental sites. The spatial-average θ from a network of GS3 sensors based on the new calibration fairly agreed with the independent area-wide θ from the Cosmic Ray Neutron Sensor (CRNS). This study provided a temperature-corrected calibration to increase the accuracy of commercial sensors, especially under dry conditions, at two experimental sites. [ABSTRACT FROM AUTHOR]- Published
- 2024
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17. Accounting for seasonal isotopic patterns of forest canopy intercepted precipitation in streamflow modeling
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Stockinger, Michael P., Lücke, Andreas, Vereecken, Harry, and Bogena, Heye R.
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- 2017
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18. Can a Sparse Network of Cosmic Ray Neutron Sensors Improve Soil Moisture and Evapotranspiration Estimation at the Larger Catchment Scale?
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Li, Fang, Bogena, Heye Reemt, Bayat, Bagher, Kurtz, Wolfgang, and Hendricks Franssen, Harrie‐Jan
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SOIL moisture ,COSMIC rays ,STANDARD deviations ,NEUTRONS ,EVAPOTRANSPIRATION ,KALMAN filtering - Abstract
Cosmic‐ray neutron sensors (CRNS) fill the gap between locally measured in‐situ soil moisture (SM) and remotely sensed SM by providing accurate SM estimation at the field scale. This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40% in 2016 and 16% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15% RMSE reduction of monthly ET in the wet year and 9% in the dry year. Key Points: Assimilation of soil moisture from a network of cosmic‐ray neutron sensors improves soil moisture characterization at the catchment scaleSoil moisture characterization improved more in a wet year than in a very dry yearEvapotranspiration and river discharge simulation are only slightly improved, despite better estimations of soil moisture [ABSTRACT FROM AUTHOR]
- Published
- 2024
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19. Editorial: Impact of anthropogenic disturbances on agroforestry ecosystems
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Nasta, Paolo, Adane, Zablon, Baatz, Roland, Schönbrodt-Stitt, Sarah, and Bogena, Heye Reemt
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Water Science and Technology - Published
- 2023
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20. Tracer sampling frequency influences estimates of young water fraction and streamwater transit time distribution
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Stockinger, Michael P., Bogena, Heye R., Lücke, Andreas, Diekkrüger, Bernd, Cornelissen, Thomas, and Vereecken, Harry
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- 2016
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21. Simultaneous soil moisture and properties estimation for a drip irrigated field by assimilating cosmic-ray neutron intensity
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Han, Xujun, Hendricks Franssen, Harrie-Jan, Jiménez Bello, Miguel Ángel, Rosolem, Rafael, Bogena, Heye, Alzamora, Fernando Martínez, Chanzy, André, and Vereecken, Harry
- Published
- 2016
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22. Scale dependent parameterization of soil hydraulic conductivity in 3D simulation of hydrological processes in a forested headwater catchment
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Fang, Zhufeng, Bogena, Heye, Kollet, Stefan, and Vereecken, Harry
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- 2016
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23. Inter-comparison of three distributed hydrological models with respect to seasonal variability of soil moisture patterns at a small forested catchment
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Koch, Julian, Cornelissen, Thomas, Fang, Zhufeng, Bogena, Heye, Diekkrüger, Bernd, Kollet, Stefan, and Stisen, Simon
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- 2016
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24. Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach.
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Boas, Theresa, Bogena, Heye Reemt, Ryu, Dongryeol, Vereecken, Harry, Western, Andrew, and Hendricks Franssen, Harrie-Jan
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CROP yields ,SOIL moisture ,LONG-range weather forecasting ,DOWNSCALING (Climatology) ,WEATHER forecasting - Abstract
Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha -1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50 % inter-annual differences in recorded yields and up to 17 % inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 % inter-annual differences in recorded yields and up to 5 % in simulated yields). The high- and low-yield seasons (2020 and 2018) among the 4 simulated years were clearly reproduced in the forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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25. Spatio-temporal validation of long-term 3D hydrological simulations of a forested catchment using empirical orthogonal functions and wavelet coherence analysis
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Fang, Zhufeng, Bogena, Heye, Kollet, Stefan, Koch, Julian, and Vereecken, Harry
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- 2015
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26. Effects of heterogeneous soil moisture distributions in cosmic-ray neutron sensing - the case of irrigation monitoring
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Bogena, Heye, Brogi, Cosimo, Köhli, Markus, Hendricks-Franssen, Harrie-Jan, Dombrowski, Olga, and Huisman, Johan Alexander
- Abstract
European Geoscience Union General Assembly, EGU 2023, Vienna, Austria, 24 Apr 2023 - 28 Apr 2023; doi:10.5194/egusphere-egu23-3095, Soil moisture (SM) sensors are widely used to monitor soil water dynamics and support irrigation management with the aim of achieving better yields while reducing water consumption. Unfortunately, due to the small measuring volume of point-scale sensors, their soil moisture readings are often not representative for heterogeneous agricultural fields. Therefore, in such cases, sensors with larger sensing volume are needed to address spatially variable SM. A suitable technique is the cosmic ray neutron sensor (CRNS) as it integrates SM over a large volume with a radius of ~130-210 m and a penetration depth of ~15-85 cm. The CRNS method is based on the inverse relationship between measured environmental neutron density and the presence of hydrogen pools (e.g., SM) in the instrument surroundings. However, the ability of CRNS to accurately monitor areas with complex SM heterogeneities (e.g., small irrigated fields) and the influence of detector design were not yet investigated. In this study, we used the neutron transport model URANOS to simulate the effect of SM variations on a CRNS placed in the centre of squared irrigated fields (0.5 to 8 ha dimensions). For this, SM in the irrigated field and in the surrounding was altered between 0.05 and 0.50 cm3 cm-3 (500 simulations in total). In addition, we investigated the effect of employing high-density polyethylene (HDPE) moderators with different thickness (5 to 35 mm) as well as a 25 mm HDPE moderator with an additional gadolinium oxide thermal shielding. Results showed that, in heterogeneous SM scenarios, the 2 e-folding lengths footprint (R86) can become smaller or larger than what previous studies showed in homogeneous SM distributions. In addition, a thin HDPE moderator will result in relatively smaller R86 whereas thicker moderators and the addition of a thermal shielding will result in relatively larger R86. However, we found that a relatively small footprint is not directly related to a better monitoring of SM nearby the instrument. In fact, in all the investigated field dimensions, the 25mm HDPE moderator with gadolinium shielding showed the largest values of R86 but also the largest variations of detected neutrons with changing SM. In addition, such moderator showed the highest chances of detecting irrigation events that increase SM by 0.05 or 0.10 cm3 cm-3 in the irrigated area. Generally, detection was uncertain only for SM variations of 0.05 cm3 cm-3 in fields of 0.5 ha when initial SM was 0.02 cm3 cm-3 or higher. Although the results of this study suggest the feasibility of monitoring and informing irrigation with CRNS, we found that SM variations outside the irrigated field have a considerable influence on CRNS measurements. Especially in fields of 0.5 and 1 ha dimension, it can be impossible to distinguish whether a relative change in detected neutrons is due to irrigation or to SM variations in the surroundings. These results are relevant for irrigation monitoring and the combination of neutron transport simulations and real-world installations has the potential to establish CRNS as a decision support system for irrigation management.
- Published
- 2023
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27. Potential of catchment-wide soil water content prediction using electromagnetic induction in a forest ecosystem
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Altdorff, Daniel, von Hebel, Christian, Borchard, Nils, van der Kruk, Jan, Bogena, Heye Reemt, Vereecken, Harry, and Huisman, Johan Alexander
- Published
- 2017
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28. MONITORING AND MODELING THE TERRESTRIAL SYSTEM FROM PORES TO CATCHMENTS : The Transregional Collaborative Research Center on Patterns in the Soil–Vegetation–Atmosphere System
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Simmer, Clemens, Thiele-Eich, Insa, Masbou, Matthieu, Amelung, Wulf, Bogena, Heye, Crewell, Susanne, Diekkrüger, Bernd, Ewert, Frank, Franssen, Harrie-Jan Hendricks, Huisman, Johan Alexander, Kemna, Andreas, Klitzsch, Norbert, Kollet, Stefan, Langensiepen, Matthias, Löhnert, Ulrich, Rahman, A. S. M. Mostaquimur, Rascher, Uwe, Schneider, Karl, Schween, Jan, Shao, Yaping, Shrestha, Prabhakar, Stiebler, Maik, Sulis, Mauro, Vanderborght, Jan, Vereecken, Harry, van der Kruk, Jan, Waldhoff, Guido, and Zerenner, Tanja
- Published
- 2015
29. SUPPLEMENT : MONITORING AND MODELING THE TERRESTRIAL SYSTEM FROM PORES TO CATCHMENTS The Transregional Collaborative Research Center on Patterns in the Soil—Vegetation—Atmosphere System
- Author
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Simmer, Clemens, Thiele-Eich, Insa, Masbou, Matthieu, Amelung, Wulf, Bogena, Heye, Crewell, Susanne, Diekkrüger, Bernd, Ewert, Frank, Franssen, Harrie-Jan Hendricks, Huisman, Johan Alexander, Kemna, Andreas, Klitzsch, Norbert, Kollet, Stefan, Langensiepen, Matthias, Löhnert, Ulrich, Rahman, A. S. M. Mostaquimur, Rascher, Uwe, Schneider, Karl, Schween, Jan, Shao, Yaping, Shrestha, Prabhakar, Stiebler, Maik, Sulis, Mauro, Vanderborght, Jan, Vereecken, Harry, van der Kruk, Jan, Zerenner, Tanja, and Waldhoff, Guido
- Published
- 2015
30. Significance of scale and lower boundary condition in the 3D simulation of hydrological processes and soil moisture variability in a forested headwater catchment
- Author
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Cornelissen, Thomas, Diekkrüger, Bernd, and Bogena, Heye R.
- Published
- 2014
- Full Text
- View/download PDF
31. Soil moisture retrieval from airborne L-band passive microwave using high resolution multispectral data
- Author
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Hasan, Sayeh, Montzka, Carsten, Rüdiger, Christoph, Ali, Muhammad, R. Bogena, Heye, and Vereecken, Harry
- Published
- 2014
- Full Text
- View/download PDF
32. Point-scale multi-objective calibration of the Community Land Model (version 5.0) using in situ observations of water and energy fluxes and variables.
- Author
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Denager, Tanja, Sonnenborg, Torben O., Looms, Majken C., Bogena, Heye, and Jensen, Karsten H.
- Subjects
COMMUNITIES ,LEAF area index ,SINGULAR value decomposition ,LATENT heat ,SOIL moisture ,CALIBRATION ,GROUNDWATER recharge ,SOIL texture - Abstract
This study evaluates water and energy fluxes and variables in combination with parameter optimization of version 5 of the state-of-the-art Community Land Model (CLM5) land surface model, using 6 years of hourly observations of latent heat flux, sensible heat flux, groundwater recharge, soil moisture and soil temperature from an agricultural observatory in Denmark. The results show that multi-objective calibration in combination with truncated singular value decomposition and Tikhonov regularization is a powerful method to improve the current practice of using lookup tables to define parameter values in land surface models. Using measurements of turbulent fluxes as the target variable, parameter optimization is capable of matching simulations and observations of latent heat, especially during the summer period, whereas simulated sensible heat is clearly biased. Of the 30 parameters considered, the soil texture, monthly leaf area index (LAI) in summer, stomatal conductance and root distribution have the highest influence on the local-scale simulation results. The results from this study contribute to improvements of the model characterization of water and energy fluxes. This work highlights the importance of performing parameter calibration using observations of hydrologic and energy fluxes and variables to obtain the optimal parameter values for a land surface model. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
33. Evaluation of Three Soil Moisture Profile Sensors Using Laboratory and Field Experiments.
- Author
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Nieberding, Felix, Huisman, Johan Alexander, Huebner, Christof, Schilling, Bernd, Weuthen, Ansgar, and Bogena, Heye Reemt
- Subjects
FIELD research ,DIELECTRIC measurements ,DETECTORS ,DIELECTRIC properties ,SOIL profiles - Abstract
Soil moisture profile sensors (SMPSs) have a high potential for climate-smart agriculture due to their easy handling and ability to perform simultaneous measurements at different depths. To date, an accurate and easy-to-use method for the evaluation of long SMPSs is not available. In this study, we developed laboratory and field experiments to evaluate three different SMPSs (SoilVUE10, Drill&Drop, and SMT500) in terms of measurement accuracy, sensor-to-sensor variability, and temperature stability. The laboratory experiment features a temperature-controlled lysimeter to evaluate intra-sensor variability and temperature stability of SMPSs. The field experiment features a water level-controlled sandbox and reference TDR measurements to evaluate the soil water measurement accuracy of the SMPS. In both experiments, a well-characterized fine sand was used as measurement medium to ensure homogeneous dielectric properties in the measurement domain of the sensors. The laboratory experiments with the lysimeter showed that the Drill&Drop sensor has the highest temperature sensitivity with a decrease of 0.014 m
3 m−3 per 10 °C, but at the same time showed the lowest intra- and inter-sensor variability. The field experiment with the sandbox showed that all three SMPSs have a similar performance (average RMSE ≈ 0.023 m3 m−3 ) with higher uncertainties at intermediate soil moisture contents. The presented combination of laboratory and field tests were found to be well suited to evaluate the performance of SMPSs and will be used to test additional SMPSs in the future. [ABSTRACT FROM AUTHOR]- Published
- 2023
- Full Text
- View/download PDF
34. Assessing Impacts of Land Use and Land Cover (LULC) Change on Stream Flow and Runoff in Rur Basin, Germany.
- Author
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Shukla, Saurabh, Meshesha, Tesfa Worku, Sen, Indra S., Bol, Roland, Bogena, Heye, and Wang, Junye
- Abstract
Understanding the impact of land use/land cover (LULC) change on hydrology is the key to sustainable water resource management. In this study, we used the Soil and Water Assessment Tool (SWAT) to evaluate the impact of LULC change on the runoff in the Rur basin, Germany. The SWAT model was calibrated against the observed data of stream flow and runoff at three sites (Stah, Linnich, and Monschau) between 2000 and 2010 and validated between 2011 and 2015. The performance of the hydrological model was assessed by using statistical parameters such as the coefficient of determination (R
2 ), p-value, r-value, and percentage bias (PBAIS). Our analysis reveals that the average R2 values for model calibration and validation were 0.68 and 0.67 (n = 3), respectively. The impacts of three change scenarios on stream runoff were assessed by replacing the partial forest with urban settlements, agricultural land, and grasslands compared to the 2006 LULC map. The SWAT model captured, overall, the spatio-temporal patterns and effects of LULC change on the stream runoffs despite the heterogeneous runoff responses related to the variable impacts of the different LULC. The results show that LULC change from deciduous forest to urban settlements, agricultural land, or grasslands increased the overall basin runoff by 43%, 14%, and 4%, respectively. [ABSTRACT FROM AUTHOR]- Published
- 2023
- Full Text
- View/download PDF
35. Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem
- Author
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Schönbrodt-Stitt, Sarah, Ahmadian, Nima, Conrad, Christopher, Kurtenbach, Markus, Romano, Nunzio, Bogena, Heye, Vereecken, Harry, Nasta, Paolo, Schönbrodt-Stitt, Sarah, Ahmadian, Nima, Kurtenbach, Marku, Conrad, Christopher, Romano, Nunzio, Bogena, Heye R., Vereecken, Harry, and Nasta, Paolo
- Subjects
terrain parameters ,Sentinel-1 single-look complex data ,near-surface soil moisture ,Alento hydrological observatory ,Mediterranean environment ,ddc:333.7 ,near-surface soil moisture, Sentinel-1 single-look complex data, SAR backscatters, terrain parameters, Alento hydrological observatory, Mediterranean environment ,SAR backscatters ,Environmental technology. Sanitary engineering ,ddc:526 ,TD1-1066 - Abstract
Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters (θNov\(^{SAR}\)) or the combination of 43 SAR and 10 terrain parameters (θNov\(^{SAR+Terrain}\)). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model (θNov\(^{SAR+Terrain}\) with 0.025 and 0.020 m3 m\(^{−3}\), and 89% compared to θNov\(^{SAR}\) with 0.028 and 0.022 m\(^3\) m\(^{−3}\, and 86% in terms of RMSE, MAE, and R2). The higher explanatory power for θNov\(^{SAR+Terrain}\) is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon.
- Published
- 2021
36. Evapotranspiration prediction for European forest sites does not improve with assimilation of in-situ soil water content data.
- Author
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Strebel, Lukas, Bogena, Heye, Vereecken, Harry, Andreasen, Mie, Aranda, Sergio, and Franssen, Harrie-Jan Hendricks
- Subjects
SOIL moisture ,SOIL moisture measurement ,EVAPOTRANSPIRATION ,EDDY flux ,WATER use ,FOREST soils - Abstract
Land surface models (LSM) are an important tool for advancing our knowledge of the Earth system. LSM are constantly improved to represent the various terrestrial processes in more detail. High quality data, freely available from various observation networks, are providing being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSM with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for thirteen forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in-situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point scale measurements from, however for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that state-of-the-art LSM such as CLM5 still suffer from uncertainties in the representation of soil hydrological processes in forests, e.g. deep root water uptake, or highly uncertain vegetation parameters. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
37. Monitoring Irrigation in Small Orchards with Cosmic-Ray Neutron Sensors.
- Author
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Brogi, Cosimo, Pisinaras, Vassilios, Köhli, Markus, Dombrowski, Olga, Hendricks Franssen, Harrie-Jan, Babakos, Konstantinos, Chatzi, Anna, Panagopoulos, Andreas, and Bogena, Heye Reemt
- Subjects
IRRIGATION ,IRRIGATION management ,WATER in agriculture ,NEUTRONS ,APPLE orchards ,SMALL-angle neutron scattering ,COSMIC rays - Abstract
Due to their unique characteristics, cosmic-ray neutron sensors (CRNSs) have potential in monitoring and informing irrigation management, and thus optimising the use of water resources in agriculture. However, practical methods to monitor small, irrigated fields with CRNSs are currently not available and the challenges of targeting areas smaller than the CRNS sensing volume are mostly unaddressed. In this study, CRNSs are used to continuously monitor soil moisture (SM) dynamics in two irrigated apple orchards (Agia, Greece) of ~1.2 ha. The CRNS-derived SM was compared to a reference SM obtained by weighting a dense sensor network. In the 2021 irrigation period, CRNSs could only capture the timing of irrigation events, and an ad hoc calibration resulted in improvements only in the hours before irrigation (RMSE between 0.020 and 0.035). In 2022, a correction based on neutron transport simulations, and on SM measurements from a non-irrigated location, was tested. In the nearby irrigated field, the proposed correction improved the CRNS-derived SM (from 0.052 to 0.031 RMSE) and, most importantly, allowed for monitoring the magnitude of SM dynamics that are due to irrigation. The results are a step forward in using CRNSs as a decision support system in irrigation management. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
38. Seasonal crop yield prediction with SEAS5 long-range meteorological forecasts in a land surface modelling approach.
- Author
-
Boas, Theresa, Bogena, Heye Reemt, Dongryeol Ryu, Vereecken, Harry, Western, Andrew, and Franssen, Harrie-Jan Hendricks
- Abstract
Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications that usually require high resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017-2020 forced with subseasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia and the Australian state of Victoria. We found that, after pre-processing of the forecast products (temporal downscaling of precipitation and incoming shortwave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to generate a model system response very close to reference simulation results forced by reanalysis data. Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual variability in crop yield across the Australian domain (approximately 50 % inter-annual variability in recorded yields and up to 17 % in simulated yields) compared to the German domain (approximately 15 % inter-annual variability in recorded yields and up to 5 % in simulated yields). The high and low yield seasons (2020 and 2018) among the four simulated years were clearly reproduced in forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the German domain. However, the simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic over- and underestimations in both the forecast and the reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency's (ESA CCI). These observed biases of soil moisture as well as the low inter-annual variability of simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
39. Modeling the Water Footprint of Mediterranean Fruit Orchards with CLM5-FruitTree
- Author
-
Dombrowski, Olga, Brogi, Cosimo, Hendricks-Franssen, Harrie-Jan, Bogena, Heye, Pisinaras, Vassilios, Panagopoulos, Andreas, Chatzi, Anna, Tsakmakis, Ioannis, and Babakos, Konstantinos
- Abstract
Land surface models (LSMs) are increasingly being used to study how irrigated agriculture, the largest consumer of fresh water globally, affects crop growth, water resources status, and climate. This is especially of interest in dry and semi-dry agricultural regions such as the Mediterranean, where water scarcity, overexploitation and expected climate change impacts threaten local water resources. The simulation of these agricultural ecosystems necessitates comprehensive crop modules. Such modules must consider local irrigation patterns, crop types, and crop specific management practices to understand their influence on water and energy fluxes under present and future climates. This study explores the water footprint of fruit orchards in the Pinios Hydrological Observatory (PHO) in central Greece, using CLM5-FruitTree, a recent development of the Community Land Model version 5, to include deciduous fruit orchards and associated management practices. Initially, CLM5-FruitTree was setup and validated at field scale using data from two highly instrumented irrigated apple orchards within the PHO. The simulations used local climate, soil, crop management and phenology information. Model results were compared to observed apple yield, sap flow, irrigation amounts, and soil moisture. The latter was obtained from a distributed sensor network measuring soil moisture in three depths at 12 locations per field as well as two cosmic-ray neutron soil moisture sensors. The model was able to reproduce the soil moisture response to irrigation satisfactorily when the local irrigation schedule was considered. The simulated irrigation amount indicated that around 45% less water than the amount applied by the farmer could be used without reduction in yield. This suggests potential improvements in irrigation efficiency by reducing losses through evaporation or deep percolation. However, possible model weaknesses in the representation of soil properties and water fluxes should be further addressed. Successively, a modeling case for the PHO is set up to study the regional irrigation water consumption and the local groundwater aquifer recharge. Results from this study could help local authorities in the definition of water policies and serve as a basis for climate impact studies on regional irrigation management.
- Published
- 2022
40. Deforestation alters dissolved organic carbon and sulfate dynamics in a mountainous headwater catchment—A wavelet analysis
- Author
-
Wang, Qiqi, Qu, Yuquan, Robinson, Kerri-Leigh, Bogena, Heye, Graf, Alexander, Vereecken, Harry, Tietema, Albert, and Bol, Roland
- Subjects
Global and Planetary Change ,Ecology ,ddc:630 ,Forestry ,Environmental Science (miscellaneous) ,Nature and Landscape Conservation - Abstract
Deforestation has a wide range of effects on hydrological and geochemical processes. Dissolved organic carbon (DOC) dynamics, a sensitive environmental change indicator, is expected to be affected by deforestation, with changes in atmospheric sulfur (S) deposition compounding this. However, how precisely anthropogenic disturbance (deforestation) under a declining atmospheric S input scenario affects the underlying spatiotemporal dynamics and relationships of river DOC and sulfate with hydro-climatological variables e.g., stream water temperature, runoff, pH, total dissolved iron (Fetot), and calcium (Ca2+) remains unclear. We, therefore, examined this issue within the TERENO Wüstebach catchment (Eifel, Germany), where partial deforestation had taken place in 2013. Wavelet transform coherence (WTC) analysis was applied based on a 10-year time series (2010–2020) from three sampling stations, whose (sub) catchment areas have different proportions of deforested area (W10: 31%, W14: 25%, W17: 3%). We found that water temperature and DOC, sulfate, and Fetot concentrations showed distinct seasonal patterns, with DOC averaging concentrations ranging from 2.23 (W17) to 4.56 (W10) mg L–1 and sulfate concentration ranging from 8.04 (W10) to 10.58 (W17) mg L–1. After clear-cut, DOC significantly increased by 59, 58% in the mainstream (W10, W14), but only 26% in the reference stream. WTC results indicated that DOC was negatively correlated with runoff and sulfate, but positively correlated with temperature, Ca2+, and Fetot. The negative correlation between DOC with runoff and sulfate was apparent over the whole examined 10-year period in W17 but did end in W10 and W14 after the deforestation. Sulfate (SO4) was highly correlated with stream water temperature, runoff, and Fetot in W10 and W14 and with a longer lag time than W17. Additionally, pH was stronger correlated (higher R2) with sulfate and DOC in W17 than in W10 and W14. In conclusion, WTC analysis indicates that within this low mountainous forest catchment deforestation levels over 25% (W10 and W14) affected the coupling of S and C cycling substantially more strongly than “natural” environmental changes as observed in W17.
- Published
- 2022
41. Cosmic-ray Neutron Sensing in Support of Precision Irrigation or: How a FairlySimple Question Yields a Puzzling Answer
- Author
-
Brogi, Cosimo, Bogena, Heye, Köhli, Markus, Pisinaras, Vasilios, Hendricks-Franssen, Harrie-Jan, Panagopoulos, Andreas, Dombrowski, Olga, Chatzi, Anna, and Babakos, Konstantinos
- Abstract
The agricultural sector is increasingly reliant on water availability, especially given expected increase of agricultural droughts related to climate change. Thus, improved soil moisture (SM) monitoring tools are needed to support more efficient water management strategies such as precision irrigation. A novel and non-invasive method is cosmic-ray neutron sensing (CRNS). It is characterized by a large footprint (~240m) and relies on the negative correlation between fast neutrons originating from cosmic radiation and SM. Despite promising results in the monitoring of SM dynamics and patterns, only a few studies explored the use of CRNS for irrigation management. In this study, two apple orchards of ~1.2 ha located in the Pinios Hydrological Observatory (Greece) were provided with CRNS probes. These were supported by extensive monitoring of SM and climate data in the context of the H2020 ATLAS project. In capturing irrigation events, the agreement between the CRNS and the validation measurements depended largely on a) the timing of irrigation, b) the CRNS calibration strategy, c) precipitation, and d) the management of the surrounding fields. In parallel, we performed neutron transport simulations of multiple scenarios with variable irrigated area and soil moisture by using the URANOS model. This allowed the study of how the surrounding environment influences the effectiveness of a CRNS sensor when its footprint is larger than the area of interest. This combination of simulations and experiments is providing key insights on how CRNS methods can move from a proof o concept to a relevant tool in actual precisionirrigation scenarios.
- Published
- 2022
42. Hydrological forecasting for climate resilient water resources management over Germany with ParFlow
- Author
-
Belleflamme, Alexandre, Görgen, Klaus, Wagner, Niklas, Hammoudeh, Suad, Bogena, Heye, Nieberding, Felix, Ney, Patrizia, and Kollet, Stefan
- Published
- 2022
43. Soil moisture observation in a forested headwater catchment: combining a dense cosmic-ray neutron sensor network with roving and hydrogravimetry at the TERENO site Wüstebach
- Author
-
Heistermann, Maik, Bogena, Heye, Francke, Till, Güntner, Andreas, Jakobi, Jannis, Rasche, Daniel, Schrön, Martin, Döpper, Veronika, Fersch, Benjamin, Groh, Jannis, Patil, Amol, Pütz, Thomas, Reich, Marvin, Zacharias, Steffen, Zengerle, Carmen, and Oswald, Sascha
- Subjects
Earth sciences ,ddc:550 ,General Earth and Planetary Sciences ,Institut für Geowissenschaften ,550 Geowissenschaften ,Extern - Abstract
Cosmic-ray neutron sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of metres and a depth of decimetres. Recent studies proposed operating CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km2 Wüstebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation) and features a topographically distinct catchment boundary. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published data set (available at https://doi.org/10.23728/b2share.756ca0485800474e9dc7f5949c63b872; Heistermann et al., 2022) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land–atmosphere exchange as well as hydrological and hydrogeological processes at the hillslope and the catchment scale; and to support the retrieval of soil water content from airborne and spaceborne remote sensing platforms., Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe; 1272
- Published
- 2022
44. The Impact of Partial Deforestation on Solute Fluxes and Stream Water Ionic Composition in a Headwater Catchment.
- Author
-
Płaczkowska, Eliza, Mostowik, Karolina, Bogena, Heye Reemt, and Leuchner, Michael
- Subjects
LAND cover ,DEFORESTATION ,WATERSHEDS ,SOIL erosion ,ENVIRONMENTAL monitoring ,CHEMICAL denudation - Abstract
To ensure the good chemical status of surface water across Europe, it is necessary to increase research on the comprehensive impact of land use and land cover changes, i.e., deforestation, on the natural environment. For this reason, we used data from 9-year environmental monitoring in the Wüstebach experimental catchment of the TERENO (Terrestrial Environmental Observatories) network to determine the impact of partial deforestation on solute fluxes and stream water ionic composition. In 2013, a partial deforestation experiment was conducted in the study area using a cut-to-length logging method. To this end, two headwater catchments were compared: one partially deforested (22% of the catchment area) and one untreated control catchment. The concentrations of ions in stream water, groundwater, and precipitation were analyzed: Ca
2+ , Mg2+ , Na+ , K+ , Al3+ , Fetot , Mn2+ , NO3 − , SO4 − , and Cl− . Most of the ions (Na+ , Ca2+ , Mg2+ , Cl− , and SO4 − ) showed decreasing trends in concentrations after deforestation, indicating a dilution effect in stream water due to the reduction of the supply of solutes with precipitation in the open deforested area. The fluxes of these ions decreased by 5–7% in the first year after deforestation, although the stream runoff increased by 5%. In the second year, the decrease in ion fluxes was greater, from 6% to 24%. This finding confirms that only limited soil erosion occurred after the deforestation because the soil was well protected during logging works by covering harvester lanes with branches. Only K+ and NO3 − ions showed increasing trends in both concentrations and fluxes in the partially deforested catchment in the first two to three years after deforestation. Spruce die-offs, common in Europe, may decrease the concentration and fluxes of base cations in surface water in a nutrient-limited environment. However, the simultaneous planting of young broad-leaved trees with post-harvesting regrowth could create a nutrient sink that protects the catchment area from nutrient depletion. [ABSTRACT FROM AUTHOR]- Published
- 2023
- Full Text
- View/download PDF
45. High-resolution drought simulations and comparison to soil moisture observations in Germany.
- Author
-
Boeing, Friedrich, Rakovec, Oldrich, Kumar, Rohini, Samaniego, Luis, Schrön, Martin, Hildebrandt, Anke, Rebmann, Corinna, Thober, Stephan, Müller, Sebastian, Zacharias, Steffen, Bogena, Heye, Schneider, Katrin, Kiese, Ralf, Attinger, Sabine, and Marx, Andreas
- Subjects
DROUGHT management ,SOIL moisture ,DISTRIBUTED sensors ,SOIL dynamics ,SENSOR networks ,HYDROLOGIC cycle ,DROUGHTS - Abstract
Germany's 2018–2020 consecutive drought events resulted in multiple sectors – including agriculture, forestry, water management, energy production, and transport – being impacted. High-resolution information systems are key to preparedness for such extreme drought events. This study evaluates the new setup of the one-kilometer German drought monitor (GDM), which is based on daily soil moisture (SM) simulations from the mesoscale hydrological model (mHM). The simulated SM is compared against a set of diverse observations from single profile measurements, spatially distributed sensor networks, cosmic-ray neutron stations, and lysimeters at 40 sites in Germany. Our results show that the agreement of simulated and observed SM dynamics in the upper soil (0–25 cm) are especially high in the vegetative active period (0.84 median correlation R) and lower in winter (0.59 median R). The lower agreement in winter results from methodological uncertainties in both simulations and observations. Moderate but significant improvements between the coarser 4 km resolution setup and the ≈ 1.2 km resolution GDM in the agreement to observed SM dynamics is observed in autumn (+ 0.07 median R) and winter (+ 0.12 median R). Both model setups display similar correlations to observations in the dry anomaly spectrum, with higher overall agreement of simulations to observations with a larger spatial footprint. The higher resolution of the second GDM version allows for a more detailed representation of the spatial variability of SM, which is particularly beneficial for local risk assessments. Furthermore, the results underline that nationwide drought information systems depend both on appropriate simulations of the water cycle and a broad, high-quality, observational soil moisture database. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
46. Recent Developments in Wireless Soil Moisture Sensing to Support Scientific Research and Agricultural Management.
- Author
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Bogena, Heye Reemt, Weuthen, Ansgar, and Huisman, Johan Alexander
- Subjects
- *
WIRELESS sensor networks , *SOIL moisture , *SOIL moisture measurement , *SOIL formation , *GEOPHYSICAL instruments , *AGRICULTURAL research - Abstract
In recent years, wireless sensor network (WSN) technology has emerged as an important technique for wireless sensing of soil moisture from the field to the catchment scale. This review paper presents the current status of wireless sensor network (WSN) technology for distributed, near real-time sensing of soil moisture to investigate seasonal and event dynamics of soil moisture patterns. It is also discussed how WSN measurements of soil measurements contribute to the validation and downscaling of satellite data and non-invasive geophysical instruments as well as the validation of distributed hydrological models. Finally, future perspectives for WSN measurements of soil moisture are highlighted, which includes the improved integration of real-time WSN measurements with other information sources using the latest wireless communication techniques and cyberinfrastructures. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
47. CLM5-FruitTree: a new sub-model for deciduous fruit trees in the Community Land Model (CLM5).
- Author
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Dombrowski, Olga, Brogi, Cosimo, Hendricks Franssen, Harrie-Jan, Zanotelli, Damiano, and Bogena, Heye
- Subjects
ORCHARDS ,DECIDUOUS plants ,FRUIT trees ,PLANT phenology ,APPLE orchards ,PLANT biomass ,FOOD crops - Abstract
The inclusion of perennial, woody crops in land surface models (LSMs) is crucial for addressing their role in carbon (C) sequestration, food production, and water requirements under climate change. To help quantify the biogeochemical and biogeophysical processes associated with these agroecosystems, we developed and tested a new sub-model, CLM5-FruitTree, for deciduous fruit orchards within the framework of the Community Land Model version 5 (CLM5). The model development included (1) a new perennial crop phenology description, (2) an adapted C and nitrogen allocation scheme, considering both storage and photosynthetic growth of annual and perennial plant organs, (3) typical management practices associated with fruit orchards, and (4) the parameterization of an apple plant functional type. CLM5-FruitTree was tested using extensive field measurements from an apple orchard in South Tyrol, Italy. Growth and partitioning of biomass to the individual plant components were well represented by CLM5-FruitTree, and average yield was predicted within 2.3 % of the observed values despite low simulated inter-annual variability compared to observations. The simulated seasonal course of C, energy, and water fluxes was in good agreement with the eddy covariance (EC) measurements owing to the accurate representation of the prolonged growing season and typical leaf area development of the orchard. We found that gross primary production, net radiation, and latent heat flux were highly correlated (r>0.94) with EC measurements and showed little bias (<±5 %). Simulated respiration components, sensible heat, and soil heat flux were less consistent with observations. This was attributed to simplifications in the orchard structure and to the presence of additional management practices that are not yet represented in CLM5-FruitTree. Finally, the results suggested that the representation of microbial and autotrophic respiration and energy partitioning in complex, discontinuous canopies in CLM5 requires further attention. The new CLM5-FruitTree sub-model improved the representation of agricultural systems in CLM5 and can be used to study land surface processes in fruit orchards at the local, regional, or larger scale. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
48. Optimal Temporal Filtering of the Cosmic-Ray Neutron Signal to Reduce Soil Moisture Uncertainty.
- Author
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Davies, Patrick, Baatz, Roland, Bogena, Heye Reemt, Quansah, Emmanuel, and Amekudzi, Leonard Kofitse
- Subjects
SOIL moisture ,NEUTRONS ,KALMAN filtering ,POISSON distribution ,SOIL dynamics ,COSMIC rays - Abstract
Cosmic ray neutron sensors (CRNS) are increasingly used to determine field-scale soil moisture (SM). Uncertainty of the CRNS-derived soil moisture strongly depends on the CRNS count rate subject to Poisson distribution. State-of-the-art CRNS signal processing averages neutron counts over many hours, thereby accounting for soil moisture temporal dynamics at the daily but not sub-daily time scale. This study demonstrates CRNS signal processing methods to improve the temporal accuracy of the signal in order to observe sub-daily changes in soil moisture and improve the signal-to-noise ratio overall. In particular, this study investigates the effectiveness of the Moving Average (MA), Median filter (MF), Savitzky–Golay (SG) filter, and Kalman filter (KF) to reduce neutron count error while ensuring that the temporal SM dynamics are as good as possible. The study uses synthetic data from four stations for measuring forest ecosystem–atmosphere relations in Africa (Gorigo) and Europe (SMEAR II (Station for Measuring Forest Ecosystem–Atmosphere Relations), Rollesbroich, and Conde) with different soil properties, land cover and climate. The results showed that smaller window sizes (12 h) for MA, MF and SG captured sharp changes closely. Longer window sizes were more beneficial in the case of moderate soil moisture variations during long time periods. For MA, MF and SG, optimal window sizes were identified and varied by count rate and climate, i.e., estimated temporal soil moisture dynamics by providing a compromise between monitoring sharp changes and reducing the effects of outliers. The optimal window for these filters and the Kalman filter always outperformed the standard procedure of simple 24-h averaging. The Kalman filter showed its highest robustness in uncertainty reduction at three different locations, and it maintained relevant sharp changes in the neutron counts without the need to identify the optimal window size. Importantly, standard corrections of CRNS before filtering improved soil moisture accuracy for all filters. We anticipate the improved signal-to-noise ratio to benefit CRNS applications such as detection of rain events at sub-daily resolution, provision of SM at the exact time of a satellite overpass, and irrigation applications. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
49. The sarsense campaign:Air‐ and space‐borne c‐ and l‐band sar for the analysis of soil and plant parameters in agriculture
- Author
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Mengen, David, Montzka, Carsten, Jagdhuber, Thomas, Fluhrer, Anke, Brogi, Cosimo, Baum, Stephani, Schüttemeyer, Dirk, Bayat, Bagher, Bogena, Heye, Coccia, Alex, Masalias, Gerard, Trinkel, Verena, Jakobi, Jannis, Jonard, François, Ma, Yueling, Mattia, Francesco, Palmisano, Davide, Rascher, Uwe, Satalino, Giuseppe, Schumacher, Maike, Koyama, Christian, Schmidt, Marius, and Vereecken, Harry
- Subjects
C‐band ,Airborne campaign ,L‐band ,Plant parameters ,Soil moisture ,ROSE‐L ,SAR - Abstract
With the upcoming L‐band Synthetic Aperture Radar (SAR) satellite mission Radar Ob-serving System for Europe L‐band SAR (ROSE‐L) and its integration into existing C‐band satellite missions such as Sentinel‐1, multi‐frequency SAR observations with high temporal and spatial resolution will become available. The SARSense campaign was conducted between June and August 2019 to investigate the potential for estimating soil and plant parameters at the agricultural test site in Selhausen (Germany). It included C‐ and L‐band air‐ and space‐borne observations accompanied by extensive in situ soil and plant sampling as well as unmanned aerial system (UAS) based multi-spectral and thermal infrared measurements. In this regard, we introduce a new publicly available SAR data set and present the first analysis of C‐ and L‐band co‐ and cross‐polarized backscattering signals regarding their sensitivity to soil and plant parameters. Results indicate that a multi‐fre-quency approach is relevant to disentangle soil and plant contributions to the SAR signal and to identify specific scattering mechanisms associated with the characteristics of different crop type, especially for root crops and cereals.
- Published
- 2021
50. The SARSense Campaign: Air- and Space-Borne C- and L-Band SAR for the Analysis of Soil and Plant Parameters in Agriculture
- Author
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Mengen, David, Montzka, Carsten, Jagdhuber, Thomas, Fluhrer, Anke, Brogi, Cosimo, Baum, Stephanie, Schüttemeyer, Dirk, Bayat, Bagher, Bogena, Heye, Coccia, Alexander, Masalias, Gerard, Trinkel, Verena, Jakobi, Jannis, Jonard, Francois, Ma, Yueling, Mattia, Francesco, Palmisano, Davide, Rascher, Uwe, Satalino, Giuseppe, Schumacher, Maike, Koyama, Christian, Schmidt, Marius, and Vereecken, Harry
- Subjects
ROSE_L ,Science ,fungi ,Aufklärung und Sicherheit ,airborne campaign ,food and beverages ,L-band ,ROSE-L ,plant parameters ,ddc:550 ,ddc:620 ,soil moisture ,C-band ,SAR - Abstract
With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Ob-serving System for Europe L-band SAR (ROSE-L) and its integration into existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. The SARSense campaign was conducted between June and August 2019 to investigate the potential for estimating soil and plant parameters at the agricultural test site in Selhausen (Germany). It included C- and L-band air- and space-borne observations accompanied by extensive in situ soil and plant sampling as well as unmanned aerial system (UAS) based multi-spectral and thermal infrared measurements. In this regard, we introduce a new publicly available SAR data set and present the first analysis of C- and L-band co- and cross-polarized backscattering signals regarding their sensitivity to soil and plant parameters. Results indicate that a multi-fre-quency approach is relevant to disentangle soil and plant contributions to the SAR signal and to identify specific scattering mechanisms associated with the characteristics of different crop type, especially for root crops and cereals.
- Published
- 2021
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