Articles | Volume 14, issue 3
https://doi.org/10.5194/bg-14-733-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-14-733-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
15 Feb 2017
Research article |
| 15 Feb 2017
Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors
Bob van der Meij
CORRESPONDING AUTHOR
Faculty of Geosciences, Utrecht University, P.O. Box 80.115, 3508 TC
Utrecht, the Netherlands
Lammert Kooistra
Laboratory of Geo-Information Science and Remote Sensing, Wageningen
University and Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Juha Suomalainen
Laboratory of Geo-Information Science and Remote Sensing, Wageningen
University and Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Janna M. Barel
Department of Soil Quality, Wageningen University and Research,
P.O. Box 47, 6700 AA Wageningen, the Netherlands
Gerlinde B. De Deyn
CORRESPONDING AUTHOR
Department of Soil Quality, Wageningen University and Research,
P.O. Box 47, 6700 AA Wageningen, the Netherlands
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- Plant–Soil Feedback: Bridging Natural and Agricultural Sciences P. Mariotte et al. 10.1016/j.tree.2017.11.005
- A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses J. Barbedo 10.3390/drones3020040
- Frontline remote sensing tool to locate hidden traits in root and tuber crops E. Joseph Fernando et al. 10.1016/j.molp.2022.08.010
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- The use of machine learning methods to estimate aboveground biomass of grasslands: A review T. Morais et al. 10.1016/j.ecolind.2021.108081
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- Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production M. Domingues Franceschini et al. 10.3390/s17061428
28 citations as recorded by crossref.
- Machine learning for yield prediction in Fergana valley, Central Asia M. Singh Boori et al. 10.1016/j.jssas.2022.07.006
- Using Hybrid Artificial Intelligence and Evolutionary Optimization Algorithms for Estimating Soybean Yield and Fresh Biomass Using Hyperspectral Vegetation Indices M. Yoosefzadeh-Najafabadi et al. 10.3390/rs13132555
- Quantifying Biochemical Traits over the Patagonian Sub-Antarctic Forests and Their Relation to Multispectral Vegetation Indices R. Taylor-Zavala et al. 10.3390/rs13214232
- Plant trait‐based approaches to improve nitrogen cycling in agroecosystems D. Abalos et al. 10.1111/1365-2664.13489
- Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops S. Jay et al. 10.1016/j.rse.2018.09.011
- The Terrestrial Carbon Sink T. Keenan & C. Williams 10.1146/annurev-environ-102017-030204
- Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice H. Zheng et al. 10.3389/fpls.2018.00936
- Detecting vegetation stress as a soil contamination proxy: a review of optical proximal and remote sensing techniques A. Gholizadeh & V. Kopačková 10.1007/s13762-019-02310-w
- Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz) M. Selvaraj et al. 10.1186/s13007-020-00625-1
- High spatio-temporal monitoring of century-old biochar effects on evapotranspiration through the ETLook model: a case study with UAV and satellite image fusion based on additive wavelet transform (AWT) R. Heidarian Dehkordi et al. 10.1080/15481603.2021.2016262
- Using unmanned aerial systems and deep learning for agriculture mapping in Dubai L. El Hoummaidi et al. 10.1016/j.heliyon.2021.e08154
- Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring U. Ahmad et al. 10.3390/agronomy12030555
- Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality M. Maimaitiyiming et al. 10.3390/rs11070740
- Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning L. Feng et al. 10.3390/rs12122028
- Influence of skidder traffic on soil bulk density, aspen regeneration, and vegetation indices following winter harvesting in the Duck Mountain Provincial Park, SK L. Sealey & K. Van Rees 10.1016/j.foreco.2019.01.017
- Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity R. Nuijten et al. 10.3390/drones3030054
- Multi-temporal estimation of vegetable crop biophysical parameters with varied nitrogen fertilization using terrestrial laser scanning J. Reji et al. 10.1016/j.compag.2021.106051
- Correlation between Geochemical and Multispectral Patterns in an Area Severely Contaminated by Former Hg-As Mining C. Boente et al. 10.3390/ijgi9120739
- Potato Leaf Area Index Estimation Using Multi-Sensor Unmanned Aerial Vehicle (UAV) Imagery and Machine Learning T. Yu et al. 10.3390/rs15164108
- Plant–Soil Feedback: Bridging Natural and Agricultural Sciences P. Mariotte et al. 10.1016/j.tree.2017.11.005
- A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses J. Barbedo 10.3390/drones3020040
- Frontline remote sensing tool to locate hidden traits in root and tuber crops E. Joseph Fernando et al. 10.1016/j.molp.2022.08.010
- UAV based soil salinity assessment of cropland K. Ivushkin et al. 10.1016/j.geoderma.2018.09.046
- The role of soils in habitat creation, maintenance and restoration G. De Deyn & L. Kooistra 10.1098/rstb.2020.0170
- Invasive Vaucheria aff. compacta (Xanthophyceae) and its distribution over a high Arctic tidal flat in Svalbard J. Elster et al. 10.1016/j.ecss.2022.108206
- The use of machine learning methods to estimate aboveground biomass of grasslands: A review T. Morais et al. 10.1016/j.ecolind.2021.108081
- Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery Y. Niu et al. 10.3390/rs11111261
- Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production M. Domingues Franceschini et al. 10.3390/s17061428
Latest update: 19 Apr 2024
Short summary
Plant–soil feedback (PSF) is an important mechanism to explain plant performance in natural and agricultural systems but is hard to quantify in field experiments. We used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way at high resolution using UAV-based optical sensors.
Plant–soil feedback (PSF) is an important mechanism to explain plant performance in natural and...
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