Articles | Volume 13, issue 24
https://doi.org/10.5194/bg-13-6545-2016
© Author(s) 2016. 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-13-6545-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Crop water stress maps for an entire growing season from visible and thermal UAV imagery
Helene Hoffmann
CORRESPONDING AUTHOR
Department of Geosciences and Natural Resource Management, University
of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, Denmark
Rasmus Jensen
Department of Geosciences and Natural Resource Management, University
of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, Denmark
Anton Thomsen
Department of Agroecology, Aarhus University, Nordre Ringgade 1, 8000
Aarhus, Denmark
Hector Nieto
Instituto de Agricultura Sostenible (IAS) Consejo Superior de
Investigaciones Científicas (CSIC), Campus Alameda del Obispo, Av.
Menéndez Pidal s/n, 14004 Córdoba, Spain
Jesper Rasmussen
Department of Plant and Environmental Sciences, University of
Copenhagen, Højbakkegaard Allé 9, 2630 Taastrup, Denmark
Thomas Friborg
Department of Geosciences and Natural Resource Management, University
of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, Denmark
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- Development of a new UAV-thermal imaging based model for estimating pecan evapotranspiration E. Mokari et al. 10.1016/j.compag.2022.106752
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- Landscape-level vegetation classification and fractional woody and herbaceous vegetation cover estimation over the dryland ecosystems by unmanned aerial vehicle platform H. Wang et al. 10.1016/j.agrformet.2019.107665
- Leaf temperatures and environmental conditions predict daily stem radial variations in a temperate coniferous forest W. Weygint et al. 10.1002/ecs2.4465
- Pre-harvest weed mapping of Cirsium arvense in wheat and barley with off-the-shelf UAVs J. Rasmussen et al. 10.1007/s11119-018-09625-7
- Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications J. Kim et al. 10.1109/ACCESS.2019.2932119
- The digitization of agricultural industry – a systematic literature review on agriculture 4.0 R. Abbasi et al. 10.1016/j.atech.2022.100042
- Unmanned-Aerial-Vehicle Data as an Effective Tool for the Evaluation of Ancient Khorasan and Modern Kabot Spring Wheat Varieties under Different Tillage Systems K. Balážová et al. 10.3390/agronomy14010147
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- Water deficit index to evaluate water stress status and drought tolerance of rainfed barley genotypes in cold semi-arid area of Iran V. Feiziasl et al. 10.1016/j.agwat.2021.107395
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- Assessing Different Plant‐Centric Water Stress Metrics for Irrigation Efficacy Using Soil‐Plant‐Atmosphere‐Continuum Simulation J. Zhang et al. 10.1029/2021WR030211
- A Comparative Estimation of Maize Leaf Water Content Using Machine Learning Techniques and Unmanned Aerial Vehicle (UAV)-Based Proximal and Remotely Sensed Data H. Ndlovu et al. 10.3390/rs13204091
- Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications Z. Zhou et al. 10.1016/j.compag.2021.106019
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- Using UAV-thermal imaging to calculate crop water use and irrigation efficiency in a flood-irrigated pecan orchard A. Garcia-Vasquez et al. 10.1016/j.agwat.2022.107824
- An Automatic Non-Destructive Method for the Classification of the Ripeness Stage of Red Delicious Apples in Orchards Using Aerial Video S. Sabzi et al. 10.3390/agronomy9020084
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- Detection of oak decline using radiative transfer modelling and machine learning from multispectral and thermal RPAS imagery A. Hornero et al. 10.1016/j.jag.2024.103679
- A systematic review on the application of UAV-based thermal remote sensing for assessing and monitoring crop water status in crop farming systems H. Ndlovu et al. 10.1080/01431161.2024.2368933
- Development of a novel and fast XRF instrument for large area heavy metal detection integrated with UAV F. Huang et al. 10.1016/j.envres.2022.113841
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- UAS-based high resolution mapping of evapotranspiration in a Mediterranean tree-grass ecosystem J. Simpson et al. 10.1016/j.agrformet.2022.108981
- Crop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies E. Chakhvashvili et al. 10.1007/s11119-024-10168-3
- Mapping Root-Zone Soil Moisture Using a Temperature–Vegetation Triangle Approach with an Unmanned Aerial System: Incorporating Surface Roughness from Structure from Motion S. Wang et al. 10.3390/rs10121978
- Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review K. Berger et al. 10.1016/j.rse.2022.113198
- Estimation of Water Stress in Grapevines Using Proximal and Remote Sensing Methods A. Matese et al. 10.3390/rs10010114
- Challenges and Best Practices for Deriving Temperature Data from an Uncalibrated UAV Thermal Infrared Camera J. Kelly et al. 10.3390/rs11050567
- Droplet deposition density of organic liquid fertilizer at low altitude UAV aerial spraying in rice cultivation M. Abd. Kharim et al. 10.1016/j.compag.2019.105045
- A New Technique to Estimate Sensible Heat Fluxes around Micrometeorological Towers Using Small Unmanned Aircraft Systems T. Lee et al. 10.1175/JTECH-D-17-0065.1
- Burrow-Nesting Seabird Survey Using UAV-Mounted Thermal Sensor and Count Automation J. Virtue et al. 10.3390/drones7110674
- Estimating rice yield by assimilating UAV-derived plant nitrogen concentration into the DSSAT model: Evaluation at different assimilation time windows H. Ge et al. 10.1016/j.fcr.2022.108705
- Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems C. Brenner et al. 10.1080/01431161.2018.1471550
- AI meets UAVs: A survey on AI empowered UAV perception systems for precision agriculture J. Su et al. 10.1016/j.neucom.2022.11.020
- Correlation between Ground Measurements and UAV Sensed Vegetation Indices for Yield Prediction of Common Bean Grown under Different Irrigation Treatments and Sowing Periods A. Lipovac et al. 10.3390/w14223786
- Evaluation of the Water Conditions in Coffee Plantations Using RPA S. Santos et al. 10.3390/agriengineering5010005
- Manual geo-rectification to improve the spatial accuracy of ortho-mosaics based on images from consumer-grade unmanned aerial vehicles (UAVs) S. Azim et al. 10.1007/s11119-019-09647-9
- On the Use of Rotary-Wing Aircraft to Sample Near-Surface Thermodynamic Fields: Results from Recent Field Campaigns T. Lee et al. 10.3390/s19010010
- Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk M. Ayaz et al. 10.1109/ACCESS.2019.2932609
- Multi-Sensor UAV Tracking of Individual Seedlings and Seedling Communities at Millimetre Accuracy T. Buters et al. 10.3390/drones3040081
- Advance control strategies using image processing, UAV and AI in agriculture: a review I. Syeda et al. 10.1108/WJE-09-2020-0459
- Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture N. Khan et al. 10.3390/su13094883
- High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms X. Jin et al. 10.1109/MGRS.2020.2998816
- High-throughput field crop phenotyping: current status and challenges S. Ninomiya 10.1270/jsbbs.21069
- Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high-resolution evapotranspiration estimations R. Guzinski & H. Nieto 10.1016/j.rse.2018.11.019
- Latent heat flux variability and response to drought stress of black poplar: A multi-platform multi-sensor remote and proximal sensing approach to relieve the data scarcity bottleneck F. Tauro et al. 10.1016/j.rse.2021.112771
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Discussed (final revised paper)
Latest update: 23 Nov 2024
Short summary
This study investigates whether the UAV (drone) based WDI can determine crop water stress from fields with open canopies (land surface consisting of both soil and canopy) and from fields where canopies are starting to senesce. This utility could solve issues that arise when applying the commonly used CWSI stress index. The WDI succeeded in providing accurate, high-resolution estimates of crop water stress at different growth stages of barley.
This study investigates whether the UAV (drone) based WDI can determine crop water stress from...
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Final-revised paper
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