Articles | Volume 27, issue 1
https://doi.org/10.5194/hess-27-39-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-39-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jan 2023
Research article |
| 02 Jan 2023
| 02 Jan 2023
Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth
Matthias Forkel
CORRESPONDING AUTHOR
Faculty of Environmental Sciences, Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, 01062 Dresden, Germany
Luisa Schmidt
Faculty of Environmental Sciences, Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, 01062 Dresden, Germany
Ruxandra-Maria Zotta
Department of Geodesy and Geoinformation, Technische Universität Wien, 1040 Vienna, Austria
Wouter Dorigo
Department of Geodesy and Geoinformation, Technische Universität Wien, 1040 Vienna, Austria
Marta Yebra
Fenner School of Environment and Society, Australian National
University, ACT 2601 Canberra, Australia
School of Engineering, Australian National University, ACT 2601 Canberra, Australia
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- Advancing global vegetation water content observations: the potential of Moon-based SAR for ecological risk assessment Y. Geng et al. https://doi.org/10.1088/1748-9326/adf764
- Retrieval Model for Surface Dead Fuel Moisture Data Based on Microwave Soil Moisture Content Y. Gao et al. https://doi.org/10.1109/TGRS.2025.3645258
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- Divergence between in situ and satellite-based estimates of forest canopy water content J. Kesselring et al. https://doi.org/10.1016/j.rse.2025.115097
- Vegetation optical depth as a key predictor for fire risk escalation D. Kankanige et al. https://doi.org/10.1016/j.ecoinf.2025.103050
43 citations as recorded by crossref.
- Gazing into the flames: A guide to assessing the impacts of climate change on landscape fire H. Clarke et al. https://doi.org/10.1126/sciadv.adz2429
- Estimation of Live fuel moisture content via spectral–meteorological time-series fusion: a TCN–BiGRU deep learning framework C. Wang et al. https://doi.org/10.1080/01431161.2026.2633780
- Global L-band equivalent AI-based vegetation optical depth dataset O. Skulovich et al. https://doi.org/10.1038/s41597-024-03810-2
- Remote sensing of vegetation phenology in the northern hemisphere from multi-channel passive microwave measurements of Chinese FengYun-3D satellite Y. Wang et al. https://doi.org/10.1016/j.rse.2025.114997
- Deep learning for wildfire risk prediction: Integrating remote sensing and environmental data Z. Xu et al. https://doi.org/10.1016/j.isprsjprs.2025.06.002
- A review of spaceborne synthetic aperture radar for invasive alien plant research G. Shennan & R. Crabbe https://doi.org/10.1016/j.rsase.2024.101358
- Estimation of Forest Water Potential From Ground-Based L-Band Radiometry T. Jagdhuber et al. https://doi.org/10.1109/JSTARS.2025.3533567
- Mapping global drought-induced forest mortality based on multiple satellite vegetation optical depth data X. Zhang et al. https://doi.org/10.1016/j.rse.2024.114406
- Contrasting age-dependent leaf acclimation strategies drive vegetation greening across deciduous broadleaf forests in mid- to high latitudes F. Wang et al. https://doi.org/10.1038/s41477-025-02096-5
- Advancing global vegetation water content observations: the potential of Moon-based SAR for ecological risk assessment Y. Geng et al. https://doi.org/10.1088/1748-9326/adf764
- Retrieval Model for Surface Dead Fuel Moisture Data Based on Microwave Soil Moisture Content Y. Gao et al. https://doi.org/10.1109/TGRS.2025.3645258
- Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties L. Schmidt et al. https://doi.org/10.5194/bg-20-1027-2023
- Constraining Plant Hydraulics With Microwave Radiometry in a Land Surface Model: Impacts of Temporal Resolution N. Holtzman et al. https://doi.org/10.1029/2023WR035481
- Estimation of All-Sky High-Resolution Gross Primary Production Across Different Biome Types Using Active Microwave Satellite Images and Environmental Data J. Chen & Z. Zhang https://doi.org/10.1109/JSTARS.2024.3422795
- Modeling of Multi-Frequency Microwave Backscatter and Emission of Land Surface by a Community Land Active Passive Microwave Radiative Transfer Modeling Platform H. Zhao et al. https://doi.org/10.34133/remotesensing.0415
- Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy D. Chaparro et al. https://doi.org/10.1016/j.rse.2024.113993
- Remote sensing of vegetation and soil moisture content in Atlantic humid mountains with Sentinel-1 and 2 satellite sensor data A. Monteiro et al. https://doi.org/10.1016/j.ecolind.2024.112123
- Combining Microwave and Optical Remote Sensing to Characterize Global Vegetation Water Status X. Wang et al. https://doi.org/10.1109/TGRS.2023.3294948
- Proximal remote sensing: an essential tool for bridging the gap between high‐resolution ecosystem monitoring and global ecology Z. Pierrat et al. https://doi.org/10.1111/nph.20405
- Relevance of earth observations of essential climate variables in wildfire adaptation S. Seitzinger et al. https://doi.org/10.1016/j.rse.2025.115082
- Towards an Integrated Approach to Wildfire Risk Assessment: When, Where, What and How May the Landscapes Burn E. Chuvieco et al. https://doi.org/10.3390/fire6050215
- Development of UAV Spectral Models for Estimating Leaf Water Content at Canopy Level for Eucalyptus globulus and Pinus radiata M. de Armendaiz et al. https://doi.org/10.1007/s12524-025-02399-2
- Vegetation Water Content Estimation Using Optical and Microwave Remote Sensing J. Zhou et al. https://doi.org/10.1109/JSTARS.2026.3682497
- Divergent Responses of Multi-frequency Vegetation Optical Depth Products to Climate Variations in China M. He et al. https://doi.org/10.34133/remotesensing.1028
- VODCA v2: multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring R. Zotta et al. https://doi.org/10.5194/essd-16-4573-2024
- Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements L. Oberhagemann et al. https://doi.org/10.5194/gmd-18-2021-2025
- Satellite canopy water content from Sentinel-2, Landsat-8 and MODIS: Principle, algorithm and assessment H. Ma et al. https://doi.org/10.1016/j.rse.2025.114801
- A Review on Soil Moisture Dynamics Monitoring in Semi-Arid Ecosystems: Methods, Techniques, and Tools Applied at Different Scales E. Duarte & A. Hernandez https://doi.org/10.3390/app14177677
- Resilience Indicators for Tropical Rainforests in a Dynamic Vegetation Model S. Bathiany et al. https://doi.org/10.1111/gcb.17613
- Application of leaf water content measurement to improve herbicide efficacy for effective weed management in a changing climate – A review T. Abbas et al. https://doi.org/10.1016/j.cropro.2025.107138
- Widespread and complex drought effects on vegetation physiology inferred from space W. Li et al. https://doi.org/10.1038/s41467-023-40226-9
- Robustness of Vegetation Optical Depth Retrievals Based on L-Band Global Radiometry D. Chaparro et al. https://doi.org/10.1109/TGRS.2022.3201581
- Improving AMSR2 vegetation optical depth retrievals via land parameter retrieval model parameter optimisation R. Zotta et al. https://doi.org/10.1016/j.rse.2026.115286
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- Assessing the effects of forest structure on vegetation optical depth in boreal forests N. Ajithkumar et al. https://doi.org/10.1080/02827581.2026.2633371
- Proximal measurements of microwave reflectance using GNSS-IR track semi-arid grassland vegetation dynamics during greening and browning phases C. Devine et al. https://doi.org/10.1016/j.agrformet.2026.111136
- Assessing plant water status: Part 2 – Non‐destructive and remote sensing approaches N. Farooq et al. https://doi.org/10.1002/jsfa.70833
- Understanding drivers and biases of simulated CO emissions from the INFERNO fire model over South America M. Velásquez-García et al. https://doi.org/10.5194/bg-23-1341-2026
- A global fuel characteristic model and dataset for wildfire prediction J. McNorton & F. Di Giuseppe https://doi.org/10.5194/bg-21-279-2024
- Enhancing two-week live fuel moisture content forecasts through biophysical modelling and remote sensing data assimilation Q. Jia et al. https://doi.org/10.1016/j.rse.2026.115267
- Divergence between in situ and satellite-based estimates of forest canopy water content J. Kesselring et al. https://doi.org/10.1016/j.rse.2025.115097
- Vegetation optical depth as a key predictor for fire risk escalation D. Kankanige et al. https://doi.org/10.1016/j.ecoinf.2025.103050
Saved (final revised paper)
Latest update: 29 Sep 2026
Short summary
The live fuel moisture content (LFMC) of vegetation canopies is a driver of wildfires. We investigate the relation between LFMC and passive microwave satellite observations of vegetation optical depth (VOD) and develop a method to estimate LFMC from VOD globally. Our global VOD-based estimates of LFMC can be used to investigate drought effects on vegetation and fire risks.
The live fuel moisture content (LFMC) of vegetation canopies is a driver of wildfires. We...
