Articles | Volume 24, issue 7
https://doi.org/10.5194/hess-24-3431-2020
© Author(s) 2020. 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-24-3431-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jul 2020
Research article |
| 07 Jul 2020
| 07 Jul 2020
Assimilation of vegetation optical depth retrievals from passive microwave radiometry
Sujay V. Kumar
CORRESPONDING AUTHOR
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Thomas R. Holmes
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Rajat Bindlish
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Richard de Jeu
Vandersat, Haarlem, the Netherlands
Christa Peters-Lidard
Hydrosphere, Biosphere, and Geophysics, Earth Sciences Division at NASA GSFC, Greenbelt, MD, USA
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Cited
29 citations as recorded by crossref.
- Global Unsupervised Assessment of Multifrequency Vegetation Optical Depth Sensitivity to Vegetation Cover C. Olivares-Cabello et al. 10.1109/JSTARS.2022.3226001
- Exploring the use of satellite observations of soil moisture, solar-induced chlorophyll fluorescence and vegetation optical depth to monitor droughts across India M. Likith et al. 10.1007/s12040-022-01848-7
- Global ecosystem-scale plant hydraulic traits retrieved using model–data fusion Y. Liu et al. 10.5194/hess-25-2399-2021
- Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land S. Modanesi et al. 10.5194/hess-25-6283-2021
- Soil moisture estimation in South Asia via assimilation of SMAP retrievals J. Ahmad et al. 10.5194/hess-26-2221-2022
- Joint assimilation of satellite-based surface soil moisture and vegetation conditions into the Noah-MP land surface model Z. Heyvaert et al. 10.1016/j.srs.2024.100129
- Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data R. Ma et al. 10.5194/gmd-15-6637-2022
- Using soil moisture information to better understand and predict wildfire danger: a review of recent developments and outstanding questions E. Krueger et al. 10.1071/WF22056
- Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties L. Schmidt et al. 10.5194/bg-20-1027-2023
- Assimilation of passive microwave vegetation optical depth in LDAS-Monde: a case study over the continental USA A. Mucia et al. 10.5194/bg-19-2557-2022
- An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. 10.1029/2022MS003259
- Interconnected hydrologic extreme drivers and impacts depicted by remote sensing data assimilation T. Lahmers et al. 10.1038/s41598-023-30484-4
- Constraining Plant Hydraulics With Microwave Radiometry in a Land Surface Model: Impacts of Temporal Resolution N. Holtzman et al. 10.1029/2023WR035481
- Impact of temperature and water availability on microwave-derived gross primary production I. Teubner et al. 10.5194/bg-18-3285-2021
- A consistent record of vegetation optical depth retrieved from the AMSR-E and AMSR2 X-band observations M. Wang et al. 10.1016/j.jag.2021.102609
- Towards effective drought monitoring in the Middle East and North Africa (MENA) region: implications from assimilating leaf area index and soil moisture into the Noah-MP land surface model for Morocco W. Nie et al. 10.5194/hess-26-2365-2022
- Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe S. Scherrer et al. 10.5194/hess-27-4087-2023
- Changes in land use enhance the sensitivity of tropical ecosystems to fire-climate extremes S. Kumar et al. 10.1038/s41598-022-05130-0
- The joint assimilation of satellite observed LAI and soil moisture for the global root zone soil moisture production and its impact on land surface and ecosystem variables Y. Xu et al. 10.1016/j.agrformet.2024.110299
- Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF‐Hydro System T. Lahmers et al. 10.1029/2021WR029867
- Challenges and benefits of quantifying irrigation through the assimilation of Sentinel-1 backscatter observations into Noah-MP S. Modanesi et al. 10.5194/hess-26-4685-2022
- Assimilation of ASCAT Radar Backscatter Coefficients over Southwestern France T. Corchia et al. 10.3390/rs15174258
- Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication G. De Lannoy et al. 10.3389/frwa.2022.981745
- Assimilation of Remotely Sensed Leaf Area Index Enhances the Estimation of Anthropogenic Irrigation Water Use W. Nie et al. 10.1029/2022MS003040
- Impacts of Fully Coupling Land Surface and Flood Models on the Simulation of Large Wetlands' Water Dynamics: The Case of the Inner Niger Delta A. Getirana et al. 10.1029/2021MS002463
- Assimilating ASCAT normalized backscatter and slope into the land surface model ISBA-A-gs using a Deep Neural Network as the observation operator: Case studies at ISMN stations in western Europe X. Shan et al. 10.1016/j.rse.2024.114167
- An alternative AMSR2 vegetation optical depth for monitoring vegetation at large scales M. Wang et al. 10.1016/j.rse.2021.112556
- The 2019–2020 Australian Drought and Bushfires Altered the Partitioning of Hydrological Fluxes S. Kumar et al. 10.1029/2020GL091411
- Improving Dynamic Vegetation Modeling in Noah‐MP by Parameter Optimization and Data Assimilation Over China's Loess Plateau Z. Shu et al. 10.1029/2022JD036703
29 citations as recorded by crossref.
- Global Unsupervised Assessment of Multifrequency Vegetation Optical Depth Sensitivity to Vegetation Cover C. Olivares-Cabello et al. 10.1109/JSTARS.2022.3226001
- Exploring the use of satellite observations of soil moisture, solar-induced chlorophyll fluorescence and vegetation optical depth to monitor droughts across India M. Likith et al. 10.1007/s12040-022-01848-7
- Global ecosystem-scale plant hydraulic traits retrieved using model–data fusion Y. Liu et al. 10.5194/hess-25-2399-2021
- Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land S. Modanesi et al. 10.5194/hess-25-6283-2021
- Soil moisture estimation in South Asia via assimilation of SMAP retrievals J. Ahmad et al. 10.5194/hess-26-2221-2022
- Joint assimilation of satellite-based surface soil moisture and vegetation conditions into the Noah-MP land surface model Z. Heyvaert et al. 10.1016/j.srs.2024.100129
- Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data R. Ma et al. 10.5194/gmd-15-6637-2022
- Using soil moisture information to better understand and predict wildfire danger: a review of recent developments and outstanding questions E. Krueger et al. 10.1071/WF22056
- Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties L. Schmidt et al. 10.5194/bg-20-1027-2023
- Assimilation of passive microwave vegetation optical depth in LDAS-Monde: a case study over the continental USA A. Mucia et al. 10.5194/bg-19-2557-2022
- An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. 10.1029/2022MS003259
- Interconnected hydrologic extreme drivers and impacts depicted by remote sensing data assimilation T. Lahmers et al. 10.1038/s41598-023-30484-4
- Constraining Plant Hydraulics With Microwave Radiometry in a Land Surface Model: Impacts of Temporal Resolution N. Holtzman et al. 10.1029/2023WR035481
- Impact of temperature and water availability on microwave-derived gross primary production I. Teubner et al. 10.5194/bg-18-3285-2021
- A consistent record of vegetation optical depth retrieved from the AMSR-E and AMSR2 X-band observations M. Wang et al. 10.1016/j.jag.2021.102609
- Towards effective drought monitoring in the Middle East and North Africa (MENA) region: implications from assimilating leaf area index and soil moisture into the Noah-MP land surface model for Morocco W. Nie et al. 10.5194/hess-26-2365-2022
- Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe S. Scherrer et al. 10.5194/hess-27-4087-2023
- Changes in land use enhance the sensitivity of tropical ecosystems to fire-climate extremes S. Kumar et al. 10.1038/s41598-022-05130-0
- The joint assimilation of satellite observed LAI and soil moisture for the global root zone soil moisture production and its impact on land surface and ecosystem variables Y. Xu et al. 10.1016/j.agrformet.2024.110299
- Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF‐Hydro System T. Lahmers et al. 10.1029/2021WR029867
- Challenges and benefits of quantifying irrigation through the assimilation of Sentinel-1 backscatter observations into Noah-MP S. Modanesi et al. 10.5194/hess-26-4685-2022
- Assimilation of ASCAT Radar Backscatter Coefficients over Southwestern France T. Corchia et al. 10.3390/rs15174258
- Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication G. De Lannoy et al. 10.3389/frwa.2022.981745
- Assimilation of Remotely Sensed Leaf Area Index Enhances the Estimation of Anthropogenic Irrigation Water Use W. Nie et al. 10.1029/2022MS003040
- Impacts of Fully Coupling Land Surface and Flood Models on the Simulation of Large Wetlands' Water Dynamics: The Case of the Inner Niger Delta A. Getirana et al. 10.1029/2021MS002463
- Assimilating ASCAT normalized backscatter and slope into the land surface model ISBA-A-gs using a Deep Neural Network as the observation operator: Case studies at ISMN stations in western Europe X. Shan et al. 10.1016/j.rse.2024.114167
- An alternative AMSR2 vegetation optical depth for monitoring vegetation at large scales M. Wang et al. 10.1016/j.rse.2021.112556
- The 2019–2020 Australian Drought and Bushfires Altered the Partitioning of Hydrological Fluxes S. Kumar et al. 10.1029/2020GL091411
- Improving Dynamic Vegetation Modeling in Noah‐MP by Parameter Optimization and Data Assimilation Over China's Loess Plateau Z. Shu et al. 10.1029/2022JD036703
Latest update: 27 Feb 2025
Short summary
Vegetation optical depth (VOD) is a byproduct of the soil moisture retrieval from passive microwave instruments. This study demonstrates that VOD information can be utilized for improving land surface water budget and carbon conditions through data assimilation.
Vegetation optical depth (VOD) is a byproduct of the soil moisture retrieval from passive...
