28 citations as recorded by crossref.

1. Global Unsupervised Assessment of Multifrequency Vegetation Optical Depth Sensitivity to Vegetation Cover C. Olivares-Cabello et al. 10.1109/JSTARS.2022.3226001
2. 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
3. Global ecosystem-scale plant hydraulic traits retrieved using model–data fusion Y. Liu et al. 10.5194/hess-25-2399-2021
4. Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land S. Modanesi et al. 10.5194/hess-25-6283-2021
5. Soil moisture estimation in South Asia via assimilation of SMAP retrievals J. Ahmad et al. 10.5194/hess-26-2221-2022
6. 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
7. 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
8. 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
9. Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties L. Schmidt et al. 10.5194/bg-20-1027-2023
10. 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
11. 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
12. Interconnected hydrologic extreme drivers and impacts depicted by remote sensing data assimilation T. Lahmers et al. 10.1038/s41598-023-30484-4
13. Constraining Plant Hydraulics With Microwave Radiometry in a Land Surface Model: Impacts of Temporal Resolution N. Holtzman et al. 10.1029/2023WR035481
14. Impact of temperature and water availability on microwave-derived gross primary production I. Teubner et al. 10.5194/bg-18-3285-2021
15. 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
16. 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
17. 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
18. Changes in land use enhance the sensitivity of tropical ecosystems to fire-climate extremes S. Kumar et al. 10.1038/s41598-022-05130-0
19. 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
20. 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
21. Assimilation of ASCAT Radar Backscatter Coefficients over Southwestern France T. Corchia et al. 10.3390/rs15174258
22. 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
23. Assimilation of Remotely Sensed Leaf Area Index Enhances the Estimation of Anthropogenic Irrigation Water Use W. Nie et al. 10.1029/2022MS003040
24. 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
25. 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
26. An alternative AMSR2 vegetation optical depth for monitoring vegetation at large scales M. Wang et al. 10.1016/j.rse.2021.112556
27. The 2019–2020 Australian Drought and Bushfires Altered the Partitioning of Hydrological Fluxes S. Kumar et al. 10.1029/2020GL091411
28. 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