32 citations as recorded by crossref.

1. 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
2. PSInet: a new global water potential network A. Restrepo-Acevedo et al. https://doi.org/10.1093/treephys/tpae110
3. 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
4. The intricacies of vegetation responses to changing moisture conditions J. Green https://doi.org/10.1111/nph.20182
5. Retrieval of ground, snow, and forest parameters from space borne passive L band observations. A case study over Sodankylä, Finland M. Holmberg et al. https://doi.org/10.1016/j.rse.2024.114143
6. 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
7. Temporal Variations in $L$-Band Reflectivity Profiles of a Boreal Forest During Growth and Drought P. Bennet et al. https://doi.org/10.1109/JSTARS.2026.3690549
8. The pulse of global vegetation: do individual rainfall events create a lasting imprint on photosynthesis? A. Feldman https://doi.org/10.1111/nph.70463
9. Detection of tree stress from sub-daily sap flow variability A. Schackow et al. https://doi.org/10.5194/bg-23-3365-2026
10. On the real-time tropospheric delay estimates using low-cost GNSS receivers and antennas L. Li et al. https://doi.org/10.1007/s10291-024-01655-1
11. Exploring the Potential of Sub-daily Microwave Remote Sensing Observations for Estimating Evaporation in Forests E. Tronquo et al. https://doi.org/10.1109/JSTARS.2026.3679245
12. 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
13. The Ecosystem as Super-Organ/ism, Revisited: Scaling Hydraulics to Forests under Climate Change J. Wood et al. https://doi.org/10.1093/icb/icae073
14. Estimation of Forest Water Potential From Ground-Based L-Band Radiometry T. Jagdhuber et al. https://doi.org/10.1109/JSTARS.2025.3533567
15. 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
16. An Improved Model for Wheat Volumetric Water Content Estimation Using GNSS Refractometry Y. Li et al. https://doi.org/10.1109/LGRS.2025.3554789
17. A Semantic Segmentation-Based GNSS Signal Occlusion Detection and Optimization Method Z. Yue et al. https://doi.org/10.3390/rs17152725
18. SMAP Validation Experiment 2019–2022 (SMAPVEX19–22): Field Campaign to Improve Soil Moisture and Vegetation Optical Depth Retrievals in Temperate Forests A. Colliander et al. https://doi.org/10.1109/JSTARS.2025.3553085
19. A Forward Model and Inversion Algorithm for Near-Surface Soil Moisture Estimation With GNSS Refraction Pattern Technique Y. Li et al. https://doi.org/10.1109/TGRS.2024.3381441
20. Sap flow monitoring in environmental research networks: lessons learned from SAPFLUXNET and challenges for the future R. Poyatos et al. https://doi.org/10.17660/ActaHortic.2025.1419.6
21. A Novel Large Vision Foundation Model-Based Approach for Generating High-Resolution Canopy Height Maps in Plantations for Precision Forestry Management S. Tan et al. https://doi.org/10.34133/remotesensing.0880
22. Agriculture in silico: Perspectives on radiative transfer optimization using vegetation modeling Y. Wang & Y. Yin https://doi.org/10.1016/j.crope.2023.07.003
23. A review of forward modelling and retrieval approaches for forest soil moisture and vegetation optical depth using L-band radiometry A. Colliander et al. https://doi.org/10.1016/j.rse.2025.115158
24. A Probabilistic STA-Bayesian Algorithm for GNSS-R Retrieval of Arctic Soil Freeze–Thaw States X. Meng et al. https://doi.org/10.1109/JSTARS.2026.3668267
25. Separating Water-Level Variations and Phenological Changes in Rice Paddies: Integrating SAR with Ground-Based GNSS-IR Observations D. Kobayashi et al. https://doi.org/10.3390/rs18071055
26. 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
27. Estimating Vegetation Optical Depth With Mobile GNSS Transmissiometry in Temperate Forests During SMAPVEX22 A. Ghosh et al. https://doi.org/10.1109/JSTARS.2025.3541182
28. Using GNSS-based vegetation optical depth, tree sway motion, and eddy covariance to examine evaporation of canopy-intercepted rainfall in a subalpine forest S. Burns et al. https://doi.org/10.5194/bg-22-5741-2025
29. Deep Learning Framework for High-Resolution Large-Scale Vegetation Optical Depth Mapping Using Airborne LiDAR and Mobile GNSS-T Data A. Ghosh et al. https://doi.org/10.1109/JSTARS.2026.3675561
30. A UGV-Based Forest Vegetation Optical Depth Mapping Using GNSS Signals A. Ghosh et al. https://doi.org/10.1109/JSTARS.2024.3365798
31. Continuous monitoring of plant water potential: sensor‐based approaches and best practices A. Restrepo‐Acevedo et al. https://doi.org/10.1111/nph.71194
32. Deciphering tree drought responses across species: linking leaf water potentials with remote sensing greenness and photoprotection dynamics P. D’Odorico et al. https://doi.org/10.1016/j.agrformet.2025.110856