17 citations as recorded by crossref.

1. Evaluation of the RF-Based Downscaled SMAP and SMOS Products Using Multi-Source Data over an Alpine Mountains Basin, Northwest China Y. Wen et al. https://doi.org/10.3390/w13202875
2. Field scale computer modeling of soil moisture with dynamic nudging assimilation algorithm O. Kozhushko et al. https://doi.org/10.23939/mmc2022.02.203
3. Unlocking the Potential of Remote Sensing in Wind Erosion Studies: A Review and Outlook for Future Directions L. Lackoóvá et al. https://doi.org/10.3390/rs15133316
4. Using multimodal remote sensing data to estimate regional-scale soil moisture content: A case study of Beijing, China M. Cheng et al. https://doi.org/10.1016/j.agwat.2021.107298
5. Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques M. Tahmouresi et al. https://doi.org/10.1038/s41598-024-77050-0
6. Runoff simulation of Mengjiang River Basin based on distributed hydrological model X. Chen et al. https://doi.org/10.1051/e3sconf/202562802007
7. Combining physical models and machine learning for enhanced soil moisture estimation M. Li et al. https://doi.org/10.1016/j.compag.2026.111480
8. Hydrologically relevant bias correction of GPM-IMERG: Performance across quantiles in upland tropical watershed E. Suhartanto et al. https://doi.org/10.1016/j.rineng.2026.110760
9. On the relation between antecedent basin conditions and runoff coefficient for European floods C. Massari et al. https://doi.org/10.1016/j.jhydrol.2023.130012
10. Improving soil moisture assimilation efficiency via model calibration using SMAP surface soil moisture climatology information J. Zhou et al. https://doi.org/10.1016/j.rse.2022.113161
11. Inverse Dynamic Parameter Identification for Remote Sensing of Soil Moisture From SMAP Satellite Observations R. Zhang et al. https://doi.org/10.1109/JSTARS.2024.3457941
12. Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning M. Cheng et al. https://doi.org/10.1016/j.agwat.2022.107530
13. Variational Assimilation of the SMAP Surface Soil Moisture Retrievals into an Integrated Urban Land Model C. Meng et al. https://doi.org/10.3103/S1068373924060037
14. Earth data assimilation in hydrologic models: recent advances S. Jeyalakshmi et al. https://doi.org/10.1080/00207233.2021.1875303
15. 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. https://doi.org/10.3389/frwa.2022.981745
16. Soil Moisture Data Assimilation in MISDc for Improved Hydrological Simulation in Upper Huai River Basin, China Z. Ding et al. https://doi.org/10.3390/w14213476
17. Assimilation of remotely sensed evapotranspiration products for streamflow simulation based on the CAMELS data sets C. Deng et al. https://doi.org/10.1016/j.jhydrol.2023.130574