16 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.
2. Field scale computer modeling of soil moisture with dynamic nudging assimilation algorithm O. Kozhushko et al.
3. Unlocking the Potential of Remote Sensing in Wind Erosion Studies: A Review and Outlook for Future Directions L. Lackoóvá et al.
4. Using multimodal remote sensing data to estimate regional-scale soil moisture content: A case study of Beijing, China M. Cheng et al.
5. Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques M. Tahmouresi et al.
6. Runoff simulation of Mengjiang River Basin based on distributed hydrological model X. Chen et al.
7. Combining physical models and machine learning for enhanced soil moisture estimation M. Li et al.
8. On the relation between antecedent basin conditions and runoff coefficient for European floods C. Massari et al.
9. Improving soil moisture assimilation efficiency via model calibration using SMAP surface soil moisture climatology information J. Zhou et al.
10. Inverse Dynamic Parameter Identification for Remote Sensing of Soil Moisture From SMAP Satellite Observations R. Zhang et al.
11. Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning M. Cheng et al.
12. Variational Assimilation of the SMAP Surface Soil Moisture Retrievals into an Integrated Urban Land Model C. Meng et al.
13. Earth data assimilation in hydrologic models: recent advances S. Jeyalakshmi et al.
14. 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.
15. Soil Moisture Data Assimilation in MISDc for Improved Hydrological Simulation in Upper Huai River Basin, China Z. Ding et al.
16. Assimilation of remotely sensed evapotranspiration products for streamflow simulation based on the CAMELS data sets C. Deng et al.