10 citations as recorded by crossref.

1. Soil Drought Anomalies in MODIS GPP of a Mediterranean Broadleaved Evergreen Forest J. Liu et al. https://doi.org/10.3390/rs70101154
2. Ground-Penetrating Radar and Electromagnetic Induction: Challenges and Opportunities in Agriculture S. Pathirana et al. https://doi.org/10.3390/rs15112932
3. Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method H. Yan et al. https://doi.org/10.1109/TGRS.2015.2432067
4. Coupled land surface–subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra A. Tran et al. https://doi.org/10.5194/tc-11-2089-2017
5. Particle filter–based data assimilation in dynamic data-driven simulation: sensitivity analysis of three critical experimental conditions Y. Huang et al. https://doi.org/10.1177/00375497221143988
6. On the uncertainty of initial condition and initialization approaches in variably saturated flow modeling D. Yu et al. https://doi.org/10.5194/hess-23-2897-2019
7. Performance of SMAP, AMSR-E and LAI for weekly agricultural drought forecasting over continental United States D. Liu et al. https://doi.org/10.1016/j.jhydrol.2017.07.049
8. Evaluating uncertainties in multi-layer soil moisture estimation with support vector machines and ensemble Kalman filtering D. Liu et al. https://doi.org/10.1016/j.jhydrol.2016.04.021
9. Estimation of the Soil Moisture Content in a Desert Steppe on the Mongolian Plateau Based on Ground-Penetrating Radar K. Li et al. https://doi.org/10.3390/su16198558
10. Joint estimation of soil moisture profile and hydraulic parameters by ground‐penetrating radar data assimilation with maximum likelihood ensemble filter A. Tran et al. https://doi.org/10.1002/2013WR014583