16 citations as recorded by crossref.

1. Evaluation of Remotely Sensed Soil Moisture for Landslide Hazard Assessment L. Zhuo et al.
2. Bayesian data fusion for combining maps of predicted soil classes: A case study using legacy soil profiles and DEM covariates in Iran Z. Rasaei & P. Bogaert
3. A comparison of CMIP5 and CMIP6 climate model projections for hydrological impacts in China Y. Lei et al.
4. A data‐driven approach for flood prediction using grid‐based meteorological data Y. Wang et al.
5. Performance improvement of hydrological models using Unscented Kalman Filter-type data assimilation and data fusion P. Almasi et al.
6. Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources S. Salcedo-Sanz et al.
7. Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends C. Gonzales-Inca et al.
8. Enhancing Global Surface Soil Moisture Estimation From ESA CCI and SMAP Product With a Conditional Variational Autoencoder C. Shi et al.
9. Streamflow Simulation with High-Resolution WRF Input Variables Based on the CNN-LSTM Hybrid Model and Gamma Test Y. Wang et al.
10. Spatial filtering and Bayesian data fusion for mapping soil properties: A case study combining legacy and remotely sensed data in Iran Z. Rasaei & P. Bogaert
11. Sensitive Feature Evaluation for Soil Moisture Retrieval Based on Multi-Source Remote Sensing Data with Few In-Situ Measurements: A Case Study of the Continental U.S. L. Zhang et al.
12. Soil moisture sensor network design for hydrological applications L. Zhuo et al.
13. Geo-Intelligent Retrieval Framework Based on Machine Learning in the Cloud Environment: A Case Study of Soil Moisture Retrieval Z. Li et al.
14. Evaluation of Soil Water Index of distributed tank model in a small basin with field data S. Vasconcellos et al.
15. Recent advancements of landslide hydrology R. Greco et al.
16. Study on Ensemble Calibration of Flood Forecasting Based on Response Curve of Rainfall Dynamic System and LSTM L. Tian et al.