32 citations as recorded by crossref.

1. Suitability of modelled and remotely sensed essential climate variables for monitoring Euro-Mediterranean droughts C. Szczypta et al. https://doi.org/10.5194/gmd-7-931-2014
2. The joint assimilation of satellite observed LAI and soil moisture for the global root zone soil moisture production and its impact on land surface and ecosystem variables Y. Xu et al. https://doi.org/10.1016/j.agrformet.2024.110299
3. Assimilation of SMOS soil moisture into a distributed hydrological model and impacts on the water cycle variables over the Ouémé catchment in Benin D. Leroux et al. https://doi.org/10.5194/hess-20-2827-2016
4. Towards precision irrigation scheduling from UAV-based observations: Model structure M. Cao et al. https://doi.org/10.1016/j.envsoft.2026.107065
5. Enhancing Model Skill by Assimilating SMOPS Blended Soil Moisture Product into Noah Land Surface Model J. Yin et al. https://doi.org/10.1175/JHM-D-14-0070.1
6. Estimation of the L-Band Effective Scattering Albedo of Tropical Forests Using SMOS Observations M. Parrens et al. https://doi.org/10.1109/LGRS.2017.2703637
7. Assimilation of passive microwave vegetation optical depth in LDAS-Monde: a case study over the continental USA A. Mucia et al. https://doi.org/10.5194/bg-19-2557-2022
8. Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe S. Scherrer et al. https://doi.org/10.5194/hess-27-4087-2023
9. Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review G. Balsamo et al. https://doi.org/10.3390/rs10122038
10. Simultaneous assimilation of in situ soil moisture and streamflow in the SWAT model using the Extended Kalman Filter L. Sun et al. https://doi.org/10.1016/j.jhydrol.2016.10.040
11. Global ecosystem-scale plant hydraulic traits retrieved using model–data fusion Y. Liu et al. https://doi.org/10.5194/hess-25-2399-2021
12. Assimilation of Leaf Area Index and Soil Water Index from Satellite Observations in a Land Surface Model in Hungary H. Tóth & B. Szintai https://doi.org/10.3390/atmos12080944
13. Review of the Kalman-type hydrological data assimilation L. Sun et al. https://doi.org/10.1080/02626667.2015.1127376
14. Assimilation of SMOS‐derived soil moisture in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer model in Western Denmark M. Ridler et al. https://doi.org/10.1002/2014WR015392
15. SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact N. Rodríguez-Fernández et al. https://doi.org/10.3390/rs11111334
16. Warm Season Evaluation of Soil Moisture Prediction in the Soil, Vegetation, and Snow (SVS) Scheme N. Alavi et al. https://doi.org/10.1175/JHM-D-15-0189.1
17. Comparing the ensemble and extended Kalman filters for in situ soil moisture assimilation with contrasting conditions D. Fairbairn et al. https://doi.org/10.5194/hess-19-4811-2015
18. Soil moisture background error covariance and data assimilation in a coupled land‐atmosphere model L. Lin et al. https://doi.org/10.1002/2015WR017548
19. New Approach for Calculating the Effective Dielectric Constant of the Moist Soil for Microwaves C. Park et al. https://doi.org/10.3390/rs9070732
20. Characteristics of Background Error Covariance of Soil Moisture and Atmospheric States in Strongly Coupled Land–Atmosphere Data Assimilation L. Lin & Z. Pu https://doi.org/10.1175/JAMC-D-18-0050.1
21. Global-scale surface roughness effects at L-band as estimated from SMOS observations M. Parrens et al. https://doi.org/10.1016/j.rse.2016.04.006
22. Assimilation of Screen-level Observations for Soil Moisture Analysis in the INM RAS-MSU Multilayer Soil Model Included in the SL-AV Global Atmospheric Modeling System S. Travova & M. Tolstykh https://doi.org/10.3103/S1068373922080015
23. Impact of quality control of satellite soil moisture data on their assimilation into land surface model J. Yin et al. https://doi.org/10.1002/2014GL060659
24. Modeling soil evaporation efficiency in a range of soil and atmospheric conditions using a meta‐analysis approach O. Merlin et al. https://doi.org/10.1002/2015WR018233
25. Impact of leaf area index assimilation and gauge-corrected precipitation on land surface variables in LDAS-Monde: a case study over China E. Liu et al. https://doi.org/10.1016/j.jhydrol.2025.133304
26. Continental satellite soil moisture data assimilation improves root-zone moisture analysis for water resources assessment L. Renzullo et al. https://doi.org/10.1016/j.jhydrol.2014.08.008
27. Considering combined or separated roughness and vegetation effects in soil moisture retrievals M. Parrens et al. https://doi.org/10.1016/j.jag.2016.11.001
28. Reducing multiplicative bias of satellite soil moisture retrievals K. Kornelsen & P. Coulibaly https://doi.org/10.1016/j.rse.2015.04.031
29. Improving Soil Moisture Estimation via Assimilation of Remote Sensing Product into the DSSAT Crop Model and Its Effect on Agricultural Drought Monitoring H. Zhou et al. https://doi.org/10.3390/rs14133187
30. Impact of INSAT‐3D land surface temperature assimilation via simplified extended Kalman filter‐based land data assimilation system on forecasting of surface fields over India A. Lodh et al. https://doi.org/10.1002/met.70019
31. Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area C. Albergel et al. https://doi.org/10.5194/gmd-10-3889-2017
32. The Impact of Spatial Dynamic Error on the Assimilation of Soil Moisture Retrieval Products X. Bai et al. https://doi.org/10.3390/rs17020239