70 citations as recorded by crossref.

1. 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
2. Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model E. Gelati et al. https://doi.org/10.5194/hess-22-2091-2018
3. Modelagem de Fluxos Superficiais entre uma Cultura de Soja e a Atmosfera A. Santos et al. https://doi.org/10.1590/0102-7786344061
4. Comparison of remote sensing and simulated soil moisture datasets in Mediterranean landscapes M. Escorihuela & P. Quintana-Seguí https://doi.org/10.1016/j.rse.2016.02.046
5. Application of SMOS Soil Moisture and Brightness Temperature at High Resolution With a Bias Correction Operator K. Kornelsen et al. https://doi.org/10.1109/JSTARS.2015.2474266
6. Simultaneous assimilation of SMOS soil moisture and atmospheric CO2 in-situ observations to constrain the global terrestrial carbon cycle M. Scholze et al. https://doi.org/10.1016/j.rse.2016.02.058
7. Validation of a new SAFRAN-based gridded precipitation product for Spain and comparisons to Spain02 and ERA-Interim P. Quintana-Seguí et al. https://doi.org/10.5194/hess-21-2187-2017
8. Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation C. Albergel et al. https://doi.org/10.3390/rs11050520
9. Using SMOS soil moisture data combining CO2 flask samples to constrain carbon fluxes during 2010–2015 within a Carbon Cycle Data Assimilation System (CCDAS) M. Wu et al. https://doi.org/10.1016/j.rse.2020.111719
10. Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA O. Rojas-Munoz et al. https://doi.org/10.3390/rs15174329
11. NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment M. Jasinski et al. https://doi.org/10.1175/JHM-D-17-0234.1
12. Validation of global moderate resolution leaf area index (LAI) products over croplands in northeastern China H. Fang et al. https://doi.org/10.1016/j.rse.2019.111377
13. Monitoring and Predicting Agricultural Droughts for a Water-Limited Subcontinental Region by Integrating a Land Surface Model and Microwave Remote Sensing Y. Sawada et al. https://doi.org/10.1109/TGRS.2019.2927342
14. Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France D. Shamambo et al. https://doi.org/10.3390/rs11232842
15. Explaining Anomalies in SAR and Scatterometer Soil Moisture Retrievals From Dry Soils With Subsurface Scattering K. Morrison & W. Wagner https://doi.org/10.1109/TGRS.2019.2954771
16. Simultaneous Assimilation of Remotely Sensed Soil Moisture and FAPAR for Improving Terrestrial Carbon Fluxes at Multiple Sites Using CCDAS M. Wu et al. https://doi.org/10.3390/rs11010027
17. 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
18. Joint assimilation of satellite soil moisture and vegetation conditions improves estimates of gross primary production and evapotranspiration over South Asia A. Chakraborty & M. Saharia https://doi.org/10.1016/j.agrformet.2025.110765
19. High-Resolution Soil Moisture Retrieval With ASCAT D. Lindell & D. Long https://doi.org/10.1109/LGRS.2016.2557321
20. Assimilation of Remotely Sensed Leaf Area Index Enhances the Estimation of Anthropogenic Irrigation Water Use W. Nie et al. https://doi.org/10.1029/2022MS003040
21. Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF‐Hydro System T. Lahmers et al. https://doi.org/10.1029/2021WR029867
22. Parameter optimisation for a better representation of drought by LSMs: inverse modelling vs. sequential data assimilation H. Dewaele et al. https://doi.org/10.5194/hess-21-4861-2017
23. Assimilation of Remotely Sensed LAI Into CLM4CN Using DART X. Ling et al. https://doi.org/10.1029/2019MS001634
24. Combined assimilation of streamflow and satellite soil moisture with the particle filter and geostatistical modeling H. Yan & H. Moradkhani https://doi.org/10.1016/j.advwatres.2016.06.002
25. An ensemble square root filter for the joint assimilation of surface soil moisture and leaf area index within the Land Data Assimilation System LDAS-Monde: application over the Euro-Mediterranean region B. Bonan et al. https://doi.org/10.5194/hess-24-325-2020
26. The International Soil Moisture Network: serving Earth system science for over a decade W. Dorigo et al. https://doi.org/10.5194/hess-25-5749-2021
27. Effect of Assimilating SMAP Soil Moisture on CO2 and CH4 Fluxes through Direct Insertion in a Land Surface Model Z. Zhang et al. https://doi.org/10.3390/rs14102405
28. The first global soil moisture and vegetation optical depth product retrieved from fused SMOS and SMAP L-band observations X. Li et al. https://doi.org/10.1016/j.rse.2022.113272
29. Vegetation recovery drivers at short-term after fire are plant community-dependent in mediterranean burned landscapes J. Fernández-Guisuraga et al. https://doi.org/10.1016/j.foreco.2023.121034
30. Improved ET assimilation through incorporating SMAP soil moisture observations using a coupled process model: A study of U.S. arid and semiarid regions S. Li et al. https://doi.org/10.1016/j.jhydrol.2020.125402
31. Interconnected hydrologic extreme drivers and impacts depicted by remote sensing data assimilation T. Lahmers et al. https://doi.org/10.1038/s41598-023-30484-4
32. From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States A. Mucia et al. https://doi.org/10.3390/rs12122020
33. Soil Moisture Assimilation Improves Terrestrial Biosphere Model GPP Responses to Sub-Annual Drought at Continental Scale X. Xing et al. https://doi.org/10.3390/rs15030676
34. GEOCLIM: A global climatology of LAI, FAPAR, and FCOVER from VEGETATION observations for 1999–2010 A. Verger et al. https://doi.org/10.1016/j.rse.2015.05.027
35. Assimilation of vegetation optical depth retrievals from passive microwave radiometry S. Kumar et al. https://doi.org/10.5194/hess-24-3431-2020
36. 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
37. Improving the Seasonal Representation of ASCAT Soil Moisture and Vegetation Dynamics in a Temperate Climate I. Pfeil et al. https://doi.org/10.3390/rs10111788
38. Assimilation of Remotely Sensed Leaf Area Index into the Noah-MP Land Surface Model: Impacts on Water and Carbon Fluxes and States over the Continental United States S. Kumar et al. https://doi.org/10.1175/JHM-D-18-0237.1
39. Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems M. Scholze et al. https://doi.org/10.5194/bg-14-3401-2017
40. TerrSysMP–PDAF (version 1.0): a modular high-performance data assimilation framework for an integrated land surface–subsurface model W. Kurtz et al. https://doi.org/10.5194/gmd-9-1341-2016
41. Satellite observed aboveground carbon dynamics in Africa during 2003–2021 M. Wang et al. https://doi.org/10.1016/j.rse.2023.113927
42. ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better? C. Albergel et al. https://doi.org/10.5194/hess-22-3515-2018
43. Cross-ECV consistency at global scale: LAI and FAPAR changes B. Mota et al. https://doi.org/10.1016/j.rse.2021.112561
44. How does precipitation data influence the land surface data assimilation for drought monitoring? K. Gavahi et al. https://doi.org/10.1016/j.scitotenv.2022.154916
45. Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area D. Leroux et al. https://doi.org/10.3390/rs10081199
46. Towards a Long-Term Reanalysis of Land Surface Variables over Western Africa: LDAS-Monde Applied over Burkina Faso from 2001 to 2018 M. Tall et al. https://doi.org/10.3390/rs11060735
47. How Does Precipitation Data Influence the Land Surface Data Assimilation Drought Monitoring? K. Gavahi et al. https://doi.org/10.2139/ssrn.3993358
48. NCA-LDAS Land Analysis: Development and Performance of a Multisensor, Multivariate Land Data Assimilation System for the National Climate Assessment S. Kumar et al. https://doi.org/10.1175/JHM-D-17-0125.1
49. Towards ecohydrological drought monitoring and prediction using a land data assimilation system: A case study on the Horn of Africa drought (2010–2011) Y. Sawada & T. Koike https://doi.org/10.1002/2015JD024705
50. 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
51. Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters M. Maertens et al. https://doi.org/10.5194/hess-25-4099-2021
52. 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
53. A parameterization of SEVIRI and MODIS daily surface albedo with soil moisture: Calibration and validation over southwestern France S. Liu et al. https://doi.org/10.1016/j.rse.2014.01.016
54. LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the Contiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables C. Albergel et al. https://doi.org/10.3390/rs10101627
55. Impact of climate, vegetation, soil and crop management variables on multi-year ISBA-A-gs simulations of evapotranspiration over a Mediterranean crop site S. Garrigues et al. https://doi.org/10.5194/gmd-8-3033-2015
56. SMOS brightness temperature forward modelling and long term monitoring at ECMWF P. de Rosnay et al. https://doi.org/10.1016/j.rse.2019.111424
57. Assimilation of ASCAT Radar Backscatter Coefficients over Southwestern France T. Corchia et al. https://doi.org/10.3390/rs15174258
58. Analysis of current validation practices in Europe for space-based climate data records of essential climate variables Y. Zeng et al. https://doi.org/10.1016/j.jag.2015.06.006
59. Development of Surface Data Assimilation Using Simplified Extended Kalman Filter in AROME Model in Hungary H. Tóth et al. https://doi.org/10.3390/atmos16060709
60. The effect of satellite-derived surface soil moisture and leaf area index land data assimilation on streamflow simulations over France D. Fairbairn et al. https://doi.org/10.5194/hess-21-2015-2017
61. Evaluation of an Adaptive Soil Moisture Bias Correction Approach in the ECMWF Land Data Assimilation System D. Fairbairn et al. https://doi.org/10.3390/rs16030493
62. Assimilation of surface soil moisture into a multilayer soil model: design and evaluation at local scale M. Parrens et al. https://doi.org/10.5194/hess-18-673-2014
63. Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces C. Albergel et al. https://doi.org/10.5194/hess-24-4291-2020
64. Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review R. Khandan et al. https://doi.org/10.3390/rs14030770
65. Joint assimilation of satellite-based surface soil moisture and vegetation conditions into the Noah-MP land surface model Z. Heyvaert et al. https://doi.org/10.1016/j.srs.2024.100129
66. SMOS near-real-time soil moisture product: processor overview and first validation results N. Rodríguez-Fernández et al. https://doi.org/10.5194/hess-21-5201-2017
67. 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
68. Towards effective drought monitoring in the Middle East and North Africa (MENA) region: implications from assimilating leaf area index and soil moisture into the Noah-MP land surface model for Morocco W. Nie et al. https://doi.org/10.5194/hess-26-2365-2022
69. ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions W. Dorigo et al. https://doi.org/10.1016/j.rse.2017.07.001
70. A numerical analysis of aggregation error in evapotranspiration estimates due to heterogeneity of soil moisture and leaf area index Q. Chen et al. https://doi.org/10.1016/j.agrformet.2019.02.017