74 citations as recorded by crossref.

1. 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
2. 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
3. The Retrieval of 30-m Resolution LAI from Landsat Data by Combining MODIS Products J. Zhou et al. https://doi.org/10.3390/rs10081187
4. 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
5. Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions A. Rajib et al. https://doi.org/10.3390/rs12132148
6. Assimilation of Remotely Sensed LAI Into CLM4CN Using DART X. Ling et al. https://doi.org/10.1029/2019MS001634
7. The influence of assimilating leaf area index in a land surface model on global water fluxes and storages X. Zhang et al. https://doi.org/10.5194/hess-24-3775-2020
8. When does a parsimonious model fail to simulate floods? Learning from the seasonality of model bias P. Astagneau et al. https://doi.org/10.1080/02626667.2021.1923720
9. Towards constraining soil and vegetation dynamics in land surface models: Modeling ASCAT backscatter incidence-angle dependence with a Deep Neural Network X. Shan et al. https://doi.org/10.1016/j.rse.2022.113116
10. Assimilation of vegetation optical depth retrievals from passive microwave radiometry S. Kumar et al. https://doi.org/10.5194/hess-24-3431-2020
11. A synthetic experiment to investigate the potential of assimilating LAI through direct insertion in a land surface model A. Rahman et al. https://doi.org/10.1016/j.hydroa.2020.100063
12. 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
13. Quantifying Drought Propagation from Soil Moisture to Vegetation Dynamics Using a Newly Developed Ecohydrological Land Reanalysis Y. Sawada https://doi.org/10.3390/rs10081197
14. Absolute versus temporal anomaly and percent of saturation soil moisture spatial variability for six networks worldwide L. Brocca et al. https://doi.org/10.1002/2014WR015684
15. 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
16. Simulating the effects of weather and climate on large wildfires in France R. Barbero et al. https://doi.org/10.5194/nhess-19-441-2019
17. Monitoring Soil Moisture Drought over Northern High Latitudes from Space J. Blyverket et al. https://doi.org/10.3390/rs11101200
18. Improving Dynamic Vegetation Modeling in Noah‐MP by Parameter Optimization and Data Assimilation Over China's Loess Plateau Z. Shu et al. https://doi.org/10.1029/2022JD036703
19. Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area D. Leroux et al. https://doi.org/10.3390/rs10081199
20. A First Assessment of the SMOS Soil Moisture Product With In Situ and Modeled Data in Italy and Luxembourg T. Lacava et al. https://doi.org/10.1109/TGRS.2012.2186819
21. 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
22. 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
23. Impacts of shifting phenology on boundary layer dynamics in North America in the CESM X. Li et al. https://doi.org/10.1016/j.agrformet.2022.109286
24. 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
25. 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
26. 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
27. Long-term Simulation of Gross Primary Productivity and its Impact Evaluation Based on a Mechanism and Data Co-Driven Model X. Zhang et al. https://doi.org/10.1007/s11269-025-04239-x
28. Two perspectives on the coupled carbon, water and energy exchange in the planetary boundary layer M. Combe et al. https://doi.org/10.5194/bg-12-103-2015
29. Improved meteorology from an updated WRF/CMAQ modeling system with MODIS vegetation and albedo L. Ran et al. https://doi.org/10.1002/2015JD024406
30. Modelagem de Fluxos Superficiais entre uma Cultura de Soja e a Atmosfera A. Santos et al. https://doi.org/10.1590/0102-7786344061
31. Editorial for the Special Issue “Assimilation of Remote Sensing Data into Earth System Models” J. Calvet et al. https://doi.org/10.3390/rs11182177
32. A land data assimilation system for simultaneous simulation of soil moisture and vegetation dynamics Y. Sawada et al. https://doi.org/10.1002/2014JD022895
33. Global Assimilation of Remotely Sensed Leaf Area Index: The Impact of Updating More State Variables Within a Land Surface Model A. Rahman et al. https://doi.org/10.3389/frwa.2021.789352
34. Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method L. Zeng et al. https://doi.org/10.3390/rs11030284
35. Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation C. Albergel et al. https://doi.org/10.3390/rs11050520
36. European heatwaves: Link to large‐scale circulation patterns and intraseasonal drivers E. Rouges et al. https://doi.org/10.1002/joc.8024
37. Impact of precipitation and land biophysical variables on the simulated discharge of European and Mediterranean rivers C. Szczypta et al. https://doi.org/10.5194/hess-16-3351-2012
38. Evaluation of a Data Assimilation System for Land Surface Models Using CLM4.5 A. Fox et al. https://doi.org/10.1029/2018MS001362
39. An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. https://doi.org/10.1029/2022MS003259
40. An Exponential Filter Model-Based Root-Zone Soil Moisture Estimation Methodology from Multiple Datasets Y. Yang et al. https://doi.org/10.3390/rs14081785
41. Characterization and intercomparison of global moderate resolution leaf area index (LAI) products: Analysis of climatologies and theoretical uncertainties H. Fang et al. https://doi.org/10.1002/jgrg.20051
42. Land‐surface interactions with the atmosphere over the Iberian Semi‐arid Environment (LIAISE): First mesoscale modelling intercomparison M. Jiménez et al. https://doi.org/10.1002/qj.4949
43. Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France D. Shamambo et al. https://doi.org/10.3390/rs11232842
44. The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes V. Masson et al. https://doi.org/10.5194/gmd-6-929-2013
45. Plant water-stress parameterization determines the strength of land–atmosphere coupling M. Combe et al. https://doi.org/10.1016/j.agrformet.2015.11.006
46. Comparison and Analysis of Data Assimilation Algorithms for Predicting the Leaf Area Index of Crop Canopies Y. Dong et al. https://doi.org/10.1109/JSTARS.2012.2208943
47. Comparison of different sequential assimilation algorithms for satellite-derived leaf area index using the Data Assimilation Research Testbed (version Lanai) X. Ling et al. https://doi.org/10.5194/gmd-12-3119-2019
48. Simultaneous assimilation of satellite and eddy covariance data for improving terrestrial water and carbon simulations at a semi-arid woodland site in Botswana T. Kato et al. https://doi.org/10.5194/bg-10-789-2013
49. Use of agricultural statistics to verify the interannual variability in land surface models: a case study over France with ISBA-A-gs J. Calvet et al. https://doi.org/10.5194/gmd-5-37-2012
50. Sensitivity of the Weather Research and Forecast/Community Multiscale Air Quality modeling system to MODIS LAI, FPAR, and albedo L. Ran et al. https://doi.org/10.1002/2015JD023424
51. Investigating the impact of leaf area index temporal variability on soil moisture predictions using remote sensing vegetation data M. Chen et al. https://doi.org/10.1016/j.jhydrol.2014.12.027
52. An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications H. Fang et al. https://doi.org/10.1029/2018RG000608
53. 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
54. Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region L. Fan et al. https://doi.org/10.1016/j.rse.2017.11.020
55. Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA O. Rojas-Munoz et al. https://doi.org/10.3390/rs15174329
56. A Comparison of Land Surface Phenology in the Northern Hemisphere Derived from Satellite Remote Sensing and the Community Land Model X. Li et al. https://doi.org/10.1175/JHM-D-21-0169.1
57. High-Resolution SMAP-Derived Root-Zone Soil Moisture Using an Exponential Filter Model Calibrated per Land Cover Type V. Stefan et al. https://doi.org/10.3390/rs13061112
58. 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
59. A multi-sourced assessment of the spatiotemporal dynamics of soil moisture in the MARINE flash flood model J. Eeckman et al. https://doi.org/10.5194/hess-25-1425-2021
60. Impact of Real‐Time Satellite‐Derived Green Vegetation Fraction on National Water Model Fluxes in Humid Alabama Watersheds N. Elmer et al. https://doi.org/10.1111/1752-1688.12982
61. Estimating root zone soil moisture using near-surface observations from SMOS T. Ford et al. https://doi.org/10.5194/hess-18-139-2014
62. Assimilation of Deformation Data for Eruption Forecasting: Potentiality Assessment Based on Synthetic Cases M. Bato et al. https://doi.org/10.3389/feart.2017.00048
63. Modelling LAI, surface water and carbon fluxes at high-resolution over France: comparison of ISBA-A-gs and ORCHIDEE S. Lafont et al. https://doi.org/10.5194/bg-9-439-2012
64. 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
65. Improving Surface Soil Moisture Estimation Through Assimilating Satellite Land Surface Temperature With a Linear SM-LST Relationship J. Tian et al. https://doi.org/10.1109/JSTARS.2023.3305888
66. Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France A. Barbu et al. https://doi.org/10.5194/hess-18-173-2014
67. Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review C. Montzka et al. https://doi.org/10.3390/s121216291
68. 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
69. 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
70. Assimilation of Surface- and Root-Zone ASCAT Soil Moisture Products Into Rainfall–Runoff Modeling L. Brocca et al. https://doi.org/10.1109/TGRS.2011.2177468
71. Global Gap-Free MERIS LAI Time Series (2002–2012) M. Tum et al. https://doi.org/10.3390/rs8010069
72. Assimilation of Remotely Sensed Leaf Area Index for Improving Land Surface Simulation Performance at a Global Scale X. Ling et al. https://doi.org/10.1109/JSTARS.2024.3388006
73. Assimilating ASCAT normalized backscatter and slope into the land surface model ISBA-A-gs using a Deep Neural Network as the observation operator: Case studies at ISMN stations in western Europe X. Shan et al. https://doi.org/10.1016/j.rse.2024.114167
74. 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