55 citations as recorded by crossref.

1. Soil Moisture Data Assimilation in a Hydrological Model: A Case Study in Belgium Using Large-Scale Satellite Data P. Baguis & E. Roulin 10.3390/rs9080820
2. Estimating snow depth by combining satellite data and ground-based observations over Alaska: A deep learning approach J. Wang et al. 10.1016/j.jhydrol.2020.124828
3. Evaluating vegetation vulnerability under compound dry and hot conditions using vine copula across global lands G. Zhang et al. 10.1016/j.jhydrol.2024.130775
4. The Potential of Earth Observation for the Analysis of Cold Region Land Surface Dynamics in Europe—A Review Z. Hu et al. 10.3390/rs9101067
5. Remote sensing techniques for water management and climate change monitoring in drought areas: case studies in Egypt and Tunisia G. Ramat et al. 10.1080/22797254.2022.2157335
6. Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms L. Karthikeyan et al. 10.1016/j.advwatres.2017.09.006
7. On the synergy of SMAP, AMSR2 AND SENTINEL-1 for retrieving soil moisture E. Santi et al. 10.1016/j.jag.2017.10.010
8. Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization Q. Liu et al. 10.3390/s22041611
9. Editorial for the Special Issue “Microwave Indices from Active and Passive Sensors for Remote Sensing Applications” S. Paloscia & E. Santi 10.3390/rs11050561
10. Soil moisture retrieval from remote sensing measurements: Current knowledge and directions for the future Z. Li et al. 10.1016/j.earscirev.2021.103673
11. Merging active and passive microwave observations in soil moisture data assimilation J. Kolassa et al. 10.1016/j.rse.2017.01.015
12. Analysis of Microwave Emission and Related Indices Over Snow using Experimental Data and a Multilayer Electromagnetic Model E. Santi et al. 10.1109/TGRS.2016.2636363
13. Possibility of Estimating Seasonal Snow Depth Based Solely on Passive Microwave Remote Sensing on the Greenland Ice Sheet in Spring H. Tsutsui & T. Maeda 10.3390/rs9060523
14. Quantifying Uncertainties in Passive Microwave Remote Sensing of Soil Moisture via a Bayesian Probabilistic Inversion Method C. Ma et al. 10.1109/TGRS.2021.3123464
15. Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications S. Schilling et al. 10.3390/rs16061085
16. Monitoring of Alpine snow using satellite radiometers and artificial neural networks E. Santi et al. 10.1016/j.rse.2014.01.012
17. Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in-situ observations J. Zeng et al. 10.1016/j.rse.2015.03.008
18. Reliability of using vegetation optical depth for estimating decadal and interannual carbon dynamics Y. Dou et al. 10.1016/j.rse.2022.113390
19. Synergistic Evaluation of Passive Microwave and Optical/IR Data for Modelling Vegetation Transmissivity towards Improved Soil Moisture Retrieval M. Moradizadeh et al. 10.3390/s22041354
20. Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China Z. Lu et al. 10.3390/rs9060574
21. Application of artificial neural networks for the soil moisture retrieval from active and passive microwave spaceborne sensors E. Santi et al. 10.1016/j.jag.2015.08.002
22. A New Operational Northern Hemisphere Snow Water Equivalent Retrieval Algorithm for FY-3F/MWRI-II Based on Pixel-Based Regression Coefficients J. Yang et al. 10.1109/TGRS.2024.3479452
23. Deep learning in environmental remote sensing: Achievements and challenges Q. Yuan et al. 10.1016/j.rse.2020.111716
24. Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges J. Du et al. 10.3390/rs11161952
25. Review of snow water equivalent microwave remote sensing J. Shi et al. 10.1007/s11430-015-5225-0
26. Integration of microwave data from SMAP and AMSR2 for soil moisture monitoring in Italy E. Santi et al. 10.1016/j.rse.2018.04.039
27. A Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product Over United States and Europe Using Ground-Based Measurements J. Zeng et al. 10.1109/TGRS.2016.2553085
28. The Added Value of the VH/VV Polarization-Ratio for Global Soil Moisture Estimations From Scatterometer Data F. Greifeneder et al. 10.1109/JSTARS.2018.2865185
29. Vegetation Water Content Retrieval by Means of Multifrequency Microwave Acquisitions From AMSR2 E. Santi et al. 10.1109/JSTARS.2017.2703629
30. Validation of remotely sensed estimates of snow water equivalent using multiple reference datasets from the middle and high latitudes of China J. Yang et al. 10.1016/j.jhydrol.2020.125499
31. High-Resolution Mapping of Soil Moisture by AMSR2 Data Disaggregation Based on Sentinel-1 and Machine Learning E. Santi et al. 10.1109/JSTARS.2024.3445111
32. Digitizing the thermal and hydrological parameters of land surface in subtropical China using AMSR-E brightness temperatures Y. Su et al. 10.1080/17538947.2016.1247472
33. The Sensitivity of Cosmo-SkyMed Backscatter to Agricultural Crop Type and Vegetation Parameters S. Paloscia et al. 10.1109/JSTARS.2014.2345475
34. Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. A Hydrological Approach D. Alexakis et al. 10.3390/s17061455
35. Detecting Trends in Wetland Extent from MODIS Derived Soil Moisture Estimates T. Gumbricht 10.3390/rs10040611
36. Fusing Microwave and Optical Satellite Observations to Simultaneously Retrieve Surface Soil Moisture, Vegetation Water Content, and Surface Soil Roughness Y. Sawada et al. 10.1109/TGRS.2017.2722468
37. Improving soil moisture retrieval accuracy of Advanced Microwave Scanning Radiometer 2 in vegetated areas using land surface parameters of Visible Infrared Imaging Radiometer Suite S. Arab & G. Easson 10.1117/1.JRS.13.044520
38. Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach J. Yang et al. 10.5194/tc-14-1763-2020
39. Robust Assessment of an Operational Algorithm for the Retrieval of Soil Moisture From AMSR-E Data in Central Italy E. Santi et al. 10.1109/JSTARS.2016.2575361
40. Evaluation and Adjustment of the AMSR2 Snow Depth Algorithm for the Northern Xinjiang Region, China R. Zhang et al. 10.1109/JSTARS.2016.2620521
41. Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index P. Yao et al. 10.3390/rs9010035
42. Radiometric Microwave Indices for Remote Sensing of Land Surfaces S. Paloscia et al. 10.3390/rs10121859
43. A simplified physically-based algorithm for surface soil moisture retrieval using AMSR-E data J. Zeng et al. 10.1007/s11707-014-0412-4
44. A Novel Downscaling Approach Based on Multifrequency Microwave Radiometry Toward Finer Scale Global Soil Moisture Mapping P. Song et al. 10.1109/TGRS.2024.3490758
45. A Comprehensive Evaluation of Microwave Emissivity and Brightness Temperature Sensitivities to Soil Parameters Using Qualitative and Quantitative Sensitivity Analyses C. Ma et al. 10.1109/TGRS.2016.2618903
46. Improving snow depth estimation by coupling HUT-optimized effective snow grain size parameters with the random forest approach J. Yang et al. 10.1016/j.rse.2021.112630
47. Estimation of soil water content in watershed using artificial neural networks M. Campos de Oliveira et al. 10.1080/02626667.2017.1364844
48. Airborne 6GHz passive microwave observation of winter ground conditions in Alaska N. ALIMASI et al. 10.5331/seppyo.78.4_185
49. Remote monitoring of soil moisture using passive microwave-based techniques — Theoretical basis and overview of selected algorithms for AMSR-E I. Mladenova et al. 10.1016/j.rse.2014.01.013
50. Winter-spring transition of ground conditions over Alaska derived by airborne 6GHz microwave and infrared observations N. ALIMASI et al. 10.5331/seppyo.78.6_365
51. A review of passive microwave observations of snow-covered areas over complex Arctic terrain N. ALIMASI 10.5331/bgr.18W01
52. Method for Soil Moisture and Surface Temperature Estimation in the Tibetan Plateau Using Spaceborne Radiometer Observations J. Zeng et al. 10.1109/LGRS.2014.2326890
53. An Approach for Monitoring Global Vegetation Based on Multiangular Observations From SMOS Q. Cui et al. 10.1109/JSTARS.2015.2388698
54. Soil Moisture Retrieval Using Neural Networks: Application to SMOS N. Rodriguez-Fernandez et al. 10.1109/TGRS.2015.2430845
55. Global sensitivity analysis of the radiative transfer model M. Neelam & B. Mohanty 10.1002/2014WR016534