Author Info
Name
Affiliation
Papers
RODRIGUEZ-FERNANDEZ, N.
CESBIO, UPS, Toulouse, France|c|
22
Collaborators
Citations 
PageRank 
121
23
8.09
Referers 
Referees 
References 
140
271
91
Search Limit
100271
Title
Citations
PageRank
Year
L-Band Data for Numerical Weather Prediction and Emergency Services at ECMWF.00.342021
Connected and Unconnected Synthetic Aperture Imaging Radiometry - A Preliminary Design for SMOS-Next Array.00.342021
A Follow-Up for the Soil Moisture and Ocean Salinity Mission.00.342021
Towards the Removal of Model Bias from ESA CCI SM by Using an L-Band Scaling Reference.00.342021
Global Assessment of Droughts in the Last Decade from SMOS Root Zone Soil Moisture.00.342021
Global Estimation of Surface Soil Moisture Using Neural Networks Trained by In-Situ Measurements and Passive L-Band Telemetry.00.342021
Soil moisture downscaling using multiple modes of the DISPATCH algorithm in a semi-humid/humid region00.342021
Influence of Surface Water Variations on Vod and Biomass Estimates from Passive Microwave Sensors.00.342021
Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records.00.342020
Preliminary System Studies on a High-Resolution SMOS Follow-On: SMOS-HR00.342019
Soil Moisture Remote Sensing across Scales.00.342019
After Almost 10 Years in Orbit - First Glance at Synergisms and New Results.00.342019
SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact.00.342019
SMOS-HR: A High Resolution L-Band Passive Radiometer for Earth Science and Applications00.342019
Evaluation of SMOS, SMAP, ASCAT and Sentinel-1 Soil Moisture Products at Sites in Southwestern France.40.442018
The Effect of Three Different Data Fusion Approaches on the Quality of Soil Moisture Retrievals from Multiple Passive Microwave Sensors.00.342018
SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product.90.552017
Global SMOS Soil Moisture Retrievals from The Land Parameter Retrieval Model80.602016
Smos After Six Years In Operations: First Glance At Climatic Trends And Anomalies00.342016
Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data.00.342016
First Application Of Regression Analysis To Retrieve Soil Moisture From Smap Brightness Temperature Observations Consistent With Smos00.342016
Soil moisture retrieval from SMOS observations using neural networks20.422014