Evaluation of L-Band SMAP Scatterometer Tropical Cyclone Wind Retrieval Using UHR σ° Measurements

Tropical cyclones pose a significant hazard to coastal populations and shipping infrastructure. By helping provide timely warnings, ocean scatterometers can mitigate this threat with ocean wind estimates. In this study, the Soil Moisture Active/Passive (SMAP) mission is used to evaluate the effectiveness of wind estimates using L-band scatterometry for tropical cyclones. Standard-resolution SMAP scatterometer normalized radar backscatter (σ∘) measurements are enhanced to a much finer spatial resolution using the Ultra-High Resolution (UHR) algorithm, from which high-to-severe winds are estimated. The influence of σ∘ polarization (Horizontal HH-, Vertical VV-, and Cross X-pol) on wind estimates at high-to-severe winds is assessed. In this study, a new wind model is created with tropical cyclone Best-Track data, SMAP σ∘ measurements, and Numerical Weather Prediction winds. The Best-Track-derived wind model and SMAP UHR σ∘ measurements are used to create a Geophysical Model Function (GMF), and wind retrieval is performed. Analysis of wind retrieval estimates demonstrates that the HH- and VV-pol σ∘ tend to saturate at high-to-severe winds while X-pol measurements do not. Retrieved UHR winds generally align with the Best-Track derived wind model, although the errors can exceed desirable tolerances for speed and direction (i.e., less than 10% m/s and 20° Root Mean Square Error). Ultimately, these results suggest that L-band scatterometer wind estimation has potential for tropical cyclone monitoring by increasing the SNR of X-pol σ∘ measurements.

Authors

Institutions

Publication Details

Journal
Remote Sensing
Published
2026-10-15
DOI
https://doi.org/10.3390/rs18203530
Primary Topic
Ocean Waves and Remote Sensing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluation of L-Band SMAP Scatterometer Tropical Cyclone Wind Retrieval Using UHR σ° Measurements

David G. Long, Benjamin J. Fogg
Remote Sensing
Ocean Waves and Remote Sensing
article

Evaluation of L-Band SMAP Scatterometer Tropical Cyclone Wind Retrieval Using UHR σ° Measurements

David G. Long, Benjamin J. Fogg
article en

Abstract

Tropical cyclones pose a significant hazard to coastal populations and shipping infrastructure. By helping provide timely warnings, ocean scatterometers can mitigate this threat with ocean wind estimates. In this study, the Soil Moisture Active/Passive (SMAP) mission is used to evaluate the effectiveness of wind estimates using L-band scatterometry for tropical cyclones. Standard-resolution SMAP scatterometer normalized radar backscatter (σ∘) measurements are enhanced to a much finer spatial resolution using the Ultra-High Resolution (UHR) algorithm, from which high-to-severe winds are estimated. The influence of σ∘ polarization (Horizontal HH-, Vertical VV-, and Cross X-pol) on wind estimates at high-to-severe winds is assessed. In this study, a new wind model is created with tropical cyclone Best-Track data, SMAP σ∘ measurements, and Numerical Weather Prediction winds. The Best-Track-derived wind model and SMAP UHR σ∘ measurements are used to create a Geophysical Model Function (GMF), and wind retrieval is performed. Analysis of wind retrieval estimates demonstrates that the HH- and VV-pol σ∘ tend to saturate at high-to-severe winds while X-pol measurements do not. Retrieved UHR winds generally align with the Best-Track derived wind model, although the errors can exceed desirable tolerances for speed and direction (i.e., less than 10% m/s and 20° Root Mean Square Error). Ultimately, these results suggest that L-band scatterometer wind estimation has potential for tropical cyclone monitoring by increasing the SNR of X-pol σ∘ measurements.

Remote SensingVol. 18(20)
Brigham Young University (US)
Climate action
Openalex Percentile: Top 17%
Ocean Waves and Remote Sensing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.