Machine-Learning-Based Algorithm for Significant Wave Height Retrieval From Microwave Radiometer During Tropical Cyclones

This study develops a machine-learning-based approach to retrieve significant wave height (SWH) from soil moisture active passive (SMAP) radiometer data under tropical cyclone (TC) conditions, accounting for limited fetch effects. Using over 1000 SMAP measurements within 400 km of the eye from 400 T...

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Vydané v:	IEEE geoscience and remote sensing letters Ročník 22; s. 1 - 5
Hlavní autori:	Hu, Yuyi, Shao, Weizeng, Xu, Ying, Jiang, Xingwei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Altimeters Artificial neural networks Cyclones Extraterrestrial measurements Hurricanes Learning algorithms Machine learning Maximum winds Microwave imaging Microwave radiometers Microwave radiometry Microwave theory and techniques Moisture content Neural networks Radio frequency Radiometers Remote sensing Root-mean-square errors Sea measurements Sea surface Significant wave height significant wave height (SWH) Soil moisture tropical cyclone (TC) Tropical cyclones Wave height Wind speed
ISSN:	1545-598X, 1558-0571
On-line prístup:	Získať plný text
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