An Effective Land Type Labeling Approach for Independently Exploiting High-Resolution Soil Moisture Products Based on CYGNSS Data

An Effective Land Type Labeling Approach for Independently Exploiting High-Resolution Soil Moisture Products Based on CYGNSS Data

Recently, soil moisture (SM) has been estimated using Cyclone Global Navigation Satellite System (CYGNSS) data. Machine learning (ML) algorithms for CYGNSS SM estimation can minimize unpredictable influences and help improve the accuracy of SM retrieval. However, ML-based CYGNSS SM estimation requir...

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Bibliographic Details
Main Authors: Yan Jia, Shuanggen Jin, Qingyun Yan, Patrizia Savi, Rongchun Zhang, Wenmei Li
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Cyclone-GNSS (CYGNSS)
global navigation satellite system-reflectometry (GNSS-R)
machine learning (ML)
soil moisture (SM)
soil moisture active passive (SMAP)
Online Access:https://ieeexplore.ieee.org/document/9779408/

Internet

https://ieeexplore.ieee.org/document/9779408/

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