Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach

Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach

Global navigation satellite system-reflectometry (GNSS-R) can retrieve Earth's surface parameters, such as soil moisture (SM) using the reflected signals from GNSS constellations with advantages of noncontact, all-weather, real-time, and continuity, particularly the space-borne cyclone GN...

Full description

Bibliographic Details
Main Authors: Yan Jia, Shuanggen Jin, Haolin Chen, Qingyun Yan, Patrizia Savi, Yan Jin, Yuan Yuan
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
CYGNSS
GNSS-Reflectometry
preclassifica- tion
SMAP
soil moisture
XGBoost
Online Access:https://ieeexplore.ieee.org/document/9419719/

Internet

https://ieeexplore.ieee.org/document/9419719/

Similar Items