학술논문
Machine Learning Applications for Classification and Retrieval of Surface Parameters from GNSS-R
Document Type
Conference
Author
Source
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2023 - 2023 IEEE International. :1174-1177 Jul, 2023
Subject
Language
ISSN
2153-7003
Abstract
This study focuses on the retrieval of soil moisture (SMC) and forest Aboveground Biomass (AGB), and on the classification of fire disturbances in forests by using the NASA’s Cyclone GNSS (CyGNSS) data over land. Retrieval and classification algorithms, based on machine learning (ML) techniques, as Supported vector machines (SVM), Artificial Neural Networks (ANN) and Random Forests are implemented and validated against reference data from in-situ measurements and EO products.The research, which was carried out in the framework of two ESA project, has the twofold aim of further assessing the potential of GNSS-R for land applications and of defining retrieval concepts to be applied to the ESA’s SCOUT 2 HydroGNSS satellite mission.