Cargando…
Daily Soil Moisture Retrieval by Fusing CYGNSS and Multi-Source Auxiliary Data Using Machine Learning Methods
The Cyclone Global Navigation Satellite System (CYGNSS), a publicly accessible spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data, provides a new alternative opportunity for large-scale soil moisture (SM) retrieval, but with interference from complex environmental conditions (...
Autores principales: | Yang, Ting, Wang, Jundong, Sun, Zhigang, Li, Sen |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10674751/ https://www.ncbi.nlm.nih.gov/pubmed/38005454 http://dx.doi.org/10.3390/s23229066 |
Ejemplares similares
-
Daily Spatial Complete Soil Moisture Mapping Over Southeast China Using CYGNSS and MODIS Data
por: Yang, Ting, et al.
Publicado: (2022) -
Towards Wind Vector and Wave Height Retrievals Over Inland Waters Using CYGNSS
por: Loria, Eric, et al.
Publicado: (2021) -
Soil Moisture Retrieval in Farmland Areas with Sentinel Multi-Source Data Based on Regression Convolutional Neural Networks
por: Liu, Jian, et al.
Publicado: (2021) -
High-resolution European daily soil moisture derived with machine learning (2003–2020)
por: O, Sungmin, et al.
Publicado: (2022) -
Machine learning based estimation of field-scale daily, high resolution, multi-depth soil moisture for the Western and Midwestern United States
por: Xia, Yushu, et al.
Publicado: (2022)