Cargando…
Global soil moisture data derived through machine learning trained with in-situ measurements
While soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learn...
Autores principales: | O., Sungmin, Orth, Rene |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275613/ https://www.ncbi.nlm.nih.gov/pubmed/34253737 http://dx.doi.org/10.1038/s41597-021-00964-1 |
Ejemplares similares
-
High-resolution European daily soil moisture derived with machine learning (2003–2020)
por: O, Sungmin, et al.
Publicado: (2022) -
Global long term daily 1 km surface soil moisture dataset with physics informed machine learning
por: Han, Qianqian, et al.
Publicado: (2023) -
Global soil moisture data fusion by Triple Collocation Analysis from 2011 to 2018
por: Xie, Qiuxia, et al.
Publicado: (2022) -
A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019)
por: Yao, Panpan, et al.
Publicado: (2021) -
A global daily soil moisture dataset derived from Chinese FengYun Microwave Radiation Imager (MWRI)(2010–2019)
por: Yao, Panpan, et al.
Publicado: (2023)