Global soil moisture data derived through machine learning trained with in-situ measurements
Measurement(s) wetness of soil Technology Type(s) machine learning Factor Type(s) soil layer • temporal interval • geographic location Sample Characteristic - Environment soil Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14790510
Guardado en:
Autores principales: | Sungmin O., Rene Orth |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/a2321a8be2714558afbe5e683daafb00 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Soil moisture signature in global weather balloon soundings
por: Jasper M. C. Denissen, et al.
Publicado: (2021) -
Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach
por: Yan Jia, et al.
Publicado: (2021) -
Soil moisture dominates dryness stress on ecosystem production globally
por: Laibao Liu, et al.
Publicado: (2020) -
Soil moisture data using citizen science technology cross-validated by satellite data
por: Mohammad Karamouz, et al.
Publicado: (2021) -
A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019)
por: Panpan Yao, et al.
Publicado: (2021)