Micrometeorological, evapotranspiration, and soil-moisture data at the Amargosa Desert Research site in Nye County near Beatty, Nevada, 2006-11

Data Series ◽  
10.3133/ds725 ◽  
2012 ◽  
Author(s):  
Jonathan M. Arthur ◽  
Michael J. Johnson ◽  
C. Justin Mayers ◽  
Brian J. Andraski
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Data ◽  
Research Site ◽  
Nye County ◽  
Desert Research

scholarly journals Portable chamber measurements of evapotranspiration at the Amargosa Desert Research Site near Beatty, Nye County, Nevada, 2003-06

2008 ◽  
Author(s):  
C. Amanda Garcia ◽  
Michael J. Johnson ◽  
Brian J. Andraski ◽  
Keith J. Halford ◽  
C. Justin Mayers
Keyword(s):  
Research Site ◽  
Nye County ◽  
Chamber Measurements ◽  
Desert Research

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile 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 learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

Drought Monitoring using High Spatial Resolution Soil Moisture Data over Australia in 2015-2019

2021 ◽  
pp. 125960
Author(s):  
Bin Fang ◽  
Prakrut Kansara ◽  
Chelsea Dandridge ◽  
Venkat Lakshmi
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Drought Monitoring ◽  
Soil Moisture Data

scholarly journals Web Geoprocessing Services for Disseminating and Analyzing SMAP Derived Soil Moisture Data Products

2020 ◽  
Author(s):  
Chen Zhang ◽  
Zhengwei Yang ◽  
Liping Di ◽  
Eugene Yu ◽  
Li Lin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Data ◽  
Data Products ◽  
Geoprocessing Services

scholarly journals Potential of bias correction for downscaling passive microwave and soil moisture data

2015 ◽  
Vol 120 (13) ◽  
pp. 6460-6479 ◽  
Author(s):  
Kurt C. Kornelsen ◽  
Michael H. Cosh ◽  
Paulin Coulibaly
Keyword(s):  
Soil Moisture ◽  
Bias Correction ◽  
Passive Microwave ◽  
Soil Moisture Data
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