scholarly journals Estimating profile soil moisture and groundwater variations using GRACE and Oklahoma Mesonet soil moisture data

2008 ◽  
Vol 44 (1) ◽  
Author(s):  
Sean Swenson ◽  
James Famiglietti ◽  
Jeffrey Basara ◽  
John Wahr
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Data ◽  
Oklahoma Mesonet

scholarly journals Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data

2008 ◽  
Vol 35 (22) ◽  
Author(s):  
Yingxin Gu ◽  
Eric Hunt ◽  
Brian Wardlow ◽  
Jeffrey B. Basara ◽  
Jesslyn F. Brown ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Drought Monitoring ◽  
Modis Ndvi ◽  
Soil Moisture Data ◽  
Oklahoma Mesonet

scholarly journals New Soil Property Database Improves Oklahoma Mesonet Soil Moisture Estimates*

2013 ◽  
Vol 30 (11) ◽  
pp. 2585-2595 ◽  
Author(s):  
Bethany L. Scott ◽  
Tyson E. Ochsner ◽  
Bradley G. Illston ◽  
Christopher A. Fiebrich ◽  
Jeffery B. Basara ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Large Scale ◽  
Water Retention ◽  
Soil Property ◽  
Volumetric Water Content ◽  
Water Retention Curve ◽  
Retention Curve ◽  
Soil Moisture Data ◽  
Oklahoma Mesonet

Abstract Soil moisture data from the Oklahoma Mesonet are widely used in research efforts spanning many disciplines within Earth sciences. These soil moisture estimates are derived by translating measurements of matric potential into volumetric water content through site- and depth-specific water retention curves. The objective of this research was to increase the accuracy of the Oklahoma Mesonet soil moisture data through improved estimates of the water retention curve parameters. A comprehensive field sampling and laboratory measurement effort was conducted that resulted in new measurements of the percent of sand, silt, and clay; bulk density; and volumetric water content at −33 and −1500 kPa. These inputs were provided to the Rosetta pedotransfer function, and parameters for the water retention curve and hydraulic conductivity functions were obtained. The resulting soil property database, MesoSoil, includes 13 soil physical properties for 545 individual soil layers across 117 Oklahoma Mesonet sites. The root-mean-square difference (RMSD) between the resulting soil moisture estimates and those obtained by direct sampling was reduced from 0.078 to 0.053 cm3 cm−3 by use of the new water retention curve parameters, a 32% improvement. A >0.15 cm3 cm−3 high bias on the dry end was also largely eliminated by using the new parameters. Reanalysis of prior studies that used Oklahoma Mesonet soil moisture data may be warranted given these improvements. No other large-scale soil moisture monitoring network has a comparable published soil property database or has undergone such comprehensive in situ validation.

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 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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