The Oklahoma Mesonet: A Pilot Study of Environmental Sensor Data Citations

Martens, Betsy Van der Veer; Illston, Bradley G.; Fiebrich, Christopher A.

doi:10.5334/dsj-2017-047

Cited by 10 publications

(2 citation statements)

References 72 publications

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“…The calibration equation of Schneider et al (2003) has been widely used to estimate matric potential and subsequently soil volumetric water content for the Oklahoma Mesonet, one of the world's longest running automated soil moisture monitoring networks. Those soil water content estimates have been utilized in an extensive array of research projects and publications (Van der Veer Martens et al, 2017) by researchers around the world. We found that this Schneider et al's equation performed the worst among the equations that we tested, having a MAE = 246 kPa and increasingly overestimating matric potentials as the imposed matric potential decreased.…”

Section: Discussionmentioning

confidence: 99%

Recalibration of Sensors in One of The World's Longest Running Automated Soil Moisture Monitoring Networks

Zhang

Ochsner

Fiebrich

et al. 2019

Soil Science Soc of Amer J

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Obtaining accurate soil moisture data from sensors in automated monitoring networks is critical as these data are increasingly used for research in soil hydrology, ecohydrology, and related disciplines. One of the earliest such networks is the Oklahoma Mesonet, which monitors soil matric potential using heat dissipation sensors. Various calibration equations have been proposed for those sensors, and there is a need to compare and validate the performance of those equations, especially for matric potentials < –150 kPa. A laboratory experiment was conducted in silt loam soil using a sand‐kaolin box and a pressure plate apparatus with matric potentials ranging from 0 to –1500 kPa. The calibration equations included Starks’ equation, Flint et al.'s equation, Schneider et al.'s equation, and a new logistic equation. The upper limit of the sensors was ‐9 kPa, and the sensors remained responsive at ‐1500 kPa matric potential. The logistic equation produced the lowest root mean squared error (34 kPa), followed by Flint et al.'s equation (192 kPa), Starks’ equation (295 kPa), and Schneider et al.'s equation (463 kPa). After recalibration of the coefficients in the three preexisting equations, their performances improved, with all RMSE values ≤ 251 kPa; however, the logistic equation still provided superior accuracy. The logistic equation effectively removed an ∼ 0.02 cm3 cm−3 positive bias in soil water content that resulted from use of the original parameterization of Schneider et al.'s equation. This logistic equation is recommended for use with past and future data from the Oklahoma Mesonet's heat dissipation sensors. Core Ideas Soil matric potential data from the Oklahoma Mesonet are widely used. Those data are estimated using a calibration equation shown here to be inaccurate. The logistic equation developed here produces more accurate matric potential estimates.

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Section: Discussionmentioning

confidence: 99%

Recalibration of Sensors in One of The World's Longest Running Automated Soil Moisture Monitoring Networks

Zhang

Ochsner

Fiebrich

et al. 2019

Soil Science Soc of Amer J

Self Cite

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“…Therefore, a wide variety of studies have explored the potential of SM for drought monitoring. First, for regions renowned for their advanced plant product industries, more ground stations could be arranged in cropland when establishing SM networks [18,179,180]. This arrangement style reflects the emphasis attached to cultivationrelated drought monitoring by acquiring multi-depth SM recordings in real-time.…”

Section: Drought Monitoringmentioning

confidence: 99%

Advances in the Quality of Global Soil Moisture Products: A Review

Liu

Yang

2022

Remote Sensing

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Soil moisture is a crucial component of land–atmosphere interaction systems. It has a decisive effect on evapotranspiration and photosynthesis, which then notably impacts the land surface water cycle, energy transfer, and material exchange. Thus, soil moisture is usually treated as an indispensable parameter in studies that focus on drought monitoring, climate change, hydrology, and ecology. After consistent efforts for approximately half a century, great advances in soil moisture retrieval from in situ measurements, remote sensing, and reanalysis approaches have been achieved. The quality of soil moisture estimates, including spatial coverage, temporal span, spatial resolution, time resolution, time latency, and data precision, has been remarkably and steadily improved. This review outlines the recently developed techniques and algorithms used to estimate and improve the quality of soil moisture estimates. Moreover, the characteristics of each estimation approach and the main application fields of soil moisture are summarized. The future prospects of soil moisture estimation trends are highlighted to address research directions in the context of increasingly comprehensive application requirements.

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Mesometeorological Networks

Brotzge¹,

Fiebrich²

2021

Springer Handbooks

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The Oklahoma Mesonet: A Pilot Study of Environmental Sensor Data Citations

Cited by 10 publications

References 72 publications

Recalibration of Sensors in One of The World's Longest Running Automated Soil Moisture Monitoring Networks

Recalibration of Sensors in One of The World's Longest Running Automated Soil Moisture Monitoring Networks

Advances in the Quality of Global Soil Moisture Products: A Review

Mesometeorological Networks

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