scholarly journals Mapping high-resolution soil moisture and properties using distributed temperature sensing data and an adaptive particle batch smoother

2016 ◽  
Vol 52 (10) ◽  
pp. 7690-7710 ◽  
Author(s):  
Jianzhi Dong ◽  
Susan C. Steele-Dunne ◽  
Tyson E. Ochsner ◽  
Christine E. Hatch ◽  
Chadi Sayde ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Temperature Sensing ◽  
Distributed Temperature Sensing ◽  
Sensing Data

scholarly journals Assessment of a vertical high-resolution distributed-temperature-sensing system in a shallow thermohaline environment

2011 ◽  
Vol 15 (3) ◽  
pp. 1081-1093 ◽  
Author(s):  
F. Suárez ◽  
J. E. Aravena ◽  
M. B. Hausner ◽  
A. E. Childress ◽  
S. W. Tyler
Keyword(s):  
High Resolution ◽  
Temperature Sensing ◽  
Distributed Temperature Sensing ◽  
Temperature Resolution ◽  
Spatial And Temporal Scales ◽  
Solar Pond ◽  
Salt Gradient ◽  
Time Averages ◽  
Corrosive Environments ◽  
And Storage

Abstract. In shallow thermohaline-driven lakes it is important to measure temperature on fine spatial and temporal scales to detect stratification or different hydrodynamic regimes. Raman spectra distributed temperature sensing (DTS) is an approach available to provide high spatial and temporal temperature resolution. A vertical high-resolution DTS system was constructed to overcome the problems of typical methods used in the past, i.e., without disturbing the water column, and with resistance to corrosive environments. This paper describes a method to quantitatively assess accuracy, precision and other limitations of DTS systems to fully utilize the capacity of this technology, with a focus on vertical high-resolution to measure temperatures in shallow thermohaline environments. It also presents a new method to manually calibrate temperatures along the optical fiber achieving significant improved resolution. The vertical high-resolution DTS system is used to monitor the thermal behavior of a salt-gradient solar pond, which is an engineered shallow thermohaline system that allows collection and storage of solar energy for a long period of time. The vertical high-resolution DTS system monitors the temperature profile each 1.1 cm vertically and in time averages as small as 10 s. Temperature resolution as low as 0.035 °C is obtained when the data are collected at 5-min intervals.

Successful Flow Profiling of Gas Wells Using Distributed Temperature Sensing Data

2006 ◽  
Author(s):  
David Oliver Johnson ◽  
Jose R. Sierra ◽  
Jiten D. Kaura ◽  
Dan Gualtieri
Keyword(s):  
Temperature Sensing ◽  
Distributed Temperature Sensing ◽  
Gas Wells ◽  
Sensing Data

scholarly journals Actively heated high-resolution fiber-optic-distributed temperature sensing to quantify streambed flow dynamics in zones of strong groundwater upwelling

2016 ◽  
Vol 52 (7) ◽  
pp. 5179-5194 ◽  
Author(s):  
Martin A. Briggs ◽  
Sean F. Buckley ◽  
Amvrossios C. Bagtzoglou ◽  
Dale D. Werkema ◽  
John W. Lane
Keyword(s):  
High Resolution ◽  
Fiber Optic ◽  
Flow Dynamics ◽  
Temperature Sensing ◽  
Distributed Temperature Sensing

High-resolution distributed temperature sensing with the multiphoton-timing technique

1995 ◽  
Vol 34 (16) ◽  
pp. 2955 ◽  
Author(s):  
M. Höbel ◽  
J. Ricka ◽  
M. Wüthrich ◽  
Th. Binkert
Keyword(s):  
High Resolution ◽  
Temperature Sensing ◽  
Distributed Temperature Sensing

scholarly journals Feasibility of soil moisture estimation using passive distributed temperature sensing

2010 ◽  
Vol 46 (3) ◽  
Author(s):  
S. C. Steele-Dunne ◽  
M. M. Rutten ◽  
D. M. Krzeminska ◽  
M. Hausner ◽  
S. W. Tyler ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temperature Sensing ◽  
Distributed Temperature Sensing ◽  
Moisture Estimation

scholarly journals DTSGUI: A Python Program to Process and Visualize Fiber‐Optic Distributed Temperature Sensing Data

Ground Water ◽  
2020 ◽  
Author(s):  
Marian Domanski ◽  
Daven Quinn ◽  
Frederick D. Day‐Lewis ◽  
Martin A. Briggs ◽  
Dale Werkema ◽  
...  
Keyword(s):  
Fiber Optic ◽  
Temperature Sensing ◽  
Distributed Temperature Sensing ◽  
Sensing Data
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