Untangling fiber optic Distributed Temperature Sensing: Getting the right temperature and getting there smoothly

Distributed Temperature Sensing (DTS) using fiber optic cables is a promising technique capable of filling in critical gaps between point observations and remote sensing. While DTS only directly measures the fiber temperature, it has been used to make spatially distributed observations of air temperature, wet bulb temperature, wind speed, and more, on the scales of centimeters to kilometers at temporal resolutions as fine as a second. Of particular interest for the flux community, the spatially distributed nature of DTS allows us to place point observations within a spatial context, highlighting missing physics and linking processes across scales.

However, DTS is not without its drawbacks. It is not a push button operation – each DTS array is unique, requiring an exceptional investment in time for the deployment and for turning DTS observations into physically-meaningful results. Characteristics of DTS observations change with the DTS device used, but also with, e.g., the type of the fiber, the layout of the fiber optic array, and properties of the reference sections used in calibration. These issues create two main challenges in processing DTS data: 1) the need for a robust calibration and 2) management of data that can exceed a terabyte, especially with large or long-term installations. To address these challenges and simplify the use of this powerful technique we present two tools, which can be used both standalone and in conjunction with each other.

First is ‘python-dts-calibration’, a Python package which is aimed at performing thorough calibration of DTS data, as calibration by DTS devices is often lacking in quality. It is able to perform a more robust calibration than the device default, and provides confidence intervals for the calibrated temperature. The confidence intervals vary along the fiber and over time and are different for every setup. The second tool, ‘pyfocs’, is a Python package meant for managing larger, long term installations. This tool automates the workflow including checking data integrity, calibration, and physically mapping the data. pyfocs incorporates ‘python-dts-calibration’ at its core, allowing the tool to robustly calibrate any DTS configuration. Lastly, the package provides the option for calculating other parameters, such as wind speed.

Both tools are open-source and hosted on GitHub^[1][2], allowing for everyone to check the code and suggest changes. By sharing our tools, we hope to make the use of fiber optic DTS in geosciences easier and open the door of this new technology to non-specialists.

 

[1] https://github.com/dtscalibration/python-dts-calibration

[2] https://github.com/klapo/pyfocs