Accurate methods are needed to measure changing soil water content from meter to kilometer scales. Laboratory results demonstrate the feasibility of the heat pulse method implemented with fiber optic temperature sensing to obtain accurate distributed measurements of soil water content. A fiber optic cable with an electrically conductive armoring was buried in variably saturated sand and heated via electrical resistance to create thermal pulses monitored by observing the distributed Raman backscatter. A new and simple interpretation of heat data that takes advantage of the characteristics of fiber optic temperature measurements is presented. The accuracy of the soil water content measurements varied approximately linearly with water content. At volumetric moisture content of 0.05 m³/m³ the standard deviation of the readings was 0.001 m³/m³, and at 0.41 m³/m³ volumetric moisture content the standard deviation was 0.046 m³/m³. This uncertainty could be further reduced by averaging several heat pulse interrogations and through use of a higher‐performance fiber optic sensing system.

Taylors' frozen turbulence hypothesis suggests that all turbulent eddies are advected by the mean streamwise velocity, without changes in their properties. This hypothesis has been widely invoked to compute Reynolds averaging using temporal turbulence data measured at a single point in space. However, in the atmospheric surface layer, the exact relationship between convection velocity and wave number k has not been fully revealed since previous observations were limited by either their spatial resolution or by the sampling length. Using Distributed Temperature Sensing (DTS), acquiring turbulent temperature fluctuations at high temporal and spatial frequencies, we computed convection velocities across wave numbers using a phase spectrum method. We found that convection velocity decreases as k^−1/3 at the higher wave numbers of the inertial subrange instead of being independent of wave number as suggested by Taylor's hypothesis. We further corroborated this result using large eddy simulations. Applying Taylor's hypothesis thus systematically underestimates turbulent spectrum in the inertial subrange. A correction is proposed for point‐based eddy‐covariance measurements, which can improve surface energy budget closure and estimates of CO₂ fluxes.

We present a novel technique to simultaneously measure wind speed (U) at thousands of locations continuously in time based on measurement of velocity‐dependent heat transfer from a heated surface. Measuring temperature differences between paired passive and actively heated fiber‐optic (AHFO) cables with a distributed temperature sensing system allowed estimation ofUat over 2000 sections along the 230 m transect (resolution of 0.375 m and 5.5 s). The underlying concept is similar to that of a hot wire anemometer extended in space. The correlation coefficient betweenUmeasured by two colocated sonic anemometers and the AHFO were 0.91 during the day and 0.87 at night. The combination of classical passive and novel AHFO provides unprecedented dynamic observations of both air temperature and wind speed spanning 4 orders of magnitude in spatial scale (0.1–1000 m) while resolving individual turbulent motions, opening new opportunities for testing basic theories for near‐surface geophysical flows.

The Actively Heated Fiber Optic (AHFO) method is shown to be capable of measuring soil water content several times per hour at 0.25 m spacing along cables of multiple kilometers in length. AHFO is based on distributed temperature sensing (DTS) observation of the heating and cooling of a buried fiber‐optic cable resulting from an electrical impulse of energy delivered from the steel cable jacket. The results presented were collected from 750 m of cable buried in three 240 m colocated transects at 30, 60, and 90 cm depths in an agricultural field under center pivot irrigation. The calibration curve relating soil water content to the thermal response of the soil to a heat pulse of 10 W m⁻¹ for 1 min duration was developed in the lab. This calibration was found applicable to the 30 and 60 cm depth cables, while the 90 cm depth cable illustrated the challenges presented by soil heterogeneity for this technique. This method was used to map with high resolution the variability of soil water content and fluxes induced by the nonuniformity of water application at the surface.

Core Ideas: Soil moisture sensors have varying accuracies that can be improved with calibration. In situ sensors require scaling to improve their representativeness of large areas. Soil moisture sensors in profile have decreasing ability to accurately represent the surface soil moisture. In situ soil moisture monitoring networks are critical to the development of soil moisture remote sensing missions as well as agricultural and environmental management, weather forecasting, and many other endeavors. These in situ networks utilize a variety of sensors and installation practices, which confounds the development of a unified reference database for satellite calibration and validation programs. As part of the Soil Moisture Active Passive Mission, the Marena, Oklahoma, In Situ Sensor Testbed (SMAP‐MOISST) was initiated to perform inter‐comparisons and study sensor limitations. Soil moisture sensors that are deployed in major monitoring networks were included in the study, along with new and emerging technologies, such as the Cosmic Ray Soil Moisture Observing System (COSMOS), passive/active distributed temperature sensing (DTS), and global positioning system reflectometers (GPSR). Four profile stations were installed in May of 2010, and soil moisture was monitored to a depth of 1 m on an hourly basis. The four stations were distributed within a circular domain of approximately 600 m diameter, adequate to encompass the sensing range of COSMOS. The sensors included in the base station configuration included the Stevens Water Hydra Probe, Campbell Scientific 616 and 229, Decagon EC‐TM, Delta‐T Theta Probe, Acclima, and Sentek EnviroSMART capacitance system. In addition, the Pico TRIME system and additional time‐domain reflectometry (TDR) systems were deployed when available. It was necessary to apply site‐specific calibration to most sensors to reach an RMSE below 0.04 m ³ m ⁻³ . For most sensor types, a single near surface sensor could be scaled to represent the areal‐average of a field domain by simple linear regression, resulting in RMSE values around 0.03 m ³ m ⁻³ .