Chadi Sayde

Feasibility of soil moisture monitoring with heated fiber optics

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.

Mapping variability of soil water content and flux across 1-1000 m scales using the Actively Heated Fiber Optic method

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.

High-resolution wind speed measurements using actively heated fiber optics

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 of U at 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 between U measured 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.

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

This study demonstrated a new method for mapping high resolution (spatial: 1 m, and temporal: 1 hour) soil moisture by assimilating distributed temperature sensing (DTS) observed soil temperatures at intermediate scales. In order to provide robust soil moisture and property estimates, we first proposed an adaptive particle batch smoother algorithm (APBS). In the APBS, a tuning factor, which can avoid severe particle weight degeneration, is automatically determined by maximizing the reliability of the soil temperature estimates of each batch window. A multiple truth synthetic test was used to demonstrate the APBS can robustly estimate soil moisture and properties using observed soil temperatures at two shallow depths. The APBS algorithm was then applied to DTS data along a 71 m transect, yielding an hourly soil moisture map with meter resolution. Results show the APBS can draw the prior guessed soil hydraulic and thermal properties significantly closer to the field measured reference values. The improved soil properties in turn remove the soil moisture biases between the prior guessed and reference soil moisture, which was particularly noticeable at depth above 20 cm. This high resolution soil moisture map demonstrates the potential of characterizing soil moisture temporal and spatial variability and reflects patterns consistent with previous studies conducted using intensive point scale soil moisture samples. The intermediate scale high spatial resolution soil moisture information derived from the DTS may facilitate remote sensing soil moisture product calibration and validation. In addition, the APBS algorithm proposed in this study would also be applicable to general hydrological data assimilation problems for robust model state and parameter estimation. This article is protected by copyright. All rights reserved.

Calibration of soil moisture sensing with subsurface heated fiber optics using numerical simulation

The heat pulse probe method can be implemented with actively heated fiber optics (AHFO) to obtain distributed measurements of soil water content (θ) by using reported soil thermal responses measured by Distributed Temperature Sensing (DTS) and with a soil-specific calibration relationship. However, most reported applications have been calibrated to homogeneous soils in a laboratory, while inexpensive efficient in situ calibration procedures useful in heterogeneous soils are lacking. Here we employed the Hydrus 2-D/3-D code to define a soil-specific calibration curve. We define a 2-D geometry of the fiber optic cable and the surrounding soil media, and simulate heat pulses to capture the soil thermal response at different soil water contents. The model was validated in an irrigated field using DTS data from two locations along the FO deployment in which reference moisture sensors were installed. Results indicate that θ was measured with the model-based calibration with accuracy better than 0.022 m3 m−3.

A Web-Based Advisory Service for Optimum Irrigation Management

Conventional irrigation practices are predicated on maximizing crop yield – a biological objective. As worldwide competition for water intensifies, a fundamentally new paradigm for irrigation management is emerging predicated on maximizing net returns to water – an economic objective. Maximizing returns to water generally involves some degree of deficit irrigation, particularly when water supplies or system constraints limit the availability of water, but few farmers are well equipped to deal with the analytical challenges associated with managing water deficits. This paper presents a web-based advisory service for irrigation management. The system is being used initially in a pilot program for conventional irrigation scheduling. However, it is designed explicitly to assist irrigation managers with planning and implementing optimum irrigation strategies when water supplies are limited or expensive. Though originally developed for use in Oregon, discussions have been initiated to make the system available in other regions of the country. This paper provides an overview of the analytical framework and demonstrates primary features of the user interface.

Optimizing Estimates of Soil Moisture for Irrigation Scheduling

Irrigation scheduling commonly involves a water balance analysis in which daily estimates of ET are used to compute cumulative soil water depletion, and occasional soil moisture measurements are used to ‘correct’ the estimate of depletion. However, both ET estimates and soil moisture measurements are characterized by significant uncertainty, and both produce uncertain estimates of depletion. When water use is not limited the problem of uncertainty can be avoided by maintaining soil moisture at higher than critical levels, relying on moisture measurements to decide when to irrigate, and keeping some soil water in reserve as a hedge against uncertainty. On the other hand, deficit irrigation, an increasingly common strategy for maximizing net economic returns to limited water, must be managed differently. Deficit irrigation strategies allow the crop to be stressed to some degree,which implies there will be no soil moisture held in reserve. But errors in management of crop stress can be costly.

A Feedback System to Optimize Crop Water Use Estimates in Irrigation Scheduling

This paper deals with errors in estimation of soil water depletion inirrigation management. Such errors can reduce net economic returns to water, increase economic risk and motivate risk averse farm managers to adopt less profitable strategies. Two common methods of estimating depletion are discussed, one based on cumulative ET the other on soil moisture measurements. Both are characterized by significant uncertainty. It is common practice to rely on one or the other of these estimators for irrigation scheduling. This paper proposes an alternative approach that utilizes both estimators in combination. Rather than treating them as deterministic quantities, they are treated as random variables. The probability distributions of each are combined in a Bayesian analysis to derive a probability distribution of depletion, which then provides a better basis for irrigation decisions.