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Lentil (Lens culinaris L. subsp. culinaris) is an important grain legume grown worldwide. As its popularity grows among consumers and more acres are produced, new root rot complexes have become more prevalent. This work sought to develop methods for studying root rot caused by Fusarium avenaceum in lentil using controlled environments. The objectives were to (i) find an effective and seed-safe sterilization technique, (ii) optimize the inoculation technique and lentil growing environment, and (iii) develop visual and automated disease scoring systems. Results showed the use of detergent and a low concentration (0.1%) of NaClO (the active ingredient in bleach) maintained germinability and effectively eliminated bacterial and fungal contamination on seeds. Other treatments, such as ethanol, reduced seed germination or failed to kill pathogenic fungi such as Fusarium spp. Placing inoculum at a moderate rate of 1 × 106 spores both directly on the seed and on top of the media covering the seed improved severity scores and reduced escapes compared with placement on top of the media only. Visual severity scoring systems and diagrammatic scales were developed for scoring the cotyledon region and roots. A computer vision algorithm was designed to improve the efficiency of scoring the cotyledon region and roots for disease severity using a simple RGB camera and lightbox. Visual and computer scores were best correlated when images were visually scored on a monitor, and multiple images were averaged. The scores generated from the computer vision algorithm had better correlations with visual scores for cotyledon rot (r = 0.92 and β1 = 0.96) than root rot (r = 0.62 and β1 = 0.67).

The advancement of vital sign radar technology has proven to be a useful tool in assessing various physiological dynamics, including heartbeat and respiration. There remains several signal processing challenges in this field, which include overcoming the nonlinearities and harmonics that populate the power spectrum. Respiration harmonics distort and overwhelm the measurement of heartbeat due to the large signal amplitude. A supervised machine learning algorithm, the gamma filter, offers an efficient, calibration-free solution to model the time series heartbeat signal given respiration and respiration artifacts. The measured signal is provided by a 5.8-GHz quadrature Doppler radar and a modified electrocardiogram signal is used as the ground truth for training the filter. Experimental results show that the heartbeat is independent and separable from respiration and the algorithm can be implemented in real time.

Passive microwave signatures have been used to retrieve geophysical parameters, such as soil temperature [Njoku and Li, 1999], moisture [Jackson et al., 1995], and surface roughness [Wegmüller and Mätzler, 1999]. One of the challenges in the parameter retrieval is the effect of soil surface roughness on the microwave emission. We conducted soil surface roughness measurements as part of our fourth Microwave Water and Energy Balance Experiment (MicroWEX-4) to understand the effects of surface roughness on microwave signatures at 6.7 GHz (λ = 4.48 cm). The dataset will also be used to develop and validate surface roughness models. In this report, we summarize briefly the theoretical background of surface roughness characteristics and discuss methodology and results of the roughness experiments. This document is Circular 1483, one of a series of the Department of Agricultural and Biological Engineering, UF/IFAS Extension. Original publication date November 2005. CIR1483/AE363: Measurements of Soil Surface Roughness During the Fourth Microwave Water and Energy Balance Experiment: April 18–June 13, 2005 (ufl.edu)

The goal of MicroWEX-2 was to understand the land-atmosphere interactions during the growing season of corn, and their effect on observed microwave brightness signatures at 6.7 GHz, matching that of the satellite-based microwave radiometer, AMSR. This document is Circular 1480, one of a series of the Department of Agricultural and Biological Engineering, UF/IFAS Extension. Original publication date November 2005. CIR1480/AE360: Field Observations During the Second Microwave Water and Energy Balance Experiment (MicroWEX-2): from March 17 through June 3, 2004 (ufl.edu)

The goal of MicroWEX-4 was to understand the land-atmosphere interactions during the growing season of corn, and their effect on observed microwave brightness signatures at 6.7 GHz, matching that of the satellite based microwave radiometer, AMSR. This document is Circular 1482, one of a series of the Department of Agricultural and Biological Engineering, UF/IFAS Extension. Original publication date November 2005. CIR1482/AE362: Field Observations During the Fourth Microwave Water and Energy Balance Experiment (MicroWEX-4): March 10–June 14, 2005 (ufl.edu)

The goal of MicroWEX-3 was to understand the land-atmosphere interactions during the growing season of cotton, and their effect on observed microwave brightness signatures at 6.7 GHz, matching that of the two satellite-based microwave radiometers (AMSR-E and AMSR). This document is Circular 1481, one of a series of the Department of Agricultural and Biological Engineering, UF/IFAS Extension. Original publication date November 2005. CIR1481/AE361: Field Observations During the Third Microwave Water and Energy Balance Experiment (MicroWEX-3): June 16–December 21, 2004 (ufl.edu)

Vital signs radar has proven to be an interesting and useful tool; however it is still limited by a few key problems. One of these is the generation of harmonics due to nonlinearities arising from the large signal amplitude of respiration when compared to that of heartbeat. As a result, harmonics arise in the spectrum which confound accurate measurement of either. The gamma filter is a supervised machine learning based approach that offers a calibration-free and computationally efficient solution for many nonlinear filtering applications. Here, it is demonstrated for the first time as a tool for real-time heart rate estimation using the baseband signal from a non-contact vital sign signal measured from a 5.8-GHz quadrature Doppler radar. Experimental results show that the proposed filter for removing respiration harmonics can accurately measure heart rate even if it is weak or overwhelmed by the respiratory movement.

Sensor systems capable of monitoring soil water content can provide a useful tool for irrigation control. Current systems are limited by installation depth and labor, accuracy, and cost. Time domain reflectometry (TDR) is an approach for monitoring soil water content that relates the travel time of an electromagnetic pulse on a waveguide to the water content of the soil. This article discusses the design, installation, lab testing, and field testing of a novel TDR sensor, using a multi-segmented, waveguide-on-access-tube (WOAT) geometry. The segmented WOAT approach allows for ease of assembly and installation of the sensor stack to the desired depth. Additionally, this sensor incorporates circuits embedded in the sensor body itself, directly coupled to the waveguides, eliminating problems associated with cabling encountered in previous WOAT embodiments. Despite some initial problems with mechanical strength of the prototype sensors, the WOAT equipment provided quality TDR waveforms and long-time reflection coefficients on a daily basis at multiple depths over the course of several months, providing data for both soil water content and bulk electrical conductivity estimates. When properly calibrated, the WOAT data were comparable to concurrent measurements of soil water content using a neutron probe, though there were differences due to the disparity in sensing volumes. Overall, we show that this is a promising new sensor design.

Soil water measurement is important in water management for irrigation and in hydrologic sciences. The purpose of this paper is to develop and test the design of a cylindrical access-tube mounted waveguide for use in time-domain reflectometry (TDR) for in situ soil water content sensing. Several prototypes with varying geometrical parameters were constructed. The sensors were compared by evaluating the characteristics of reflected waveforms from a (200-ps) step pulse in different media, including air, triethylene glycol, deionized water, and over a range of water contents in sand and a clay loam soil. Sensors with greater separation between electrodes, achieved by means different tube diameters or the separation angles, tend to have greater field penetration in both sand and clay. In addition, sensors with the shortest electrode separation show greater sensitivity to soil electrical conductivity. Together, these trends demonstrate that the propagating electromagnetic fields above 0 Hz do not take the transverse electromagnetic form commonly assumed in the analysis of TDR probes.

The soil water balance can be solved for evapotranspiration (ET) using soil water change in storage data from either weighing lysimetry or soil water sensing and measurement, along with data on the other components of the water balance. Weighing lysimeters are expensive and, although accurate, are difficult to manage and afford little replication. Direct soil water measurement by coring is accurate enough, but plagued by spatial variability that induces unwanted variability in the change in soil water storage between dates, and is destructive and time/labor consuming. Here we focus on soil water sensing using the neutron probe and various electromagnetic (EM) sensors (capacitance, time domain reflectometry (TDR) and quasi-TDR) with respect to the relative levels of uncertainty in profile water content, change in soil water storage, and estimates of deep flux; and their impact on estimated ET and water use efficiency (WUE). Studies consistently showed errors up to and >0.05 m 3 m −3 for capacitance sensors used in access tubes, which implied errors in soil water flux estimation of up to 50 mm day −1 , and calibrations that were so sensitive to soil bulk electrical conductivity ( σ dc ) and temperature that water content and change in storage estimates were rendered unreliable. Also, larger spatial variability of water contents reported by capacitance sensors was tied to the EM field penetration in structured soils around access tubes being non-uniform and influenced by the random arrangement of soil micro-scale water content, σ dc and bulk density distribution. Thus, we recommend that profiling sensor systems based on capacitance technology not be used for studies of water balance, ET and WUE, nor for irrigation scheduling. Recommended methods include the neutron probe, direct volumetric soil sampling and, in some cases, conventional time domain reflectometry with waveform capture and analysis. New sensor development efforts should focus on waveguide approaches using TDR technology.

Accurate soil profile water content monitoring at multiple depths has heretofore been possible only using the neutron probe (NP) but with great effort and at infrequent time intervals. Despite the existence of several frequency domain electromagnetic (EM) sensor systems for profile water content measurements, accuracy and spatial representativeness has been precluded by fundamental problems related to soil conductivity and structure effects on the volume explored by the EM field of these sensors, which causes nonrealistic spatial variation in reported profile water contents. Time domain reflectometry (TDR) methods have the distinct advantage of employing a moving EM field that must pass through and be affected by both the drier and wetter soil structures in which the TDR electrodes are embedded. This article describes a profiling water content system based on TDR. The design, laboratory calibration, and field testing is detailed. The sensor system provides unattended, real-time, data acquisition. And, it can be installed without disturbing the soil around the access tube on the outside of which the TDR electrodes are embedded. The correlation coefficient between neutron probe and TDR measured soil water content was 0.94 with a slope of 1.40 and an intercept of -0.08 m3m-3. Bias between TDR and NP readings (TDR-NP) was positive at all depths below 10 cm, ranging from 0.021 and 0.096 m3 m-3. Uncertainty in data of ~0.012 m3 m-3 for soil water, and uncertainty in bulk electrical conductivity of 0.030 S m-1 (both partly due to unreliable mechanical electrical connections) shows that improvements must be made before such a system is acceptable for widespread use.

A method to determine various operating modes of a high-efficiency inductive wireless power transfer system which is capable of supporting more than one receiver is proposed. The three operating modes are no-load, safe, and fault modes. The detection scheme probes the transmitter circuitry periodically to determine the operating mode. For power saving, the transmitter is powered down when there is no valid receiver placed on the transmitting coil. If any conductive or magnetic object that can affect the total effective inductance of the transmitting coil is located nearby, the system will enter the fault mode and shut down the transmitter so that it will not be damaged. The safe mode is the nominal operation mode when the power transmission efficiency is high with minimum power loss and zero-voltage switching operation of the class-E transmitter is achieved. The determination of the operating mode is achieved by analyzing the transmitting coil voltage and supply current space, requiring no communication link between the transmitter and receiver. The linear relationship between the power delivery and the supply current can be used to calculate the power delivered to the load(s).

A new type of dual-function structure is presented, a 3-D fractal heatsink antenna. This design is simulated at different fractal iterations in Ansoft HFSS and ePhysics to assess its electromagnetic and thermal performance, relative to a patch antenna and a finned heatsink antenna. It compares favorably to a patch antenna, improving radiation efficiency (up to 0.98) and directivity (up to 8.21 dB). As a heatsink, it decreases thermal resistance in comparison to a typical finned heatsink by an order of magnitude.

In wireless power systems for charging battery-operated devices, the selection of component values guaranteeing certain desired performance characteristics can be a tedious trial-and-error process, either sweeping component values in circuit simulations or changing components by hand. This difficulty is compounded by the variable nature of the load resistance presented by a device under charge. This brief considers component selection for a specific wireless power system architecture, which is an open-loop class-e inverter using a series-parallel arrangement for load impedance transformation. Formulas for the optimal receiver, transmitter, and class-e components are derived given a set of constraints on the resistance, phase, quality factor, and drain voltage waveform. Using a 16 cm times 18 cm primary and a 4 cm times 5 cm secondary coil, the derived formulas are used to build a wireless power system. We show that the system has desirable performance characteristics, including a power delivery of over 3.7 W, peak efficiency of over 66%, and decreasing power delivery with increasing load resistance.

Wireless power transfer is demonstrated mathematically and experimentally for M primary coils coupled to N secondary coils. Using multiple primary coils in parallel has advantages over a single primary coil. First, the reduced inductance of the transmitting coils makes the amplifier less sensitive to component variations. Second, with multiple receiving coils, the power delivery to an individual receiver is less sensitive to changes in the loads attached to other coils. By using a 16 cm by 18 cm primary and a 6 cm by 8 cm secondary coil, going from a 1:2 coupling to a 2:2 coupling, we show an increase in received power from 1.8 to 9.5 W, with only a small change in coupling efficiency. The advantages of the multiple primary coil topology increase the feasibility of charging multiple wireless portable devices simultaneously.

In this paper, we develop a model for estimating canopy opacity tau for sweet corn. We estimate the refractive index based upon moisture distribution in the corn during different stages of growth. The moisture distribution was observed during two season-long field experiments. We found that the moisture content decreased linearly as the height of the corn increased, with the distribution closer to Gaussian in the fruit region during reproductive stages. The tau obtained from our model was compared to that estimated using a widely used Jackson model. In general, our tau estimates were higher than those obtained using the Jackson model, with a root mean-square difference (rmsd) of up to 0.23 Np between the two models. The tau values were used in a microwave emission model at C-band, and the model estimates of brightness were compared with field observations. We found that the model brightness temperatures matched well with observations, with rmsd values of 5.13 and 4.88 K, using our model and the Jackson model for tau, respectively.

Current production of sweet corn (Zea mays L.) in the United States is 4.0 million Mg with a value of $807 million. The fresh market component amounts to three-fourths of this value with California, Florida, and Georgia harvesting half of the U.S. fresh market production. Existing maize simulation models have limited potential to assist sweet corn production as a result of the distinctive nature of the marketed end product (i.e., fresh market ears versus dry mature kernels). The purpose of this study was to develop a sweet corn simulation model. The Cropping System Model-Crop-Environment Resource Synthesis CSM-CERES-Maize simulation model, version 4.0, was modified to improve the simulation of ear growth, to predict ear fresh market yield, and to predict fresh market ear quality according to U.S. standards. A field experiment conducted in Florida in 2003 was used for model development. Five nitrogen fertilization levels (0, 67, 133, 200, and 267 kg·ha−1 N) were applied to a sh2-based commercial hybrid with a Bt gene sown at 8.2 plants/m2. Three additional experiments conducted in 2002, 2004, and 2005 provided independent data to evaluate the new model. In 2002, the treatments and hybrid were the same as mentioned, but the population density was 5.5 plants/m2. A yellow sh2-based hybrid with a Bt gene was planted at 6.1 plants/m2 in 2004. In 2005, a bicolor sh2-based hybrid with a Bt gene was planted at 8.1 plants/m2. The 2004 and 2005 experiments had 100% and 150% of the Florida N recommendations applied to the crop. Results indicated that the new model was able to simulate adequately crop and ear growth of sweet corn. The ear dry weight simulation was improved as indicated by 30% reduction of root mean square of the error (RMSE) when the new model was compared with the original CSM-CERES-Maize. Total ear fresh weight yield and marketable yield were also simulated reasonably well with RMSE values of 3367 and 3502 kg·ha−1, respectively. The simulation of ear quality was consistently overpredicted at intermediate levels of N fertilization, indicating the need to further examine the impact of limited N on ear quality.

Stored water, i.e., soil moisture in the root zone, is the most important factor governing energy and moisture fluxes at the land atmosphere interface. Knowledge of stored water is critical for accurate modeling of crop development and yield. Even though crop model simulations have become increasingly realistic, their estimates of crop growth diverge from reality over time. A promising way to improve model estimates is to incorporate independent observations in the model, e.g. remotely sensed data that are sensitive to soil moisture, such as microwave observations at low frequencies. Integrated crop growth-microwave models can utilize satellite observations to improve model estimates of evapotranspiration, biomass, and yield. This research aims to calibrate a crop growth model for a growing season of corn in North-Central Florida. This calibrated model will be linked to a microwave brightness model to estimate brightness signatures of a dynamic canopy. The CERES-Maize model was implemented for weather and soil conditions in North-Central Florida and calibrated using data from our second Microwave Water and Energy Balance Experiment (MicroWEX-2). MicroWEX-2 was an extensive field experiment conducted by the Center for Remote Sensing to monitor a growing season of corn from Day of Year (DoY) 78 to DoY 154 in 2004. During MicroWEX-2, we observed micrometeorological, soil, and vegetative conditions along with microwave signatures for a nine-acre field. The model was ported from MS Windows to the Linux OS and calibrated using a combination of grid search and simulated annealing methods. The six cultivar coefficients (P1, P2, P5, G1, G3, and PHINT) were modified to minimize the normalized sum of square errors (SSE) of biomass and LAI, the two most important canopy parameters for the microwave brightness model. Overall the model estimated realistic total biomass with a root mean square error (RMSE) of 0.880 Mg/ha. However, the partitioning of total biomass into stem and leaf biomasses were under and overestimated, respectively. LAI matched well with the observations with a RMSE of 0.105. The model estimated realistic daily latent heat flux with an RMSE of 42.07 W/m2 This error is similar in magnitude to the experimental errors using Eddy covariance systems. The soil moisture and temperature profiles of deeper soil layers matched reasonably well with observations, with RMSE of 1- 3.5% and 1.4-3.7 K, respectively. Near surface soil moisture and temperature (0-5 cm) were less realistic because the hydrological processes near the surface need to be modeled on a much shorter timestep than is allowed by the crop model.

PURPOSE: To assess intraocular pressure measured with a Goldmann tonometer over a period of 1 year after implantation of a Worst-Fechner anterior chamber phakic intraocular lens to correct myopia of -7.00 D or more. METHODS: We performed a prospective, nonmasked study of 100 patients (100 myopic eyes) who had a Worst-Fechner phakic intraocular lens implanted to correct myopia of -7.00 D or greater. Central corneal Goldmann tonometry was performed before surgery and 1, 3, 6, and 12 months after surgery. The influence of different variables was assessed. RESULTS: IOP showed a significant increase of of a mean 2.1 mmHg at 3 months after surgery (P=. 001). There was not a statistically significant difference between IOP before surgery and 6 months after PIOL implantation (P=.244) or 1 year after surgery (P=.845). In one eye, the lens was explanted 11 months after surgery because of the presence of ocular hypertension. No variables studied showed a statistically significant difference from before to after surgery. CONCLUSIONS: Worst-Fechner anterior chamber phakic intraocular lens implantation led to a slight transitory increase of intraocular pressure during the first 3 months after surgery, but returned to preoperative levels by 6 months after implantation. [J Refract Surg 2003; 19:131-136]

Knowledge of crop abiotic and biotic stress is important for optimal irrigation management. While spectral reflectance and infrared thermometry provide a means to quantify crop stress remotely, these measurements can be cumbersome. Computer vision offers an inexpensive way to remotely detect crop stress independent of vegetation cover. This paper presents a technique using computer vision to detect disease stress in wheat. Digital images of differentially stressed wheat were segmented into soil and vegetation pixels using expectation maximization (EM). In the first season, the algorithm to segment vegetation from soil and distinguish between healthy and stressed wheat was developed and tested using digital images taken in the field and later processed on a desktop computer. In the second season, a wireless camera with near real-time computer vision capabilities was tested in conjunction with the conventional camera and desktop computer. For wheat irrigated at different levels and inoculated with wheat streak mosaic virus (WSMV), vegetation hue determined by the EM algorithm showed significant effects from irrigation level and infection. Unstressed wheat had a higher hue (118.32) than stressed wheat (111.34). In the second season, the hue and cover measured by the wireless computer vision sensor showed significant effects from infection (p = 0.0014), as did the conventional camera (p < 0.0001). Vegetation hue obtained through a wireless computer vision system in this study is a viable option for determining biotic crop stress in irrigation scheduling. Such a low-cost system could be suitable for use in the field in automated irrigation scheduling applications.

Wireless power transfer systems using near-field magnetic coupling are attractive as they allow power transfer with high efficiency and do not require an unobstructed path between transmitter and receiver. In this work a two coil wireless power transmission system is analyzed, including the driving amplifier, and a demonstration system is built and characterized. The system achieves 76% efficiency for a distance of 1 meter for 40W transferred power. The effects of changes to the geometry of the system (pitch angle of coils, separation distance) are also examined, and the effect on amplifier topologies analyzed.

We developed the design of a waveguide on the exterior of an access tube for use in time-domain reflectometry (TDR) for in-situ soil water content sensing. In order to optimize the design with respect to sampling volume and losses, we derived the electromagnetic (EM) fields produced by a TDR sensor with this geometry. Using this analytical derivation, the effects on sampling area and waveform shape of varying geometry and soil water content were examined. The theoretical results were compared to laboratory measurements of different design variations in air, triethylene glycol, deionized water, sand, and clay in order to evaluate sensor performance and model accuracy. Both theoretical results and lab measurements indicated a positive, though not strong, relationship between electrode separation distance and (EM) field penetration into the soil or other medium with which sensor prototypes were surrounded. Results indicated good correspondence between the hybrid mode EM model predictions and measurements, indicating the value of the hybrid mode analysis. The relationship between measured pulse travel time and soil volumetric water content was quadratic rather than linear as in conventional TDR. Different quadratic calibration equations were obtained for sand and clay soils, indicating that soil-specific calibrations will be required for this design.

A high-efficiency wireless power transfer system which is capable of supporting more than one receiver using class E operation for transmitter via inductive coupling has been designed and fabricated. The design approach of the system is also presented in this paper. The system requires no complex external control system but relies on its natural impedance response to achieve the desired power delivery profile across a wide range of load resistances while maintaining high efficiency to prevent any heating issues. A switch circuit is used to decouple the fully charged receiver from the system so that power delivery to the other receiver can be improved. The fabricated system at 12 V supply voltage is compact and capable of approximately 2.5 W of power delivery to each of the two receivers in a dual receiver setup and 5 W to a single receiver alone or when the other receiver is decoupled by the receiver switch. During high-power delivery state, the system efficiency is between 67.5% and 77.5%.

A wireless power system via magnetic induction that can deliver 32 W to a laptop is designed and fabricated. A 60% peak end-to-end regulated efficiency is achieved. A load detection scheme is also implemented, which detects when a device is placed on the transmitter and can shut down the system if faults are detected. The system eliminates the need to plug the laptop into AC power when running or charging.

A 20 cm by 20 cm transmitting coil is designed for an inductively-coupled power transfer system. The coil design is a spiral, whose geometry is optimized to ensure an even magnetic field distribution. This guarantees uniform power delivery regardless of recieving coil position. The transmitting coil is tested using Litz wire, a switchmode power amplifier, and a 6 cm by 8 cm recieving coil connected to a rectifier and a variable load. The system achieves a maximum efficiency of 80.9% and a maximum power delivery of 11.8 W. At a fixed load, the power delivery has a coefficient of variation of 2.2% as the recieving coil's position is varied on the transmitter. In general, the system efficiency is high and insensitive to receiver placement as well as loading conditions.

[1] A Soil Vegetation Atmosphere Transfer (SVAT) model, viz. Land Surface Process (LSP) model, is coupled with a widely used crop-growth model, DSSAT, to estimate energy and moisture fluxes at the land surface and in the vadose zone for growing vegetation. In this study, we present detailed observations of soil and crop characteristics, and various components of energy and water balance during a season-long field experiment for sweet corn. The data set is used to calibrate the LSP with Latin Hypercube Sampling and Pareto ranking. We compare the observations with model estimates of crop growth and development, land surface fluxes, soil moisture and temperature profiles from both the stand-alone LSP and coupled LSP-DSSAT models. We find that the model estimates of radiation fluxes, soil moisture, and soil temperature, by both the LSP and LSP-DSSAT are very similar, indicating that the LSP-DSSAT model can be used to simulate fluxes for dynamic vegetation without the need of in situ vegetation observations. Moreover, because coupling was achieved without structurally changing either of the models, the methodology in this study can be extended to coupling other SVAT and vegetation growth models.

Circular 1515, a 56-page illustrated report by Fei Yan, Hwan Hee Han, Ruofan Yang, Joaquin Casanova, Jasmeet Judge, Jennifer Jacobs, Orlando Lanni, and Larry Miller, presents the results of experiments using microwave remote sensing to determine root-zone soil moisture at UF/IFAS PSREU. Published by the UF Department of Agricultural and Biological Engineering, May 2007. CIR 1515/AE409: Field Observations During the Sixth Microwave Water and Energy Balance Experiment (MicroWEX-6): from June 19 through November 30, 2006 (ufl.edu)

In this paper, we develop a canopy opacity model for sweet corn. The model, calculated using a simple refractive emission model, is based upon observed moisture distribution in the corn canopy for vegetative and reproductive stages during our Fourth Microwave Water and Energy Balance Experiment (MicroWEX-4). The moisture content decreases linearly with height of the corn height during the vegetative stages. During the reproductive stage, the moisture distribution in the fruit region can be estimated as Gaussian, with the spread dependent on grain biomass. The optical depth increased during the growing season to a maximum of 1.34 on DoY 153, corresponding to the canopy biomass of 3.48 kg/m2 and canopy height of 1.72 m.

Time domain reflectometry (TDR) is an established method for the determination of apparent dielectric permittivity and water content in soils. Using current waveform interpretation procedures, signal attenuation and variation in dielectric media properties along the transmission line can significantly increase sampling error in estimating the time, t2, at which the pulse arrives at the end of the probe. Additionally, manual adjustment of waveform analysis parameters is frequently required in current software to accommodate changes in media properties when processing large time series of TDR measurements. Our objectives were to reevaluate conventional propagation time analysis and difficulties with these methods, introduce the AWIGF (adaptive waveform interpretation with Gaussian filtering) algorithm that circumvents these problems, and compare interpretation methods using waveforms obtained with different TDR instruments and under widely varying media properties. The AWIGF algorithm filters signal noise using Gaussian kernels with an adaptively estimated standard deviation based on the maximum gradient of the reflection at the termination of the probe. Two fitted parameters are required to scale the smoothing level for a given step pulse generator. Additionally, the maximum second derivative is used to evaluate t2. The AWIGF-determined t2 was compared with TACQ, a standard waveform interpretation algorithm. The strategies of AWIGF permitted the determination of t2 without parameter adjustment when the loss characteristics of the medium changed, such as with an increase in soil water content and bulk electrical conductivity. Using the new method, the sampling error of t2 was

Bulk electrical conductivity (σ a ) can dominate the low frequency dielectric loss spectrum in soils, causing changes in the permittivity and errors in estimated water content. We examined the dependence of measured apparent permittivity ( K a ) on σ a in contrasting soils using time-domain reflectometry (TDR), a digital time-domain transmission (TDT) sensor, and a capacitance sensor (5TE) during near saturated solute displacement experiments. Sensors were installed in columns packed with fine sand or a clay loam soil. Displacement experiments were completed by first equilibrating columns with 0.25 dS m −1 CaCl 2 , introducing a step pulse of ∼4.7 dS m −1 CaCl 2 and, after equilibration, displacing the resident solution with 0.25 dS m −1 CaCl 2 . Using TDR, measured K a increased with increasing σ a ; however, the slope of this response averaged 3.47 m dS −1 for clay loam compared with 0.19 m dS −1 for sand. The large response in the clay loam was attributed to relaxation losses that narrowed the effective bandwidth from 821 to an estimated 164 MHz. In contrast, the effective frequency in sand averaged 515 MHz. Permittivity measured using the TDT probe exhibited little or no sensitivity to σ a ( −1 ) in both media. Measured K a using the 5TE probe declined with increasing σ a up to 1 to 1.8 dS m −1 and then increased thereafter with net negative responses for sand (Δ K a /Δσ a = −3.1 m dS −1 ) and net positive responses for the clay loam (Δ K a /Δσ a = 2.9 m dS −1 ). Consideration of the K a –σ a response is required for accurate soil water content estimation (±0.03 m 3 m −3 ) in the presence of solution EC variations using TDR in fine-textured soils or the 5TE sensor in all media. Large differences in the sampling volumes between 5TE-measured bulk EC and permittivity confounded the K a –σ a response in the presence of a concentration gradient.