KEYWORDS: Polarization, Polarimetry, Clouds, Radiometry, Soil science, Signal to noise ratio, Mass attenuation coefficient, Antennas, Microwave radiation, Nose
Polarimetric signatures of terrain features and man-made objects have been measured using unique Direct Detection
Polarimetric Radiometers (DDPR). The DDPRs are lightweight inexpensive systems operating at 35 and 94 GHz. Each
system consists of a single antenna, amplifier, and a truncated cylindrical waveguide that directly measures Q, U, and V.
The highly portable DDPRs are ideal for obtaining the Stokes vectors needed to study the physical characteristics of
natural and man-made features. Field evaluations using the DDPR systems include measurements from an airborne
platform over different terrain features and water, and ground based measurements of the polarimetric signature of grass,
asphalt, buildings, and concealed munitions. The DDPR can function as a bistatic system by using an active source of
polarization. Using this configuration and a soil chamber, we have investigated the effect of soil type and soil moisture
on linear and circular polarization. This report will describe the DDPR and present the analysis of the airborne and
ground based measurements, including the effects of soil type and soil moisture on sources of linear and circular
polarization.
The multi-agency Flight in Icing Remote Sensing Team (FIRST), a consortium of the National Aeronautics and
Space Administration (NASA), the Federal Aviation Administration (FAA), the National Center for Atmospheric
Research (NCAR), the National Oceanographic and Atmospheric Administration (NOAA), and the Army Corps of
Engineers (USACE), has developed technologies for remotely detecting hazardous inflight icing conditions. The
USACE Cold Regions Research and Engineering Laboratory (CRREL) assessed the potential of onboard passive
microwave radiometers for remotely detecting icing conditions ahead of aircraft. The dual wavelength system
differences the brightness temperature of Space and clouds, with greater differences potentially indicating closer and
higher magnitude Cloud Liquid Water Content (CLWC). The Air Force RADiative TRANsfer model (RADTRAN)
was enhanced to assess the flight track sensing concept, and a "flying" RADTRAN was developed to simulate a
radiometer system flying through simulated clouds. Neural network techniques were developed to invert brightness
temperatures and obtain integrated cloud liquid water. In addition, a dual wavelength Direct-Detection Polarimeter
Radiometer (DDPR) system was built for detecting hazardous drizzle drops. This paper reviews technology
development to date and addresses initial polarimeter performance.
The Engineering Research and Development Center participated in several field programs, mainly in desert areas, using
ground-based and airborne thermal imagers and radiometers to investigate the thermal signatures of disturbed and
undisturbed soils, including disturbed soils over buried munitions. Analysis of the thermal imagery indicates the thermal
temperature difference between the disturbed and undisturbed soil varies diurnally. The thermal temperature differences
have similar diurnal patterns for the different field programs and different environmental conditions. This paper presents
the analysis of the field measurements and model simulations used to quantify the observed thermal temperature
differences.
Electromagnetic signatures of terrain exhibit significant spatial heterogeneity on a range of scales as well as considerable
temporal variability. A statistical characterization of the spatial heterogeneity and spatial scaling algorithms of terrain
electromagnetic signatures are required to extrapolate measurements to larger scales. Basic terrain elements including
bare soil, grass, deciduous, and coniferous trees were studied in a quasi-laboratory setting using instrumented test sites in
Hanover, NH and Yuma, AZ. Observations were made using a visible and near infrared spectroradiometer (350 - 2500
nm) and hyperspectral camera (400 - 1100 nm). Results are reported illustrating: i) several difference scenes; ii) a terrain
scene time series sampled over an annual cycle; and iii) the detection of artifacts in scenes. A principal component
analysis indicated that the first three principal components typically explained between 90 and 99% of the variance of
the 30 to 40-channel hyperspectral images. Higher order principal components of hyperspectral images are useful for
detecting artifacts in scenes.
The ability to detect buried land mines under a wide variety of environmental conditions is an important Army requirement. Both for interpreting signatures of mines and to ensure appropriate modeling of mine and background signatures, it is important to understand the phenomena that result in different signature patterns. The dynamic signatures can change quickly in time due to changing meteorological conditions and their impact on the mine, the soil, and on the mine-soil interaction. In field tests, infrared measurements of surface and near surface mines have shown anomalous concentric thermal signatures around the mine. The cause of these irregularities is not known. We conduct numerical multidimensional finite element calculations to investigate interactions between the meteorological conditions, the mine, and the nearby soil to elucidate the cause for these signatures. Both in-situ temperature measurements and model results show that thermal interactions between the mine and the soil are responsible for the signatures. The warm area around the mine in the nearby soil is predominant primarily at night. The warm ring effect is most likely to exist in dry soil and for mines whose heat capacity exceeds that of the soil, resulting in thermal dominance of the mine in the coupled mine-soil thermal regime. Wet soils are less likely to display the thermal contrast of the warm ring. Improved understanding of physical interactions between the mine and the background may facilitate improved discrimination between signatures of mines and of false alarms.
In this paper, we present our first results towards understanding high temporal frequency thermal infrared response from a dense grass canopy. The model is driven by slowly varying, time-averaged meteorological conditions and by high frequency measurements of local and within canopy profiles of relative humidity and wind speed, and compared to high frequency thermal infrared observations. Previously, we have employed three-dimensional ray tracing to compute the intercepted and scattered solar and IR radiation fluxes and for final scene rendering. For the turbulent fluxes, simple resistance models for latent and sensible heat with one-dimensional profiles of relative humidity and wind speed are used. Our modeling approach has proven successful in capturing the directional and diurnal variation in background thermal infrared signatures. We hypothesize that at these scales, where the model is typically driven by time-averaged, local meteorological conditions, the primary source of thermal variance arises from the spatial distribution of sunlit and shaded foliage elements within the canopy and the associated radiative interactions.
In recent experiments, we have begun to focus on the high temporal frequency response of plant canopies in the thermal infrared at 1 sec to 5 min intervals. At these scales, we hypothesize turbulent mixing plays a more dominant role. Our results indicate that in the high frequency domain, the vertical profile of temperature change is tightly coupled to the within canopy wind speed. In the results reported here, the canopy cools from the top down with increased wind velocities and heats from the bottom up at low wind velocities.
KEYWORDS: Clouds, Solar radiation models, Atmospheric modeling, Data modeling, 3D modeling, Solar energy, Thermal modeling, Absorption, Visible radiation, Near infrared
The ability of sensors to discriminate objects in scenes depends on the scene composition and interactions with the available incident radiation. As sensors and camouflage techniques become more complex the nature of the energy interactions become more important to model accurately. Specific areas of interest are the influences of fluctuations in incident total solar loading radiation on terrain surfaces. The means used to produce 3D radiative calculations over the solar spectrum involves coupling the Air Force's Moderate- resolution Transmission (MODTRAN) code to the Army's 3D Atmospheric Illumination Module (AIM). The solar loading outputs calculated by these coupled codes are then used as input to the Army Smart Weapons Operability Enhancement (SWOE) thermal models. Variations in incident radiation produce surface temperature variations of up to 8 degrees Celsius. In the paper we describe the means of evaluating solar loading effects using a correlated-k-distribution-like algorithm to compress spectral processing, and show comparisons between measured and modeled results.
The smart weapons operability enhancement (SWOE) program has developed a synthetic scene generation process that incorporates formal experimental design, random sampling procedures, data collection methods, physics models, and numerically repeatable validation procedures. The SWOE synthetic scene generation procedure uses an assemblage of measurements, static and dynamic information databases, thermal and radiance models, and rendering techniques to simulate a wide range of environmental conditions. The models provide a spatial and spectral agility that permits the simulation of a wide range of sensor systems for varied environmental conditions. Comprehensive validation efforts have been conducted for two locations: Grayling, Michigan and Yuma, Arizona, and for two spectral bands: shortwave (3 - 5 micrometers ) and longwave (8 - 12 micrometers ) IR. The intended use of the validated SWOE process is synthetic battlefield scene generation. The users of the SWOE process are the smart weapons system designers, developers, testers and evaluators, including developers of automatic target recognition algorithms and techniques.
The primary objective of the Smart Weapons Operability Enhancement (SWOE) Joint Test and Evaluation (JT&E) Program is to validate the SWOE Process for the Office of the Secretary of Defense. The SWOE Process is a physics based scene generation capability that will enhance the performance of future smart weapon systems for a global variety of battlefield environments. This process is focused on generating complex background environmental scenes, including targets, for a world wide range of battlefield conditions. The SWOE program is a DoD wide partnership incorporating capabilities from the Army, Navy, Marine Corps and Air Force. The principal thrusts of SWOE are to quantitatively define the environmental factors and processes and to provide the capabilities to measure, model, render and extrapolate their impact on smart weapon system performance. The Grayling I exercise is the first in a series of four coordinate field deployments focused on validation of the SWOE Process. This paper describes the experimental design, sampling plans, measurement efforts and summarizes preliminary results of the Grayling I exercise.
The use of backscatter lidars as a research tool to remotely sense atmospheric parameters has been well established. But, the use of lidars as an operational tool to predict the performance of electro-optical (EO) systems during periods of adverse weather has not. A model correlating lidar derived atmospheric transmission and FLIR (forward looking infrared) performance has been developed and an international field program, FLAPIR, has been conducted to collect the data necessary to evaluate the model.
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