Inclusion of a Simple Vegetation Layer in Terrain Temperature Models for Thermal IR Signature Prediction

Balick, Lee K.; Scoggins, Randy K.; Link, Lewis E.

doi:10.1109/tgrs.1981.350343

Cited by 24 publications

(5 citation statements)

References 15 publications

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“…When foliage is present, the height at which the logarithmic wind profile goes to zero is displaced upward by an amount defined as the zero displacement height Z d (m). The zero displacement height and the foliage roughness length are calculated from (Balick et al 1981b) Table 2 is used. …”

Section: Sensible Heat Fluxmentioning

confidence: 99%

“…The energy budget of a simple vegetation layer on a soil surface is modeled using a steady-state semi-infinite plane parallel model (Deardorff 1978, Balick et al 1981b, which is described by the foliage emissivity ε f and albedo α f , a foliage height Z and the foliage fractional coverage σ f . The vegetation model (in this section the terms foliage and vegetation are used interchangeably) consists of a single, homogeneous layer that is infinite in the x and y directions.…”

Section: Introductionmentioning

confidence: 99%

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FASST Vegetation Models

Frankenstein¹,

Koenig²

2004

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The one-dimensional dynamic state of the ground model FASST (Fast All-season Soil Strength) is a state of the ground model developed by Frankenstein and Koenig (2004) as part of the Army's Battlespace Terrain Reasoning and Awareness (BTRA) research program. In its original form, the only effects vegetation had on FASST were to change the surface albedo and emissivity. Recently, a two tier, multilayer vegetation algorithm was added. These can be implemented separately or together. Both alter the soil surface energy and moisture budgets. In this report we will discuss the energy balance equations used to solve for the low vegetation, canopy and ground temperatures. In solving these equations, the effects of precipitation interception and soil moisture modification attributable to rootuptake are incorporated. DISCLAIMER:The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. FASST Vegetation Models iii CONTENTS

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Section: Sensible Heat Fluxmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FASST Vegetation Models

Frankenstein¹,

Koenig²

2004

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“…Models combining thermal remote sensing data and ground-based meteorological data to estimate regional surface sensible heat flux, H, were first developed in the early 1980s [11][12][13] . While the complexity of these models has increased considerably over the last 20 years the underlying principles are unchanged: atmospheric surface layer similarity theory 14,15 is used to relate H to the bulk properties of the atmospheric surface layer and the difference in temperature between T a and the aerodynamic temperature of the surface at the effective level of heat exchange, T 0 .…”

Section: Modelmentioning

confidence: 99%

Turbulence-induced spatial variation of surface temperature in high-resolution thermal IR satellite imagery

2003

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Atmospheric eddies cause transient spatial and temporal variations of surface temperature and can limit the precision of satellite surface temperature retrievals. If a thermal IR sensor has sufficiently high spatial resolution, the effects of these transient changes of temperature will be seen as variations of the thermal spatial pattern. Nine thermal IR images of a uniform emissivity area on Mauna Loa caldera are carefully compared to document spatial differences between them. These images were obtained from the Dept. of Energy Multispectral Thermal Imager satellite at about 20m GSD. Spatial patterns with a 1C -6C magnitude are present but not repeated in any of the images. In order to better understand the characteristics and impact of turbulence induced temperature fluctuations for quantitative remote thermal IR sensing, an effort to model the spatial variation of surface temperature as driven by turbulent energy fluxes has been initiated. Stochastic models initially examined showed a close coupling between surface temperature and turbulent fluxes but were not successful. Traditional energy balance models used in this type of simulation are insufficient to model skin temperature because of the importance of the skin layer and its small depth compared to soil depths used in the models. A new treatment based on surface renewal theory is introduced.

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“…The energy budget of a simple vegetation layer on a soil surface is modeled using a steady-state semi-infmite plane parallel model (Balick et al 1981), which is described by the foliage emissivity and albedo, a foliage height, the foliage fractional coverage and a foliage state parameter. The vegetation consists of a single homogeneous layer that is infinite in both x and y directions.…”

Section: Vegetation Thermal Modelmentioning

confidence: 99%

Solar Flux Initialization Schemes for Distributed Surface Energy Budget Modeling

Koenig¹,

Tofsted²

2003

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Inclusion of a Simple Vegetation Layer in Terrain Temperature Models for Thermal IR Signature Prediction

Cited by 24 publications

References 15 publications

FASST Vegetation Models

FASST Vegetation Models

Turbulence-induced spatial variation of surface temperature in high-resolution thermal IR satellite imagery

Solar Flux Initialization Schemes for Distributed Surface Energy Budget Modeling

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