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Updated thermal model using simplified short-wave radiosity calculations

Abstract

An extension to a forest canopy thermal radiance model is described that computes the short-wave energy flux absorbed within the canopy by solving simplified radiosity equations describing flux transfers between canopy ensemble classes partitioned by vegetation layer and leaf slope. Integrated short-wave reflectance and transmittance-factors obtained from measured leaf optical properties were found to be nearly equal for the canopy studied. Short-wave view factor matrices were approximated by combining the average leaf scattering coefficient with the long-wave view factor matrices already incorporated in the model. Both the updated and original models were evaluated for a dense spruce fir forest study site in Central Maine. Canopy short-wave absorption coefficients estimated from detailed Monte Carlo ray tracing calculations were 0.60, 0.04, and 0.03 for the top, middle, and lower canopy layers corresponding to leaf area indices of 4.0, 1.05, and 0.25. The simplified radiosity technique yielded analogous absorption values of 0.55, 0.03, and 0.01. The resulting root mean square error in modeled versus measured canopy temperatures for all layers was less than 1°C with either technique. Maximum error in predicted temperature using the simplified radiosity technique was approximately 2°C during peak solar heating. (author)
Publication Date:
Feb 15, 1994
Product Type:
Journal Article
Resource Relation:
Journal Name: Remote Sensing of Environment; Journal Volume: 47; Journal Issue: 2; Other Information: FAO/AGRIS record; ARN: US9445474; Country of input: International Atomic Energy Agency (IAEA)
Subject:
54 ENVIRONMENTAL SCIENCES; CANOPIES; FIRS; FORESTS; LEAVES; MONTE CARLO METHOD; SOLAR ENERGY; SOLAR HEATING; SPRUCES; THERMAL RADIATION
OSTI ID:
22440033
Country of Origin:
FAO
Language:
English
Other Identifying Numbers:
Journal ID: ISSN 0034-4257; TRN: XF15A4169009567
Submitting Site:
INIS
Size:
page(s) 167-175
Announcement Date:
Mar 07, 2016

Citation Formats

Smith, J. A., and Goltz, S. M. Updated thermal model using simplified short-wave radiosity calculations. FAO: N. p., 1994. Web. doi:10.1016/0034-4257(94)90153-8.
Smith, J. A., & Goltz, S. M. Updated thermal model using simplified short-wave radiosity calculations. FAO. doi:10.1016/0034-4257(94)90153-8.
Smith, J. A., and Goltz, S. M. 1994. "Updated thermal model using simplified short-wave radiosity calculations." FAO. doi:10.1016/0034-4257(94)90153-8. https://www.osti.gov/servlets/purl/10.1016/0034-4257(94)90153-8.
@misc{etde_22440033,
title = {Updated thermal model using simplified short-wave radiosity calculations}
author = {Smith, J. A., and Goltz, S. M.}
abstractNote = {An extension to a forest canopy thermal radiance model is described that computes the short-wave energy flux absorbed within the canopy by solving simplified radiosity equations describing flux transfers between canopy ensemble classes partitioned by vegetation layer and leaf slope. Integrated short-wave reflectance and transmittance-factors obtained from measured leaf optical properties were found to be nearly equal for the canopy studied. Short-wave view factor matrices were approximated by combining the average leaf scattering coefficient with the long-wave view factor matrices already incorporated in the model. Both the updated and original models were evaluated for a dense spruce fir forest study site in Central Maine. Canopy short-wave absorption coefficients estimated from detailed Monte Carlo ray tracing calculations were 0.60, 0.04, and 0.03 for the top, middle, and lower canopy layers corresponding to leaf area indices of 4.0, 1.05, and 0.25. The simplified radiosity technique yielded analogous absorption values of 0.55, 0.03, and 0.01. The resulting root mean square error in modeled versus measured canopy temperatures for all layers was less than 1°C with either technique. Maximum error in predicted temperature using the simplified radiosity technique was approximately 2°C during peak solar heating. (author)}
doi = {10.1016/0034-4257(94)90153-8}
journal = {Remote Sensing of Environment}
issue = {2}
volume = {47}
journal type = {AC}
place = {FAO}
year = {1994}
month = {Feb}
}