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Title: Assimilation of surface albedo and vegetation states from satellite observations and their impact on numerical weather prediction

Authors:
; ; ; ;
Publication Date:
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1373311
Grant/Contract Number:
FG02-04ER63917; FG02-04ER63911
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Remote Sensing of Environment
Additional Journal Information:
Journal Volume: 163; Journal Issue: C; Related Information: CHORUS Timestamp: 2017-10-04 15:38:34; Journal ID: ISSN 0034-4257
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

Citation Formats

Boussetta, Souhail, Balsamo, Gianpaolo, Dutra, Emanuel, Beljaars, Anton, and Albergel, Clement. Assimilation of surface albedo and vegetation states from satellite observations and their impact on numerical weather prediction. United States: N. p., 2015. Web. doi:10.1016/j.rse.2015.03.009.
Boussetta, Souhail, Balsamo, Gianpaolo, Dutra, Emanuel, Beljaars, Anton, & Albergel, Clement. Assimilation of surface albedo and vegetation states from satellite observations and their impact on numerical weather prediction. United States. doi:10.1016/j.rse.2015.03.009.
Boussetta, Souhail, Balsamo, Gianpaolo, Dutra, Emanuel, Beljaars, Anton, and Albergel, Clement. Wed . "Assimilation of surface albedo and vegetation states from satellite observations and their impact on numerical weather prediction". United States. doi:10.1016/j.rse.2015.03.009.
@article{osti_1373311,
title = {Assimilation of surface albedo and vegetation states from satellite observations and their impact on numerical weather prediction},
author = {Boussetta, Souhail and Balsamo, Gianpaolo and Dutra, Emanuel and Beljaars, Anton and Albergel, Clement},
abstractNote = {},
doi = {10.1016/j.rse.2015.03.009},
journal = {Remote Sensing of Environment},
number = C,
volume = 163,
place = {United States},
year = {Wed Apr 01 00:00:00 EDT 2015},
month = {Wed Apr 01 00:00:00 EDT 2015}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.rse.2015.03.009

Citation Metrics:
Cited by: 8works
Citation information provided by
Web of Science

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  • A method was investigated to estimate broadband surface shortwave albedo from the narrowband reflectances obtained by the Advanced Very High Resolution Radiometers (AVHRRs) on board the polar orbiting satellites. Field experiments were conducted to measure simultaneously multispectral narrowband reflectances and broadband albedo over various vegetation and soil surfaces. These data were combined to examine the behavior of narrowband-to-broadband (NTB) conversion factors for different surfaces. Many previous studies have used constant NTB conversion factors for the AVHRR data. The results from this investigation indicate that the optimal NTB conversion factors for AVHRR channels 1 and 2 have a strong dependence onmore » the amount of green vegetation within the field of view. Two conversion factors, f1 and f2, were established to quantify, respectively, (1) the relationship between the reflectance in the narrow red wave band and the total reflectance within the whole visible subregion (0.3-0.685 m) and (2) the relationship between the reflectance in the narrow near-infrared wave band and the total reflectance within the whole near-infrared subregion (0.685-2.8 m). Values of f1 and f2, calculated from field data, correlated well with the normalized difference vegetation index (NDVI), largely because of the unique characteristics of light absorption and scattering within the red and near-infrared wave bands by green leaves. The f1-NDVI and f2-NDVI relationships developed from this study were used to infer empirical coefficients needed to estimate surface albedo from AVHRR data. The surface albedo values calculated by the new method agreed with ground-based measurements within a root-mean-square error of 0.02, which is better than other methods that use constant empirical coefficients. Testing with albedo measurements made by unmanned aerospace vehicles during a field campaign also indicates that the new method provides an improved estimate of surface albedo.« less
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