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Proceedings Paper

Scaling from instantaneous remote-sensing-based latent heat flux to daytime integrated value with the help of SiB2
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Paper Abstract

This research dealt with a daytime integration method with the help of Simple Biosphere Model, Version 2 (SiB2). The field observations employed in this study were obtained at the Yingke (YK) oasis super-station, which includes an Automatic Meteorological Station (AMS), an eddy covariance (EC) system and a Soil Moisture and Temperature Measuring System (SMTMS). This station is located in the Heihe River Basin, the second largest inland river basin in China. The remotely sensed data and field observations employed in this study were derived from Watershed Allied Telemetry Experimental Research (WATER). Daily variations of EF in temporal and spatial scale would be detected by using SiB2. An instantaneous midday EF was calculated based on a remote-sensing-based estimation of surface energy budget. The invariance of daytime EF was examined using the instantaneous midday EF calculated from a remote-sensing-based estimation. The integration was carried out using the constant EF method in the intervals with a steady EF. Intervals with an inconsistent EF were picked up and ET in these intervals was integrated separately. The truth validation of land Surface ET at satellite pixel scale was carried out using the measurement of eddy covariance (EC) system.

Paper Details

Date Published: 7 October 2011
PDF: 8 pages
Proc. SPIE 8174, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 81741W (7 October 2011); doi: 10.1117/12.897951
Show Author Affiliations
Yi Song, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Institute of Earth Environment (China)
Mingguo Ma, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Xin Li, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Xufeng Wang, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Graduate Univ. of Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 8174:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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