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Proceedings Paper

An improved model for sensible heat flux estimation based on landcover classification
Author(s): Ti Zhou; Xiaozhou Xin; Jingjun Jiao; Zhiqing Peng
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Paper Abstract

Remote sensing (RS) has been recognized as the most feasible means to provide spatially distributed regional evapotranspiration (ET). However, classical RS flux algorithms (SEBS, S-SEBI, SEBAL, etc.) can hardly be used with coarser resolution RS data from sensors like MODIS or AVHRR for no consideration of surface heterogeneity in mixed pixels even they are suitable for assessing the surface fluxes with high resolution RS data.A new model named FAFH is developed in this study to enhance the accuracy of flux estimation in mixed pixels based on high resolution landcover classification data. The area fraction and relative sensible heat fraction of each heterogeneous land use type calculated within coarse resolution pixels are calculated firstly, and then used for the weighted average of modified sensible heat. The study is carried out in the core agricultural land of Zhangye, the middle reaches of Heihe river based on the flux and landcover classification product of HJ-1B in our earlier work. The result indicates that FAFH increases the accuracy of sensible heat by 5% absolutely, 10.64% relatively in the whole research area.

Paper Details

Date Published: 29 October 2014
PDF
Proc. SPIE 9239, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI, 92392F (29 October 2014); doi: 10.1117/12.2067354
Show Author Affiliations
Ti Zhou, Institute of Remote Sensing and Digital Earth (China)
Xiaozhou Xin, Institute of Remote Sensing and Digital Earth (China)
Jingjun Jiao, Institute of Remote Sensing and Digital Earth (China)
Zhiqing Peng, Institute of Remote Sensing and Digital Earth (China)


Published in SPIE Proceedings Vol. 9239:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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