29 October 2014 An improved model for sensible heat flux estimation based on landcover classification
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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.
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Ti Zhou, Xiaozhou Xin, Jingjun Jiao, Zhiqing Peng, "An improved model for sensible heat flux estimation based on landcover classification", Proc. SPIE 9239, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI, 92392F (29 October 2014); doi: 10.1117/12.2067354; https://doi.org/10.1117/12.2067354
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