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IGARSS11_HongboSu_ver3.ppt
1. A new algorithm to automatically determine the boundary of the scatter plot in the triangle method for evapotranspiration retrieval Hongbo Su 1,2 , Jing Tian 2 , Shaohui Chen 2 , Renhua Zhang 2 Yuan Rong 2 , Yongmin Yang 2 , Xinzhai Tang 2 and Julio Garcia 1 1. Department of Environmental Engineering, Texas A&M University at Kingsville, Kingsville, TX 78363, USA 2. The Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China IGARSS2011 Session: WE4.T09 Parameter Estimation
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3. What is Evapotranspiration? Evapotranspiration (ET) is the combination of water that is evaporated from the surface and transpired by plants as a part of their metabolic processes. Background and Motivation
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13. Methodology Three different algorithms were developed to automatically determine the boundary of the triangle shape in the scatter plots. It is assumed that x denotes the variable in the X dimension, y stands for the variable in the Y dimension in the two-dimensional scatter plot, the number of pixel is N and the threshold is α ( 0<α<0.5)
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15. Methodology For some particular shape of the scatter plot (see Figure on the right, albedo V.S. Vegetation Fraction ), the above algorithm couldn’t converge because of the forked shape on the right hand side.
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17. Methodology Algorithm III is quite different with the above two. Firstly, the x-y space is divided equally into n (here n is assigned to be 15) domains according to their x values. For vegetation fraction, it is in the range of 0 and 1. Secondly, after sorting the y values in each of the 15 sub-domains, the α and (1- α ) quintile of the y values is retrieved. Thirdly, the lower boundary line is fitted using the 15 α quintile y values and the corresponding x values. Similarly, the upper boundary line is fitted using the 15 (1- α ) quintile y values and the corresponding x values.
18. Methodology Examples of the determination of the boundary of the scatter plot Figure 2 Albedo V.S. Vegetation Fraction for (a) date 03/14/2006; (b) date03/28/2006
21. Findings and Conclusion The study area is the Northern China Plain, which is flat and has a wide range of soil wetness and fractional vegetation cover. MODIS land data products, including land surface temperature, albedo, vegetation index, together with the necessary meteorological variables (mainly the surface downward and upward radiative fluxes) from the GDAS (Global Data Assimilation System) database developed by NOAA/NCEP, are used to test the proposed algorithm. Figure 3 Evapotranspiration estimate for the Northern China Plain based on the new algorithm
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23. Thanks for your attention! Contact Info: Hongbo Su [email_address] [email_address]
Notes de l'éditeur
The principle consists of performing a multiresolution decomposition on high resolution panchromatic image (HRPI) using AWT. The approximation component and low resolution multispectral image (LRMI) are fused through an intrinsic mode functions (IMFs) based model. Subsequently, the sharpening approximation component produced is substituted for the old one. High resolution multispectrall image (HRMI) is then obtained through an inverse AWT (IAWT). QuickBird images are used to illustrate the advantage of this method over the traditional AWT and EMD based methods both visually and quantitatively
The principle consists of performing a multiresolution decomposition on high resolution panchromatic image (HRPI) using AWT. The approximation component and low resolution multispectral image (LRMI) are fused through an intrinsic mode functions (IMFs) based model. Subsequently, the sharpening approximation component produced is substituted for the old one. High resolution multispectrall image (HRMI) is then obtained through an inverse AWT (IAWT). QuickBird images are used to illustrate the advantage of this method over the traditional AWT and EMD based methods both visually and quantitatively
The principle consists of performing a multiresolution decomposition on high resolution panchromatic image (HRPI) using AWT. The approximation component and low resolution multispectral image (LRMI) are fused through an intrinsic mode functions (IMFs) based model. Subsequently, the sharpening approximation component produced is substituted for the old one. High resolution multispectrall image (HRMI) is then obtained through an inverse AWT (IAWT). QuickBird images are used to illustrate the advantage of this method over the traditional AWT and EMD based methods both visually and quantitatively
The principle consists of performing a multiresolution decomposition on high resolution panchromatic image (HRPI) using AWT. The approximation component and low resolution multispectral image (LRMI) are fused through an intrinsic mode functions (IMFs) based model. Subsequently, the sharpening approximation component produced is substituted for the old one. High resolution multispectrall image (HRMI) is then obtained through an inverse AWT (IAWT). QuickBird images are used to illustrate the advantage of this method over the traditional AWT and EMD based methods both visually and quantitatively
The principle consists of performing a multiresolution decomposition on high resolution panchromatic image (HRPI) using AWT. The approximation component and low resolution multispectral image (LRMI) are fused through an intrinsic mode functions (IMFs) based model. Subsequently, the sharpening approximation component produced is substituted for the old one. High resolution multispectrall image (HRMI) is then obtained through an inverse AWT (IAWT). QuickBird images are used to illustrate the advantage of this method over the traditional AWT and EMD based methods both visually and quantitatively
The principle consists of performing a multiresolution decomposition on high resolution panchromatic image (HRPI) using AWT. The approximation component and low resolution multispectral image (LRMI) are fused through an intrinsic mode functions (IMFs) based model. Subsequently, the sharpening approximation component produced is substituted for the old one. High resolution multispectrall image (HRMI) is then obtained through an inverse AWT (IAWT). QuickBird images are used to illustrate the advantage of this method over the traditional AWT and EMD based methods both visually and quantitatively
The principle consists of performing a multiresolution decomposition on high resolution panchromatic image (HRPI) using AWT. The approximation component and low resolution multispectral image (LRMI) are fused through an intrinsic mode functions (IMFs) based model. Subsequently, the sharpening approximation component produced is substituted for the old one. High resolution multispectrall image (HRMI) is then obtained through an inverse AWT (IAWT). QuickBird images are used to illustrate the advantage of this method over the traditional AWT and EMD based methods both visually and quantitatively
The principle consists of performing a multiresolution decomposition on high resolution panchromatic image (HRPI) using AWT. The approximation component and low resolution multispectral image (LRMI) are fused through an intrinsic mode functions (IMFs) based model. Subsequently, the sharpening approximation component produced is substituted for the old one. High resolution multispectrall image (HRMI) is then obtained through an inverse AWT (IAWT). QuickBird images are used to illustrate the advantage of this method over the traditional AWT and EMD based methods both visually and quantitatively
The principle consists of performing a multiresolution decomposition on high resolution panchromatic image (HRPI) using AWT. The approximation component and low resolution multispectral image (LRMI) are fused through an intrinsic mode functions (IMFs) based model. Subsequently, the sharpening approximation component produced is substituted for the old one. High resolution multispectrall image (HRMI) is then obtained through an inverse AWT (IAWT). QuickBird images are used to illustrate the advantage of this method over the traditional AWT and EMD based methods both visually and quantitatively
The principle consists of performing a multiresolution decomposition on high resolution panchromatic image (HRPI) using AWT. The approximation component and low resolution multispectral image (LRMI) are fused through an intrinsic mode functions (IMFs) based model. Subsequently, the sharpening approximation component produced is substituted for the old one. High resolution multispectrall image (HRMI) is then obtained through an inverse AWT (IAWT). QuickBird images are used to illustrate the advantage of this method over the traditional AWT and EMD based methods both visually and quantitatively
The principle consists of performing a multiresolution decomposition on high resolution panchromatic image (HRPI) using AWT. The approximation component and low resolution multispectral image (LRMI) are fused through an intrinsic mode functions (IMFs) based model. Subsequently, the sharpening approximation component produced is substituted for the old one. High resolution multispectrall image (HRMI) is then obtained through an inverse AWT (IAWT). QuickBird images are used to illustrate the advantage of this method over the traditional AWT and EMD based methods both visually and quantitatively