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Analysis of the Spatial Variability of Land Surface Variables for ET Estimation: Case Study in HiWATER Campaign

State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China
Joint Center for Global Change Studies (JCGCS), Beijing 100875, China
University of Chinese Academy of Sciences, Beijing 100049, China
College of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China
Inner Mongolia Ecological and Agricultural Meteorological Center, Hohhot 010051, China
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(1), 91;
Received: 13 December 2017 / Revised: 8 January 2018 / Accepted: 10 January 2018 / Published: 11 January 2018
PDF [3647 KB, uploaded 11 January 2018]


Heterogeneity, including the inhomogeneity of landscapes and surface variables, significantly affects the accuracy of evapotranspiration (ET) (or latent heat flux, LE) estimated from remote sensing satellite data. However, most of the current research uses statistical methods in the mixed pixel to correct the ET or LE estimation error, and there is a lack of research from the perspective of the remote sensing model. The method of using frequency distributions or generalized probability density functions (PDFs), which is called the “statistical-dynamical” approach to describe the heterogeneity of land surface characteristics, is a good way to solve the problem. However, in attempting to produce an efficient PDF-based parameterization of remotely sensed ET or LE, first and foremost, it is necessary to systematically understand the variables that are most consistent with the heterogeneity (i.e., variability for a fixed target area or landscape, where the variation in the surface parameter value is primarily concerned with the PDF-based model) of surface turbulence flux. However, the use of PDF alone does not facilitate direct comparisons of the spatial variability of surface variables. To address this issue, the objective of this study is to find an indicator based on PDF to express variability of surface variables. We select the dimensionless or dimensional consistent coefficient of variation (CV), Gini coefficient and entropy to express variability. Based on the analysis of simulated data and field experimental data, we find that entropy is more stable and accurate than the CV and Gini coefficient for expressing the variability of surface variables. In addition, the results of the three methods show that the variability of the leaf area index (LAI) is greater than that of the land surface temperature (LST). Our results provide a suitable method for comparing the variability of different variables. View Full-Text
Keywords: spatial heterogeneity; variability; evapotranspiration; land surface variables; probability density function; HiWATER spatial heterogeneity; variability; evapotranspiration; land surface variables; probability density function; HiWATER

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Li, X.; Xin, X.; Peng, Z.; Zhang, H.; Yi, C.; Li, B. Analysis of the Spatial Variability of Land Surface Variables for ET Estimation: Case Study in HiWATER Campaign. Remote Sens. 2018, 10, 91.

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