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

作者:Li, XJ (Li, Xiaojun)[ 1,2,3,4 ] ; Xin, XZ (Xin, Xiaozhou)[ 1,2,3 ] ; Peng, ZQ (Peng, Zhiqing)[ 1,2,4 ] ; Zhang, HL (Zhang, Hailong)[ 1,2,3 ] ; Yi, CX (Yi, Chuanxiang)[ 5 ] ; Li, B (Li, Bin)[ 6 ]

REMOTE SENSING

卷: 10  期: 1

文献号: 91

DOI: 10.3390/rs10010091

出版年: JAN 2018

摘要

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.

通讯作者地址: Xin, XZ (通讯作者)

Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China.

通讯作者地址: Xin, XZ (通讯作者)

Beijing Normal Univ, Beijing 100101, Peoples R China.

通讯作者地址: Xin, XZ (通讯作者)

JCGCS, Beijing 100875, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China

[ 2 ] Beijing Normal Univ, Beijing 100101, Peoples R China

[ 3 ] JCGCS, Beijing 100875, Peoples R China

[ 4 ] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

[ 5 ] Nanjing Univ Informat Sci & Technol, Coll Appl Meteorol, Nanjing 210044, Jiangsu, Peoples R China

[ 6 ] Inner Mongolia Ecol & Agr Meteorol Ctr, Hohhot 010051, Peoples R China