Is scale really a challenge in evapotranspiration estimation? A multi-scale study in the Heihe oasis using thermal remote sensing and the three-temperature model

Wang, Yong Qiang; Xiong, Yujiu; Qiu, Guo Yu; Zhang, Qing Tao

doi:10.1016/j.agrformet.2016.03.012

Cited by 43 publications

(40 citation statements)

References 83 publications

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“…In Scheme 3, we add 1 to the original random number generated in Scheme 2 so that the interval range becomes [1,2], as shown in Figure 4b. The variability of each simulated data in Scheme 3 is also calculated using three methods, and the results are shown in Table 3.…”

Section: Analysis Based On Simulation Datamentioning

confidence: 99%

“…HiWATER was designed as a comprehensive ecohydrological experiment to address problems that include heterogeneity, uncertainty, scaling, and closing the water cycle at the watershed scale implemented in the HRB, an inland river watershed [2]. MUSOEXE, a component of HiWATER, was implemented to reveal the spatial heterogeneities of heat and water fluxes by constructing a flux matrix (Figure 1c) from May to September 2012.…”

Section: Ground Observation Datamentioning

confidence: 99%

“…The processing steps included spike removal, lag time correction, coordinate rotation (2D rotation), frequency response correction, corrections for density fluctuations (WPL-correction), and averaging to half-hourly fluxes [39,41]. Third, a four-step procedure was performed to control the quality of the EC data; for more details, see [1,2]. Because night turbulence is weak and considerable data are either missing or of poor quality, this study used data from 10:00 to 18:00 Beijing time to ensure the quality of turbulent flux observation data.…”

Section: Ground Observation Datamentioning

confidence: 99%

“…The heterogeneities of surface variables are regarded as two different aspects with or without considering the spatial patterns of surface variables [26,27]. By considering spatial patterns or spatial arrangement as well as the value of the gray scale of the pixels, the changes in spatial patterns with more or less chaotic spatial arrangements correspond to greater or smaller spatial heterogeneity [2,10]. In the other way, only the heterogeneity caused by the change in the variable value or gray value (i.e., spatial variability of the surface property over the observed scene) is considered [26].…”

Section: Introductionmentioning

confidence: 99%

“…Evapotranspiration plays a key role in the Earth's surface energy and water balances, and it has substantial effects on global climate change, water management and crop yield [1][2][3]. Satellite-based remote sensing has been identified as a suitable means of mapping the spatial distribution of ET or LE.…”

Section: Introductionmentioning

confidence: 99%

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

Xin

Peng

et al. 2018

Remote Sensing

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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.

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Section: Analysis Based On Simulation Datamentioning

confidence: 99%

Section: Ground Observation Datamentioning

confidence: 99%