Validity of Five Satellite-Based Latent Heat Flux Algorithms for Semi-arid Ecosystems

Abstract

Accurate estimation of latent heat flux (LE) is critical in characterizing semiarid ecosystems. Many LE algorithms have been developed during the past few decades. However, the algorithms have not been directly compared, particularly over global semiarid ecosystems. In this paper, we evaluated the performance of five LE models over semiarid ecosystems such as grassland, shrub, and savanna using the Fluxnet dataset of 68 eddy covariance (EC) sites during the period 2000-2009. We also used a modern-era retrospective analysis for research and applications (MERRA) dataset, the Normalized Difference Vegetation Index (NDVI) and Fractional Photosynthetically Active Radiation (FPAR) from the moderate resolution imaging spectroradiometer (MODIS) products; the leaf area index (LAI) from the global land surface satellite (GLASS) products; and the digital elevation model (DEM) from shuttle radar topography mission (SRTM30) dataset to generate LE at region scale during the period [2003][2004][2005][2006]. The models were the moderate resolution imaging spectroradiometer LE (MOD16) algorithm, revised remote sensing based Penman-Monteith LE algorithm (RRS), the Priestley-Taylor LE algorithm of the Jet Propulsion Laboratory (PT-JPL), the modified satellite-based Priestley-Taylor LE algorithm (MS-PT), and the semi-empirical Penman LE algorithm (UMD). Direct comparison with ground measured LE showed the PT-JPL and MS-PT algorithms had relative high performance over semiarid ecosystems with the coefficient of determination (R 2 ) ranging from 0.6 to 0.8 and root mean squared error (RMSE) of approximately 20 W/m 2 . Empirical parameters in the structure algorithms of MOD16 and RRS, and calibrated coefficients of the UMD algorithm may be the cause of the reduced performance of these LE algorithms with R 2 ranging from 0.5 to 0.7 and RMSE ranging from 20 to 35 W/m 2 for MOD16, RRS and UMD. Sensitivity analysis showed that radiation and vegetation terms were the dominating variables affecting LE Fluxes in global semiarid ecosystem.

Parameter sensitivity analysis and optimization for a satellite‐based evapotranspiration model across multiple sites using Moderate Resolution Imaging Spectroradiometer and flux data

“…Among them, the Priestly‐Taylor Jet Propulsion Laboratory (PT‐JPL) model proposed by Fisher et al . [] has been widely used to estimate ET because of its minimal requirements for ground‐based measurements and its good performance [ Feng et al ., ; Michel et al ., ; Zhu et al ., ]. For example, Ershadi et al .…”

“…Remote sensing-based ET estimation methods fall into two broad categories: (1) empirical/statistical methods that relate ET to some easily obtained satellitederived variables (e.g., radiation, land surface temperature, and vegetation index) and (2) process-based methods, which estimate ET on the basis of the Penman-Monteith equation [Monteith, 1965;Cleugh et al, 2007;Mu et al, 2007Mu et al, , 2011, the Priestley-Taylor approach [Priestley and Taylor, 1972;Fisher et al, 2008], or the residual method of the energy balance equation [Bastiaanssen et al, 1998;Su, 2002;Norman et al, 1995]. Among them, the Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) model proposed by Fisher et al [2008] has been widely used to estimate ET because of its minimal requirements for ground-based measurements and its good performance [Feng et al, 2015;Michel et al, 2016;Zhu et al, 2016]. For example, Ershadi models across 20 flux towers.…”

Long-term spatial distributions and trends of the latent heat fluxes over the global cropland ecosystem using multiple satellite-based models

“…LE estimates by the four models showed high correlations with ground observations in humid mild regions such as east of Asia and central Europe, whereas low in arid and cold regions such the Mediterranean region and west of Asia. Compared with other methods, PT-JPL and MS-PT were more identical with ground observations [ 36 , 38 ], especially at East Asia sites. RRS, PT-JPL and MS-PT had negative bias ranging from 10 to 20 W/m 2 at most sites.…”

“…Validation by Yao et al (2015) [ 37 ] showed that the MS-PT algorithm had good performance over global forest biomes. Feng et al (2015) [ 38 ] found that the MS-PT algorithm improved LE estimation over global grassland, shrubland and savanna.…”

A hierarchical Bayesian approach for multi‐site optimization of a satellite‐based evapotranspiration model

“…Among these methods, modelling provides a powerful tool and is becoming more and more popular (Shugart, ). The Priestly–Taylor Jet Propulsion Laboratory (PT‐JPL) model (Fisher, Tu, & Baldocchi, ), which has a process‐based structure to partition total ET into its different components, is physically sound and rigorous, and has been widely used in previous studies due to its minimal requirements for ground‐based measurements and its good performance (Feng et al, ; Michel et al, ; Zhang et al, ; Zhu et al, ).…”