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Remote Sens. 2015, 7(12), 16733-16755; doi:10.3390/rs71215853

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

1
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
2
Landscape Ecology & Ecosystem Science (LEES) Lab. Center for Global Change and Earth Observations (CGCEO), Michigan State University, East Lansing, MI 48823, USA
3
State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China
4
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
5
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100049, China
6
University of Chinese Academy of Sciences, Beijing 100049, China
7
The Research Institute of Petroleum Exploration and Development, China Petroleum Pipeline Bureau, Beijing 100083, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Richard Müller and Prasad S. Thenkabail
Received: 4 October 2015 / Revised: 25 November 2015 / Accepted: 27 November 2015 / Published: 9 December 2015
View Full-Text   |   Download PDF [5582 KB, uploaded 9 December 2015]   |  

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 2003–2006. 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 (R2) ranging from 0.6 to 0.8 and root mean squared error (RMSE) of approximately 20 W/m2. 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 R2 ranging from 0.5 to 0.7 and RMSE ranging from 20 to 35 W/m2 for MOD16, RRS and UMD. Sensitivity analysis showed that radiation and vegetation terms were the dominating variables affecting LE Fluxes in global semiarid ecosystem. View Full-Text
Keywords: latent heat flux; grassland ecosystems; revised remote sensing based Penman–Monteith LE algorithm; MOD16; modified satellite-based Priestley–Taylor LE algorithm; semi-empirical Penman LE algorithm latent heat flux; grassland ecosystems; revised remote sensing based Penman–Monteith LE algorithm; MOD16; modified satellite-based Priestley–Taylor LE algorithm; semi-empirical Penman LE algorithm
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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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MDPI and ACS Style

Feng, F.; Chen, J.; Li, X.; Yao, Y.; Liang, S.; Liu, M.; Zhang, N.; Guo, Y.; Yu, J.; Sun, M. Validity of Five Satellite-Based Latent Heat Flux Algorithms for Semi-arid Ecosystems. Remote Sens. 2015, 7, 16733-16755.

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