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Open AccessArticle

Estimation of Vegetation Latent Heat Flux over Three Forest Sites in ChinaFLUX using Satellite Microwave Vegetation Water Content Index

1
School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China
2
Atmospheric Science Research Center, State University of New York, Albany, NY 12203, USA
3
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
4
Institut de recherche sur les forêts, Université du Québec en Abitibi-Témiscamingue (UQAT), Rouyn-Noranda, QC J9X 5E4, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1359; https://doi.org/10.3390/rs11111359
Received: 18 March 2019 / Revised: 28 May 2019 / Accepted: 28 May 2019 / Published: 6 June 2019
(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)
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Abstract

Latent heat flux (LE) and the corresponding water vapor lost from the Earth’s surface to the atmosphere, which is called Evapotranspiration (ET), is one of the key processes in the water cycle and energy balance of the global climate system. Satellite remote sensing is the only feasible technique to estimate LE over a large-scale region. While most of the previous satellite LE methods are based on the optical vegetation index (VI), here we propose a microwave-VI (EDVI) based LE algorithm which can work for both day and night time, and under clear or non-raining conditions. This algorithm is totally driven by multiple-sensor satellite products of vegetation water content index, solar radiation, and cloud properties, with some aid from a reanalysis dataset. The satellite inputs and the performance of this algorithm are validated with in situ measurements at three ChinaFLUX forest sites. Our results show that the selected satellite observations can indeed serve as the inputs for the purpose of estimating ET. The instantaneous estimations of LE (LEcal) from this algorithm show strong positive temporal correlations with the in situ measured LE (LEobs) with the correlation coefficients (R) of 0.56–0.88 in the study years. The mean bias is kept within 16.0% (23.0 W/m2) across the three sites. At the monthly scale, the correlations between the retrieval and the in situ measurements are further improved to an R of 0.84–0.95 and the bias is less than 14.3%. The validation results also indicate that EDVI-based LE method can produce stable LEcal under different cloudy skies with good accuracy. Being independent of any in situ measurements as inputs, this algorithm shows great potential for estimating ET under both clear and cloudy skies on a global scale for climate study. View Full-Text
Keywords: Microwave emissivity difference vegetation index (EDVI); evapotranspiration (ET); satellite remote sensing; cloudy sky; clouds and earth’s radiation energy system (CERES); ChinaFLUX Microwave emissivity difference vegetation index (EDVI); evapotranspiration (ET); satellite remote sensing; cloudy sky; clouds and earth’s radiation energy system (CERES); ChinaFLUX
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Wang, Y.; Li, R.; Min, Q.; Zhang, L.; Yu, G.; Bergeron, Y. Estimation of Vegetation Latent Heat Flux over Three Forest Sites in ChinaFLUX using Satellite Microwave Vegetation Water Content Index. Remote Sens. 2019, 11, 1359.

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