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

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 (LE cal ) from this algorithm show strong positive temporal correlations with the in situ measured LE (LE obs ) 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/m 2 ) 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 LE cal 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.

Spatiotemporal Variations of Satellite Microwave Emissivity Difference Vegetation Index in China Under Clear and Cloudy Skies

“…A series of studies in midlatitude forest and Amazonian rainforest have confirmed its validity (Li & Min, 2013; Min et al, 2010; Min & Lin, 2006b). Recently, it has also been used to estimate the evapotranspiration rates at some forest sites in East Asia (Wang, Li, Min, Fu, et al, 2019, Wang, Li, Min, Zhang, et al, 2019). However, its spatiotemporal features over large‐scale complex land cover mixed with vegetation (forest, crops, and grass), bare soil, and open water were not clear yet.…”

“…The temporal correlation coefficient between satellite observed EDVI and ground measured evapotranspiration fraction can be as high as 0.79 (Min & Lin, 2006a). Using EDVI as dynamic variable in the vegetation conductivity and environmental stress function, satellite algorithms for retrieving ET rate have been developed for Harvard forest site (Li et al, 2009) and East Asia forest sites (Wang, Li, Min, Fu, et al, 2019; Wang, Li, Min, Zhang, et al, 2019). Instantaneous satellite retrieved ET under all sky conditions are found to have a bias of 16.5 W/m 2 (13.7%) and a root mean square error of 63.6 W/m 2 (52.5%) over East Asia forest sites.…”

Satellite Retrieval of Microwave Land Surface Emissivity under Clear and Cloudy Skies in China Using Observations from AMSR-E and MODIS

“…The rate of water vapor exchange and carbon exchange processes between the land and the atmosphere strongly depends on the characteristics of the vegetation water content. The MLSE results in this study were successfully used in the retrieval of vegetation water content [70], the estimation of water vapor evapotranspiration rate in mid-latitude forests [15,16,71], and the volatile organic gas emissions [17]. The influence of clouds on the evapotranspiration process has also been studied using MLSE-based products [72].…”

“…First, MLSE largely determines the background brightness temperature in the over-land emission-based retrievals of precipitation [8][9][10]. Second, MLSE has wide applications in monitoring/estimating surface properties and processes, including but not limited to soil moisture, surface evapotranspiration, and forest carbon uptake [11][12][13][14][15][16][17], assessment of wildfire risk [18,19], determination of vegetation physiology activity (respiration and photosynthesis), and the estimation of gross primary production and above ground biomass [20][21][22].…”

Editorial for the Special Issue “Remote Sensing of the Terrestrial Hydrologic Cycle”

“…The key hydrologic variables such as evapotranspiration (ET) and precipitation were derived and evaluated at varying spatial and temporal resolutions. Wang et al [4] proposed a latent heat flux (LE) algorithm based on a novel microwave vegetation index (EDVI) instead of the optical vegetation index that was commonly used in previous studies. This algorithm was driven by multiple-sensor satellite products of vegetation water content index, solar radiation, and cloud properties, with some aid from a reanalysis dataset.…”