In a study published in Remote Sensing of Environment, a research team led by Prof. LI Rui from University of Science and Technology of China (USTC) of the Chinese Academy of Sciences developed a new satellite-based light use effective (LUE) model coupled with a passive microwave vegetation index (emissivity difference vegetation index, EDVI) to monitor the gross primary production (GPP) of terrestrial ecosystems. This is the first step toward the integration of microwave-derived variables into LUE model for daily GPP estimation.
Terrestrial ecosystems fix CO2 in the atmosphere through plant photosynthesis and generate GPP. This process is essential in the carbon cycle between land and atmosphere. However, due to the complexity of terrestrial geographical systems, to study regional GPP, it is necessary to use satellite-based remote sensing technology.
Most satellite methods use optical vegetation indices to describe the characteristics of vegetation, and inputs specific models to estimate the exchange rate of carbon between the atmosphere and vegetation. One problem with these models is that optical sensing relies upon reflected sunlight, rendering it powerless at night or in cloudy weather. In contrast, microwave remote sensing monitors the heat radiation of the ground and the vegetation itself regardless of sunlight. Microwaves have a good penetration effect on clouds.
The researchers in this study independently developed the index EDVI, and proposed EDVI-LUE, a light utilizing efficiency model based on microwave EDVI. This model has been applied to representative areas of the forest, grassland and farmland vegetation in China.
Based on data collected from seven flux tower sites (four forests, two grasslands and one cropland) of ChinaFLUX network, the researchers found that in certain environments, EDVI has advantages in coping with complex weather and estimate accuracy
To be specific, normalized EDVI, an indicator of canopy-scale leaf development and biomass change, was used as a proxy of fraction of photosynthetically active radiation. Compared with optical indicators, normalized EDVI (nEDVI) based FPAR is less likely to saturate in forests, and it has higher sensitivity, and better relativity to GPP observed at sites.
In the situation of changing cloud condition over evergreen broadleaf forests, the estimate accuracy indicator of EDVI-LUE showed results with more stable accuracies, representing the advantage in accurately estimating GPP in cloud-covered thick forest areas.
This study offers a new way to quantify the carbon fixing ability of terrestrial ecosystems in China, and it provides scientific background for achieving carbon neutrality and carbon peak.
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