Analysis of light use efficiency and gross primary productivity based on remote sensing data over a phragmites-dominated wetland in Zhangye, China

Guoqing Jiang; Rui Sun; Lei Zhang; Shaomin Liu; Ziwei Xu; Chen Qiao

doi:10.1117/12.2068840

8 November 2014 Analysis of light use efficiency and gross primary productivity based on remote sensing data over a phragmites-dominated wetland in Zhangye, China

Guoqing Jiang, Rui Sun, Lei Zhang, Shaomin Liu, Ziwei Xu, Chen Qiao

Author Affiliations +

Proceedings Volume 9260, Land Surface Remote Sensing II; 926033 (2014) https://doi.org/10.1117/12.2068840
Event: SPIE Asia-Pacific Remote Sensing, 2014, Beijing, China

Abstract

Light use efficiency (LUE) is a critical parameter for estimating carbon exchange in many ecosystem models, especially those models based on remote sensing algorithms. Estimation and monitoring of LUE and gross primary productivity (GPP) over wetland is very important for the global carbon cycle research and modelling, since the wetland plays a vital role in the ecosystem balance. In this paper, carbon flux data observed with an eddy covariance tower over a reedsdominated wetland in Zhangye, northwest of China, was used to calculate LUE. Through the postprocessing of carbon flux data and estimation of ecosystem respiration, daily GPP was calculated firstly. Combining with fraction of absorbed photosynthetically active radiation (FPAR) inversed from HJ-1 satellite, LUE was determined. The maximum value of LUE was 1.03 g C·MJ^-1 occurred in summer. Furthermore, a regional vegetation productivity model based on meteorological data and remote sensing data was used to estimate the wetland GPP. The results show that the modeled GPP results were consistent with in situ data.

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Guoqing Jiang, Rui Sun, Lei Zhang, Shaomin Liu, Ziwei Xu, and Chen Qiao "Analysis of light use efficiency and gross primary productivity based on remote sensing data over a phragmites-dominated wetland in Zhangye, China", Proc. SPIE 9260, Land Surface Remote Sensing II, 926033 (8 November 2014); https://doi.org/10.1117/12.2068840

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KEYWORDS

Data modeling

Carbon

Ecosystems

Remote sensing

Vegetation

Atmospheric modeling

Data conversion

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