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Remote Sensing Technology and Application  2020, Vol. 35 Issue (5): 1015-1027    DOI: 10.11873/j.issn.1004-0323.2020.5.1015
    
Spatial and Temporal Differences of GPP Simulated by Different Satellite-derived LAI in China
Jiyu Hou1(),Yanlian Zhou1(),Yang Liu2
1.School of Geography and Ocean science,Nanjing University,Nanjing,Jiangsu 210023,China
2.Institute of Geographical Sciences and Resources,Chinese Academy of Sciences,Beijing 100101,China
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Abstract  

Terrestrial Gross Primary Production (GPP) is a key component of the carbon cycle, which represents the ability of plants to absorb and fix CO2 in the atmosphere. Light Use Efficiency (LUE) model is commonly used in regional simulation of GPP. Leaf Area Index (LAI) is a key input data in TL-LUE model. There are great spatial and temporal difference between various LAI data. Difference in spatial and temporal patterns between GPP simulations derived with different LAI needs to be investigated further. In this study, three satellite-derived LAI data, MCD15, GLASS and GlobMap, were used to simulate GPP in China from 2003 to 2017. Firstly, three LAI data were compared to investigate the difference in the spatial and temporal patterns. Then, GPP simulated by three LAI data were compared to investigate the difference. Results showed that spatial and temporal patterns of LAI differed substantially among different LAI data, and there were great differences in forest regions. Averaged annual value of three LAI data showed significant increasing trends from 2003 to 2017(p<0.01). However, the interannual variation of the annual mean value of different LAI data were obviously different. The GPP simulated by GLASS LAI had high correlation with EC GPP. Mean annual total GPP in China simulated with different LAI data has great difference, varied from 6.39 Pg C a-1 (GlobMap) to 7.46 Pg C a-1 (GLASS). Annual total GPP in China simulated by three LAI data showed significant increasing trends from 2003 to 2017 (p<0.05). However, the interannual variation of different annual total GPP were obviously different. The spatial and temporal patterns of GPP differed substantially among different simulated GPP, and there were great differences in forest and crop regions. This study was helpful to assess the uncertainties of regional GPP simulation derived from input data.

Key words:  Leaf Area Index (LAI)      Gross Primary Productivity (GPP)      Two Leaf Light Use Efficiency (TL-LUE) model      Spatial and temporal difference     
Received:  04 February 2020      Published:  26 November 2020
ZTFLH:  TP75  
Corresponding Authors:  Yanlian Zhou     E-mail:  18061790512@163.com;zhouyl@nju.edu.cn
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Jiyu Hou
Yanlian Zhou
Yang Liu

Cite this article: 

Jiyu Hou,Yanlian Zhou,Yang Liu. Spatial and Temporal Differences of GPP Simulated by Different Satellite-derived LAI in China. Remote Sensing Technology and Application, 2020, 35(5): 1015-1027.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2020.5.1015     OR     http://www.rsta.ac.cn/EN/Y2020/V35/I5/1015

LAI数据集版本数据源空间分辨率时间分辨率时间范围参考文献
MCD15C6MODIS500 m8 d2003~2017Myneni等[8]
GLASSV4.0MODIS C61 km8 d2003~2017Xiao等[20]
GlobMapV3.0MODIS C6500 m8 d2003~2017Liu等[9]
Table 1  Characteristic of the LAI data(MCD15,GLASS and GlobMap)
Fig.1  Land cover map of China in 2017
植被类型DBFDNFEBFENFMFSHRGRACROSAVWS
聚集度指数Ω0.80.60.80.60.80.80.90.90.80.8
反照率α0.180.150.180.150.170.230.230.230.160.16
εmsun(g CMJ-1)0.620.400.490.590.660.460.891.190.50.48
εmsh(g CMJ-1)2.171.731.961.871.842.112.824.811.531.70
VPDmax(kpa)4.14.14.14.14.14.14.14.14.14.1
VPDmin(kpa)0.930.930.930.930.930.930.930.930.930.93
Tamin,h (℃)7.9410.449.098.318.511.3912.0212.028.6111.39
Tamin,l (℃)-8-8-8-8-8-8-8-8-8-8
Table 2  Parameters of the TL-LUE model
Fig.2  Spatial patterns of annual mean LAI-MCD15, LAI-GLASS, LAI-GlobMap (a~c) and the differences (d~f) between each two LAI data and the trends of LAI (g~i) in China during 2003~2017 (after M-K trend detection, positive values represent significant increase trends (p< 0.05), negative values represent significant decrease trends (p < 0.05), gray represents no significant trends (p > 0.05)) (Uint: m2 m-2)
Fig.3  Multiyear averaged annual LAI of China during 2003~2017 (Uint: m2 m-2)
Fig.4  Annual mean LAI of China during 2003~2017 (Unit: m2 m-2)
Fig.5  Comparison of annual total GPP-EC and annual total GPP-MCD15, GPP-GLASS, GPP-GlobMap at 8 Chinaflux sites
Fig.6  Spatial patterns of GPP-MCD15,GPP-GLASS and GPP-GlobMap and the differences between each two GPP and the trends of annual mean GPP in China during 2003–2017(after M-K trend detection, positive values represent significant increase trends (p < 0.05), negative values represent significant decrease trends (p < 0.05), gray represents no significant trends (p > 0.05))
Fig.7  Annual total GPP of China during 2003~2017
Fig.8  Interannual variation of China’ annual total GPP during 2003~2017
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