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Improved estimations of gross primary production using satellite-derived photosynthetically active radiation

 

Wenwen Cai¹, Wenping Yuan^1,2, Shunlin Liang^3,4, Xiaotong Zhang³, Wenjie Dong¹, Jiangzhou Xia³,Yang Fu¹, Yang Chen¹, Dan Liu¹, and Qiang Zhang⁵

 

1State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China,

2State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Science, Lanzhou, China,

3State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China,

4Department of Geographical Sciences, University of Maryland, College Park, MaryLand, USA,

5Key laboratory of Arid Climatic Change and Disaster Reduction of Gansu Province/Key Open Laboratory of Arid Climatic Change and Disaster Reduction of China Meteorological Administration, Institute of Arid Meteorology, Lanzhou, China

 

ABSTRACT

Terrestrial vegetation gross primary production (GPP) is an important variable in determining the global carbon cycle as well as the interannual variability of the atmospheric CO2 concentration. The accuracy of GPP simulation is substantially affected by several critical model drivers, one of the most important of which is photosynthetically active radiation (PAR) which directly determines the photosynthesis processes of plants. In this study, we examined the impacts of uncertainties in radiation products on GPP estimates in China. Two satellite-based radiation products (GLASS and ISCCP), three reanalysis products (MERRA, ECMWF, and NCEP), and a blended product of reanalysis and observations (Princeton) were evaluated based on observations at hundreds of sites. The results revealed the highest accuracy for two satellite-based products over various temporal and spatial scales. The three reanalysis products and the Princeton product tended to overestimate radiation. The GPP simulation driven by the GLASS product exhibited the highest consistency with those derived from site observations. Model validation at 11 eddy covariance sites suggested the highest model performance when utilizing the GLASS product. Annual GPP in China driven by GLASS was 5.55 Pg C yr−1, which was 68.85%–94.87% of those derived from the other products. The results implied that the high spatial resolution, satellite-derived GLASS PAR significantly decreased the uncertainty of the GPP estimates at the regional scale.

 

KEY WORDS: Global LAnd Surface Satellite Product, International Satellite Cloud Climatology Project, EC-LUE, Gross Primary Production

 

PUBLISHED BY: JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES, 2014, 119 (1): 110-123

 

SOURCE: http://onlinelibrary.wiley.com/doi/10.1002/2013JG002456/abstract