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Remote Sens. 2014, 6(3), 2108-2133; doi:10.3390/rs6032108
Article

Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia

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Received: 14 June 2013; in revised form: 4 February 2014 / Accepted: 19 February 2014 / Published: 7 March 2014
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Abstract: Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics.
Keywords: Gross Primary Productivity (GPP); GIMMS NDVI3g; GLOPEM-CEVSA; GIMMS NDVI1g; MOD15A2; MOD17A2; Southeast Asia Gross Primary Productivity (GPP); GIMMS NDVI3g; GLOPEM-CEVSA; GIMMS NDVI1g; MOD15A2; MOD17A2; Southeast Asia
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Wang, J.; Dong, J.; Liu, J.; Huang, M.; Li, G.; Running, S.W.; Smith, W.K.; Harris, W.; Saigusa, N.; Kondo, H.; Liu, Y.; Hirano, T.; Xiao, X. Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia. Remote Sens. 2014, 6, 2108-2133.

AMA Style

Wang J, Dong J, Liu J, Huang M, Li G, Running SW, Smith WK, Harris W, Saigusa N, Kondo H, Liu Y, Hirano T, Xiao X. Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia. Remote Sensing. 2014; 6(3):2108-2133.

Chicago/Turabian Style

Wang, Junbang; Dong, Jingwei; Liu, Jiyuan; Huang, Mei; Li, Guicai; Running, Steven W.; Smith, W. K.; Harris, Warwick; Saigusa, Nobuko; Kondo, Hiroaki; Liu, Yunfen; Hirano, Takashi; Xiao, Xiangming. 2014. "Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia." Remote Sens. 6, no. 3: 2108-2133.


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