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Large Differences in Terrestrial Vegetation Production Derived 
from Satellite-Based Light Use Efficiency Models

 

Wenwen Cai ¹, Wenping Yuan ^1,* , Shunlin Liang ^2,3, Shuguang Liu ⁴, Wenjie Dong ¹, Yang Chen ¹, Dan Liu ¹ and Haicheng Zhang ¹

 

1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China

2 State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

3 Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

4 Geospatial Science Center of Excellence, South Dakota State University, 1021 Medary Ave, Wecota Hall 115, Box 506B, Brookings, SD 57007, USA

 

ABSTRACT

Terrestrial gross primary production (GPP) is the largest global CO2 flux and determines other ecosystem carbon cycle variables. Light use efficiency (LUE) models may have the most potential to adequately address the spatial and temporal dynamics of GPP, but recent studies have shown large model differences in GPP simulations. In this study, we investigated the GPP differences in the spatial and temporal patterns derived from seven widely used LUE models at the global scale. The result shows that the global annual GPP estimates over the period 2000–2010 varied from 95.10 to 139.71 Pg C？yr−1 among models. The spatial and temporal variation of global GPP differs substantially between models, due to different model structures and dominant environmental drivers. In almost all models, water availability dominates the interannual variability of GPP over large vegetated areas. Solar radiation and air temperature are not the primary controlling factors for interannual variability of global GPP estimates for most models. The disagreement among the current LUE models highlights the need for further model improvement to quantify the global carbon cycle.

 

KEY WORDS: gross primary production (GPP); light use efficiency (LUE); CASA; CFix; CFlux; EC-LUE; MODIS; VPM; VPRM

 

PUBLISHED BY: REMOTE SENSING, 2014, 6 (9): 8945-8965

 

SOURCE: http://www.mdpi.com/2072-4292/6/9/8945