National Earth System Science Data Center GLASS Gross Primary Production (GPP) Download:
Introduction:
GLASS-GPP algorithm originates from EC-LUE model (Eddy Covariance – Light Use Efficiency) (Yuan et al. 2007). On the basis of the theory of light use efficiency, the EC-LUE model relies on two assumptions: first, that the fraction of absorbed PAR (fPAR) is a linear function of NDVI; second, that the realized light use efficiency, calculated from a biome-independent invariant potential LUE, is controlled by air temperature or soil moisture, whichever is most limiting. The original EC-LUE is driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux (used to calculate moisture stress) (Yuan et al., 2007).
The later version of EC-LUE substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET (Yuan et al. 2010).
To accurately indicate the long-term changes of GPP, the GLASS-GPP product used the latest version of EC-LUE, which integrates the impacts of several environmental variables: atmospheric CO2 concentration, radiation components and atmospheric water vapor pressure (VPD) (Yuan et al., in preparation).
The EC-LUE model has been validated widely throughout North America, Europe and East Asia by using the measurements of eddy covariance towers (Yuan et al., 2007; 2010)(Li et al. 2013; Yuan et al. 2014). These validations showed that the EC-LUE model can successfully reproduce the spatial and temporal variabilities of GPP over the various ecosystem types.
Several model comparisons also indicate the better performance of EC-LUE than other LUE models. Previous study compared EC-LUE model and MODIS-GPP products based on the measurements of eddy covariance towers at southeastern China, and found the EC-LUE model performed better than the MODIS algorithms (Xu et al., 2013). A recent study compared eight satellite-based GPP models over various major grassland ecosystem types and found the EC-LUE model performed best (Jia et al., 2018).
References:
[1]Yuan, W.P., Liu, S., Zhou, G.S., Zhou, G.Y., Tieszen, L.L., Baldocchi, D., Bernhofer, C., Gholz, H., Goldstein, A.H., Goulden, M.L., Hollinger, D.Y., Hu, Y., Law, B.E., Stoy, P.C., Vesala, T., Wofsy, S.C., & AmeriFlux, C. (2007). Deriving a light use efficiency model from eddy covariance flux data for predicting dailygross primary production across biomes. Agricultural and Forest Meteorology, 143, 189-207
[2]Yuan, W.P., Liu, S.G., Yu, G.R., Bonnefond, J.M., Chen, J.Q., Davis, K., Desai, A.R., Goldstein, A.H., Gianelle, D., Rossi, F., Suyker, A.E., & Verma, S.B. (2010). Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sensing of Environment, 114, 1416-1431
[3]Li, X., Liang, S., Yu, G., Yuan, W., Cheng, X., Xia, J., Zhao, T., Feng, J., Ma, Z., Ma, M., Liu, S., Chen, J., Shao, C., Li, S., Zhang, X., Zhang, Z., Chen, S., Ohta, T., Varlagin, A., Miyata, A., Takagi, K., Saiqusa, N., & Kato, T. (2013). Estimation of gross primary production over the terrestrial ecosystems in China. Ecological Modelling, 261–262, 80-92
[4]Yuan, W., Cai, W., Xia, J., Chen, J., Liu, S., Dong, W., Merbold, L., Law, B., Arain, A., & Beringer, J. (2014). Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database. Agricultural and Forest Meteorology, 192, 108-120