An Improved Methodology to Estimate Terrestrial Net Primary Productivity by Integrating MODIS-LAI to Ecosystem Model SimCYCLE

Net Primary Productivity (NPP) is the difference between total photosynthesis and total plant respiration in an ecosystem. Estimating terrestrial NPP accurately is important as world's forest plays a vital role in the global carbon budget and overall environmental sustainability. Existing ecosystem models synthesize disparate time/space data into single coherent analysis of terrestrial carbon fluxes by incorporating known parameterizations of different ecosystem processes. However, because of the differences in mechanisms between the model and natural ecosystems, often the simulation of key parameters within the model is not accurate. Leaf area index (LAI) is one such key parameter simulated inside most ecosystem models based on averaged climate and soil conditions, carbon allocation scheme and fixed specific leaf area assumed for each biome. It is the second most important factor to NPP after growth period, and now with advanced modeling techniques, realistic and accurate estimates of LAI can be estimated globally from remote sensing imageries such as MODIS. In a previous study (Scheme I), considerable improvement was achieved in estimating global NPP at 0.5 degree spatial scale by constraining simulated LAI in an ecosystem model SimCYCLE with MODIS-derived LAI(MODIS- LAI). In this study (Scheme II), we used a similar strategy to Scheme I, but employed an improved methodology to estimate global NPP by integrating MODIS-LAI to ecosystem model SimCYCLE. Validation and comparison of results were done using GPPDI (Global Primary Productivity Data Initiative) ground-truth NPP dataset at 0.5 degree spatial scale. With this new integration scheme (Scheme II), estimation accuracy improved considerably (R2: 0.67, RMSE: 1.27 MgC ha-1yr-1, Stdev:3.46 MgC ha-1yr-1), when compared with Scheme I (R2: 0.56, RMSE: 1.88 MgC ha-1yr-1, Stdev:7.52 MgC ha-1yr-1) or with the model-alone estimates without integration of MODIS-LAI (R2: 0.44, RMSE: 2.40 MgC ha-1yr-1, Stdev:8.59 MgC ha-1yr-1). Validation at several locations in the tropics with another dataset also showed the new Scheme II producing better estimates (R2: 0.44) when compared with Scheme I (R2:0.01) or with the model estimates using climate and soil data alone (R2: 0.11).