Investigating the potential of GIMMS and MODIS NDVI data sets for estimating gross primary productivity in Harvard Forest

Abstract

Accurate estimation of CO 2 fluxes is significant for studying the interaction between the terrestrial biosphere and the atmosphere, which is also highly relevant to the climate-policy making. Gross primary productivity (GPP), defined as the overall rate of fixation of carbon through the process of vegetation photosynthesis, is the total influx of carbon into an ecosystem. Many remote sensing approaches based on light use efficiency (LUE) model have been developed to estimate GPP at regional or global scale. A standard suite of global products characterizing GPP at the 1km spatial resolution is now being produced operationally based on observations from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. In this study, we investigated the potential of two normalized difference vegetation index (NDVI) data sets from global inventory modeling and mapping studies (GIMMS) and MODIS for estimating GPP in Harvard Forest. The result showed that only using NDVI and photosynthetically active radiation (PAR) can explain 74% of GPP for this site, which indicates GPP can be predicted by using long time period NDVI data sets at reasonable accuracy.