How accurately does gross primary productivity derived from remote sensing-based models represent the products from field measurements? Case studies of tropical vegetation in Borneo, Southeast Asia

The total quantity of carbon fixed by photosynthesis per unit time in an ecosystem is referred to as gross primary productivity (GPP). This is an important activity in the Earth’s carbon cycle. In near-equilibrium conditions, GPP is calculated as the sum of net carbon exchange during the day plus ecosystem respiration. In our study, we compared the GPP from satellite-based model estimates with the actual GPP calculated by the eddy covariance method available from the FLUXNET database in Borneo, Southeast Asia. We found that the GPP models were unable to capture the actual daily fluctuations of GPP in tropical vegetation in Borneo, although there were moderate correlations when comparing GPP from two different remote sensing models (e.g. the GPP derived from the Vegetation Photosynthesis Model (VPM) has a moderate correlation with GPP products from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua for Maludam tropical peat swamp forest vegetation). Parameterization was required to improve the GPP models, which included reanalyzing each model parameter. These parameter include LUI (light use efficiency), which is a challenging model parameter to measure but is critical in determining GPP, and cloud cover on MODIS satellite data, which determines the quality of remote sensing indices such as LSWI (land surface water index), due to the importance of this index as a proxy for W_scalar to estimate VPM GPP.