An Evaluation of the MOD17 Gross Primary Production Algorithm in a Mangrove Forest

Though coastal wetlands are only a small fraction of the Earth, they are extremely active ecosystems and play a significant role in the global carbon budget. However, coastal wetlands are still poorly understood, especially when compared to the open ocean and terrestrial ecosystems. This is partly due to the limited in situ observations in these areas. One of the ways to get around the limited in situ data is to use remote sensing products, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), to estimate productivity in coastal wetlands. However, problems arise when the gross primary productivity (GPP) from MODIS is compared to in situ data. Looking specifically at a flux tower located in a mangrove forest in the Everglades, MODIS has difficulty accurately reproducing GPP. The algorithm MODIS uses to calculate GPP was evaluated in an attempt to identify possible sources of error and ways to improve the algorithm. This was done by using data downloaded from MODIS and NCEP/DOE Reanalysis – 2 and data from the flux tower. MODIS makes certain assumptions about GPP and PAR (photosynthetically active radiation) that do not hold true in the mangroves and can affect GPP's accuracy. It was found that salinity seems to decrease GPP in the mangroves, but MODIS does not take salinity into account when calculating GPP, so including it in the algorithm would hopefully improve its accuracy. The accuracy of the meteorological data from the reanalysis also has an effect on the accuracy of GPP. The reanalysis tends to underestimate the tower observations which can introduce significant error when MODIS is calculating GPP. Another possible problem with the reanalysis data is the coarse spatial resolution when compared to MODIS. An attempt was made to reproduce GPP by interpolating the data and using the MODIS algorithm, however the MODIS GPP could not be accurately reproduced. The calculated GPP tends to be larger than MODIS GPP.