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

Though coastal wetlands occupy a small fraction of the Earth's surface, 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 open-ocean and terrestrial ecosystems. This is partly due to the limited in situ observations in these areas. One of the ways around the limited in situ data is to use remote sensing products. Here we present the first evaluation of the MOD17 remote sensing algorithm of gross primary productivity (GPP) in a mangrove forest using data from a flux tower in the Florida Everglades. MOD17 utilizes remote sensing products from the Moderate Resolution Imaging Spectroradiometer and meteorological fields from the NCEP/DOE Reanalysis 2. MOD17 is found to capture the long-term mean and seasonal amplitude of GPP but has significant errors describing the interannual variability, intramonthly variability, and the phasing of the annual cycle in GPP. Regarding the latter, MOD17 overestimates GPP when salinity is high and underestimates it when it is low, consistent with the fact that MOD17 ignores salinity and salinity tends to decrease GPP. Including salinity in the algorithm would then most likely improve its accuracy. MOD17 also assumes that GPP is linear with respect to PAR (photosynthetically active radiation), which does not hold true in the mangroves. Finally, the estimated PAR and air temperature inputs to MOD17 were found to be significantly lower than observed. In summary, while MOD17 captures some aspects of GPP variability at this mangrove site, it appears to be doing so for the wrong reasons.