Assessing the ability of MODIS EVI to estimate terrestrial ecosystem gross primary production of multiple land cover types

Publication Type:
Journal Article
Ecological Indicators, 2017, 72 pp. 153 - 164
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tTerrestrial ecosystem gross primary production (GPP) is the largest component in the global carbon cycle.The enhanced vegetation index (EVI) has been proven to be strongly correlated with annual GPP withinseveral biomes. However, the annual GPP-EVI relationship and associated environmental regulationshave not yet been comprehensively investigated across biomes at the global scale. Here we exploredrelationships between annual integrated EVI (iEVI) and annual GPP observed at 155 flux sites, whereGPP was predicted with a log-log model: ln(GPP) = a × ln(iEVI) + b. iEVI was computed from MODISmonthly EVI products following removal of values affected by snow or cold temperature and withoutcalculating growing season duration. Through categorisation of flux sites into 12 land cover types, theability of iEVI to estimate GPP was considerably improved (R2from 0.62 to 0.74, RMSE from 454.7 to368.2 g C m−2yr−1). The biome-specific GPP-iEVI formulae generally showed a consistent performancein comparison to a global benchmarking dataset (R2= 0.79, RMSE = 387.8 g C m−2yr−1). Specifically, iEVIperformed better in cropland regions with high productivity but poorer in forests. The ability of iEVI inestimating GPP was better in deciduous biomes (except deciduous broadleaf forest) than in evergreendue to the large seasonal signal in iEVI in deciduous biomes. Likewise, GPP estimated from iEVI was ina closer agreement to global benchmarks at mid and high-latitudes, where deciduous biomes are morecommon and cloud cover has a smaller effect on remote sensing retrievals. Across biomes, a significant andnegative correlation (R2= 0.37, p < 0.05) was observed between the strength (R2) of GPP-iEVI relationshipsand mean annual maximum leaf area index (LAImax), and the relationship between the strength andmean annual precipitation followed a similar trend. LAImaxalso revealed a scaling effect on GPP-iEVIrelationships. Our results suggest that iEVI provides a very simple but robust approach to estimate spatialpatterns of global annual GPP whereas its effect is comparable to various light-use-efficiency and data-driven models. The impact of vegetation structure on accuracy and sensitivity of EVI in estimating spatialGPP provides valuable clues to improve EVI-based models.
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