Assessing the ability of MODIS EVI to estimate terrestrial ecosystem gross primary production of multiple land cover types. (January 2017)

• Record Type: Journal Article
• Authors: Shi, Hao; Li, Longhui; Eamus, Derek; Huete, Alfredo; Cleverly, James; Tian, Xin; Yu, Qiang; Wang, Shaoqiang; Montagnani, Leonardo; Magliulo, Vincenzo; Rotenberg, Eyal; Pavelka, Marian; Carrara, Arnaud
• Is Part Of: Ecological indicators. Volume 72(2017)
• Page Start: 153
• Page End: 164
• Publication Date: 2017-01
• Subjects: Remote sensing -- MODIS -- Enhanced vegetation index -- Gross primary production -- Land cover types -- Leaf area index; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2016.08.022
• Languages: English
• ISSNs: 1470-160X

Abstract

Abstract: Terrestrial 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 within several biomes. However, the annual GPP-EVI relationship and associated environmental regulations have not yet been comprehensively investigated across biomes at the global scale. Here we explored relationships between annual integrated EVI (iEVI) and annual GPP observed at 155 flux sites, where GPP was predicted with a log-log model: ln ( G P P ) = a × ln ( i E V I ) + b . iEVI was computed from MODIS monthly EVI products following removal of values affected by snow or cold temperature and without calculating growing season duration. Through categorisation of flux sites into 12 land cover types, the ability of iEVI to estimate GPP was considerably improved ( R 2 from 0.62 to 0.74, RMSE from 454.7 to 368.2 g C m −2 yr −1 ). The biome-specific GPP-iEVI formulae generally showed a consistent performance in comparison to a global benchmarking dataset ( R 2 = 0.79, RMSE = 387.8 g C m −2 yr −1 ). Specifically, iEVI performed better in cropland regions with high productivity but poorer in forests. The ability of iEVI in estimating GPP was better in deciduous biomes (except deciduous broadleaf forest) than in evergreen due to the large seasonal signal in iEVI in deciduous biomes. Likewise, GPP estimated from iEVI was in a closer agreement to global benchmarks at mid and high-latitudes, where deciduous biomes are more common and cloud cover has a smaller effect on remote sensing retrievals. Across biomes, a significant and negative correlation ( R 2 = 0.37, p < 0.05) was observed between the strength ( R 2 ) of GPP-iEVI relationships and mean annual maximum leaf area index (LAImax ), and the relationship between the strength and mean annual precipitation followed a similar trend. LAImax also revealed a scaling effect on GPP-iEVI relationships. Our results suggest that iEVI provides a very simple but robust approach to estimate spatial patterns 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 spatial GPP provides valuable clues to improve EVI-based models.