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

Shi, H; Li, L; Eamus, D; Huete, A; Cleverly, J; Tian, X; Yu, Q; Wang, S; Montagnani, L; Magliulo, V; Rotenberg, E; Pavelka, M; Carrara, A

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

Shi, H Li, L

Eamus, D

Huete, A

Cleverly, J

Tian, X Yu, Q

Wang, S Montagnani, L Magliulo, V Rotenberg, E Pavelka, M Carrara, A

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Publication Type:: Journal Article
Citation:: Ecological Indicators, 2017, 72 pp. 153 - 164
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Shi, H	en_US
dc.contributor.author	Li, L https://orcid.org/0000-0001-9983-6681	en_US
dc.contributor.author	Eamus, D https://orcid.org/0000-0003-2765-8040	en_US
dc.contributor.author	Huete, A https://orcid.org/0000-0003-2809-2376	en_US
dc.contributor.author	Cleverly, J https://orcid.org/0000-0002-2731-7150	en_US
dc.contributor.author	Tian, X	en_US
dc.contributor.author	Yu, Q https://orcid.org/0000-0001-6950-1821	en_US
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Montagnani, L	en_US
dc.contributor.author	Magliulo, V	en_US
dc.contributor.author	Rotenberg, E	en_US
dc.contributor.author	Pavelka, M	en_US
dc.contributor.author	Carrara, A	en_US
dc.date.available	2020-05-25T19:02:36Z
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Ecological Indicators, 2017, 72 pp. 153 - 164	en_US
dc.identifier.issn	1470-160X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115611
dc.description.abstract	© 2016 Elsevier Ltd 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(GPP)=a×ln(iEVI)+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 (R2 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 (R2 = 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 (R2 = 0.37, p < 0.05) was observed between the strength (R2) 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.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE120103022
dc.relation	http://purl.org/au-research/grants/arc/DP140102698
dc.relation	http://purl.org/au-research/grants/arc/DP130101566
dc.relation.ispartof	Ecological Indicators	en_US
dc.relation.isbasedon	10.1016/j.ecolind.2016.08.022	en_US
dc.subject.classification	Ecology	en_US
dc.title	Assessing the ability of MODIS EVI to estimate terrestrial ecosystem gross primary production of multiple land cover types	en_US
dc.type	Journal Article
utslib.citation.volume	72	en_US
utslib.for	0602 Ecology	en_US
utslib.for	03 Chemical Sciences	en_US
utslib.for	05 Environmental Sciences	en_US
utslib.for	06 Biological Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	72	en_US

Abstract:

© 2016 Elsevier Ltd 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(GPP)=a×ln(iEVI)+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 (R2 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 (R2 = 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 (R2 = 0.37, p < 0.05) was observed between the strength (R2) 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.

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http://hdl.handle.net/10453/114836