Comparing the performance of vegetation indices for improving urban vegetation GPP estimation via eddy covariance flux data and Landsat 5/7 data Article Swipe
Qi Zeng
,
Xuehe Lu
,
Sanmei Chen
,
Xuan Cui
,
Haidong Zhang
,
Qian Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecoinf.2025.103023
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecoinf.2025.103023
Urban vegetation is pivotal in enhancing regional ecological balance and sequestering significant amounts of carbon dioxide (CO2) through photosynthesis, thereby contributing substantially to regional carbon budgets. However, the gross primary productivity (GPP) of urban vegetation remains underexplored due to the absence of robust estimation methodologies, often leading to its exclusion from global and regional carbon budgets. Advances in vegetation indices (VIs) offer promising solutions for improving the accuracy and spatial resolution of urban GPP estimation. In this study, we compared the performance of the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and kernel normalized difference vegetation index (kNDVI) calculated from Landsat 5/7 images in estimating flux-site-level GPP and incorporated meteorological factors to construct a high-performance VI-GPP model for urban GPP estimation. Our findings demonstrated that the EVI, NIRv, and kNDVI exhibited stronger correlations with GPP dynamics and higher R2 values than did the NDVI in linear VI-GPP relationships across most plant functional types (PFTs). Exceptions were observed in evergreen broadleaf forest (EBF), evergreen needle-leaf forest (ENF), and savanna (SAV), where GPP variations were strongly influenced by temperature, shortwave radiation, and vapor pressure. Incorporating these meteorological factors significantly enhanced GPP estimation accuracy for these PFTs. Among the indices, the NIRv achieved the highest overall model performance, with an R2 of 0.60 and a root-mean-square error (RMSE) of 2.05 g C m−2 d−1 across PFTs. The kNDVI demonstrated unique advantages for specific PFTs, such as deciduous broadleaf forest (DBF) and ENF. Compared with existing VI-GPP relationships created with coarse-spatial-resolution remote sensing data, our model was more suitable for high-spatial-resolution GPP estimation in urban areas. Our results highlight the performance of the NIRv and kNDVI in urban vegetation GPP estimation and provide a solution for estimating fine-resolution GPP to reveal the importance of urban vegetation to regional carbon budgets.
Related Topics
Concepts
Eddy covariance
Vegetation (pathology)
Environmental science
Flux (metallurgy)
Covariance
Estimation
Enhanced vegetation index
Remote sensing
Vegetation Index
Normalized Difference Vegetation Index
Ecosystem
Geography
Statistics
Geology
Ecology
Mathematics
Climate change
Oceanography
Metallurgy
Management
Materials science
Pathology
Medicine
Biology
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ecoinf.2025.103023
- OA Status
- gold
- Cited By
- 7
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406596421
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406596421Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ecoinf.2025.103023Digital Object Identifier
- Title
-
Comparing the performance of vegetation indices for improving urban vegetation GPP estimation via eddy covariance flux data and Landsat 5/7 dataWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-19Full publication date if available
- Authors
-
Qi Zeng, Xuehe Lu, Sanmei Chen, Xuan Cui, Haidong Zhang, Qian ZhangList of authors in order
- Landing page
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https://doi.org/10.1016/j.ecoinf.2025.103023Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.ecoinf.2025.103023Direct OA link when available
- Concepts
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Eddy covariance, Vegetation (pathology), Environmental science, Flux (metallurgy), Covariance, Estimation, Enhanced vegetation index, Remote sensing, Vegetation Index, Normalized Difference Vegetation Index, Ecosystem, Geography, Statistics, Geology, Ecology, Mathematics, Climate change, Oceanography, Metallurgy, Management, Materials science, Pathology, Medicine, Biology, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7Per-year citation counts (last 5 years)
- References (count)
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60Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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