Comparison of Phenology Estimated from Reflectance-Based Indices and Solar-Induced Chlorophyll Fluorescence (SIF) Observations in a Temperate Forest Using GPP-Based Phenology as the Standard
Abstract
We assess the performance of optical images, reflectance-based vegetation indices and solar induced chlorophyll fluorescence (SIF) datasets with various spatial and temporal resolutions in monitoring the GPP-based phenology in a temperate deciduous forest. If negative impacts due to coarse spatial and temporal resolutions are effectively reduced, we find that all these data can serve as good indicators of phenological metrics in the spring that are derived from GPP time series. However, the autumn phenological metrics derived from all reflectance-based datasets are later than the tower-based GPP estimates. This is because the reflectance-based observations estimate phenology by tracking physiological properties including leaf area index (LAI) and leaf chlorophyll content (Chl), which does not reflect instantaneous changes in phenophase transitions, and thus the estimated fall phenological events may be later than GPP-based phenology. In contrast, we find that SIF has a good potential to track seasonal transition of photosynthetic activities in both spring and fall seasons. The advantage of SIF in estimating the GPP-based phenology lies in its inherent link to photosynthesis activities such that SIF can respond quickly to all factors regulating phenological events. Despite uncertainties in phenological metrics estimated from current spaceborne SIF observations due to their coarse spatial andmore »
- Authors:
-
[6]
- Oregon State Univ., Corvallis, OR (United States). Dept. of Forest Ecosystems and Society; Marine Biological Lab., Woods Hole, MA (United States). Ecosystems Center
- Marine Biological Lab., Woods Hole, MA (United States). Ecosystems Center; Nanjing Univ. (China). School of Life Sciences
- Iowa State Univ., Ames, IA (United States). Dept. of Geological and Atmospheric Sciences
- Columbia Univ., New York, NY (United States). Dept. of Earth and Environmental Engineering
- Nanjing Univ. (China). School of Life Sciences
- Marine Biological Lab., Woods Hole, MA (United States). Ecosystems Center
- Publication Date:
- Research Org.:
- Marine Biological Lab., Woods Hole, MA (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1499905
- Alternate Identifier(s):
- OSTI ID: 1512441
- Report Number(s):
- PNNL-SA-130442
Journal ID: ISSN 2072-4292
- Grant/Contract Number:
- SC0006951; AC05-76RL01830
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Remote Sensing
- Additional Journal Information:
- Journal Volume: 10; Journal Issue: 6; Journal ID: ISSN 2072-4292
- Publisher:
- MDPI
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; solar-induced chlorophyll fluorescence; reflectance; phenology; fall phenological events
Citation Formats
Lu, Xiaoliang, Liu, Zhunqiao, Zhou, Yuyu, Liu, Yaling, An, Shuqing, and Tang, Jianwu. Comparison of Phenology Estimated from Reflectance-Based Indices and Solar-Induced Chlorophyll Fluorescence (SIF) Observations in a Temperate Forest Using GPP-Based Phenology as the Standard. United States: N. p., 2018.
Web. doi:10.3390/rs10060932.
Lu, Xiaoliang, Liu, Zhunqiao, Zhou, Yuyu, Liu, Yaling, An, Shuqing, & Tang, Jianwu. Comparison of Phenology Estimated from Reflectance-Based Indices and Solar-Induced Chlorophyll Fluorescence (SIF) Observations in a Temperate Forest Using GPP-Based Phenology as the Standard. United States. https://doi.org/10.3390/rs10060932
Lu, Xiaoliang, Liu, Zhunqiao, Zhou, Yuyu, Liu, Yaling, An, Shuqing, and Tang, Jianwu. Wed .
"Comparison of Phenology Estimated from Reflectance-Based Indices and Solar-Induced Chlorophyll Fluorescence (SIF) Observations in a Temperate Forest Using GPP-Based Phenology as the Standard". United States. https://doi.org/10.3390/rs10060932. https://www.osti.gov/servlets/purl/1499905.
@article{osti_1499905,
title = {Comparison of Phenology Estimated from Reflectance-Based Indices and Solar-Induced Chlorophyll Fluorescence (SIF) Observations in a Temperate Forest Using GPP-Based Phenology as the Standard},
author = {Lu, Xiaoliang and Liu, Zhunqiao and Zhou, Yuyu and Liu, Yaling and An, Shuqing and Tang, Jianwu},
abstractNote = {We assess the performance of optical images, reflectance-based vegetation indices and solar induced chlorophyll fluorescence (SIF) datasets with various spatial and temporal resolutions in monitoring the GPP-based phenology in a temperate deciduous forest. If negative impacts due to coarse spatial and temporal resolutions are effectively reduced, we find that all these data can serve as good indicators of phenological metrics in the spring that are derived from GPP time series. However, the autumn phenological metrics derived from all reflectance-based datasets are later than the tower-based GPP estimates. This is because the reflectance-based observations estimate phenology by tracking physiological properties including leaf area index (LAI) and leaf chlorophyll content (Chl), which does not reflect instantaneous changes in phenophase transitions, and thus the estimated fall phenological events may be later than GPP-based phenology. In contrast, we find that SIF has a good potential to track seasonal transition of photosynthetic activities in both spring and fall seasons. The advantage of SIF in estimating the GPP-based phenology lies in its inherent link to photosynthesis activities such that SIF can respond quickly to all factors regulating phenological events. Despite uncertainties in phenological metrics estimated from current spaceborne SIF observations due to their coarse spatial and temporal resolutions, dates of the middle spring and autumn – the two most important metrics, can still be reasonably estimated from satellite SIF. Our study reveals that SIF provides a better way to monitor GPP-based phenological metrics.},
doi = {10.3390/rs10060932},
journal = {Remote Sensing},
number = 6,
volume = 10,
place = {United States},
year = {Wed Jun 13 00:00:00 EDT 2018},
month = {Wed Jun 13 00:00:00 EDT 2018}
}
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