Monitoring nature's calendar from space: Emerging topics in land surface phenology and associated opportunities for science applications

Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant‐climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surfac...

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Published in:Global change biology Vol. 28; no. 24; pp. 7186 - 7204
Main Authors: Ma, Xuanlong, Zhu, Xiaolin, Xie, Qiaoyun, Jin, Jiaxin, Zhou, Yuke, Luo, Yunpeng, Liu, Yuxia, Tian, Jiaqi, Zhao, Yuhe
Format: Journal Article
Language:English
Published: England Blackwell Publishing Ltd 01.12.2022
John Wiley and Sons Inc
Subjects:
Adaptation
Algorithms
Atmospheric models
big data
bioclimatology
biodiversity
Biological Sciences
Biometeorology
Carbon
carbon cycle
Climate Change
Climate models
Data integration
Dynamics
Ecological adaptation
Ecosystem
Ecosystem dynamics
ecosystems
energy
Environmental impact
global change
Global climate
Interdisciplinary subjects
Monitoring
Multisensor fusion
Phenology
public health
Remote sensing
Review
Reviews
Seasonal variations
Seasons
Vegetation
vegetation dynamics
Water
biodiversity
carbon cycle
big data
vegetation dynamics
global change
public health
ISSN:1354-1013, 1365-2486, 1365-2486
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Abstract Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant‐climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field. Land surface phenology (LSP), as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.
AbstractList Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant‐climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field. Land surface phenology (LSP), as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.
Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant‐climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.
Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant-climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant-climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.
Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant‐climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field. Land surface phenology (LSP), as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.
Author Luo, Yunpeng
Zhou, Yuke
Jin, Jiaxin
Ma, Xuanlong
Zhu, Xiaolin
Xie, Qiaoyun
Liu, Yuxia
Tian, Jiaqi
Zhao, Yuhe
AuthorAffiliation 5 Key Laboratory of Ecosystem Network Observation and Modelling Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences Beijing China
8 Geospatial Sciences Center of Excellence (GSCE) South Dakota State University Brookings South Dakota USA
2 Department of Land Surveying and Geo‐Informatics The Hong Kong Polytechnic University Hong Kong China
7 Department of Environmental System Science ETH Zurich Zurich Switzerland
3 School of Life Sciences, Faculty of Science University of Technology Sydney Sydney New South Wales Australia
9 Department of Geography National University of Singapore Singapore Singapore
1 College of Earth and Environmental Sciences, Lanzhou University Lanzhou China
6 Swiss Federal Institute for Forest, Snow and Landscape Research WSL Birmensdorf Switzerland
4 College of Hydrology and Water Resources, Hohai University Nanjing China
AuthorAffiliation_xml – name: 1 College of Earth and Environmental Sciences, Lanzhou University Lanzhou China
– name: 6 Swiss Federal Institute for Forest, Snow and Landscape Research WSL Birmensdorf Switzerland
– name: 7 Department of Environmental System Science ETH Zurich Zurich Switzerland
– name: 3 School of Life Sciences, Faculty of Science University of Technology Sydney Sydney New South Wales Australia
– name: 5 Key Laboratory of Ecosystem Network Observation and Modelling Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences Beijing China
– name: 4 College of Hydrology and Water Resources, Hohai University Nanjing China
– name: 8 Geospatial Sciences Center of Excellence (GSCE) South Dakota State University Brookings South Dakota USA
– name: 9 Department of Geography National University of Singapore Singapore Singapore
– name: 2 Department of Land Surveying and Geo‐Informatics The Hong Kong Polytechnic University Hong Kong China
Author_xml – sequence: 1
  givenname: Xuanlong
  orcidid: 0000-0003-1499-8476
  surname: Ma
  fullname: Ma, Xuanlong
  email: xlma@lzu.edu.cn
  organization: College of Earth and Environmental Sciences, Lanzhou University
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  givenname: Xiaolin
  orcidid: 0000-0001-6967-786X
  surname: Zhu
  fullname: Zhu, Xiaolin
  organization: The Hong Kong Polytechnic University
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  orcidid: 0000-0002-1576-6610
  surname: Xie
  fullname: Xie, Qiaoyun
  organization: University of Technology Sydney
– sequence: 4
  givenname: Jiaxin
  orcidid: 0000-0003-4067-298X
  surname: Jin
  fullname: Jin, Jiaxin
  organization: College of Hydrology and Water Resources, Hohai University
– sequence: 5
  givenname: Yuke
  orcidid: 0000-0002-2559-0241
  surname: Zhou
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  organization: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
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  givenname: Yunpeng
  orcidid: 0000-0001-6383-8300
  surname: Luo
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  organization: ETH Zurich
– sequence: 7
  givenname: Yuxia
  orcidid: 0000-0001-6130-2991
  surname: Liu
  fullname: Liu, Yuxia
  organization: South Dakota State University
– sequence: 8
  givenname: Jiaqi
  orcidid: 0000-0002-5463-1532
  surname: Tian
  fullname: Tian, Jiaqi
  organization: National University of Singapore
– sequence: 9
  givenname: Yuhe
  orcidid: 0000-0002-5766-7522
  surname: Zhao
  fullname: Zhao, Yuhe
  organization: College of Earth and Environmental Sciences, Lanzhou University
BackLink https://www.ncbi.nlm.nih.gov/pubmed/36114727$$D View this record in MEDLINE/PubMed
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Issue 24
Keywords biodiversity
carbon cycle
big data
vegetation dynamics
global change
public health
Language English
License Attribution
2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.
This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Snippet Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant‐climate interactions. The correct representation of...
Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant-climate interactions. The correct representation of...
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SubjectTerms Adaptation
Algorithms
Atmospheric models
big data
bioclimatology
biodiversity
Biological Sciences
Biometeorology
Carbon
carbon cycle
Climate Change
Climate models
Data integration
Dynamics
Ecological adaptation
Ecosystem
Ecosystem dynamics
ecosystems
energy
Environmental impact
global change
Global climate
Interdisciplinary subjects
Monitoring
Multisensor fusion
Phenology
public health
Remote sensing
Review
Reviews
Seasonal variations
Seasons
Vegetation
vegetation dynamics
Water
Title Monitoring nature's calendar from space: Emerging topics in land surface phenology and associated opportunities for science applications
URI https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fgcb.16436
https://www.ncbi.nlm.nih.gov/pubmed/36114727
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https://pubmed.ncbi.nlm.nih.gov/PMC9827868
Volume 28
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