Evidence map›Paper›PMID 36114727›Full record

ReviewGlobal change biology2022

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

Xuanlong Ma, Xiaolin Zhu, Qiaoyun Xie, Jiaxin Jin, Yuke Zhou, Yunpeng Luo, Yuxia Liu, Jiaqi Tian, Yuhe Zhao

Abstract readReview
In one paragraph

Review in Global change biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Review
  2. Article
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  6. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Xuanlong MaCollege of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China.ORCID 0000-0003-1499-8476
Xiaolin ZhuDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China.ORCID 0000-0001-6967-786X
Qiaoyun XieSchool of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, New South Wales, Australia.ORCID 0000-0002-1576-6610
Jiaxin JinCollege of Hydrology and Water Resources, Hohai University, Nanjing, China.ORCID 0000-0003-4067-298X
Yuke ZhouKey Laboratory of Ecosystem Network Observation and Modelling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.ORCID 0000-0002-2559-0241
Yunpeng LuoSwiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland.ORCID 0000-0001-6383-8300
Yuxia LiuSchool of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, New South Wales, Australia.ORCID 0000-0001-6130-2991
Jiaqi TianDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China.ORCID 0000-0002-5463-1532
Yuhe ZhaoCollege of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China.ORCID 0000-0002-5766-7522

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Climate ChangeEcosystemCarbonSeasonsWaterCarbonWaterbig databiodiversitycarbon cycleglobal changepublic healthvegetation dynamics

Identifiers

PMID36114727
PMCPMC9827868

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.