A review of vegetation phenological metrics extraction using time-series, multispectral satellite data
Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes and variation in year-to-year vegetation productivity. Satellite remote sensing data have been...
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| Veröffentlicht in: | Remote sensing of environment Jg. 237; S. 111511 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
Elsevier Inc
01.02.2020
Elsevier BV |
| Schlagworte: | |
| ISSN: | 0034-4257, 1879-0704 |
| Online-Zugang: | Volltext |
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| Abstract | Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting general vegetation phenology stages (e.g., green onset, time or peak greenness and growing season length) often termed ‘land surface phenology’, as well as more advanced methods that estimate species-specific phenological stages (e.g., silking stage of maize). Common data processing methods, such as data smoothing, applied to prepare the time-series remote sensing observations to be applied to phenological detection methods are presented. Specific land surface phenology detection methods as well as species-specific phenology detection methods based on multispectral satellite data are then discussed. The impact of different error sources in the data on remote-sensing based phenology detection are also discussed in detail, as well as ways to reduce these uncertainties and errors. Joint analysis of multi-scale observations ranging from satellite to more recent ground-based sensors is helpful for us to understand satellite-based phenology detection mechanism and extent phenology detection to regional scale in the future. Finally, emerging opportunities to further advance remote sensing of phenology is presented that includes observations from Cubesats, near-surface observations such as PhenoCams and image data fusion techniques to improve the spatial resolution of time-series image data sets needed for phenological characterization.
•Review of satellite remote sensing-based land surface phenology detection methods.•Discussion of advantages and drawbacks of phenological metrics extraction methods.•Review of error sources and methods to reduce their effects on phenology detection.•Opportunities and challenges related to improve phonological metrics extraction. |
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| AbstractList | Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting general vegetation phenology stages (e.g., green onset, time or peak greenness and growing season length) often termed ‘land surface phenology’, as well as more advanced methods that estimate species-specific phenological stages (e.g., silking stage of maize). Common data processing methods, such as data smoothing, applied to prepare the time-series remote sensing observations to be applied to phenological detection methods are presented. Specific land surface phenology detection methods as well as species-specific phenology detection methods based on multispectral satellite data are then discussed. The impact of different error sources in the data on remote-sensing based phenology detection are also discussed in detail, as well as ways to reduce these uncertainties and errors. Joint analysis of multi-scale observations ranging from satellite to more recent ground-based sensors is helpful for us to understand satellite-based phenology detection mechanism and extent phenology detection to regional scale in the future. Finally, emerging opportunities to further advance remote sensing of phenology is presented that includes observations from Cubesats, near-surface observations such as PhenoCams and image data fusion techniques to improve the spatial resolution of time-series image data sets needed for phenological characterization. Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting general vegetation phenology stages (e.g., green onset, time or peak greenness and growing season length) often termed ‘land surface phenology’, as well as more advanced methods that estimate species-specific phenological stages (e.g., silking stage of maize). Common data processing methods, such as data smoothing, applied to prepare the time-series remote sensing observations to be applied to phenological detection methods are presented. Specific land surface phenology detection methods as well as species-specific phenology detection methods based on multispectral satellite data are then discussed. The impact of different error sources in the data on remote-sensing based phenology detection are also discussed in detail, as well as ways to reduce these uncertainties and errors. Joint analysis of multi-scale observations ranging from satellite to more recent ground-based sensors is helpful for us to understand satellite-based phenology detection mechanism and extent phenology detection to regional scale in the future. Finally, emerging opportunities to further advance remote sensing of phenology is presented that includes observations from Cubesats, near-surface observations such as PhenoCams and image data fusion techniques to improve the spatial resolution of time-series image data sets needed for phenological characterization. •Review of satellite remote sensing-based land surface phenology detection methods.•Discussion of advantages and drawbacks of phenological metrics extraction methods.•Review of error sources and methods to reduce their effects on phenology detection.•Opportunities and challenges related to improve phonological metrics extraction. |
| ArticleNumber | 111511 |
| Author | Xiang, Daxiang Hu, Shun Wardlow, Brian D. Zeng, Linglin Li, Deren |
| Author_xml | – sequence: 1 givenname: Linglin surname: Zeng fullname: Zeng, Linglin organization: College of Resources and Environment, Huazhong Agricultural University, Wuhan, 430070, China – sequence: 2 givenname: Brian D. surname: Wardlow fullname: Wardlow, Brian D. email: bwardlow2@unl.edu organization: Center for Advanced Land Management Information Technologies, School of Natural Resources, University of Nebraska-Lincoln, 3310 Holdrege St, Lincoln, 68583, USA – sequence: 3 givenname: Daxiang surname: Xiang fullname: Xiang, Daxiang organization: Changjiang River Scientifc Research Institute, Changjiang River Water Resources Commission, Wuhan, China – sequence: 4 givenname: Shun surname: Hu fullname: Hu, Shun organization: State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, Wuhan, 430072, China – sequence: 5 givenname: Deren surname: Li fullname: Li, Deren organization: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China |
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| Keywords | Phenological metrics extraction Land surface phenology Data smoothing Specie-specific phenology Remote sensing |
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| PublicationTitle | Remote sensing of environment |
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| SubjectTerms | Climate and land use Climate and vegetation corn Cubesat Data integration Data processing Data smoothing Detection Growing season Imagery information processing Land cover Land surface phenology Land use monitoring Multiscale analysis Multisensor fusion Phenological metrics extraction Phenology Remote sensing Satellite data Satellite observation Satellites spatial data Spatial discrimination Spatial resolution Specie-specific phenology Terrestrial ecosystems Time series time series analysis uncertainty Vegetation Vegetation changes |
| Title | A review of vegetation phenological metrics extraction using time-series, multispectral satellite data |
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