Extracting Deciduous Forests Spring Phenology From Sentinel-1 Cross Ratio Index

Yu, H; Yang, Y; Wang, C; Chen, R; Xie, Q; Liu, G; Yin, G

Extracting Deciduous Forests Spring Phenology From Sentinel-1 Cross Ratio Index

Yu, H Yang, Y Wang, C Chen, R Xie, Q

Liu, G Yin, G

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16, pp. 2841-2850
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Yu, H
dc.contributor.author	Yang, Y
dc.contributor.author	Wang, C
dc.contributor.author	Chen, R
dc.contributor.author	Xie, Q https://orcid.org/0000-0002-1576-6610
dc.contributor.author	Liu, G
dc.contributor.author	Yin, G
dc.date.accessioned	2024-03-11T01:05:07Z
dc.date.available	2024-03-11T01:05:07Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16, pp. 2841-2850
dc.identifier.issn	1939-1404
dc.identifier.issn	2151-1535
dc.identifier.uri	http://hdl.handle.net/10453/176430
dc.description.abstract	Deciduous forests spring phenology plays a major role in balancing the carbon cycle. The cloud cover affects images acquired from optical sensors and reduces their performance in monitoring phenology. Synthetic aperture radar (SAR) can regularly acquire images day and night independent of weather conditions, which offers more frequent observations of vegetation phenology compared to optical sensors. However, it remains unclear how SAR data-derived indices vary across different growth stages of forests. Here, we explored the relationship between the cross ratio (CR) index derived from Sentinel-1 data and the deciduous forest growth process. We proposed a deciduous forests spring phenology extraction method using CR and compared the extracted start of growing season (SOS) with those extracted using normalized difference vegetation index (NDVI) derived from Sentinel-2 optical satellite data and green chromatic coordinate (GCC) derived from ground PhenoCam data. We extracted the SOS of 41 PhenoCam sites over the Continental United States in 2018 using the dynamic threshold method. Our results showed that the variations of CR time series are closely related to the phenological processes of deciduous forests. The SOS extracted using CR data showed high consistency with those extracted using GCC (R2 = 0.46), with slightly lower accuracy compared with NDVI-derived results (R2 = 0.62). Our study illustrates the value and mechanism of deciduous forests spring phenology extraction using SAR data and provides a reference for using SAR data to improve forest phenology extraction in addition to using optical remote sensing data, especially in rainy and cloudy regions.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
dc.relation.isbasedon	10.1109/JSTARS.2023.3247833
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0406 Physical Geography and Environmental Geoscience, 0801 Artificial Intelligence and Image Processing, 0909 Geomatic Engineering
dc.subject.classification	3709 Physical geography and environmental geoscience
dc.subject.classification	4013 Geomatic engineering
dc.subject.classification	4601 Applied computing
dc.title	Extracting Deciduous Forests Spring Phenology From Sentinel-1 Cross Ratio Index
dc.type	Journal Article
utslib.citation.volume	16
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-11T01:05:06Z
pubs.publication-status	Published
pubs.volume	16

Abstract:

Deciduous forests spring phenology plays a major role in balancing the carbon cycle. The cloud cover affects images acquired from optical sensors and reduces their performance in monitoring phenology. Synthetic aperture radar (SAR) can regularly acquire images day and night independent of weather conditions, which offers more frequent observations of vegetation phenology compared to optical sensors. However, it remains unclear how SAR data-derived indices vary across different growth stages of forests. Here, we explored the relationship between the cross ratio (CR) index derived from Sentinel-1 data and the deciduous forest growth process. We proposed a deciduous forests spring phenology extraction method using CR and compared the extracted start of growing season (SOS) with those extracted using normalized difference vegetation index (NDVI) derived from Sentinel-2 optical satellite data and green chromatic coordinate (GCC) derived from ground PhenoCam data. We extracted the SOS of 41 PhenoCam sites over the Continental United States in 2018 using the dynamic threshold method. Our results showed that the variations of CR time series are closely related to the phenological processes of deciduous forests. The SOS extracted using CR data showed high consistency with those extracted using GCC (R2 = 0.46), with slightly lower accuracy compared with NDVI-derived results (R2 = 0.62). Our study illustrates the value and mechanism of deciduous forests spring phenology extraction using SAR data and provides a reference for using SAR data to improve forest phenology extraction in addition to using optical remote sensing data, especially in rainy and cloudy regions.

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http://hdl.handle.net/10453/176430