Generating Spatiotemporally Continuous Grassland Aboveground Biomass on the Tibetan Plateau Through PROSAIL Model Inversion on Google Earth Engine

Xie, J; Wang, C; Ma, D; Chen, R; Xie, Q; Xu, B; Zhao, W; Yin, G

Generating Spatiotemporally Continuous Grassland Aboveground Biomass on the Tibetan Plateau Through PROSAIL Model Inversion on Google Earth Engine

Xie, J Wang, C Ma, D Chen, R Xie, Q

Xu, B Zhao, W Yin, G

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Geoscience and Remote Sensing, 2022, 60, pp. 1-10
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Xie, J
dc.contributor.author	Wang, C
dc.contributor.author	Ma, D
dc.contributor.author	Chen, R
dc.contributor.author	Xie, Q https://orcid.org/0000-0002-1576-6610
dc.contributor.author	Xu, B
dc.contributor.author	Zhao, W
dc.contributor.author	Yin, G
dc.date.accessioned	2023-07-02T02:51:37Z
dc.date.available	2023-07-02T02:51:37Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Geoscience and Remote Sensing, 2022, 60, pp. 1-10
dc.identifier.issn	0196-2892
dc.identifier.issn	1558-0644
dc.identifier.uri	http://hdl.handle.net/10453/171085
dc.description.abstract	Spatiotemporally continuous monitoring of aboveground biomass (AGB), an important indicator of grassland productivity, is crucial for achieving sustainable grassland development. Most existing grassland AGB estimation methods are empirical, and their temporally and spatially specific nature hinders operational application at large scales. Grass is herbaceous, so its AGB can be represented as the product of leaf area index (LAI) and dry matter content (C m), both are the inputs of PROSAIL model. We, therefore, proposed a novel physical-based method through PROSAIL model inversion. Results showed that the estimated AGB presented good consistency with field-measured one, with R2= 0.87 and RMSE = 14.29 g/m2. We then implemented our method on the Google Earth Engine platform and generated daily and monthly AGB products covering the Tibetan Plateau (TP) and spanning from 2000 to 2021. These products characterized the spatiotemporally continuous dynamics of AGB on the TP. For example, it captured the decrease in dry matter caused by grazing during grassland dormancy, which is impossible for other existing AGB retrieval methods. Our method provides a promising tool to generate spatiotemporally continuous grassland AGB, which would inform the decision making for the conservation and restoration of grassland.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Geoscience and Remote Sensing
dc.relation.isbasedon	10.1109/TGRS.2022.3227565
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0404 Geophysics, 0906 Electrical and Electronic Engineering, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.title	Generating Spatiotemporally Continuous Grassland Aboveground Biomass on the Tibetan Plateau Through PROSAIL Model Inversion on Google Earth Engine
dc.type	Journal Article
utslib.citation.volume	60
utslib.for	0404 Geophysics
utslib.for	0906 Electrical and Electronic Engineering
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	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-07-02T02:51:34Z
pubs.publication-status	Published
pubs.volume	60

Abstract:

Spatiotemporally continuous monitoring of aboveground biomass (AGB), an important indicator of grassland productivity, is crucial for achieving sustainable grassland development. Most existing grassland AGB estimation methods are empirical, and their temporally and spatially specific nature hinders operational application at large scales. Grass is herbaceous, so its AGB can be represented as the product of leaf area index (LAI) and dry matter content (C m), both are the inputs of PROSAIL model. We, therefore, proposed a novel physical-based method through PROSAIL model inversion. Results showed that the estimated AGB presented good consistency with field-measured one, with R2= 0.87 and RMSE = 14.29 g/m2. We then implemented our method on the Google Earth Engine platform and generated daily and monthly AGB products covering the Tibetan Plateau (TP) and spanning from 2000 to 2021. These products characterized the spatiotemporally continuous dynamics of AGB on the TP. For example, it captured the decrease in dry matter caused by grazing during grassland dormancy, which is impossible for other existing AGB retrieval methods. Our method provides a promising tool to generate spatiotemporally continuous grassland AGB, which would inform the decision making for the conservation and restoration of grassland.

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http://hdl.handle.net/10453/171085