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A radiative transfer model-based method for the estimation of grassland aboveground biomass

Quan, Xingwen; He, Binbin; Yebra, Marta; Yin, Changming; Liao, Zhanmang; Zhang, Xueting; Li, Xing

Description

This paper presents a novel method to derive grassland aboveground biomass (AGB) based on the PROSAILH (PROSPECT + SAILH) radiative transfer model (RTM). Two variables, leaf area index (LAI, m2m−2, defined as a one-side leaf area per unit of horizontal ground area) and dry matter content (DMC, gcm−2, defined as the dry matter per leaf area), were retrieved using PROSAILH and reflectance data from Landsat 8 OLI product. The result of LAI × DMC was regarded as the estimated grassland AGB...[Show more]

dc.contributor.authorQuan, Xingwen
dc.contributor.authorHe, Binbin
dc.contributor.authorYebra, Marta
dc.contributor.authorYin, Changming
dc.contributor.authorLiao, Zhanmang
dc.contributor.authorZhang, Xueting
dc.contributor.authorLi, Xing
dc.date.accessioned2021-04-30T00:35:44Z
dc.identifier.issn1569-8432
dc.identifier.urihttp://hdl.handle.net/1885/231163
dc.description.abstractThis paper presents a novel method to derive grassland aboveground biomass (AGB) based on the PROSAILH (PROSPECT + SAILH) radiative transfer model (RTM). Two variables, leaf area index (LAI, m2m−2, defined as a one-side leaf area per unit of horizontal ground area) and dry matter content (DMC, gcm−2, defined as the dry matter per leaf area), were retrieved using PROSAILH and reflectance data from Landsat 8 OLI product. The result of LAI × DMC was regarded as the estimated grassland AGB according to their definitions. The well-known ill-posed inversion problem when inverting PROSAILH was alleviated using ecological criteria to constrain the simulation scenario and therefore the number of simulated spectra. A case study of the presented method was applied to a plateau grassland in China to estimate its AGB. The results were compared to those obtained using an exponential regression, a partial least squares regression (PLSR) and an artificial neural networks (ANN). The RTM-based method offered higher accuracy (R2 = 0.64 and RMSE = 42.67 gm−2)than the exponential regression (R2 = 0.48 and RMSE = 41.65 gm−2) and the ANN (R2 = 0.43 and RMSE = 46.26 gm−2). However, the proposed method offered similar performance than PLSR as presented better determination coefficient than PLSR (R2 = 0.55) but higher RMSE (RMSE = 37.79 gm−2). Although it is still necessary to test these methodologies in other areas, the RTMbased method offers greater robustness and reproducibility to estimate grassland AGB at large scale without the need to collect field measurements and therefore is considered the most promising methodology.
dc.description.sponsorshipThis work was supported by the National Natural Science Foundation of China (Contract No. 41471293 & 41671361),the Fundamental Research Fund for the Central Universities (Contract No. ZYGX2012Z005) and the National High-Tech Research and Development Program of China (Contract 2013AA12A302)
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherElsevier
dc.rights© 2016 Elsevier B.V
dc.sourceInternational Journal of Applied Earth Observation and Geoinformation
dc.subjectGrassland aboveground biomass
dc.subjectLandsat 8 OLI product
dc.subjectLeaf area index
dc.subjectPROSAILH
dc.subjectIll-posed inversion problem
dc.titleA radiative transfer model-based method for the estimation of grassland aboveground biomass
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume54
dc.date.issued2017
local.identifier.absfor050200 - ENVIRONMENTAL SCIENCE AND MANAGEMENT
local.identifier.absfor040600 - PHYSICAL GEOGRAPHY AND ENVIRONMENTAL GEOSCIENCE
local.identifier.ariespublicationa383154xPUB5853
local.publisher.urlhttps://www.elsevier.com/en-au
local.type.statusPublished Version
local.contributor.affiliationQuan, Xingwen, University of Electronic Science and Technology of China
local.contributor.affiliationHe, Binbin, University of Electronic Science and Technology of China
local.contributor.affiliationYebra, Marta, College of Science, ANU
local.contributor.affiliationYin, Changming, University of Electronic Science and Technology of China
local.contributor.affiliationLiao, Zhanmang, University of Electronic Science and Technology of China
local.contributor.affiliationZhang, Xueting, University of Electronic Science and Technology of China
local.contributor.affiliationLi, Xing, University of Electronic Science and Technology of China
local.description.embargo2099-12-31
local.bibliographicCitation.startpage159
local.bibliographicCitation.lastpage168
local.identifier.doi10.1016/j.jag.2016.10.002
local.identifier.absseo960600 - ENVIRONMENTAL AND NATURAL RESOURCE EVALUATION
local.identifier.absseo961000 - NATURAL HAZARDS
dc.date.updated2020-11-23T10:07:44Z
local.identifier.scopusID2-s2.0-85018630181
local.identifier.thomsonID000388776100015
CollectionsANU Research Publications

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