DOI: 10.1016/j.jag.2016.10.002
Scopus记录号: 2-s2.0-85018630181
论文题名: A radiative transfer model-based method for the estimation of grassland aboveground biomass
作者: Quan X ; , He B ; , Yebra M ; , Yin C ; , Liao Z ; , Zhang X ; , Li X
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2017
卷: 54 起始页码: 159
结束页码: 168
语种: 英语
英文关键词: Grassland aboveground biomass
; Ill-posed inversion problem
; Landsat 8 OLI product
; Leaf area index
; PROSAILH
Scopus关键词: aboveground biomass
; accuracy assessment
; artificial neural network
; estimation method
; grassland
; inverse problem
; Landsat
; leaf area index
; radiative transfer
; regression analysis
; China
英文摘要: 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 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 RTM-based 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. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79951
Appears in Collections: 气候变化事实与影响
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作者单位: School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; Fenner School of Environment and Society, The Australian National University, ACT, Canberra, Australia; Bushfire & Natural Hazards Cooperative Research Centre, Melbourne, Australia
Recommended Citation:
Quan X,, He B,, Yebra M,et al. A radiative transfer model-based method for the estimation of grassland aboveground biomass[J]. International Journal of Applied Earth Observation and Geoinformation,2017-01-01,54