Published works

Published works

Quan, X. et al. A radiative transfer model-based method for the estimation of grassland aboveground biomass. International Journal of Applied Earth Observation and Geoinformation 54, (2017).
Refereed

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

TitleA radiative transfer model-based method for the estimation of grassland aboveground biomass
Publication TypeJournal Article
Year of Publication2017
AuthorsQuan, X, He, B, Yebra, M, Yin, C, Liao, Z, Zhang, X, Li, X
JournalInternational Journal of Applied Earth Observation and Geoinformation
Volume54
Date Published02/2017
Abstract

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.

URLhttp://www.sciencedirect.com/science/article/pii/S0303243416301726
DOI10.1016/j.jag.2016.10.002
Refereed DesignationRefereed

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