@article{quanRadiativeTransferModelbased2017,
  title = {A Radiative Transfer Model-Based Method for the Estimation of Grassland Aboveground Biomass},
  author = {Quan, Xingwen and He, Binbin and Yebra, Marta and Yin, Changming and Liao, Zhanmang and Zhang, Xueting and Li, Xing},
  date = {2017-02},
  journaltitle = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {54},
  pages = {159--168},
  issn = {0303-2434},
  doi = {10.1016/j.jag.2016.10.002},
  url = {https://doi.org/10.1016/j.jag.2016.10.002},
  abstract = {[Highlights]

[::] The PROSAILH radiative transfer model was presented to estimate grassland AGB. [::] The ill-posed inversion problem was alleviated by using the ecological criteria. [::] Multi-source satellite products were used to filter the unrealistic combinations of retrieved free parameters. [::] Three empirical methods were also used to estimate the grassland AGB.

[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.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14166801,~to-add-doi-URL,artificial-neural-networks,biomass,ecology,grasslands,integration-techniques,landsat,model-comparison,regression,remote-sensing}
}

{"_id":"d9oMATWGCrtX3Lub7","bibbaseid":"quan-he-yebra-yin-liao-zhang-li-aradiativetransfermodelbasedmethodfortheestimationofgrasslandabovegroundbiomass","authorIDs":[],"author_short":["Quan, X.","He, B.","Yebra, M.","Yin, C.","Liao, Z.","Zhang, X.","Li, X."],"bibdata":{"bibtype":"article","type":"article","title":"A Radiative Transfer Model-Based Method for the Estimation of Grassland Aboveground Biomass","author":[{"propositions":[],"lastnames":["Quan"],"firstnames":["Xingwen"],"suffixes":[]},{"propositions":[],"lastnames":["He"],"firstnames":["Binbin"],"suffixes":[]},{"propositions":[],"lastnames":["Yebra"],"firstnames":["Marta"],"suffixes":[]},{"propositions":[],"lastnames":["Yin"],"firstnames":["Changming"],"suffixes":[]},{"propositions":[],"lastnames":["Liao"],"firstnames":["Zhanmang"],"suffixes":[]},{"propositions":[],"lastnames":["Zhang"],"firstnames":["Xueting"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Xing"],"suffixes":[]}],"date":"2017-02","journaltitle":"International Journal of Applied Earth Observation and Geoinformation","volume":"54","pages":"159–168","issn":"0303-2434","doi":"10.1016/j.jag.2016.10.002","url":"https://doi.org/10.1016/j.jag.2016.10.002","abstract":"[Highlights] [::] The PROSAILH radiative transfer model was presented to estimate grassland AGB. [::] The ill-posed inversion problem was alleviated by using the ecological criteria. [::] Multi-source satellite products were used to filter the unrealistic combinations of retrieved free parameters. [::] Three empirical methods were also used to estimate the grassland AGB. [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.","keywords":"*imported-from-citeulike-INRMM,~INRMM-MiD:c-14166801,~to-add-doi-URL,artificial-neural-networks,biomass,ecology,grasslands,integration-techniques,landsat,model-comparison,regression,remote-sensing","bibtex":"@article{quanRadiativeTransferModelbased2017,\n title = {A Radiative Transfer Model-Based Method for the Estimation of Grassland Aboveground Biomass},\n author = {Quan, Xingwen and He, Binbin and Yebra, Marta and Yin, Changming and Liao, Zhanmang and Zhang, Xueting and Li, Xing},\n date = {2017-02},\n journaltitle = {International Journal of Applied Earth Observation and Geoinformation},\n volume = {54},\n pages = {159--168},\n issn = {0303-2434},\n doi = {10.1016/j.jag.2016.10.002},\n url = {https://doi.org/10.1016/j.jag.2016.10.002},\n abstract = {[Highlights]\n\n[::] The PROSAILH radiative transfer model was presented to estimate grassland AGB. [::] The ill-posed inversion problem was alleviated by using the ecological criteria. [::] Multi-source satellite products were used to filter the unrealistic combinations of retrieved free parameters. [::] Three empirical methods were also used to estimate the grassland AGB.\n\n[Abstract]\n\nThis 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.},\n keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14166801,~to-add-doi-URL,artificial-neural-networks,biomass,ecology,grasslands,integration-techniques,landsat,model-comparison,regression,remote-sensing}\n}\n\n","author_short":["Quan, X.","He, B.","Yebra, M.","Yin, C.","Liao, Z.","Zhang, X.","Li, X."],"key":"quanRadiativeTransferModelbased2017","id":"quanRadiativeTransferModelbased2017","bibbaseid":"quan-he-yebra-yin-liao-zhang-li-aradiativetransfermodelbasedmethodfortheestimationofgrasslandabovegroundbiomass","role":"author","urls":{"Paper":"https://doi.org/10.1016/j.jag.2016.10.002"},"keyword":["*imported-from-citeulike-INRMM","~INRMM-MiD:c-14166801","~to-add-doi-URL","artificial-neural-networks","biomass","ecology","grasslands","integration-techniques","landsat","model-comparison","regression","remote-sensing"],"downloads":0},"bibtype":"article","biburl":"https://tmpfiles.org/dl/58794/INRMM.bib","creationDate":"2020-07-02T22:41:19.101Z","downloads":0,"keywords":["*imported-from-citeulike-inrmm","~inrmm-mid:c-14166801","~to-add-doi-url","artificial-neural-networks","biomass","ecology","grasslands","integration-techniques","landsat","model-comparison","regression","remote-sensing"],"search_terms":["radiative","transfer","model","based","method","estimation","grassland","aboveground","biomass","quan","he","yebra","yin","liao","zhang","li"],"title":"A Radiative Transfer Model-Based Method for the Estimation of Grassland Aboveground Biomass","year":null,"dataSources":["DXuKbcZTirdigFKPF"]}