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Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations
Zeng, Yelu1,2; Xu, Baodong2,3; Yin, Gaofei4; Wu, Shengbiao2; Hu, Guoqing5; Yan, Kai2; Yang, Bin6; Song, Wanjuan2; Li, Jing2
Corresponding AuthorLi, Jing(
Source PublicationREMOTE SENSING
AbstractThis paper presents a simple radiative transfer model based on spectral invariant properties (SIP). The canopy structure parameters, including the leaf angle distribution and multi-angular clumping index, are explicitly described in the SIP model. The SIP model has been evaluated on its bidirectional reflectance factor (BRF) in the angular space at the radiation transfer model intercomparison platform, and in the spectrum space by the PROSPECT+SAIL (PROSAIL) model. The simulations of BRF by SIP agreed well with the reference values in both the angular space and spectrum space, with a root-mean-square-error (RMSE) of 0.006. When compared with the widely-used Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model on fPAR, the RMSE was 0.006 and the R-2 was 0.99, which shows a high accuracy. This study also suggests the newly proposed vegetation index, the near-infrared (NIR) reflectance of vegetation (NIRv), was a good linear approximation of the canopy structure parameter, the directional area scattering factor (DASF), with an R-2 of 0.99. NIRv was not influenced much by the soil background contribution, but was sensitive to the leaf inclination angle. The sensitivity of NIRv to canopy structure and the robustness of NIRv to the soil background suggest NIRv is a promising index in future biophysical variable estimations with the support of the SIP model, especially for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) observations near the hot spot directions.
Keywordspectral invariant radiative transfer canopy structure leaf inclination angle hot spot
Indexed BySCI
Funding ProjectNational Natural Science Foundation of China[41701401] ; National Key Research and Development Program[2018YFA0605503] ; GF6 Project[30-Y20A03-9003-17/18]
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000448555800008
Funding OrganizationNational Natural Science Foundation of China ; National Key Research and Development Program ; GF6 Project
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Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorLi, Jing
Affiliation1.Carnegie Inst Sci, Dept Global Ecol, Stanford, CA 94305 USA
2.State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
3.Huazhong Agr Univ, Macro Agr Res Inst, Coll Resource & Environm, 1 Shizishan St, Wuhan 430070, Hubei, Peoples R China
4.Chinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Digital Mt & Remote Sensing Applicat, Chengdu 610041, Sichuan, Peoples R China
5.Purdue Univ, Sch Aeronaut & Astronaut, W Lafayette, IN 47907 USA
6.Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China
Recommended Citation
GB/T 7714
Zeng, Yelu,Xu, Baodong,Yin, Gaofei,et al. Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations[J]. REMOTE SENSING,2018,10(10):17.
APA Zeng, Yelu.,Xu, Baodong.,Yin, Gaofei.,Wu, Shengbiao.,Hu, Guoqing.,...&Li, Jing.(2018).Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations.REMOTE SENSING,10(10),17.
MLA Zeng, Yelu,et al."Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations".REMOTE SENSING 10.10(2018):17.
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