Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations

doi:10.3390/rs10101508

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > 数字山地与遥感应用中心

	Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations
	Zeng, Yelu 1,2; Xu, Baodong 2,3; Yin, Gaofei4 ; Wu, Shengbiao 2; Hu, Guoqing 5; Yan, Kai 2; Yang, Bin 6; Song, Wanjuan 2; Li, Jing 2
刊名	REMOTE SENSING
	2018-10-01
卷号	10 期号:10 页码:17
关键词	spectral invariant radiative transfer canopy structure leaf inclination angle hot spot
ISSN号	2072-4292
DOI	10.3390/rs10101508
通讯作者	Li, Jing(lijing01@radi.ac.cn)
英文摘要	This 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.
资助项目	National Natural Science Foundation of China[41701401] ; National Key Research and Development Program[2018YFA0605503] ; GF6 Project[30-Y20A03-9003-17/18]
WOS关键词	LEAF-AREA INDEX ; PHOTON RECOLLISION PROBABILITY ; HEIHE RIVER-BASIN ; RADIATIVE-TRANSFER ; VEGETATION CANOPIES ; REFLECTANCE MODEL ; LIGHT-SCATTERING ; PLANT CANOPIES ; RETRIEVAL ; LAI
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000448555800008
资助机构	National Natural Science Foundation of China ; National Key Research and Development Program ; GF6 Project
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/24234]
专题	成都山地灾害与环境研究所_数字山地与遥感应用中心
通讯作者	Li, Jing
作者单位	1.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
推荐引用方式 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.