张晓宁等:Potential Investigation of Linking PROSAIL with the Ross-Li BRDF Model for Vegetation Characterization
被阅读 48 次
2018-06-25
Potential Investigation of Linking PROSAIL with the Ross-Li BRDF Model for Vegetation Characterization
作者:Zhang, XN (Zhang, Xiaoning)[ 1,2 ] ; Jiao, ZT (Jiao, Ziti)[ 1,2 ] ; Dong, YD (Dong, Yadong)[ 1,2 ] ; Zhang, H (Zhang, Hu)[ 3 ] ; Li, Y (Li, Yang)[ 1,2 ] ; He, DD (He, Dandan)[ 1,2 ] ; Ding, AX (Ding, Anxin)[ 1,2 ] ; Yin, SY (Yin, Siyang)[ 1,2 ] ; Cui, L (Cui, Lei)[ 1,2 ] ; Chang, YX (Chang, Yaxuan)[ 1,2 ]
REMOTE SENSING
卷: 10  期: 3
文献号: 437
DOI: 10.3390/rs10030437
出版年: MAR 2018
文献类型:Article
 
摘要
Methods that link different models for investigating the retrieval of canopy biophysical/structural variables have been substantially adopted in the remote sensing community. To retrieve global biophysical parameters from multiangle data, the kernel-driven bidirectional reflectance distribution function (BRDF) model has been widely applied to satellite multiangle observations to model (interpolate/extrapolate) the bidirectional reflectance factor (BRF) in an arbitrary direction of viewing and solar geometries. Such modeled BRFs, as an essential information source, are then input into an inversion procedure that is devised through a large number of simulation analyses from some widely used physical models that can generalize such an inversion relationship between the BRFs (or their simple algebraic composite) and the biophysical/structural parameter. Therefore, evaluation of such a link between physical models and kernel-driven models contributes to the development of such inversion procedures to accurately retrieve vegetation properties, particularly based on the operational global BRDF parameters derived from satellite multiangle observations (e.g., MODIS). In this study, the main objective is to investigate the potential for linking a popular physical model (PROSAIL) with the widely used kernel-driven Ross-Li models. To do this, the BRFs and albedo are generated by the physical PROSAIL in a forward model, and then the simulated BRFs are input into the kernel-driven BRDF model for retrieval of the BRFs and albedo in the same viewing and solar geometries. To further strengthen such an investigation, a variety of field-measured multiangle reflectances have also been used to investigate the potential for linking these two models. For simulated BRFs generated by the PROSAIL model at 659 and 865 nm, the two models are generally comparable to each other, and the resultant root mean square errors (RMSEs) are 0.0092 and 0.0355, respectively, although some discrepancy in the simulated BRFs can be found at large average leaf angle (ALA) values. Unsurprisingly, albedos generated by the method are quite consistent, and 99.98% and 97.99% of the simulated white sky albedo (WSA) has a divergence less than 0.02. For the field measurements, the kernel-driven model presents somewhat better model-observation congruence than the PROSAIL model. The results show that these models have an overall good consistency for both field-measured and model-simulated BRFs. Therefore, there is potential for linking these two models for looking into the retrieval of canopy biophysical/structural variables through a simulation method, particularly from the current archive of the global routine MODIS BRDF parameters that were produced by the kernel-driven BRDF model; however, erectophile vegetation must be further examined.
 
通讯作者地址: Jiao, ZT (通讯作者)
Beijing Normal Univ & Inst Remote Sensing & Digit, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
通讯作者地址: Jiao, ZT (通讯作者)
Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, Beijing Engn Res Ctr Global Land Remote Sensing P, Beijing 100875, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ & Inst Remote Sensing & Digit, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, Beijing Engn Res Ctr Global Land Remote Sensing P, Beijing 100875, Peoples R China
[ 3 ] Tianjin Normal Univ, Coll Urban & Environm Sci, Tianjin 300387, Peoples R China