Hyperspectral canopy reflectance modeling and EO-1 Hyperion

2002 ◽  
Author(s):  
Jerrell R. Ballard, Jr. ◽  
James A. Smith
Keyword(s):  
Canopy Reflectance ◽  
Reflectance Modeling

scholarly journals Mapping woody plant cover in desert grasslands using canopy reflectance modeling and MISR data

2006 ◽  
Vol 33 (17) ◽  
Author(s):  
Mark J. Chopping ◽  
Lihong Su ◽  
Andrea Laliberte ◽  
Albert Rango ◽  
Debra P. C. Peters ◽  
...  
Keyword(s):  
Woody Plant ◽  
Plant Cover ◽  
Canopy Reflectance ◽  
Reflectance Modeling

scholarly journals A Modified Geometrical Optical Model of Row Crops Considering Multiple Scattering Frame

2020 ◽  
Vol 12 (21) ◽  
pp. 3600
Author(s):  
Xu Ma ◽  
Yong Liu
Keyword(s):  
Multiple Scattering ◽  
Single Scattering ◽  
Growth Stages ◽  
Canopy Reflectance ◽  
Row Crops ◽  
Reflectance Modeling ◽  
Scattering Contribution ◽  
Relationship Of ◽  
Gap Probabilities

The canopy reflectance model is the physical basis of remote sensing inversion. In canopy reflectance modeling, the geometric optical (GO) approach is the most commonly used. However, it ignores the description of a multiple-scattering contribution, which causes an underestimation of the reflectance. Although researchers have tried to add a multiple-scattering contribution to the GO approach for forest modeling, different from forests, row crops have unique geometric characteristics. Therefore, the modeling approach originally applied to forests cannot be directly applied to row crops. In this study, we introduced the adding method and mathematical solution of integral radiative transfer equation into row modeling, and on the basis of improving the overlapping relationship of the gap probabilities involved in the single-scattering contribution, we derived multiple-scattering equations suitable for the GO approach. Based on these modifications, we established a row model that can accurately describe the single-scattering and multiple-scattering contributions in row crops. We validated the row model using computer simulations and in situ measurements and found that it can be used to simulate crop canopy reflectance at different growth stages. Moreover, the row model can be successfully used to simulate the distribution of reflectances (RMSEs < 0.0404). During computer validation, the row model also maintained high accuracy (RMSEs < 0.0062). Our results demonstrate that considering multiple scattering in GO-approach-based modeling can successfully address the underestimation of reflectance in the row crops.

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