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Updated: Jun 18, 2026
13:44
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Published on: August 30, 2013
[Crop geometry identification based on inversion of semiempirical BRDF models]
Chun-jiang Zhao1, Wen-jiang Huang, Xu-han Mu
1National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China. zhaocj@nercita.org.cn
Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|December 3, 2009
Summary
Average leaf angle (ALA) significantly impacts crop canopy spectra, crucial for remote sensing applications. Understanding ALA improves leaf area index (LAI) accuracy and guides precision agriculture management.
Area of Science:
- Agricultural remote sensing
- Radiative transfer modeling
- Plant biophysics
Context:
- Remote sensing technology has advanced from single to multi-view angle observations.
- Accurate crop monitoring relies on understanding spectral reflectance properties.
- Leaf area index (LAI) and crop growth conditions are key parameters for agricultural management.
Purpose:
- To investigate the qualitative and quantitative effects of average leaf angle (ALA) on crop canopy reflected spectra.
- To analyze the influence of erective and horizontal leaf distributions using bidirectional canopy reflectance and semi-empirical models.
- To assess the sensitivity of bidirectional reflectance distribution function (BRDF) model parameters (fvol, fgeo, fiso) at red and near-infrared bands.
Summary:
- The study examined how average leaf angle (ALA) affects crop canopy spectral reflectance using multi-view angle remote sensing data.
- Bidirectional canopy reflectance and semi-empirical BRDF models were employed to analyze the impact of leaf orientation (erective vs. horizontal).
- Sensitivity analysis of BRDF model parameters at 680 nm and 800 nm bands provided insights into spectral responses.
Impact:
- Enables on-site, non-sampling crop ALA identification using remote sensing.
- Enhances the accuracy of leaf area index (LAI) inversion for crop monitoring.
- Provides valuable information for guiding fertilizer and irrigation management in precision agriculture without prior knowledge.
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