4.7 Article

Effect of Vertical Distribution of Crop Structure and Biochemical Parameters of Winter Wheat on Canopy Reflectance Characteristics and Spectral Indices

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2016.2604492

Keywords

Crop properties; scenario simulation; spectral reflectance; vertical distribution; winter wheat

Funding

  1. National Natural Science Foundation of China [41671411, 41471285]
  2. Beijing Natural Science Foundation [6132015, 4141001]

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Vertical heterogeneity of the canopy is being increasingly recognized in remote estimates of vegetative properties. Given the current limited knowledge of this issue, this paper investigated the effects of different vertical distributions of crop structure [e.g., leaf angle (LA) and leaf area index (LAI)] and biochemical parameters [e.g., chlorophyll a and b content (Chl(a+b)) and water content (W-c)] on canopy reflectance and vegetation indices (VIs). A recently developed multiple-layer canopy reflectance model (MRTM) was tested for winter wheat and used to run a simulation analysis of different canopy scenarios. The results showed that the MRTM performed well to model winter wheat canopy reflectance with regard to spikes and vertical distributions of leaf properties. The vertical profiles of LA and LAI influenced canopy reflectance at almost all wavelengths, whereas the vertical profile of Chl(a+b) mainly affected reflectance in the visible region, and that of W-c only affected reflectance in the near-infrared region. Changes in vertical distribution of the LA resulted in clear variations in VIs related to the LA, LAI, and Chl(a+b) estimates. The vertical LAI and Chl(a+b) profiles mainly influenced the VIs related to the LAI and Chl(a+b) estimates. The W-c vertical profile primarily affected the VIs used to estimate crop water properties. The sensitivities of the VIs were mainly associated with the spectral responses and penetration characteristics of the bands they used. These findings suggest that the sensitivity of VIs to the vertical distributions of crop parameters should be considered when establishing models for remote crop monitoring.

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