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Published on: August 9, 2019
[Standardization of technical methods for apple fluorescence canopy spectral detection].
Xi-Cun Zhu1, Geng-Xing Zhao, Tong Lei
1College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, China. zxc@sdau.edu.cn
Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|August 17, 2010
Summary
Standardized spectral detection methods for apple fluorescence canopy were explored. Optimal conditions include sunny days, low wind, midday detection, and vertical sensor angles for reliable spectral reflectance data.
Area of Science:
- Agricultural Science
- Remote Sensing
- Plant Physiology
Background:
- Spectral characteristics of apple fluorescence canopy are crucial for monitoring tree health and yield.
- Understanding environmental impacts on spectral data is essential for accurate remote sensing applications.
Purpose of the Study:
- To analyze the influence of weather, time, height, and angle on apple canopy spectral characteristics.
- To establish standardized spectral detection methods for apple fluorescence canopy.
Main Methods:
- Spectral detection tests were conducted on apple canopies over two years (2008-2009).
- Data were collected under varying weather conditions, detection times, heights, and angles.
- Analysis focused on spectral reflectance, particularly in near-infrared bands (760-1350 nm).
Main Results:
- Spectral reflectance showed regular patterns under different conditions, especially in near-infrared bands.
- Canopy spectral reflectance decreased with reduced sunshine; sunny, clear days are optimal.
- Stable spectral reflectance was observed below wind scale 2, with minimal discrepancy between 10:00 and 15:00.
- Vertical or near-vertical detection angles and adjusted detector height ensured stable spectral curves.
Conclusions:
- Standardized spectral detection methods for apple fluorescence canopy were proposed.
- Findings provide theoretical references for spectral detection and information extraction of apple tree canopies.
- Optimal detection conditions enhance the reliability of spectral data for agricultural applications.

