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[Study on hyperspectral estimation model of crop vegetation cover percentage]
Lei Zhu1, Jun-feng Xu, Jing-feng Huang
1Institute of Agriculture Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310029, China. zhulei112@sohu.com
Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|November 4, 2008
Summary
This study demonstrates that the Normalized Difference Vegetation Index (NDVI) is highly effective for estimating crop vegetation coverage percentage using hyperspectral remote sensing data. Optimal NDVI band combinations were identified for accurate vegetation cover estimation in large areas.
Area of Science:
- Hyperspectral Remote Sensing
- Agricultural Science
- Geospatial Analysis
Context:
- Crop vegetation coverage percentage is a critical parameter for agricultural monitoring and yield prediction.
- Accurate estimation of vegetation cover is essential for effective resource management and precision agriculture.
- Existing methods for vegetation cover estimation often lack the spectral resolution needed for detailed analysis.
Purpose:
- To investigate the efficacy of hyperspectral remote sensing for estimating crop vegetation coverage percentage.
- To develop and validate estimation models using spectral variables, particularly the Normalized Difference Vegetation Index (NDVI).
- To identify optimal spectral bands and model configurations for accurate vegetation cover estimation.
Summary:
- Canopy spectral measurements of rape, corn, and rice were correlated with vegetation cover percentage.
- The Normalized Difference Vegetation Index (NDVI) demonstrated higher correlation and effectiveness compared to red-edge variables for vegetation cover estimation.
- A simple quadratic equation using NDVI(696-921) as the independent variable provided the best estimation model, with high correlation coefficients (r > 0.8).
- Simulated NDVI from Landsat TM3 and TM4 also showed strong correlation (r=0.80) with vegetation cover, validating its use for large-area estimations.
Impact:
- Establishes hyperspectral remote sensing, specifically NDVI, as a reliable tool for crop vegetation coverage estimation.
- Provides optimized spectral band combinations for enhanced accuracy in vegetation cover assessment.
- Validates the use of readily available satellite data (Landsat TM) for large-scale vegetation cover monitoring, supporting agricultural applications.
