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Computer Vision-Based Biomass Estimation for Invasive Plants
Published on: February 9, 2024
[Progress in retrieving vegetation water content under different vegetation coverage condition based on remote
Jia-Hua Zhang1, Li Li, Feng-Mei Yao
1Laboratory for Remote Sensing and Climate Information Sciences, Chinese Academy of Meteorological Sciences, Beijing 100081, China. zhangjh@cams.cma.gov.cn
Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|August 17, 2010
Summary
This review explores remote sensing methods for estimating vegetation water content, focusing on spectral indices and radiation transfer models, especially for low crop coverage scenarios. It highlights techniques to improve accuracy by minimizing soil background effects.
Area of Science:
- Earth Observation
- Vegetation Biophysics
- Agricultural Monitoring
Background:
- Accurate estimation of vegetation water content is crucial for agricultural drought assessment and crop health monitoring.
- Traditional methods face challenges, particularly under low crop coverage conditions where soil background interference is significant.
Purpose of the Study:
- To review and analyze advancements in remote sensing techniques for retrieving vegetation water content.
- To evaluate methods effective under low crop coverage, including spectral indices and radiation transfer models.
- To discuss future trends in plant water information retrieval.
Main Methods:
- Utilizing vegetation spectral reflectance information (Visible, Infrared, Near-Infrared, Shortwave Infrared) to directly extract water content.
- Establishing vegetation water indices (WI) such as Normalized Difference Water Index (NDWI) and பாடல் Water Index (PWI).
- Employing radiation transfer (RT) models (e.g., PROSAIL) and analyzing multi-angle/bi-directional reflectance (BRDF) to estimate water content and mitigate soil background effects.
Main Results:
- Vegetation spectral reflectance and derived indices (NDWI, PWI) show potential for direct vegetation water content estimation.
- Radiation transfer models, combined with canopy physiological parameters or vegetation indices, effectively reduce soil background influence.
- Multi-angle polarized reflectance and BRDF offer improved estimation of agricultural drought and vegetation water content.
Conclusions:
- Remote sensing offers diverse methods for vegetation water content retrieval, with spectral indices and RT models showing promise.
- Addressing low crop coverage and soil background effects are key challenges, with advanced reflectance techniques showing potential.
- Future research should focus on refining these methods for more accurate and reliable plant water monitoring.
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