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Estimation of Vertical Leaf Nitrogen Distribution Within a Rice Canopy Based on Hyperspectral Data.
Jiaoyang He1,2,3,4, Xiangbin Zhang1,2,3,4, Wanting Guo1,2,3,4
1National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, China.
Frontiers in Plant Science
|March 3, 2020
Summary
Accurate leaf nitrogen (N) distribution in rice canopies is key for yield prediction. Method 2, using vegetation indices and a vertical distribution model, best estimates N concentration in different rice leaf layers.
Area of Science:
- Agricultural Science
- Plant Physiology
- Remote Sensing
Background:
- Accurate estimation of vertical leaf nitrogen (N) distribution in rice canopies is crucial for understanding nutrient dynamics and improving yield predictions.
- Leaf nitrogen concentration (LNC) varies significantly across different functional leaf layers within the canopy.
Purpose of the Study:
- To investigate the vertical distribution of LNC in rice canopies under various experimental conditions (rice varieties, N rates, planting densities).
- To establish relationships between LNC at different leaf layers (LNCLi) and canopy level (LNCCanopy).
- To develop and compare methods for estimating LNCLi using vegetation indices (VIs) and a vertical distribution model.
Main Methods:
- Conducted a two-year field experiment with varying rice varieties, N rates, and planting densities.
- Constructed a vertical distribution model for LNC based on relative canopy height.
- Analyzed relationships between VIs and LNCCanopy, LNCLi, and model parameters.
- Compared three methods for estimating LNCLi, focusing on VI-based approaches integrated with the vertical distribution model.
Main Results:
- Leaf nitrogen concentration (LNC) at the bottom of the rice canopy was most sensitive to nitrogen (N) rates.
- Changes in LNC with relative canopy height were effectively simulated by an exponential model.
- Specific VIs (R705/(R717+R491) and RDVI) accurately estimated a key parameter of the LNC vertical distribution model for indica and japonica rice, respectively.
- Method 2, which uses VIs to estimate LNC in any layer and then applies the vertical distribution model, proved most effective for estimating LNCLi across all layers (R2 up to 0.768).
Conclusions:
- Vegetation indices can effectively estimate LNC at the top of the rice canopy.
- The developed exponential model accurately simulates LNC changes with canopy height.
- Method 2 offers a robust, non-destructive approach for precise estimation of vertical leaf nitrogen distribution in rice, supporting precision agriculture practices.
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