| Cite this article: | 李军玲,李梦夏.花生叶面积指数地面高光谱遥感估算模型研究[J].灌溉排水学报,0,():-. |
| LI Jun-ling,LI Meng-xia.花生叶面积指数地面高光谱遥感估算模型研究[J].灌溉排水学报,0,():-. |
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| Estimation of Peanut LAI Based on Canopy Hyperspectral Remote Sensing Date |
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LI Jun-ling,LI Meng-xia
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Henan Key Laboratory of Agrometeorological Ensuring and Applied Technique,CMA
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| Abstract: |
| 【Objective】To explore the relationship between hyperspectral reflectance and leaf area index (LAI), 【Method】the experiment was conducted from 2019 in Zhengzhou agricultural meteorological experimental station. Peanut canopy hyperspectral data was measured at different growth stages by using the ASD HandHeld 2, Peanut LAI was collected at the same time by using LAI2200 canopy analysis system. By making the correlation analysis between the original, the speccountdown logarithms, the derivative spectral data and LAI, Peanut LAI estimation model was obtained, and the optimal estimation model was selected through the comparison of model accuracy. 【Result】The results showed that the exponential model of the most parameters in single spectral variables estimated LAI model were with larger R2, including VI1, VI2, Rr parabolic model more than 0.6, the fitting degree were the highest, while the RMSE of VI2 were minimal, followed by VI1. 【Conclusion】So that VI2 is the optimal variable parabola model, and the prediction and forecasting precision are preferable. Analysis of comprehensive multiple stepwise regression model, the sensitive spectral indices for estimating leaf area index of peanut were VI2 and VI1. The analysis shows that the accuracy of multiple regression model is higher than that of single variable estimation model. When a variety of spectral parameters can be obtained, the multiple regression model is preferred to estimate the canopy LAI of peanut. |
| Key words: Peanut; leaf area index; hyperspectral remote sensing; model accuracy |
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