| 引用本文: | 李军玲,李梦夏,李树岩,等.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,2021,(5):69-75. |
| LI Junling,LI Mengxia,LI Shuyan,et al.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,2021,(5):69-75. |
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| 摘要: |
| 【目的】掌握花生叶面积指数（LAI）及其动态变化，并用于其生长监测和作物估产。【方法】试验于2019年在郑州市农业气象试验站进行，设置了3个不同氮素水平处理，以花生（豫花40）为研究对象，利用便携式光谱仪ASD HandHeld 2测量花生冠层高光谱数据，使用LAI2200冠层分析系统采集花生叶面积指数。对原始光谱数据进行倒数对数和导数变换，并选取常见高光谱特征参数建立LAI估算模型，通过模型精度比较从中选择出最优估算模型。最后通过多元逐步回归，建立了花生LAI估算的多元回归模型。【结果】LAI单变量估算模型中，指数模型的R2较大，其中VI1（VI1=Rg/Rr。Rg绿峰反射率，Rr红谷反射率）、VI2（VI2=(Rg-Rr)/(Rg+Rr)）和Rr的指数模型R2超过0.68，拟合程度最高。同时利用检验样本计算均方根误差（RMSE），VI2的RMSE值最小，其次是VI1。【结论】以VI2为自变量的指数模型最优，其次为VI1。多元回归模型模拟精度高于单变量估算模型，当多种光谱参数均可获取的情况下，应优先选用多元回归模型对花生冠层LAI进行估算。 |
| 关键词: 花生；叶面积指数；高光谱遥感；模型精度 |
| DOI：10.13522/j.cnki.ggps.2019460 |
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| Estimating Leaf Area Index of Peanut Using Hyperspectral Remote Sensing |
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LI Junling, LI Mengxia, LI Shuyan, TIAN Hongwei
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1. Henan Key Laboratory of Agrometeorological Ensuring and Applied Technique, CMA, Zhengzhou 450003, China;2. Henan Institute of Meteorological Sciences, Zhengzhou 450003, China
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| Abstract: |
| 【Background】Leaf area index (LAI) is one of the most important plant traits. It modulates both photosynthetic rate and transpiration, and understanding its variation in response to environmental change at different growth stages is hence important to improve crop management and safeguard agricultural production.【Objective】The purpose of this paper is to present the results of an experimental study on the relationship between hyperspectral reflectance and LAI of peanut.【Method】The experiment was conducted in 2019 at Zhengzhou Agricultural Meteorological Experimental station, in which hyperspectral data of the peanut canopy at different growth stages was measured using the ASD HandHeld 2. In the meantime, we also measured LAI of the peanut using the LAI 2200 canopy analyzer. We used both the logarithm of reciprocal of the hyperspectral data and the derivatives of the hyperspectral data to estimate the LAI. Different models including models using a single parameter derived from the hyperspectral data and multiple stepwise regression models were developed to estimate the LAI. Their accuracy and reliability were compared and tested again the measured LAI. 【Result】Exponential model using a single parameter derived from the hyperspectral data is reliable with R2 being reasonably high for most tested examples. In particular, when using the parameter VI1=Rg/Rr (where Rg and Rr are the reflectance spectra of the green and red wavelength respectively), VI2=(Rg-Rr)/(Rg+Rr), or Rr, their associated R2 was all greater than 0.68. The rooted mean of square error of VI2 was the least, followed by VI1.【Conclusion】 Using a single parameter, exponential mode using VI2 was most accurate to estimate peanut LAI at different growth stages. Analysis revealed that the most sensitive spectral indices for estimating the LAI were VI2 and VI1, and the multiple stepwise regression models using multiple spectral parameters were more accurate than the single-parameter models but they are more computationally complicated. |
| Key words: peanut; leaf area index; hyperspectral remote sensing; multiple stepwise regression model |
