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基于多光谱遥感的刺槐正午叶水势反演

Inversion of Robinia pseudoacacia Midday Leaf Water Potential Based on UVA Multispectral Images

  • 摘要: 以半干旱区陕西省米脂县刺槐和半湿润区陕西省长武县刺槐为对象,通过地面实测刺槐正午叶水势,同步获取无人机多光谱影像并提取其波段反射率,从而构建植被指数。分析两地正午叶水势差异,探讨光谱指数与地面实测数据的相关性;以5波段组、5波段−10个植被指数、全子集筛选3种组合变量,分别构建随机森林、支持向量机和径向基神经网络模型。结果表明:植被指数与正午叶水势相关性良好,其中红绿比值指数和改进增强型植被指数与正午叶水势的相关性最高,相关系数分别为0.39和−0.51。全子集筛选变量后归一化植被指数、非线性指数、红绿比值指数、改进增强型植被指数为反演的最优组合。随机森林方法为最优模型构建方法,模型验证集的R2为0.76,RMSE为0.27 MPa。

     

    Abstract: The Robinia pseudoacacia in Mizhi County, located in a semi-arid region, and Changwu County, situated in a semi-humid area, within Shaanxi Province were selected as the subjects of study. By measuring the midday leaf water potential of R. pseudoacacia on the ground, multi-spectral images of R. pseudoacacia were obtained synchronously and its band reflectance was extracted to construct the vegetation index. Firstly, the difference of midday leaf water potential between the 2 places was analyzed. Secondly, the correlation between spectral index and ground measured data is analyzed. Finally, combine variables in 3 ways: 5-band combination variable, 5-band + 10 vegetation index combination variable and full subset screening variable. Random forest, support vector machine and radial basis neural network models were constructed respectively. The results showed that the correlation between vegetation index and midday leaf water potential was good, in which the ratio index of red to green was good and improved enhanced vegetation index had the highest correlation with the midday leaf water potential, and the correlation coefficients were 0.39 and −0.51, respectively. Normalized vegetation index, non-linear index, red-green ratio index and improved enhanced vegetation index were the optimal combinations of all the selected variables. Random forest method is the optimal model construction method, and the R2 and RMSE of random forest model validation set are 0.76 and 0.27 MPa.

     

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