不同种植密度烟草叶面积指数的高光谱估测模型

摘要

烟草叶面积指数(LAI)是评价其长势和预测产量的重要指标.利用高光谱遥感能够实现LAI的快速无损监测.为建立烟草LAI估算的最佳光谱指数及监测模型,通过设置不同种植密度处理,将田间观测和高光谱遥感技术结合,提取和分析了10个植被指数,并用二次多项式模型、对数模型、逐步回归模型(SMLR)和BP神经网络对烟草LAI进行估算.结果表明,NDVI、RVI、MCARI、GM1、GNDVI2和PSSRb等植被指数同烟草LAI均达到极显著正相关,相关系数均大于0.80.烟草LAI的二次多项式模型、对数模型、逐步回归模型(SMLR)和BP神经网络模型的决定系数R2分别为0.69、0.57、0.89和0.90.经检验,4个模型的均方根误差RMSE分别为0.69、0.87、0.62和0.44.表明SMLR和BP神经网络LAI都取得了较为理想的结果,其中BP神经网络的精度最高、误差最小,更适合对烟草LAI进行反演.该结果为实现不同种植密度水平下烟草LAI的精确监测提供技术支持和地域参考.%Leaf area index (LAI) is a key parameter for evaluating tobacco growth status and forecasting its yield and quality. Hyperspectral remote sensing can rapidly and nondestructively acquire LAI. By integrating traditional field monitoring and hyperspectral remote sensing, the primary objective of this study was to explore the best spectral indices and monitoring model for tobacco LAI. On the basis of different planting densities, this study extracted and analyzed 10 spectral parameters. The quadratic polynomial model, logarithmic model, stepwise multiple linear regression (SMLR) and BP neural network model were used to construct the prediction models for tobacco LAI. The results showed that the correlation between the tobacco LAI and NDVI, RVI, MCARI, GM1, GNDVI2 and PSSRb all reached extremely significant correlation (p<0.01), and the correlation coefficients were all higher than 0.80. The tobacco LAI prediction models of quadratic polynomial model, logarithmic model, SMLR and BP neural network model had theR2 value of 0.69, 0.57, 0.89 and 0.90, respectively. The validation RMSE of the four models was 0.69, 0.87, 0.62 and 0.44, respectively. Both SMLR and BP neural network models achieved good results, and the BP neural network model is the best model for inversion the tobacco LAI with the biggest accuracy and the minimum error. These results provide technical support and regional reference for accurate monitoring tobacco LAI under different planting densities.