冬小麦叶面积指数高光谱遥感反演方法对比

冬小麦叶面积指数(LAI,leafarea index)是评价其长势和预测产量的重要农学参数,高光谱遥感能够实现快速无损地监测叶面积指数。该文旨在将田间监测与高光谱遥感相结合,探索研究不同冬小麦叶面积指数高光谱反演方法的模拟精度及适应性。针对国际上普遍应用的2种高光谱遥感反演LAI模型方法,即回归分析法和BP神经网络法,在介绍2种LAI反演模型的基础上,选择位于黄淮海平原的山东省济南市长清区为研究区域,通过ASD地物光谱仪和SunScan冠层分析系统对冬小麦的冠层光谱及LAI变化进行田间观测,然后利用回归分析法和BP神经网络法构建冬小麦LAI反演模型,将模型估算LAI值和田间观测LAI值进行比对,分析评价2种方法的反演精度。结果表明,BP神经网络法较回归分析法估算冬小麦LAI的精度有较大提高,检验方程的决定系数(R2)为0.990、均方根误差(RMSE)为0.105。利用BP神经网络法构建反演模型能较好的对冬小麦LAI进行反演。研究结果可为不同冬小麦长势遥感监测提供理论和技术上的支持,并为大尺度传感器监测冬小麦长势和估产提供参考。

Comparison of two inversion methods for winter wheat leaf area index based on hyperspectral remote sensing

Abstract: Leaf area index (LAI) is an important index for evaluating winter wheat's growth status and forecasting its yield. Hyperspectral remote sensing is a new technical approach that can be used to acquire the information of winter wheat LAI immediately. By integrating hyperspectral remote sensing and traditional field monitoring, this study aims to explore the best simulation accuracy and adaptability to the different method of high spectral monitoring winter wheat leaf area index inversion. Two kinds of universal LAI inversion methods based on hyperspectral remote sensing data through regression analysis method and the BP neural network (BPNN) are introduced and used in this study. The study area is Changqing district of Jinan city, Shandong province, China's Huang-huai-hai plain. On winter wheat growth stage, the winter wheat canopy spectral reflectance and LAI were monitored in field using the ASD FieldSpec 3 and SunScan canopy analysis system. The study selected the following 6 vegetation index (RVI, DVI, NDVI, GRVI, EVI and SAVI) combined with spectral reflectance characteristics of the study area. The 6 vegetation indexes are closely related to winter wheat LAI with correlation at a significant level. After correlation analysis of the Hyperspectral Vegetation Index (HVI) and LAI, winter wheat LAI regression models and BPNN model were established. Then simulation precisions for different models were analyzed and evaluated. The 6 winter wheat LAI regression models fits were 0.696~0.775, and root mean square errors (RMSE) were 0.386-0.523. Accuracy test showed that NDVI inversion model had the highest accuracy compared to other models. It is concluded that NDVI model is the most suitable model for inverting winter wheat LAI in the study area. However, the NDVI inversion model must avoid saturation phenomenon when NDVI is close to 1. This is the model's inadequacy. Input multiple sensitive reflectivity bands contain 450, 550, 670 and 870 nm bands to the BP neural network model. Upon examination, the simulation and measured fit values was 0.990 and the RMSE was 0.105. The results show that BP neural network model inversion method can build a better LAI inversion for winter wheat varieties in different regions. Among them, the inversion model has the highest R2 (0.990) and least RMSE (0.105). The BP neural network method used to construct the inversion model is better on different varieties of winter wheat LAI inversion. However, establishing BP model needs to ensure enough samples (generally the number of samples n>50 is a large sample of events) for the research adaptability. Both methods have their advantages and disadvantages. Overall, inversion method should be selected according to the number of samples and monitoring area.