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摘要: 归一化植被指数(normalized difference vegetation index,NDVI)作为重要的植被生长状况植被指数,对其进行有效实时监测具有重要科学意义。选择4个大陆板块边界观测网(plate boundary observatory,PBO)观测站的GPS信噪比观测值,提取反射信号信噪比并计算归一化振幅,通过与MODIS(moderate resolution imaging spectroradiometer)NDVI产品时序频谱特征的相关性分析,建立GPS反射信号植被指数线性反演模型和BP神经网络反演模型。分析发现:GPS反射信号信噪比归一化振幅与NDVI指数存在显著年周期性和季候特性,NDVI线性反演模型相关系数均约为0.7,均方根误差处于0.05~0.09之间,BP神经网络反演模型相关系数提高了约5%。利用GPS反射信号反演NDVI变化趋势具有可行性,为获取高时间分辨率、低成本的NDVI指数提供了一种新思路。Abstract: NDVI (normalized difference vegetation index)is one of the most important vegetation index, which can reflect vegetation growth and coverage, and it is of great significance to perform real-time monitoring. In this paper, we first take advantage of the amplitude of single-to-noise generated from GPS reflected signal to calculate normalized amplitude, then the characteristics of time series, frequency spectrogram and correlation between the normalized amplitude and NDVI extracted from MODIS products are analyzed. At last, NDVI inversion models with linear regression and BP neural network based on GPS reflection signal are presented. The results show that there are significant annual and seasonal characteristics within the normalized amplitude calculated by GPS reflection signal and NDVI, the correlation coefficient of linear regression is about 0.7, RMSE is 0.05-0.09. Moreover, BP inversion model within consideration of soil moisture is superior to the linear inversion model, the correlation coefficient is increased by about 5%, RMSE is 0.03-0.09. It indicates that NDVI change inversion using GPS reflection signals is feasible, which will provide an alternative approach to monitoring NDVI with high temporal resolution and low cost.
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