基于GPS-IR的美国中西部地区NDVI时间序列反演

Retrieving NDVI in midwestern America using GPS-interferometric reflectometry

基于AVHRR（advanced very high resolution radiometer）、MODIS（moderate-resolution imaging spectroradiometer）等卫星遥感影像获取的归一化植被指数（normalized difference vegetation index,NDVI）存在大气噪声、土壤背景、饱和度等固有问题。GPS（global positioning system）卫星播发的L波段信号对土壤和植被水分含量变化较为敏感,GPS-IR（GPS-interferometric reflectometry）利用测地型接收机和天线记录GPS反射信号的变化,进而反演测站环境参数。该文研究了利用GPS-IR反演区域NDVI时间序列的方法。采用4个GPS参考站2007-2015年近9 a的连续观测数据,由伪距和相位观测值计算了归一化微波反射指数（normalized microwave reflection index,NMRI）,傅立叶变换显示NMRI具有明显的周期特性,其中年周期和半年周期分量普遍较为突出。利用三角多项式拟合剔除NMRI中由积雪和降雨引起的粗差点后,其波动与同时间段内MODIS NDVI的趋势一致。一元线性回归结果显示NMRI与NDVI之间存在显著线性相关,相关系数在0.697~0.818（P〈0.001）,NDVI反演误差的均方根误差在0.059~0.079,表明GPS-IR反演区域NDVI时间序列是可行的,该研究为获取准实时、低成本和高时间分辨率的NDVI提供了新的思路。

The NDVI（normalized difference vegetation index） data,routinely derived from the AVHRR（advanced very high resolution radiometer） or MODIS（moderate resolution imaging spectroradiometer） imagery,is a key indicator of vegetation status and a useful parameter in studies of terrestrial vegetation cover,it has been widely used in remote sensing studies to reflect regional and global vegetation dynamics.However,the inherent defects of NDVI,including the atmospheric noise,soil effects and saturation problems are unavoidable,and thus impede further analysis and have a risk to generating erroneous results.Global Positioning System-Interferometric Reflectometry（GPS-IR） is a bistatic radar remote sensing technique that relates temporal changes in reflected GPS signals to changes in environmental parameters surrounding a ground-based GPS site.All GPS satellites transmit signals at L-band,which is similar to those used in active microwave radar applications.L-band signals have a higher correlation with vegetation water content,therefore GPS reflections will be sensitive to water within and on the surface of vegetation,as well as water in soil and snow.The sensing footprint of GPS-IR is on the order of a thousand square meters,which depends on the antenna height and satellite elevation angle.Other than specially-designed antenna or receiver in order to estimate environmental parameters,GPS-IR utilizes geodetic-quality GPS receivers and antennas,which are currently used at many of the already-existing GPS stations.This article presents a new method to retrieve regional NDVI data using NMRI（normalized microwave reflection index）,which is an index derived from GPS observations.An experiment was conducted to evaluate the feasibility of the NDVI retrieval using NMRI.In the experiment,continuous GPS observations of four plate boundary observatory GPS reference stations in midwestern America during the interval 2008-2012 and MOD13Q1 product within the same time from MODIS were used.In the first step,the NMRI time series were calculated with the GPS pseudoranges and carrier phase observations preprocessed with an improved Turboedit method,and then NDVI time series were extracted from MOD13Q1 product.In the second step,NMRI and NDVI were compared and analyzed.The temporal fluctuations of NMRI showed a clear periodicity as well as sudden drops,which were not compatible with the gradual process of vegetation change.Fast Fourier transform revealed that the annual and semi-annual periodicities exhibited dominant amplitude.To obtain cleaned NMRI data,trigonometric polynomial fitting method was adopted to remove outliers.A relatively high correlation coefficient between NMRI and NDVI was found,the coefficients of determination varied from 0.697 to 0.818（with a significance level of P〈0.001）,showing a near linear relationship involving these variables.With regression analysis,a linear retrieve model for NDVI could be established on each reference station,the root mean square of NDVI retrieve errors varied from 0.059 to 0.079.The outcomes of this study suggested that GPS-IR would be almost equally capable of retrieving regional NDVI data,in contrast,GPS-IR had the potential to be in near real time,with low price and high temporal resolution,and what＇s more,existing GPS networks around the world had the potential to be the NDVI sensors,which could be regarded as a new opportunity to obtain NDVI data.