Abstract: Abstract: 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.