A New Algorithm for Measuring Vegetation Growth Using GNSS Interferometric Reflectometry

Abstract

The use of global navigation satellite system interferometric reflectometry (GNSS-IR) to measure vegetation growth status has become a rapidly growing technique in remote sensing. GNSS signals reflected by the soil surface affect the accuracy of vegetation growth status (vegetation cover density) measurement, and the influence of soil moisture (SM) varies. This study establishes a calibration model that can reduce the influence of SM and snow layer on reflectivity. We used a direct-reflected signal amplitude ratio and GNSS-IR altimeter based on the Lomb–Scargle Periodogram to calculate the reflectivity of vegetation and snow layer depth. GNSS data from plate boundary observation were used to verify the validity of our model. The results show that reflectivity correlates better with vegetation growth status after calibrating the influence of the SM and snow layer. Moreover, the correlation increased by nearly 0.14. This study analyzed the influence of the snow layer and found that it had a noticeable effect on vegetation growth status measurement when the snow depth was over 30 cm. Furthermore, a fusion method is proposed to improve the accuracy of vegetation growth status measurement by combining the reflectivity and normalized microwave reflection index (NMRI). The experimental results show that better performance can be obtained compared to the single observation of the reflectivity and NMRI, and the best correlation between the measured and in situ normalized difference vegetation index is over 0.91, and the root mean square error decreases to 0.1893.