This article is dedicated to the study of airborne GNSS-R signal processing techniques for water body detection and edge localization using a low-altitude airborne carrier with high rate reflectivity measurements. A GNSS-R setup on-board a carrier with reduced size and weight was developed for this application. We develop a radar technique for automatic GNSS signal segmentation in order to differentiate in-land water body surfaces based on the reflectivity measurements associated to different areas of reflection. Such measurements are derived from the GNSS signal amplitudes. We adapt a transitional model to characterize the changes in the measurements of the reflected GNSS signals from one area to another. We propose an on-line/off-line change detection algorithm for GNSS signal segmentation. A real flight experimentation took place in the context of this work obtaining reflections from different surfaces and landforms. We show, using the airborne GNSS measurements obtained, that the proposed radar technique detects in-land water body surfaces along the flight trajectory with high temporal (50 Hz ) and spatial resolution (order of 10 to 100 m${}^2$). We also show that we can localize the edges of the detected water body surfaces at meter accuracy.

This article studies the use of airborne Global Navigation Satellite System Reflectometry (GNSS-R) techniques for remote sensing applications at regional scale. The objective is to classify the reflectivity of airborne GNSS signals in order to differentiate various reflective surfaces along the satellite traces. For this purpose, we propose a segmentation algorithm based on an online change point detector and an off-line change point localization estimate. Given the presence of speckle noise in GNSS signals, a homomorphic log-transformation is applied to mitigate this noise. In this context, the Cumulative Sum (CUSUM) change point detector and the Maximum Likelihood change point localization are designed for a Log-Gamma distribution. We show that the proposed radar segmentation system is able to automatically detect different land-forms along real flight experiments that took place in the Northern Region of France. Automatic classification using K-means clustering is applied to the segmented signals allowing to distinguish different segments of the signal.

This article is dedicated to the study of Global Navigation Satellite System Reflectometry (GNSS-R) techniques for remote sensing applications, focusing on classifying the reflectivity of airborne signals to differentiate reflective surfaces along satellite traces. We propose an automatic segmentation algorithm using an online change point detector and off-line change point localization estimate. Given the presence of speckle noise in GNSS signals, a homomorphic log-transformation is applied to mitigate this noise. The system is shown to detect different land-forms in real flight experiments in France, using K-means clustering to identify sand, water bodies, and plain land.

Soil moisture remote sensing on a global scale has been an active area of research over the past few decades due to its essential role in agriculture and in the prediction of some natural disasters. In this regard, GNSS-Reflectometry (GNSS-R) is proven as an efficient tool for the measurement of soil moisture content using remote sensing techniques. GNSS-R is a bi-static radar technique that uses the L-band GNSS signals as sources of opportunity to characterize Earth's surface, due to the fact that the reflected signals are often affected by the properties of the reflecting surface. In the context of this work, it is important to detect and fastly reach the area of interest (reflecting surface) for which the soil moisture content shall be monitored. A GNSS-R setup onboard a gyrocopter meets all the requirements of our application. This paper is dedicated to the study of airborne GNSS-R techniques for soil moisture monitoring using a low-altitude airborne carrier with a high rate (1ms for GPS C/A) carrier-to-noise ratio (C/N0) observations. To cope with the rapid displacement of the satellites footprints along the receiver trajectory, high rate (1000 Hz rate) C/N0 observations are processed. For this purpose, real flight experimentation has taken place on October 19, 2020 for 45 min. During the flight, the gyrocopter maintained a low-altitude of approximately 315m above the ground with an average speed of 95 km/h. Based on that, the size of the major axis of the first Fresnel zones that constitute the detected footprints ranged between 1,316m for a minimum elevation angle of 3° and 15m for a maximum elevation angle of 75°. Concerning the temporal resolution of the application, the raw data were sampled at a frequency of 25MHz and the C/N0 estimates were realized at a rate of 1000Hz.During the flight, an average of 9 GPS satellites have been detected of which 4 GPS satellite signals were extensively analyzed to observe the reflectivity corresponding to land, beach, and sea reflections. After analyzing the Delay Doppler Maps which provides an image of the scattering cross-section in terms of time and frequency and consequently tracking the corresponding signals, the 1ms C/N0 estimations were derived using the in-phase components of the signals as observations. The reflected signals are then linked to the footprints of the satellites and thus to the reflecting surfaces from which each processed signal has reflected using the GPS time, attitude, and position provided by onboard sensors and the GPS time extracted from the digitized GNSS signals. The ultimate aim of this study is to obtain reflectivity measurements from high rate C/N0 observations in order to provide a soil moisture mapping of the studied area, where we notice that the signals reflected from the beach had the best reflectivity followed by sea then land reflections.