This paper presents a new technique to retrieve soil moisture using global navigation satellite signals reflected over the soil surface using the measurement of the power fluctuations of the signal created by the interference between the direct GPS signal and the one reflected over the soil surface. As a function of the elevation angle, power fluctuations at vertical polarization pass through a notch, which is related to the soil moisture content, while horizontal polarization exhibits a very weak dependence. Experimental results of the measurements obtained over a bare soil field are presented and discussed.

In the past years, the scientific community has placed a special interest in remotely sensing soil moisture and vegetation parameters. Radiometry and radar techniques have been widely used for years. Global Navigation Satellite Systems opportunity signals Reflected (GNSS-R) over the earth's surface are younger, but they have already shown their potential to perform these observations. This paper presents a GNSS-R technique, based on Global Positioning System (GPS) measurements, that allows the retrieval of several geophysical parameters from land surfaces. This technique measures the power of the interference signal between the direct GPS signal and the reflected one after scattering over the land, so it is called Interference Pattern Technique (IPT). This paper presents the results obtained after applying the IPT for topography, soil moisture, and vegetation height retrievals over vegetation-covered soils.

A new and efficient algorithm to compute delay-Doppler maps is presented. It improves by more than an order of magnitude the required computation time and memory resources. This approach is based on the derivation of explicit expressions of the space coordinates as a function of the delay offset and Doppler shift. Using this technique, the limitation posed by the number of sampling points of the observed surface is drastically attenuated, and a wide range of scenarios from low- to medium-height airborne-to-spaceborne scenarios can now be simulated with standard desktop computers.

Global navigation satellite system (GNSS) reflectometry has emerged recently as a promising remote sensing tool to retrieve various geophysical parameters of the Earth's surface. GNSS-reflected signals, after being received and processed by the airborne or spaceborne receiver, are available as delay correlation waveforms or as delay-Doppler maps (DDMs). In the case of a rough ocean surface, those characteristics can be related to the rms of the L-band limited slopes of the surface waves and, from there, to the surface wind speed. The raw GNSS-reflected signal can be either processed in real time by the receiver or recorded and stored on board and postprocessed in a laboratory. The latter approach leveraging a software receiver allows more flexibility while processing the raw data. This work analyzes DDMs obtained as a result of processing of the data collected by the Global Positioning System (GPS) data logger/software receiver on board the National Oceanic and Atmospheric Administration Gulfstream-IV jet aircraft. Thereafter, the DDMs were used to retrieve surface wind speed employing several different metrics that characterize the DDM extent in the Doppler frequency-delay domain. In contrast to previous works in which winds have been retrieved by fitting the theoretically modeled curves into measured correlation waveforms, here, we do not rely on any model for the determination. Instead, the approach is based on a linear regression between DDM observables and the wind speeds obtained in simultaneous GPS dropsonde measurements.

The use of global navigation satellite system reflectometry (GNSS-R) measurements for classification of inundated wetlands is presented. With the launch of NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission, space-borne GNSS-R measurements have become available over ocean and land. CYGNSS covers latitudes between ±38°, providing measurements over tropical ecosystems and benefiting new studies of wetland inundation dynamics. The GNSS-R signal over inundated wetlands is driven mainly by coherent scattering associated with the presence of surface water, producing strong forward scattering and a distinctive bistatic scattering signature. This paper presents a methodology used to classify inundation in tropical wetlands using observables derived from GNSS-R measurements and ancillary data. The methodology employs a multiple decision tree randomized (MDTR) algorithm for classification and wetland inundation maps derived from the phased-array L-band synthetic aperture radar (PALSAR-2) as reference for training and validation. The development of an innovative GNSS-R wetland classification methodology is aimed to advance mapping of global wetland distribution and dynamics, which is critical for improved estimates of natural methane production. The results obtained in this manuscript demonstrate the ability of GNSS-R signals to detect inundation under dense vegetation over the Pacaya-Samiria Natural Reserve, a tropical wetland complex located in the Peruvian Amazon. Classification results report an accuracy of 69% for regions of inundated vegetation, 87% for open water regions, and 99% for non-inundated areas. Misclassification of inundated vegetation, primarily as non-inundated area, is likely related to the combination of two factors: partial inundation within the GNSS-R scattering area, and signal attenuation from dense overstory vegetation, resulting in a low signal.