A theoretical model that describes the power of a scattered Global Positioning System (GPS) signal as a function of geometrical and environmental parameters has been developed. This model is based on a bistatic radar equation derived using the geometric optics limit of the Kirchhoff approximation. The waveform (i.e., the time-delayed power obtained in the delay-mapping technique) depends on a wave-slope probability density function, which in turn depends on wind. Waveforms obtained for aircraft altitudes and velocities indicate that altitudes within the interval 5-15 km are the best for inferring wind speed. In some regimes, an analytical solution for the bistatic radar equation is possible. This solution allows converting trailing edges of waveforms into a set of straight lines, which could be convenient for wind retrieval. A transition to satellite altitudes, together with satellite velocities, makes the peak power reduction and the Doppler spreading effect a significant problem for wind retrieval based on the delay-mapping technique. At the same time, different time delays and different Doppler shifts of the scattered GPS signal could form relatively small spatial cells on sea surface, suggesting mapping of the wave-slope probability distribution in a synthetic-aperture-radar (SAR) fashion. This may allow more accurate measurements of wind velocity and wind direction.

In traditional GNSS applications, signals arriving at a receiver's antenna from nearby reflecting surfaces (multipath) interfere with the signals received directly from the satellites which can often result in a reduction of positioning accuracy. About two decades ago researchers produced an idea to use reflected GNSS signals for remote-sensing applications. In this new concept a GNSS transmitter together with a receiver capable of processing GNSS scattered signals of opportunity becomes bistatic radar. By properly processing the scattered signal, this system can be configured either as an altimeter, or a scatterometer allowing us to estimate such characteristics of land or ocean surface as height, roughness, or dielectric properties of the underlying media. From there, using various methods the geophysical parameters can be estimated such as mesoscale ocean topography, ocean surface winds, soil moisture, vegetation, snowpack, and sea ice. Depending on the platform of the GNSS receiver (stationary, airborne, or spaceborne), the capabilities of this technique and specific methods for processing of the reflected signals may vary. In this tutorial, we describe this new remotesensing technique, discuss some of the interesting results that have been already obtained, and give an overview of current and planned spacecraft missions.

Snow is an important component of the climate system and a critical storage component in the hydrologic cycle. However, in situ observations of snow distribution are sparse, and remotely sensed products are imprecise and only available at a coarse spatial scale. GPS geodesists have long recognized that snow can affect a GPS signal, but it has not been shown that a GPS receiver placed in a standard geodetic orientation can be used to measure snow depth. In this paper, it is shown that changes in snow depth can be clearly tracked in the corresponding multipath modulation of the GPS signal. Results for two spring 2009 snowstorms in Colorado show strong agreement between GPS snow depth estimates, field measurements, and nearby ultrasonic snow depth sensors. Because there are hundreds of geodetic GPS receivers operating in snowy regions of the U.S., it is possible that GPS receivers installed for plate deformation studies, surveying, and weather monitoring could be used to also estimate snow depth.

Measurements of soil moisture, both its global distribution and temporal variations, are required to study the water and carbon cycles. A global network of in situ soil moisture stations is needed to supplement datasets from satellite sensors. We demonstrate that signals routinely recorded by Global Positioning System (GPS) receivers for precise positioning applications can also be related to surface soil moisture variations. Over a three month interval, GPS‐derived estimates from a 300 m2 area closely match soil moisture fluctuations in the top 5 cm of soil measured with conventional sensors, including the rate and amount of drying following six precipitation events. Thousands of GPS receivers that exist worldwide could be used to estimate soil moisture in near real‐time, with L‐band signals that complement future satellite missions.