Observations from spaceborne radar contain considerable information about vegetation dynamics. The ability to extract this information could lead to improved soil moisture retrievals and the increased capacity to monitor vegetation phenology and water stress using radar data. The purpose of this review paper is to provide an overview of the current state of knowledge with respect to backscatter from vegetated (agricultural) landscapes and to identify opportunities and challenges in this domain. Much of our understanding of vegetation backscatter from agricultural canopies stems from SAR studies to perform field-scale classification and monitoring. Hence, SAR applications, theory, and applications are considered here too. An overview will be provided of the knowledge generated from ground-based and airborne experimental campaigns that contributed to the development of crop classification, crop monitoring, and soil moisture monitoring applications. A description of the current vegetation modeling approaches will be given. A review of current applications of spaceborne radar will be used to illustrate the current state of the art in terms of data utilization. Finally, emerging applications, opportunities and challenges will be identified and discussed. Improved representation of vegetation phenology and water dynamics will be identified as essential to improve soil moisture retrievals, crop monitoring, and for the development of emerging drought/water stress applications.

/pdf/radar-remote-sensing-of-agricultural-canopies-a-review-53mfpiiqux.pdf

Radar Remote Sensing of Agricultural Canopies: A Review

Critical government and industry sectors (such as law enforcement, transportation, communication, and finance) are growing increasingly dependent on Global Navigation Satellite Systems (GNSS) for positioning, navigation, and timing. At the same time, the availability of low-cost GNSS jamming devices are presenting a serious threat to GNSS and increasing the likelihood of outages to infrastructures relying on GNSS. The attacks range from malicious parties intentionally jamming GNSS signals within a targeted geographical region to uninformed users causing accidental interference. This paper is an overview of different approaches adopted to date to mitigate GNSS disruption caused by intentional and unintentional jamming. The first approach outlined in this paper is the use of inertial systems to aid GNSS. The second and third approaches are the filtering of jamming/interference in the spatial and time-frequency domains, respectively. The fourth approach is vector tracking of GNSS signals in the receiver.

Protecting GNSS Receivers From Jamming and Interference

Monitoring snow variations can know how fast the snow is melted and how much water is frozen on the Earth. However, traditional ground techniques have some limitations to monitor snow variations due to high cost. Recently, global navigation satellite systems-reflectometry (GNSS-R) has been developed and used to sense snow variations, while previous studies mostly used global positioning system (GPS) observations to estimate snow depth. In this paper, snow depth is estimated from GLONASS signal-to-noise ratio (SNR) and phase-based multipath reflectometry at one IGS GANP station in Slovakia, which is compared with in situ observations. For two snow seasons, snow depth estimations from GLONASS SNR1 and SNR2 data have a good agreement with in situ results with correlations of 0.94 and 0.94 in 2012 and 0.92 and 0.89 in 2013, respectively. Compared to GPS results, snow depth results from GLONASS are almost similar, but have some differences due to low coverage of less GLONASS satellites. However, results from GLONASS geometry-free linear combination (L4) are not pretty much, which needs more works to improve it in the future. Combined GPS and GLONASS observations have no significant improvement on the precision, but improve the spatial resolution because of more satellites.

Estimation of Snow Depth From GLONASS SNR and Phase-Based Multipath Reflectometry

Ground-based GNSS-R (global navigation satellite system reflectometry) can provide the absolute vertical distance from a GNSS antenna to the reflective surface of the ocean in a common height reference frame, given that vertical crustal motion at a GNSS station can be determined using direct GNSS signals. This technique offers the advantage of enabling ground-based sea level measurements to be more accurately determined compared with traditional tide gauges. Sea level changes can be retrieved from multipath effects on GNSS, which is caused by interference of the GNSS L-band microwave signals (directly from satellites) with reflections from the environment that occur before reaching the antenna. Most of the GNSS observation types, such as pseudo-range, carrier-phase and signal-to-noise ratio (SNR), suffer from this multipath effect. In this paper, sea level altimetry determinations are presented for the first time based on geometry-free linear combinations of the carrier phase at low elevation angles from a fixed global positioning system (GPS) station. The precision of the altimetry solutions are similar to those derived from GNSS SNR data. There are different types of observation and reflector height retrieval methods used in the data processing, and to analyze the performance of the different methods, five sea level determination strategies are adopted. The solutions from the five strategies are compared with tide gauge measurements near the GPS station, and the results show that sea level changes determined from GPS SNR and carrier phase combinations for the five strategies show good agreement (correlation coefficient of 0.97–0.98 and root-mean-square error values of <0.2 m).

Sea Level Estimation Based on GNSS Dual-Frequency Carrier Phase Linear Combinations and SNR

Snow is not only a critical storage component in the hydrologic cycle but also an important data for climate research; however, snowfall observations are only sparsely available. Signal-to-noise ratio (SNR) has recently been applied for sensing snow depths. Most studies only consider either global positioning system (GPS) L1 or L2 SNR data. In the current study, a new snow depth estimation approach is proposed using multipath reflectometry and SNR combination of GPS triple frequency (i.e. L1, L2 and L5) signals. The SNR combination method describes the relationship between antenna height variation and spectral peak frequency. Snow depths are retrieved from the SNR combination data at YEL2 and KIRU sites and validated by comparing it with in situ observations. The elevation angle ranges from 5° to 25°. The correlations for the two sites are 0.99 and 0.97. The performance of the new approach is assessed by comparing it with existing models. The proposed approach presents a high correlation of 0.95 and an accuracy (in terms of Root Mean Square Error) improvement of over 30%. Findings indicate that the new approach could potentially be applied to monitor snow depths and may serve as a reference for building multi-system and multi-frequency global navigation satellite system reflectometry models.

/pdf/a-new-gps-snr-based-combination-approach-for-land-surface-1zglttn72r.pdf

A New GPS SNR-based Combination Approach for Land Surface Snow Depth Monitoring

Measuring snow depth using the GPS interferometric reflectometry is an active microwave remote sensing technique and an emerging approach because of its relatively large spatial coverage and high temporal sampling capability. The current geodetic GPS networks are capable of measuring snow depth in the vicinity of the antenna installation at no additional hardware cost. However, the performance is constrained by the geodetic GPS antenna which was originally designed to minimize the reception of the reflected signal. In our prior work, we proposed a horizontally polarized antenna which has equal gain for both direct and reflected signals and tested its performance for a single snow event. In order to comprehensively assess its performance, we set up a horizontally polarized snow monitor (HPSM) using the improved antenna at Marshall, Colorado, USA, over the 2013---2014 water year. The data from the HPSM clearly shows that the proposed design has high sensitivity to even very light snowfalls. However, some anomalies are observed from the HPSM measurements, which tend to either overestimate or underestimate the actual snow depth. We explain the observed measurement anomalies by replacing the traditional air---snow single-interface model with an air---snow---soil dual-interface model. The effectiveness of the new model is validated by comparing the simulated results to the HPSM measurements. Utilizing the dual-interface model, we simulate the error curve for snow depth estimation given various snow depths and snow permittivities. The error curve shows that the estimation biases can be observed only for shallow snow with a relatively small permittivity value.

Snow depth estimation accuracy using a dual-interface GPS-IR model with experimental results

The GNSS signal received by a GNSS receiver consists of many signals transmitted from all visible satellites at single or multiple carrier frequencies. These signals, which are from different satellites at the same frequency, can interfere with each other; while the codes and carriers of all satellite signals are related. In order to mitigate the interference between GNSS signals and to take full advantage of correlation between codes and carriers, we model the received complex baseband signal instead of outputs of correlators as a linear model and propose a joint vector tracking loop based on the linear model to track codes and carriers jointly regardless of whether the RF module and the digital processing module are driven by the same clock or not. The proposed joint vector tracking loop can mitigate the interference between GNSS signals, and it also can improve the tracking sensitivity and the tracking accuracy. Although we only take GPS L1 C/A code signal as an example herein to simplify analysis, the proposed linear model and joint vector tracking loop can be easily expanded to the case of multifrequency and multi-constellation.

Joint Vector Tracking Loop in a GNSS Receiver

The global navigation satellite system (GNSS) (GPS, Galileo, GLONASS, BeiDou (COMPASS)) would provide more available civil signals and more accurate PVT estimation. On one hand, more satellites in view would enable the user receiver to select the satellites with better geometric distribution to get a better Dilution of Precision (DOP). On the other hand, some GNSS civil signals adopted Binary Offset Carrier (BOC) modulation which could provide the advantages of better code tracking performance, multipath effect reduction, and narrow band interference rejection. However, there are two main problems in the design and implementation of multi-system GNSS receiver. Firstly, some signals adopt BOC modulations and this would arouse the ambiguity problem in signal acquisition and tracking of BOC modulated signal. Secondly, multi-system GNSS receiver would consume more hardware resources than conventional GPS receiver.

In this paper we tried to explore solutions to the two problems. To deal with the ambiguity problem, we implemented the GRASS technique in our receiver which could effectively deal with the sine-phased BOC(kn,n) signal’s ambiguity problem. We devised an FFT based acquisition method to effectively calculate the test criterion of the GRASS technique employs. In the tracking progress, we devised a PCF (Pseudo Correlation Function) based detection technique to test false lock and compared its performance with Bump-Jump technique.

To reduce the hardware consumption, we devised a universal configurable tracking channel module with which the receiver could track satellite signals of different systems. This channel architecture could reduce the channel numbers used in the multi-system GNSS receiver. We also employed some other methods to reduce hardware resources. We implemented a multi-system GNSS receiver on an Altera development board which could handle GPS L1 C/A, GALILEO E1 OS and BeiDou B1 signals.

Implementation of a GNSS Receiver with an Unambiguous Acquisition and Tracking Technique

Signal Polarization Selection for GPS-IR Remote Sensing – Theoretical Analysis and Experimental Results

Measurement of vegetation state is required both for modeling and for satellite data validation. GPS Interferometric Reflectometry (GPS-IR) is a passive microwave remote sensing technique that has been applied to several applications, such as sensing soil moisture, snow depth, ocean roughness and wind speed, and vegetation growth. Previous studies proposed that geodetic GPS data could be used to measure vegetation biophysical parameters, for example, vegetation height and vegetation water content (VWC). However, the measurement range and accuracy are limited because the geodetic GPS antenna is designed to suppress reflections. In this paper, the antenna design for vegetation sensing via GPS-IR is discussed. Then, a customized dipole antenna is developed specifically for vegetation sensing. A field experiment is conducted at Boulder, CO, USA to evaluate the performance of the proposed antenna. The GPS metric, which is the normalized signal-to-noise ratio (SNR) amplitude ${A_{{\rm{norm}}}}$ , shows a linear relationship with in situ measurements of VWC over a range of 0—6 kg/m2, which is much greater than the range from the geodetic data (0—1.0 kg/m 2). The sensitivity of ${A_{{\rm{norm}}}}$ to VWC change is −0.11 (W/W)/(kg/m2), and the average measurement error of VWC is ∼12.4%. Thus, the horizontally polarized antenna is suitable for vegetation monitoring in most environments, including agricultural settings. The effects of soil moisture on GPS SNR amplitude are also documented, but these are minor compared to the decrease in amplitude resulting from vegetation growth.

Vegetation Sensing Using GPS Interferometric Reflectometry: Experimental Results With a Horizontally Polarized Antenna

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