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Tree heights are important input for many inventory and ecosystem models. While optical and infrared sensors such as LiDAR are widely used in forest surveys, microwave radar has the unique advantage of being able to operate in bad weather or poor visibility conditions. In this work, we employed a LiDAR combined with a compact ultra-wideband frequency modulated continuous wave (FMCW) radar onboard a Single Otter aircraft to collect data over forested areas in Alaska. While the primary focus of the mission was to map the surface elevation and snow thickness of Alaskan glaciers as a part of NASA Operation IceBridge (OIB), we recorded LiDAR and radar returns during the transit flights to analyze backscattering signatures from tree-covered areas, thereby assessing the potential application of our radar to forest and vegetation remote sensing. We analyzed these measurements and estimated the tree heights along the flight trajectory. The very good agreement between the tree height profiles from the two instruments shows promising results for wide area forestry studies using microwave radar.

We designed a Ka-band prototype radar altimeter operated at a center frequency of 35 GHz with a 6 GHz bandwidth. The instrument was intended for fine-resolution verification and validation of space borne altimetry datasets. We installed it onboard the NASA C-130 aircraft in conjunction with two other wideband microwave instruments and collected airborne altimetry data over Greenland land ice and arctic sea ice during the 2015 NASA Operation IceBridge arctic campaign. Apart from the major application of verification and calibration of satellite-based measurements, data from this instrument can be used to derive snow grain size because of the dominant effect of volume scattering in radar signatures. In this paper, we briefly describe the system design and the installation on the NASA C-130, discuss the observed penetration depths of Ka-band signals into the snowpack, present sample results of optical-equivalent snow grain size estimates from radar measurements over the dry snow zone using a simplified snowpack model. We show that the observed penetration depths and the snow grain size estimates from the airborne Ka-band radar retrievals agree well with the model and in-situ snow-pit measurements.

We developed an airborne radar system capable of simultaneous operation at Ku- and Ka- bands. The system is compact and lightweight, and supports coincident altimetry measurements in both bands with ultra-wide bandwidth (up to 6 GHz). Wide-band data can be used to obtain fine vertical resolution or sub-banded to match the operational parameters of orbital instruments. We installed the system onboard a Twin Otter aircraft and collected data over the East Greenland ice sheet margin and on Arctic marine ice during a test field campaign conducted in the summer of 2019. The work was carried as an international collaboration to support the validation of ESA's CryoSat-2 and NASA's ICESat-2 measurements. In this paper, we present an overview of the instrument and its airborne test configuration and field operation; provide initial results from field trials; and outline the potential application of dual-band airborne radar data for the validation of satellite instruments.

Snow in mountainous areas provides freshwater resources for lower basins and coastal areas. Snow layering at mountain summits contains information about local seasonal snow accumulation and climate history. Knowledge of snow depths is required to estimate the snow water equivalent. However, monitoring spatial distribution and temporal changes in snow depth and accumulation over remote mountains and glaciers in wide areas is challenging because of limited accessibility for in-situ measurements. As a part of NASA Operation IceBridge missions, we took airborne radar measurements of snow over Alaskan mountains, icefields and glaciers in late May of 2018, with a compact frequency-modulated continuous wave radar system, operating from 2 GHz to 8 GHz and installed on a Single Otter aircraft. In this paper, we describe the radar instrument, its installation onto the platform, the data collection and processing activities, and report the major results from these surveys. We observed seasonal snow depth between ~0.3 m to ~15 m for elevations above sea level from ~1726 m to ~3624 m. We successfully mapped snow accumulation layers to depths exceeding ~85 m below the surface at high-elevation summits of Mount Wrangell and Bona. The traced snow depth profiles over glaciers and accumulation layers at mountain summits point to the utility of these data to the study of water resource management, hydrology modelling, and regional climate change.

This paper presents recent updates to the CReSIS radar sensor package and the platforms supporting these sensors. These sensors cover a wide frequency range (14 MHz to 38 GHz). The specific frequency bands are chosen to balance between bandwidth available and signal penetration. The wide frequency range is also used for measuring different phenomenology. CReSIS has integrated these radar systems, including antennas, on a wide variety of fixed wing crewed aircraft, several UAV platforms, and for ground-based applications. The software for processing the radar data is now open source and an overview of the capabilities and how to access and use the software are presented. Finally, example data products which explore the new capabilities of the sensors and platforms are given.

Understanding the subglacial conditions is crucial in determining the ice dynamics and predicting the stability of ice sheets. Radio echo sounding (RES) surveys have been extensively carried out to map the ice sheets of Greenland and Antarctica. Using RES data, basal conditions of ice sheets can be determined from estimated reflectivity that is affected by ice surface and bed roughness. Roughness is also one of the important parameters to explain ice dynamics. In this paper the ice surface and bed roughness of Petermann Glacier, one of the largest outlet glacier of Greenland, is estimated with the Multi-Channel Coherent Radar Depth Sounder (MCoRDS) data by fitting the echo amplitude distribution to statistical models. The roughness derived from MCoRDS data is compared and verified with the roughness estimations from Airborne Topographic Mapper (ATM) and Ku-band altimeter measurements. It has been observed that Petermann glacier has rougher ice inland and a smoother bed towards the trunk of the glacier.

Ice basal conditions determine the ice flow dynamics and are needed for the accurate modeling of ice sheets. In this paper, a large volume of data, collected over Antarctica's Byrd Glacier in 2011–2012 with multichannel radar, is analyzed to infer the basal conditions. The analysis of received radio echo sounding data includes power loss corrections for spherical spreading and roughness of the ice-surface and ice-bed interfaces, and the estimate of relative englacial attenuation and basal reflectivity values. From the estimated relative basal reflectivity values, basal conditions are inferred, and are validated using geophysical features from radar images, abruptness and coherence index values. The identified locations of high basal reflectivity values provide new insights on the subglacial hydrological network of Byrd Glacier.

We present an overview of a set of radar instruments developed at the University of Kansas for multi-spectral measurements of ice and snow properties. The systems operate at different frequency bands ranging from 14 MHz to 38 GHz, onboard manned and unmanned aircraft. The data collected with these systems are used to estimate parameters such as ice thickness, ice surface and bedrock topography, snow cover thickness on sea ice, and annual snow accumulation. We give a summary of recent field programs (including operations out of Punta Arenas, Chile) and discuss current collaborations with Chilean institutions.

In this paper, we present the development of a multi-channel VHF/UHF ultra-wideband airborne radar sounder and imager for measurements of polar ice sheets. The radar was developed at the Center for Remote Sensing of Ice Sheets (CReSIS) for operation onboard the German Alfred Wegener Institute (AWI) Basler BT-67 aircraft. The system operates from 150 to 600 MHz corresponding to a vertical resolution of 33 cm in free space. The radar is equipped with three 4-m long 8-element antenna subarrays installed under the fuselage and both wings to support 8 transmit and 24 receive channels. The radar waveform from each transmit channel can be configured individually to enable real-time transmit beamforming for wide-swath ice bed imaging of up to 10 km wide. The radar system was deployed to Greenland in the spring of 2016 as a part of the joint AWI/CReSIS test campaign to conduct measurements over glaciers. Sample radar data from this field campaign are presented to illustrate the capability of the radar.

In this paper, we investigate the performance of two robust beamformers, Chen's Quadratically Constrained Beamformer and Shahbazpanahi's General Rank Beamformer, on their performance on clutter rejection in Radar Depth Sounding of ice sheets. The investigation is carried out using both simulation and measured data with our Multichannel Coherent Radar Depth Sounder/Imager (MCoRDS/I). The results show that the two robust beamformers outperform conventional sample matrix inversion (SMI) and Loaded SMI beamformers in resolving the issue of signal self-nulling. However, the effectiveness of both robust beamformers on clutter cancellation was greatly reduced in situations of small antenna array and small number of data samples. By using MUSIC cum geometry to estimate the directions of arrivals (DOAs) of the dominant clutter signals, we have shown via simulations that we can enhance the two robust beamformers by incorporating linear constraints at the clutter signal DOAs to improve SINR by more than 10 dB.

Sea-level rise will affect populations worldwide with considerable and lasting consequences in the not-too-distant future. Accurate measurement of fast flowing outlet glaciers in Greenland and Antarctica are of vital importance to ice sheet models that predict the course of sea-level rise. The Center for the Remote Sensing of Ice Sheets (CReSIS) has developed a suite of tools designed for use with data collected by CReSIS depth sounding radar platforms. This suite includes algorithms for removing clutter and noise from coherent radar data, and the results show successful sounding of some of these fast-flowing glaciers for the first time.

On basis of reviewing the existed research results, and with digital image processing technology, this paper brings forward a rapid algorithm of detecting new and old banknotes, in which the Support Vector Machines (SVM) and Dynamic Time Warp (DTW) are combined. First, the banknotes gray histogram is aligned by DTW. Second, the aligned Gray Histogram is embedded in radial basis function (RBF) of SVM. Then, a mixed structure RBF/DTW is built. The simulation results show that the proposed structure is efficient to recognize the banknote levels of new and old.