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During the concluding phase of the NASA Operation IceBridge (OIB), we successfully completed two airborne measurement campaigns (in 2018 and 2021, respectively) using a compact S and C band radar installed on a Single Otter aircraft and collected data over Alaskan mountains, ice fields, and glaciers. This paper reports seasonal snow depths derived from radar data. We found large variations in seasonal radar-inferred depths with multi-modal distributions assuming a constant relative permittivity for snow equal to 1.89. About 34 % of the snow depths observed in 2018 were between 3.2 and 4.2 m, and close to 30 % of the snow depths observed in 2021 were between 2.5 and 3.5 m. We observed snow strata in ice facies, combined percolation and wet-snow facies, and dry-snow facies from radar data and identified the transition areas from wet-snow facies to ice facies for multiple glaciers based on the snow strata and radar backscattering characteristics. Our analysis focuses on the measured strata of multiple years at the caldera of Mount Wrangell (K'elt'aeni) to estimate the local snow accumulation rate. We developed a method for using our radar readings of multi-year strata to constrain the uncertain parameters of interpretation models with the assumption that most of the snow layers detected by the radar at the caldera are annual accumulation layers. At a 2004 ice core and 2005 temperature sensor tower site, the locally estimated average snow accumulation rate is ∼2.89 m w.e. a−1 between the years 2003 and 2021. Our estimate of the snow accumulation rate between 2005 and 2006 is 2.82 m w.e. a−1, which matches closely to the 2.75 m w.e. a−1 inferred from independent ground-truth measurements made the same year. The snow accumulation rate between the years 2003 and 2021 also showed a linear increasing trend of 0.011 m w.e. a−2. This trend is corroborated by comparisons with the surface mass balance (SMB) derived for the same period from the regional atmospheric climate model MAR (Modèle Atmosphérique Régional). According to MAR data, which show an increase of 0.86 ∘C in this area for the period of 2003–2021, the linear upward trend is associated with the increase in snowfall and rainfall events, which may be attributed to elevated global temperatures. The findings of this study confirmed the viability of our methodology, as well as its underlying assumptions and interpretation models.

During the concluding phase of the NASA Operation IceBridge (OIB), we successfully completed two airborne measurement campaigns (in 2018 and 2021, respectively) using a compact S/C band radar installed on a Single Otter aircraft and collected data over Alaskan mountains, ice fields, and glaciers. We observed snow strata in ice facies, wet-snow/percolation facies and dry snow facies from radar data. This paper reports seasonal snow depths derived from our observations. We found large variations in seasonal radar-inferred depths assuming a constant relative permittivity for snow equal to 1.89. The majority of the seasonal depths observed in 2018 were between 3.2 m and 4.2 m, and around 3 m in 2021. We also identified the transition areas from wet-snow facies to ice facies for multiple glaciers based on the snow strata and radar backscattering characteristics. Our analysis focuses on the measured strata of multiple years at the caldera of Mount Wrangell to estimate the local snow accumulation rate. We developed a method for using our radar readings of multi-year strata to constrain the uncertain parameters of interpretation models with the assumption that most of the snow layers detected by the radar at the caldera are annual accumulation layers. At a 2004 ice core and 2005 temperature sensor tower site, the locally estimated average snow accumulation rate is ~2.89 m w. e. a-1 between the years 2002 and 2021. Our estimate of the snow accumulation rate between 2005 and 2006 is 2.82 m w. e. a-1, which matches closely to the 2.75 m w. e. a-1 inferred from independent ground-truth measurements made the same year. We also found a linear increasing trend of 0.011 m w. e. a-1 per year between the years 2002 and 2021. With this trend, we extrapolated the snow accumulation back to 1992 and obtained an average accumulation rate of 2.74 w. e. a-1 between the years 1992 and 2004, which agrees well with the value of 2.66 w. e. a-1 for the same period determined from the ice core data retrieved at the caldera in 2004. The results reported here verified the efficacy of our method, its assumption, and the interpretation models.

In this article, we introduce a pH-responsive charge-reversible and photo-crosslinkable polymer nanoparticle. It is prepared via typical self-assembly from a block copolymer poly((7-(4-viny...

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.

This paper provides an update and overview of the Center for Remote Sensing of Ice Sheets (CReSIS) radars and platforms, including representative results from these systems. CReSIS radar systems operate over a frequency range of 14–38 GHz. Each radar system's specific frequency band is driven by the required depth of signal penetration, measurement resolution, allocated frequency spectra, and antenna operating frequencies (often influenced by aircraft integration). We also highlight recent system advancements and future work, including (1) increasing system bandwidth; (2) miniaturizing radar hardware; and (3) increasing sensitivity. For platform development, we are developing smaller, easier to operate and less expensive unmanned aerial systems. Next-generation platforms will further expand accessibility to scientists with vertical takeoff and landing capabilities.

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.

The path optimization problem with capacity constraints has always been a combinatorial optimization problem, and the quantum evolution algorithm has excellent performance in solving combinatorial optimization problems. Firstly, after analyzing the traditional path optimization problem and the path optimization problem with capacity constraints, the distribution center is numbered 0 based on the customer-based decimal coding method, and the quantum evolution algorithm is improved according to the capacity constraint method. Fixed quantum rotation angle to dynamic rotation angle. The improved algorithm is then applied to vehicle path optimization problems with capacity constraints. Finally, through the MATLAB and LINGO methods to verify the comparison, it can be concluded that the improved quantum evolution algorithm can accurately solve the vehicle path optimization problem, and the improved quantum evolution algorithm has a good influence on the path optimization problem.

In this paper, we present a brief overview of the state of the art in radar systems for ice and snow measurements. Emphasis is given to the instrumentation developed at the University of Kansas for multi-band data retrieval at frequencies ranging from 14 MHz to 38 GHz, which is operated onboard manned and unmanned aircraft. The information collected with these systems is 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), present sample results, and discuss recent collaborations with Chilean institutions.

In this work, novel mesoporous silica spheres were prepared by calcination of golf-shaped silica microspheres, which were fabricated via a typical sol-gel method by using synthetic amphiphilic block copolymer poly(acrylate isobutyl polyhedral oligomeric silsesquioxane)-b-poly(dimethylaminoethyl methacrylate co-styrene)) (PAPOSS-b-(PDMAEMA-co-PSt) and anionic surfactant sodium dodecyl sulfate (SDS) as dual-template. Firstly, three different POSS-based block copolymers were synthesized through subsequent homopolymerization of acrylate isobutyl POSS and chain extension of dimethylaminoethyl methacrylate (DMAEMA) and styrene with various feed ratios via reversible transfer radical polymerization (RAFT). These amphiphilic block polymers and SDS were utilized as cooperative template to golf-shaped silica microspheres. The electrostatic interaction between protonated PDMAEMA segments and anion surfactant as well as the strong interaction between PAPOSS segments and silicon source plays the dominant role in forming golf-shaped silica spheres. Moreover, the surface morphology of silica spheres can be tuned by varying molar ratio of PDMAEMA/PAPOSS. The resulting golf-shaped silica microspheres were further calcined to give mesoporous silica spheres which exhibited a BET surface area of 1031 m2 g−1, a total pore volume of 0.826 cm3 g−1, and a narrow pore size distribution centered at 3.20 nm.

Climate change is extensively affecting ice sheets resulting in accelerating mass loss in recent decades. Assessment of this reduction and its causes is required to project future ice mass loss. Annual snow accumulation is an important component of the surface mass balance of ice sheets. While in situ snow accumulation measurements are temporally and spatially limited due to their high cost, airborne radar sounders can achieve ice sheet wide coverage by capturing and tracking annual snow layers in the radar images or echograms. In this paper, we use deep learning to uniquely identify the position of each annual snow layer in the Snow Radar echograms taken across different regions over the Greenland ice sheet. We train with more than 15,000 images generated from radar echograms and estimate the thickness of each snow layer within a mean absolute error of 0.54 to 7.28 pixels, depending on dataset. A highly precise snow layer thickness can help improve weather models and, thus, support glaciological studies. Such a well-trained deep learning model can be used with ever-growing datasets to aid in the accurate assessment of snow accumulation on the dynamically changing ice sheets.

A novel halogen-free 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO)-containing co-curing agent, 6,6′-(1,4-phenylenebis(((4-(phenylamino)phenyl)amino)methylene))bis(dibenzo[c,e][1,2]oxaphosphinine 6-oxide) (DPN) was synthesized via a simple 1-pot or 2-step procedure with yield of 86.2% and 70.8%, respectively. The molecular structures of 4,4′-((1,4-phenylenebis(methanylylidene))bis(azanylylidene))bis(N-phenylaniline) (DPN intermediate) and DPN are characterized by FTIR, NMR, and MS. TGA tests show that the char yield of DPN/EP composites raises to 30.9% when the molar ratio of DPN to 4,4-diaminodiphenyl methane(DDM) is 20:80. Tg values of DPN/EP composites tested by DSC and DMA are similar to neat epoxy resin (EP), which is due to the secondary amine in DPN that participates in the cross-linking reaction of epoxy resin. The storage modulus in the rubber stage (E′-190 °C) of flame-retardant epoxy resin is close to that of neat EP, while their tanδ's are lower, which indicates the similarity of samples' cross-linking density due to the participation of DPN in the cross-linking reaction. The results show that when the molar ratio of DPN and DDM is 5:95, the epoxy has a higher Tg value and better mechanical properties than other samples. The introduction of DPN efficiently improves the flame-retardant properties of epoxy resin with V-0 rating of UL-94 vertical burning test, non-dripping, 41% of limit oxygen index (LOI) value, low peak heat release rate (PHRR), and total heat release (THR).

Frequency-modulated-continuous-wave (FM-CW) radar has been used extensively for the airborne measurement of snow thickness over sea ice and the mapping of annual accumulation over land ice. In contrast to conventional in situ measurements, FM-CW radar, when operated onboard an airborne platform, can be a useful tool for widearea surveys of snow deposition. Since the early 2000s, the Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas (KU) has designed, developed, and deployed airborne ultrawide-band (UWB) FM-CW radars, called Snow Radars, on National Science Foundation (NSF)-, NASA-, Naval Research Laboratory (NRL)-, and Alfred Wegener Institute (AWI)-provided aircraft in both Arctic and Antarctic regions and generated a large amount of snow data products. In addition to the significant standalone value of the snow-thickness measurements, these data are being used in estimating Arctic sea ice thickness, which is a key variable in the study of atmosphere-ocean-ice interactions. This article provides a review of snow remote sensing techniques with airborne FM-CW radars to document the operating principle, design, and evolution of CReSIS' UWB FM-CW radars and discuss and promote understanding of the advantages and limitations associated with these systems.

Ice crystal orientation fabric (COF) records information about past ice-sheet deformation and influences the present-day flow of ice. Polarimetric radar sounding provides a means to infer anisotropic COF patterns due to the associated birefringence of polar ice. Here, we develop a polarimetric coherence (phase-based) method to determine horizontal properties of the COF. The method utilizes the azimuth and depth dependence of the vertical gradient of the hhvv coherence phase to infer the dielectric principal axes and birefringence, which are then related to the second-order fabric orientation tensor. Specifically, under the assumption that one of the orientational eigenvectors is vertical, we can determine the horizontal eigenvectors and the difference between the horizontal eigenvalues (a measure of horizontal fabric asymmetry). The method exploits single-polarized data acquired with varying antenna orientation. It applies to ground-based ``multi-polarization'' surveys and is demonstrated using data acquired by Center for Remote Sensing of Ice Sheets (CReSIS) using Multi-Channel Coherent Radar Depth Sounder (MCRDS) from the North Greenland Eemian Ice Drilling (NEEM) ice core region in Greenland. The analysis is validated using a combination of polarimetric matrix backscatter simulations and comparison with COF data from the NEEM ice core. The results are consistent with a conventional model of ice deformation at an ice divide where a lateral tension component is present, with minor horizontal COF asymmetry and the greatest horizontal concentration of crystallographic axes orientated near parallel to the ice divide.