This study presents a deep-learning (D2S) model to generate historical vertically polarized (TBv) and horizontally polarized (TBh) brightness temperatures (TBs) at the Soil Moisture Active-Passive (SMAP) 1.4 GHz channels using TBv and TBs polarization difference from the Defense Meteorological Satellite Program (DMSP) satellite series' 19.35 GHz channels. The proposed D2S model employed the ResNet9-based generator and PatchGAN-based discriminator, with a compositve loss function synergizing mean squared error and L1 losses, similar to the Pix2Pix architecture. Training, validation, and testing were conducted using paired datasets of polarized TBs from SMAP (1.41 GHz) and DMSP (19.35 GHz) collected between July 2015 and June 2022, followed by application to historical data spanning from September 1987 to June 2015. The D2S model demonstrated robust performance in generating both SMAP TBv and TBh, achieving test set average correlation coefficients of 0.994 and 0.992, biases of 0.150 K and -0.259 K, root-mean-square errors of 6.463 K and 9.011 K, and mean absolute errors of 3.883 K and 5.518 K, respectively. Furthermore, this study showed reasonable agreement between the D2S-generated SMAP TBs and SMOS TBs and provided preliminary results of thin sea ice thickness retrievals, which were further used for evaluating the limitations of the D2S approach in specific sea ice regions. Consequently, the D2S model can provide empirically consistent SMAP-like polarized TB estimates over the Arctic, including periods prior to the L-band operational era. The D2S framework offers significant potential to enhance the reconstruction of historical Arctic sea ice conditions and to support climate change studies by providing surrogate L-band–equivalent information extending back to 1987.

Expansion of the Arctic melting season with an earlier melt onset date (MOD) is a well-known indicator of Arctic warming. Since 1979, the pan-Arctic MOD distributions usually have been estimated using passive satellite microwave radiometer observations. However, there is a poor agreement in MOD between previous MOD detection algorithms based on passive microwave measurements, raising doubts regarding the accuracy of their MOD products. Thus, this study developed a new MOD algorithm, namely TBmax algorithm, to improve the estimation accuracy of continuous melt onset. The TBmax algorithm utilizes the microwave radiation characteristics of sea ice, and the daily brightness temperature time series shows their maximum brightness temperature on MOD. By using Advanced Microwave Scanning Radiometer 2 brightness temperature data, the pan-Arctic MOD distributions estimated from 2013 to 2021 using the TBmax algorithm successfully reproduced a feature of sea ice melting that mainly during May or June over the Arctic, including the late melting tendency of ice at high latitudes and multiyear ice. Validation with independent dataset (ice mass balance buoy data) suggested that the TBmax MODs showed superior performance compared to other previous algorithms (biases of 0.1 days vs. -2.7 and 13.9 days). As MOD can provide information about surface emissivity and the energy budget of the sea ice, the improved MOD may contribute to a more precise analysis of Arctic environment change and enhanced estimation of sea ice parameters.

This study introduces case adaptive detection models for detecting asbestos-containing building materials employing short-wave infrared hyperspectral images. The models are optimized for indoor and outdoor conditions, which could be applied to asbestos detection in interior and exterior building materials. Samples were collected from the most probable types and forms of building materials for both asbestos-containing materials (ACMs) and non-asbestos-containing materials (non-ACMs) to represent real-world conditions, utilizing the largest and most diverse dataset to date (11 types, 4 directions, and 4 forms). X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) analyses demonstrated that chrysotile was the main asbestos mineral in the most of ACMs, as evidenced by characteristic chrysotile diffraction peaks, fibrous serpentine morphology, and coincident Mg-Si elemental distributions. The spectral characteristics of building materials are manifested by mineral components for ACMs and non-ACMs, where asbestos absorption (Mg-OH) identifies ACMs. The indoor RF model identified critical absorption features at 2299-2350nm, 1923-1967nm, and 2366-2426nm, corresponding to Mg-OH, OH, and CO₃²⁻ absorptions, achieving 95.3% accuracy. The outdoor RF model incorporated additional Fe-OH and Al-OH bands, achieving 96.5% accuracy. Real-world validation at three asbestos abatement sites and one large-scale site demonstrated practical applicability including scalability and computational efficiency with overall accuracies of 92.1% for indoor and 92.5% for outdoor conditions. Comparative analysis with existing benchmark methodologies confirmed superior performance, with our models significantly outperforming previous approaches. The models successfully detected ACMs across various material conditions, scales and effectively verified complete removal post-abatement with high computational speed. This approach delivers immediate practical value for compliance verification and quality assurance in real-world abatement scenarios and large-scale surveys, providing objective documentation for regulatory compliance while substantially reducing costs compared to conventional destructive sampling methods.

Snow cover on Arctic sea ice is crucial to understanding sea ice evolution and the heat budget in the rapidly changing Arctic Ocean. Despite advancements in remote sensing technology, accurately estimating snow depth, grain size, and density on a large scale remains challenging. This study examines the influence of snow density and grain size on passive microwave satellite observation data. Data was collected from December 2012 to February 2016 using AMSR2 brightness temperature data, and snow depth was estimated using ice-tethered ice mass balance (IMB) data from the Cold Regions Research and Engineering Laboratory (CRREL). A two-stream approximation radiative transfer model that accounts for the scattering effect of the snow layer over Arctic sea ice was used. The study found that the slope in the linear relationship between snow density and the brightness temperature indices (such as gradient ratio (GR) and brightness temperature ratio (BTR)) is highly sensitive to the density and grain size of the snow layer. Using this dependence, the temporal changes in physical state of the snow layer was inferred from the observation over the winter season. The radiative transfer model simulations revealed that the grain size of the snow layer decreases during winter as the snow density increases, likely due to the counterbalancing effect of new snow on the temporal variability in grain size increasing caused by snow metamorphism.

In this paper, sea-ice surface backscattering properties from synthetic aperture radar (SAR) point of view has been investigated. The backscattering profile extraction was part of a forward numerical modeling procedure in which sea state contribution to ocean wave scatterings were analyzed. The proposed modeling was implemented based on JONSWAP energy formulation, which was modified in the presence of ice floes by cosine-power Gamma probability distribution function (PDF). The simulation results show that sea state conditions including wind direction and speed, have a direct impact on ice thickness and fluctuation, defining its backscattering properties. Despite limitations such as calculation complexities, dimensionality, and variables that are hard to assess due to harsh access to polar regions, the proposed model reduces reliance on empirical instructions and provide a dataset for future remote sensor developments.

We propose a Doppler calibration scheme that effectively removes errors come from attitude anomaly and antenna mispointing which are generic for all SAR sensors. The resulting calibrated Doppler signal of the entire ENVISAT ASAR ScanSAR data for one repeat cycle (35 days) showed well-balanced inter-swath measurements and largely reduced uncertainty without relying on bias correction using land reference. Sea ice drift in the Fram Strait was derived using both the offset-tracking and the Doppler estimation. An inter-comparison of the time-averaged velocities showed overall high consistency with RMSE of 0.15 m/s.

Sentinel-1A and 1B operate in Extra Wide swath dual-polarization mode over the Arctic Seas, and the two-satellite constellation provides the most frequent SAR observation of the Arctic sea ice ever. However, the use of Sentinel-1 for sea ice classification has not been popular because of relatively higher level of system noise and radiometric calibration issues. By taking advantage of our recent development on Sentinel-1 image noise correction, we suggest a fully automated SAR image-based sea ice classification scheme which can provide a potential near-real time services of sea ice charting. The denoised images are processed into texture features and a machine learning-based classifier is trained by feeding digitized ice charts. The use of ice chart rather than manually classified reference image makes enable an automated training which minimizes the effects from biased human decision. The resulting classifier was tested over the Fram Strait area for an extensive dataset of Sentinel-1 constellation acquired from October 2017 to May 2018. The classification results are shown in comparison with the ice charts, and the feasibility of the ice chart-feeded automated classifier is discussed.

Bias assessment of sea ice concentration (SIC) derived from passive microwave sensors is very important because it has been used as an important parameter for climate change research. In this study, we analyzed biases in SICs retrieved from NASA Team (NT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithm implemented for the Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Microwave Scanning Radiometer-2 (AMSR2), respectively, in the Chukchi Sea in summer using the Korea Multi-Purpose SATellite-5 (KOMPSAT-5) synthetic aperture radar images. The root mean square error of ASI SIC was smaller than that of NT SIC. The SIC values from the sea ice algorithms showed different biases by the KOMPSAT-5 SIC range due to different sensitivities to atmospheric effects and ice surface melting conditions.