Lanqing Huang

Last Name

Huang

First Name

Lanqing

ORCID

0000-0002-5180-7491

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Publications 1 - 10 of 13

Wet Snow Mapping in the Karakoram using SAR and Topographic Data
Li, Shiyi; Huang, Lanqing; Bernhard, Philipp; et al. (2023)
EGUsphere
Wet snow is a critical component of the cryosphere, and its spatial and temporal distribution has important implications for water resources, natural hazards, and the regional climate. However, mapping wet snow in alpine regions such as the Karakoram is challenging due to complex topography, harsh weather conditions, and limited in-situ observations. Previous studies have shown that synthetic aperture radar (SAR) can effectively detect wet snow surfaces using the backscattering ratio between the current and reference images (e.g. the average of summer acquisitions). However, its regional application on a large-scale and complex terrain is hampered, as the ratio value is easily affected by the land cover, local topography, surface roughness, and snow wetness. In this study, we present a new approach for mapping wet snow in the Karakoram using a combination of SAR data and topographic information. The SAR data used in the analysis were obtained from Sentinel-1, and the topographic data included a digital elevation model (DEM), slope angle, and slope aspect ratio. We first used a Gaussian Mixture Model to classify the ratio image of Sentinel-1 into wet snow (WS) and non-wet snow (NWS) classes, then transformed the two classes into a logistic function to characterize the probability of WS based on the backscattering ratio. Secondly, we categorized the image based on the topography and calculated the likelihood of WS for each topographic bin using the WS probability. The joint WS likelihood map was finally obtained by multiplying the WS probability on the backscattering ratio with the WS likelihood on topography, and a binary WS map was generated by setting a threshold on the joint likelihood map. The proposed method was validated using snow maps generated from Sentinel-2 images. Compared with the traditional method of using only the SAR backscattering ratio, our method significantly reduced false negative detections and preserved the high true positive rate, indicating an improvement of robustness and accuracy by combining SAR and topographic data for regional wet snow mapping. This study demonstrates a practical method of merging SAR backscattering features and topographic information for robust regional wet snow mapping in complex mountain ranges. It also provides new insights into the incorporation of different datasets using a probabilistic framework. With the proposed method, the operational monitoring of wet snow distribution in the Karakoram using SAR becomes feasible and reliable.
Karakoram Glacier Elevation Change During the Recent Decade Revealed by Tandem-X Dem Series
Li, Shiyi; Huang, Lanqing; Bernhard, Philipp; et al. (2024)
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
The change of glacier surface elevation in Karakoram holds significant relevance to the study of global climate change and regional water resource management. In this work, we focused on understanding glacier dynamics in the Karakoram region through the analysis of Digital Elevation Models (DEMs) generated from a comprehensive 481 TanDEM-X CoSSC products spanning 2011 to 2020. An iterative approach based on the residual phase with respect to the reference DEM was employed to generate high resolution DEMs. The systematic approach offered a robust methodology for extracting precise glacier elevation changes in the Karakoram with high temporal and spatial resolution, provided valuable insights into the understanding of complex glacier dynamics.
Wet Snow Mapping In Karakoram Using Sar And Topographic Data
Li, Shiyi; Huang, Lanqing; Bernhard, Philipp; et al. (2023)
IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium
Mapping seasonal wet snow (WS) is essential for climate research, hydrological assessments and natural hazards management. It is especially crucial in Karakoram as the the water supply in the region is dominated by snow melting. However, the complex terrain in Karakoram brings great challenge when employing synthetic aperture radar (SAR) data for WS mapping. In this work, we present an algorithm that combines SAR and topographic data for WS mapping. The result shows that the proposed algorithm can efficiently reduce the uncertainty and generate robust WS map on a large scale.
Mapping seasonal snow melting in Karakoram using SAR and topographic data
Li, Shiyi; Huang, Lanqing; Bernhard, Philipp; et al. (2025)
The Cryosphere
Mapping seasonal snow melting is crucial for assessing its impacts on water resources, natural hazards, and regional climate in Karakoram. However, complex terrain in the high-mountain region poses great challenges to remote-sensing-based wet snow mapping methods. In this study, we developed a novel framework that incorporates synthetic aperture radar (SAR) and topographic data for robust and accurate mapping of wet snow over Karakoram. Our method adopts the Gaussian mixture model (GMM) to adaptively determine a wet snow index (WSI) and a computed topographic snow index (TSI) considering the impact of terrain on wet snow distribution to improve the accuracy of mapping. We validated the mapping results against Sentinel-2 snow cover maps, which demonstrated significantly improved accuracy using the proposed method. Applied across three major water basins in Karakoram, our method generated large-scale wet snow maps and provided valuable insights into the temporal dynamics of regional snow melting extent and duration. This study offers a practical and robust method for snow melting monitoring over challenging terrains. It can contribute to a significant step forward in better managing water resources under climate change in vulnerable regions.
Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry
Huang, Lanqing; Fischer, Georg; Hajnsek, Irena (2021)
The Cryosphere
Assessing the Transferability of Neural Network-Based Sea Ice Classification between the Arctic and Antarctic
Huang, Lanqing; Johansson, Malin; Hajnsek, Irena (2024)
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
In this study, we trained a supervised neural network for sea ice classification using X-band SAR images obtained during an Antarctic fieldwork campaign and applied it to Arctic X-band data acquired during sea ice fieldwork campaigns. The results revealed a pronounced correlation between the predicted sea ice class and measured snow depth in the Arctic. This correlation is likely attributed to the Arctic acquisitions taking place during the cold winter conditions characterized by thicker snow layers, which resemble conditions in Antarctica.
Theory of Radar Polarimetric Interferometry and Its Application to the Retrieval of Sea Ice Elevation in the Western Weddell Sea, Antarctic
Nghiem, Son V.; Huang, Lanqing; Hajnsek, Irena (2022)
Earth and Space Science
Sea ice elevation plays a crucial role in sea ice dynamic processes driven by winds and waves, for which satellite radar remote sensing becomes indispensable to monitor snow-covered sea ice across the vast polar regions regardless of darkness and clouds. To measure sea ice elevation, a theory of polarimetric interferometry for both monostatic and bistatic radars is developed based on analytic solutions of Maxwell's equations, accounting for realistic and complicated properties of snow, sea ice, and seawater. This analytic method inherently preserves phase information that is imperative for radar polarimetry and interferometry. Among a multitude of complex radar coefficients in the general polarimetric interferometric covariance matrix, the symmetry group theory is utilized to identify and select appropriate terms pertaining to the retrieval of sea ice elevation while avoiding radar parameters that may inadvertently introduce non-uniqueness and excessive uncertainty. Theoretical calculations compare well with field observations for rough and old sea ice encountered in the Operation-IceBridge and TanDEM-X Antarctic Science Campaign over the Western Weddell Sea. The results show that the magnitude of the coefficient of normalized correlation between co-polarized horizontal and co-polarized vertical radar returns is inversely related to sea ice elevation, while the associate phase term is nonlinear and noisy and should be excluded. From these analyses, a protocol is set up to measure Antarctic sea ice elevation to be presented in the next companion paper.
Sea Ice Topographic Retrieval in the Western Weddell Sea from Tandem-X
Huang, Lanqing; Hajnsek, Irena (2023)
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
Single-pass interferometric synthetic aperture radar (InSAR) offers the potential to generate sea ice digital elevation models (DEMs) despite the inherent dynamics of sea ice. However, the accuracy of sea ice DEM generation from InSAR is hindered by the varying penetration bias across different types of ice. This paper presents a new scheme to generate sea ice DEMs (i.e., snow freeboard) across different ice types from TanDEM-X images. The proposed scheme is applied to generate a sea ice topographic map over a 630 km × 19 km area, revealing a spatial pattern of sea ice elevation. The highest elevations are observed near the Antarctica Peninsula (AP), with a rapid decrease in elevation within approximately 200km from the AP coastline, followed by a gradual increase towards the south.
Investigation of Polarimetric Phase Differences in Tandem-X Data Over Young and Undeformed Sea ICE
Huang, Lanqing; Hajnsek, Irena (2022)
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Single-pass interferometric synthetic aperture radar (InSAR) is an effective technique for sea ice topographic retrieval despite the dynamics of sea ice. Co-polarimetric phase differences (CPD) provide scattering information concerning the sea ice properties. This study estimates the observational CPD from the dual-polarimetric SAR images over young ice (YI) and undeformed ice (UI) in the Western Weddell Sea. The CPD is further modelled as a function of structural anisotropy, incidence angle, ice volume fracture, and volume thickness. The good agreement between the observational and modelled CPD indicates that CPD can be a promising indication for topographic characterization over the YI and UI.
Sea Ice Elevation in the Western Weddell Sea, Antarctica: Observations From Field Campaign
Huang, Lanqing; Hajnsek, Irena; Nghiem, Son V. (2022)
Earth and Space Science
Sea ice elevation is crucial in the characterization of three-dimensional (3D) sea ice patterns, providing physical insights to advance sea ice dynamic models. Moreover, how sea ice elevation may be related to the ocean geophysical environment is still a significant knowledge gap, especially in Antarctica. A radar theory relating electromagnetic scattering mechanisms to sea ice elevation over old and deformed rough ice has been reported in a prior companion paper. This follow-up paper presents the validated model function and synthetic aperture radar (SAR)-retrieved sea ice elevations based on the field data acquired during the Operation IceBridge and TanDEM-X Antarctic Science Campaign. A high-resolution sea ice digital elevation model (DEM) is generated extensively over a 19 x 450 km sector in the Western Weddell Sea, achieving a good accuracy with a low root-mean-square error of 0.23 m. From the SAR-retrieved sea ice DEM, 3D sea ice patterns including roughness height, auto-correlation lengths, correlation ellipticity, and orientation angles are calculated over the old and deformed rough sea ice. The 3D sea ice patterns give a comprehensive characterization of sea ice topography in the Western Weddell Sea and show the potential to be used for understanding sea ice formation processes in the Antarctic.

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Lanqing Huang

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