Monitoring Snow Cover and Snowmelt Dynamics and Assessing their Influences on Inland Water Resources

Snow is one of the most vital cryospheric components owing to its wide coverage as well as its unique physical characteristics. It not only affects the balance of numerous natural systems but also influences various socio-economic activities of human beings. Notably, the importance of snowmelt water to global water resources is outstanding, as millions of populations rely on snowmelt water for daily consumption and agricultural use. Nevertheless, due to the unprecedented temperature rise resulting from the deterioration of climate change, global snow cover extent (SCE) has been shrinking significantly, which endangers the sustainability and availability of inland water resources. Therefore, in order to understand cryo-hydrosphere interactions under a warming climate, (1) monitoring SCE dynamics and snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced waterbodies, and (3) assessing the causal effect of snowmelt conditions on inland water resources are indispensable. However, for each point, there exist many research questions that need to be answered. Consequently, in this thesis, five objectives are proposed accordingly. Objective 1: Reviewing the characteristics of SAR and its interactions with snow, and exploring the trends, difficulties, and opportunities of existing SAR-based SCE mapping studies; Objective 2: Proposing a novel total and wet SCE mapping strategy based on freely accessible SAR imagery with all land cover classes applicability and global transferability; Objective 3: Enhancing total SCE mapping accuracy by fusing SAR- and multi-spectral sensor-based information, and providing total SCE mapping reliability map information; Objective 4: Proposing a cloud-free and illumination-independent inland waterbody dynamics tracking strategy using freely accessible datasets and services; Objective 5: Assessing the influence of snowmelt conditions on inland water resources

Monitoring Summertime Erosion Patterns Over an Arctic Permafrost Coast with Recent Sub-meter Resolution Microsatellite SAR Data

Arctic coasts experience some of the highest rates of erosion in the world, particularly due to permafrost degradation resulting from the recent exacerbation of climate change. Therefore, not only have coastal defense and energy facilities been threatened, but maintenance costs for the infrastructure of cold regions have also risen. To monitor the coastal erosion pattern of the circum-Arctic, earlier studies often employ spaceborne or airborne optical multi-spectral images to depict shoreline changes, which are limited by frequent clouds and haze in Arctic regions and, thus, hamper the time-series analysis. Instead, this study aims to explore the synthetic aperture radar (SAR) images, especially the recently developed microsatellite SAR data, which provide unprecedented high-resolution at a sub-meter scale, to measure the summertime spatio-temporal dynamics of an ice-rich permafrost coast along the Beaufort Sea, Alaska. The results reveal a maximum shoreline change envelope (SCE) of 64.89 m during the three-month study period. To examine the differences between the estimations and the observations derived from the conventional Sentinel-1 data, the proposed multi-stage statistical-driven scheme is used. A statistically significant positive relationship between two depicted SCEs with the presence of heteroscedasticity is confirmed. In detail, the agreement between two SCEs increases with the magnitude of the SCE, indicating that the microsatellite SAR can depict more trivial changes in coastline positions. Founded on the results and detailed discussion on the uniqueness and limitations of current SAR sensors, the promising opportunity to utilize the blooming microsatellite SAR datasets for coastal monitoring is highlighted

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments

Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique

Traditional studies on mapping wet snow cover extent (SCE) often feature limitations, especially in vegetated and mountainous areas. The aim of this study is to propose a new total and wet SCE mapping strategy based on freely accessible spaceborne synthetic aperture radar (SAR) data. The approach is transferable on a global scale as well as for different land cover types (including densely vegetated forest and agricultural regions), and is based on the use of backscattering coefficient, interferometric SAR coherence, and polarimetric parameters. Furthermore, four topographical factors were included in the simple tuning of random forest-based land cover type-dependent classification strategy. Results showed the classification accuracy was above 0.75, with an F-measure higher than 0.70, in all five selected regions of interest located around globally distributed mountain ranges. Whilst excluding forest-type land cover classes, the accuracy and F-measure increases to 0.80 and 0.75. In cross-location model set, the accuracy can also be maintained at 0.80 with non-forest accuracy up to 0.85. It has been found that the elevation and polarimetric parameters are the most critical factors, and that the quality of land cover information would also affect the subsequent mapping reliability. In conclusion, through comprehensive validation using optical satellite and in-situ data, our land cover-dependent total SCE mapping approach has been confirmed to be robustly applicable, and the holistic SCE map for different months were eventually derived

Automatic Monitoring of Oil Tank 3D Geometry and Storage Changes With Interferometric Coherence and SAR Intensity Information

Continuous monitoring of oil tanks is vital for analyzing local fuel consumption. Synthetic aperture radar (SAR) has been a popular data source as it guarantees day-and-night and all-weather sensing capacity. However, most earlier studies adopt a scene-wise and oil tank-wise scheme, which is inefficient as there can be hundreds of oil tanks on an oil depot, while only a few are dynamic. Also, no study explores both intensity coherence and interferometric coherence for oil tank dynamics mapping. This article proposes a novel three-stage strategy to detect all oil tanks, identify dynamic oil tanks, and estimate their fuel volume changes based on both the intensity and phase information of SAR in both slant-range and geocoded projections. Results indicate that the intensity coherence can perfectly differentiate dynamic and stable oil tanks (a Jeffries–Matusita distance of 1.997) and is less vulnerable to repeat-pass SAR factors, such as baselines and atmospheric conditions. Via evaluating estimations’ consistency, our scattering keypoint detection exhibits 0.23 and 0.87 m precision of tank heights and diameters, respectively. By validation with ground truth data, oil tanks exhibiting floating-roof changes larger than 0.23 m are correctly identified. Also, the estimated storage changes agree well with actual changes with an R-squared value of 0.98 and a root-mean-square error corresponding to 1.05 m biases in floating-roof heights. These quantitative assessments confirm the robustness and broad applicability of our non-in situ data-needed approach, highlighting the opportunity to utilize spotlight SAR data to automatically and comprehensively monitor oil tank dynamics in remote sites