A Review of Interferometric Synthetic Aperture Radar (InSAR) and Phase-Based Techniques for Monitoring Seasonal Snow

2025-11-25 · 1 citations

Radar phase-based techniques for measuring snow water equivalent (SWE) and snow depth, such as Interferometric Synthetic Aperture Radar (InSAR), have advanced substantially over the past 25 years. Progress has been made across a wide range of frequencies (P-, L-, C-, X-, and Ku-band) and platforms (tower, airborne, and satellite), with new methods including Signals of Opportunity (SoOp) passive bistatic radar or reflectometry further broadening the potential for snow monitoring. These phase-based approaches offer unique advantages for addressing long-standing gaps in snow remote sensing and have the potential to improve basin-scale hydrologic understanding. This review synthesizes current understanding of phase-based snow measurements, highlights key successes and limitations, and identifies knowledge gaps that must be addressed for operational monitoring. The NASA–ISRO Synthetic Aperture Radar (NISAR) mission provides near-global L-band InSAR observations relevant to snow applications; broader progress will depend on continued methodological development, robust validation, and integration with other remote sensing and modeling approaches. Our review supports the continued development and testing of phase-based and InSAR methods. If these methods reach their full potential, InSAR-capable satellites will be an integral component of any global snow measurement strategy.

Cloud-native geospatial data cube workflows with open-source tools

Journal of Open Source Education · 2025-07-04

This work contains educational modules designed to reduce barriers to interacting with large, complex, cloud-hosted remote sensing datasets using open-source computational tools and software.The goal of these materials is to demonstrate and promote the rigorous investigation of n-dimensional multi-sensor satellite imagery datasets through scientific programming.These tutorials feature publicly available satellite imagery with global coverage and commonly used sensors such as optical and synthetic aperture radar data with different levels of processing.We include thorough discussions of specific data formats and demonstrate access patterns for two popular cloud infrastructure platforms (Amazon Web Services and Microsoft Planetary Computer) as well as public cloud computational resources for remote sensing data processing at Alaska Satellite Facility (ASF).

Introducing Glaciohydrological Model Calibration Using Sentinel‐1 SAR Wet Snow Maps in the Himalaya‐Karakoram

Water Resources Research · 2025-11-25

Abstract Field‐based studies are limited in Himalaya‐Karakoram (HK); therefore, remote sensing and glaciohydrological modeling provide alternative solutions to investigate runoff evolution under changing climate conditions. Due to limited in situ runoff data in HK, glaciohydrological models are often calibrated using high‐resolution remote sensing data. This study introduces the calibration of the glaciohydrological model Spatial Processes in Hydrology (SPHY), at glacier catchment‐scale over 2000–2023 using satellite‐based Sentinel‐1 Synthetic Aperture Radar (SAR) wet snow maps, along with available geodetic mass balance estimates in the HK region. The selected calibrated model parameters are validated against in situ runoff data to test the robustness of satellite‐based calibration for Chhota Shigri Glacier (CSG), Dokriani Bamak Glacier (DBG), and Gangotri Glacier System (GGS) catchments in HK. The SPHY modeled and in situ catchment‐wide runoff estimates show good agreement. The Sentinel‐1 SAR‐derived wet snow percentage area shows strong spatial and temporal variability from 2015 to 2023. The mean annual runoff is 1.79 ± 0.15 m 3 s −1 , 1.63 ± 0.09 m 3 s −1 and 39.40 ± 3.15 m 3 s −1 over 2000–2023 for CSG, DBG and GGS catchments, respectively. Maximum annual runoff occurred in 2021/2022, mainly due to heatwaves in early spring/summer 2022. Snowmelt runoff is highest in CSG (61%) and GGS (49%), while rainfall‐runoff dominates in DBG (42%). Satellite‐based glaciohydrological model calibration offers a unique opportunity to improve runoff estimates for glacierized catchments in data‐sparse regions. Applying present study to glacierized catchments lacking in situ runoff data will strengthen our past, present, and future glaciohydrological understanding of regions such as HK and Andes.

KINEMATICS AND DEBRIS THICKNESS OF TIMPANOGOS ROCK GLACIER, UTAH USING InSAR AND ELECTRICAL RESISTIVITY TOMOGRAPHY

Abstracts with programs - Geological Society of America · 2025-01-01

An ensemble machine learning approach for filling voids in surface elevation change maps over glacier surfaces

2025-11-18

Abstract. Glacier mass balance assessments in mountainous regions often rely on digital elevation models (DEMs) to estimate surface elevation change. However, these DEMs are prone to spatial data voids, particularly during historical reconstructions using older imagery. These voids, which are most common in glacier accumulation zones, introduce uncertainty into estimates of glacier mass balance and surface elevation change. Traditional void-filling methods, such as constant and hypsometric interpolation, have limitations in capturing spatial variability in elevation change. This study introduces a machine-learning- based approach using gradient-boosted tree regression (XGBoost) to estimate glacier surface-elevation change across voids. High Mountain Asia (HMA) is an ideal study area for assessing the accuracy of different void-filling approaches across glaciers with varying morphology and climatic settings. We compare XGBoost predictions to traditional void-filling methods across the Western and Eastern Himalayas using a dataset of DEM-derived elevation changes. Results indicate that XGBoost consistently outperforms simpler methods, reducing root mean square error (RMSE) and mean absolute error (MAE) while improving alignment with observed elevation changes. The study highlights the advantages of integrating multiple glaciological and topographic predictors, demonstrating the potential of machine learning to improve assessments of glacier mass balance and elevation change. Future research should explore additional predictors, such as climate data, to further enhance predictive accuracy.

Assessing C-Band InSAR Coherence Data to Monitor Landscape Change at the Bonneville Salt Flats, Utah

IEEE Transactions on Geoscience and Remote Sensing · 2024-01-01 · 1 citations

The Bonneville Salt Flats (BSF) of Utah, with a notable history in land-speed racing and resource extraction, presents a dynamic landscape shaped by both natural processes and anthropogenic activities. This study leverages interferometric synthetic aperture radar (InSAR) techniques to monitor the BSF, focusing on the potential of coherence as an indicator of surface changes. A total of 165 Sentinel-1 A&B coherence images, with perpendicular baselines under 100 m and temporal baselines of 12, 24, and 36 days, were analyzed using spatially and temporally reduced statistics, along with median absolute deviation (MAD) variance. The findings demonstrate consistently high coherence (>0.90) during dry conditions, contrasted by significantly lower coherence (<0.40) during periods of precipitation and flooding. Coherence showed moderate reduction (<0.70) in zones of recreational activities (driving and racing), highlighting the relative impact of land-use activities on the salt crust. Coherence observations indicate a minimal difference during periods overlapping with and following official racing events compared to periods of racetrack preparation and general recreational use. Zones with the highest variability are linked to disturbances from recreation and resource extraction, while areas with minimal variability correspond to regions with thicker halite crust. The data also revealed that large precipitation events significantly influence coherence, with a clear power-law decrease in mean coherence as precipitation and floodwater accumulation increased. This research not only validates the use of coherence for monitoring environmental evolution and the influences of anthropogenic activities at the BSF but also supports the potential of future InSAR applications for accurate phase assessments during dry periods.

Use of mobile L-Band interferometric synthetic aperture radar observations to inform snow property estimation

2024-03-11

With the dawn of future L-Band satellite interferometric missions (e.g., NISAR - NASA/ISRO SAR and ESA ROSE-L) upon us, there are unique opportunities to explore the use of radar methods and techniques across a variety of applications. Moreover, through the advancement of radar remote sensing hardare and software, additional opportunities exist to specifically target and explore the development of snow estimation, snowmelt impact, and resulting soil moisture detection applications. With the development of mobile interferometric synthetic aperture (InSAR) hardware and software solutions, we present findings from field campaigns using a multi-polarization L-band (1.6 GHz) InSAR system (Gamma Remote Sensing) deployed from mobile vehicle (car), unmanned aerial vehicle (UAV), and helicopter-based platforms. These platforms allow us to control the temporal repeat of InSAR acquisitions assessing the role of changing environmental conditions on InSAR coherence, bracketing synoptic weather events to identify change in the radar signal, as well as simulating the temporal repeat of future satellite missions to estimate what may be done with these data when available. Results from time-series of InSAR acquisitions exploring snow water equivalent estimation, soil moisture, and airborne deployments (e.g., helicopter and UAV) show sensitivity to L-Band coherence and phase for application development. Future work will also be discussed exploring interferometric tomography and bistatic radar applications.

Investigation of Ground-Based Mobile L-Band InSAR Phase Response to the Application of Soil Moisture on a High-Desert Grassland

2023-07-16

In an agricultural river valley in central Idaho, USA, we conducted a case study to investigate interferometric synthetic aperture (InSAR) coherence and phase response as an indication of soil moisture change. Throughout a 3-day observational campaign, repeated observations were acquired from a mobile vehicle with a multi-polarization L-band (1.6 GHz) InSAR system, first prior to controlled irrigation and then during a dry-out period. Here, we present results that show the time series of coherence and phase in coordination with in-situ soil moisture observations at two depths over the three days of the controlled experiment. During the subsequent dry out period, the time series of interferometric coherence shows an immediate degradation of the signal with a subsequent improvement. The in-situ soil moisture observations highlight this transition of controlled irrigation to subsequent drying out of the soils. We anticipate future work to include investigation of the interferometric times series of phase change as it relates to quantitative changes in soil moisture.

ASSESSMENT OF INSAR COHERENCE DATA FOR APPLICATION TO STUDY THE BONNEVILLE SALT FLATS, UTAH: HARNESSING ALASKA SATELLITE FACILITY CLOUD-BASED INSAR DATA PROCESSING

Abstracts with programs - Geological Society of America · 2023-01-01

Constraining Mountain Streamflow Constituents by Integrating Citizen Scientist Acquired Geochemical Samples and Sentinel‐1 SAR Wet Snow Time‐Series for the Shimshal Catchment in the Karakoram Mountains of Pakistan

Water Resources Research · 2023-02-21 · 4 citations

Abstract Upper Indus Basin (UIB) streamflow originates largely from glacier and snow melt in the upstream Himalaya, Karakoram, and Hindu Kush mountain ranges and is extremely vulnerable because of its projected climate changes, dense populations, and hydropolitical tensions. Accurate knowledge of streamflow constituents is required for resilient water resources management; this is precluded by a paucity of measurement as well as climatological and topographic complexity. Here we integrate citizen scientist acquired geochemical samples, collected from October 2018 through September 2019 in the Shimshal watershed of the Karakoram Mountains of Pakistan, with Sentinel‐1 (S1) synthetic aperture radar (SAR)‐derived wet snow maps, to better understand streamflow constituents for the high altitude and heavily glaciated catchment. We use Bayesian end‐member mixture analysis to separate river flows into baseflow and meltwater constituents, using fixed and time‐variant melt end‐member values. We compare river hydrograph separation results with S1 wet snow time series maps for the same timeframe. We then utilize S1 imagery to inform end‐member mixture analysis to separate meltwaters into snow and glacier melt. For the Shimshal catchment, we find that about 85% of annual river flows are derived from snow and glacier melt; 45% of annual flows are derived from snow melt and 40% glacier melt. Engaged and committed citizen scientists enabled geochemical sample collection and analysis on a significant temporal and spatial scale. In the future, co‐produced knowledge that both implements local expertise and that is also planned and utilized by diverse stakeholders may increase climatological awareness and resilience in the UIB.