Estimating Snow Accumulation and Ablation with L-Band Interferometric Synthetic Aperture Radar (InSAR)

Snow is a critical water resource for the western United States and many regions across the globe. However, our ability to accurately measure and monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge. To confront these challenges, NASA initiated the SnowEx program, a multiyear effort to address knowledge gaps in snow remote sensing. During SnowEx 2020, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) team acquired an L-band interferometric synthetic aperture radar (InSAR) data time series to evaluate the capabilities and limitations of repeat-pass L-band InSAR for tracking changes in snow water equivalent (SWE). The goal was to develop a more comprehensive understanding of where and when L-band InSAR can provide SWE change estimates, allowing the snow community to leverage the upcoming NASA–ISRO (NASA–Indian Space Research Organization) SAR (NISAR) mission. Our study analyzed three InSAR image pairs from the Jemez Mountains, NM, between 12 and 26 February 2020. We developed a snow-focused multi-sensor method that uses UAVSAR InSAR data synergistically with optical fractional snow-covered area (fSCA) information. Combining these two remote sensing datasets allows for atmospheric correction and delineation of snow-covered pixels within the radar swath. For all InSAR pairs, we converted phase change values to SWE change estimates between the three acquisition dates. We then evaluated InSAR-derived retrievals using a combination of fSCA, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). The results of this study show that repeat-pass L-band InSAR is effective for estimating both snow accumulation and ablation with the proper measurement timing, reference phase, and snowpack conditions

Advancing the Monitoring Capabilities of Mountain Snowpack Fluctuations at Various Spatial and Temporal Scales

Snow is a critical water resource for the western US and many regions across the globe. However, our ability to accurately monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge in mountain regions. No single sensor currently has the ability to directly measure snow water equivalent (SWE) from space at a spatial scale suitable for water supply forecasting in mountain environments. This knowledge gap calls for the innovative use of remote sensing techniques, computational tools, and data science methods to advance our ability to estimate mountain snowpacks across a range of spatial and temporal scales. The goal of this dissertation is to advance our capabilities for understanding snowpack across watershed-relevant spatial and temporal scales. Two research approaches were used to accomplish this goal: quantifying the physiographic controls and sensitivities of hydrologically important snow metrics and progressing our ability to use L-band interferometric synthetic aperture radar (InSAR) to measure SWE changes. First, we quantify the physiographic controls and various snowpack metrics in the Sierra Nevada using a novel gridded SWE reanalysis dataset. Such work demonstrates the complexity of snowpack processes and the need for fine-resolution snowpack information. Next, using L-band Interferometric Synthetic Aperture Radar (InSAR) from the NASA SnowEx campaign, both snow ablation and accumulation are estimated in the Jemez Mountains, NM. The radar-derived retrievals are evaluated utilizing a combination of optical snow-cover data, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). Lastly, we compare multisensor optical-radar approaches for SWE retrievals and find that moderate-resolution legacy satellite products provide sufficient results. The results of this work show that L-band InSAR is a suitable technique for global SWE monitoring when used synergistically with optical SCA data and snowpack modeling. While two distinctive methods are present in this research, they both work towards advancing our ability to understand the dynamics of mountain snowpack

Snow Water Equivalent Retrieval Over Idaho – Part 2: Using L-Band UAVSAR Repeat-Pass Interferometry

This study evaluates using interferometry on low-frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 d temporal baseline, captured by an L-band aerial platform, NASA\u27s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved measurements shows a strong Pearson correlation (R = 0.80) and low RMSE (0.1 m, n = 64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R = 0.52, n = 57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R = 0.60, RMSD = 0.023 m, n = 3.2 × 106) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R = 0.72, RMSD = 0.013 m, n = 6.5 × 104). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy

Snow Water Equivalent Retrieval Over Idaho – Part 1: Using Sentinel-1 Repeat-Pass Interferometry

Snow water equivalent (SWE) is identified as the key element of the snowpack that impacts rivers\u27 streamflow and water cycle. Both active and passive microwave remote sensing methods have been used to retrieve SWE, but there does not currently exist a SWE product that provides useful estimates in mountainous terrain. Active sensors provide higher-resolution observations, but the suitable radar frequencies and temporal repeat intervals have not been available until recently. Interferometric synthetic aperture radar (InSAR) has been shown to have the potential to estimate SWE change. In this study, we apply this technique to a long time series of 6 d temporal repeat Sentinel-1 C-band data from the 2020–2021 winter. The retrievals show statistically significant correlations both temporally and spatially with independent in situ measurements of SWE. The SWE change measurements vary between −5.3 and 9.4 cm over the entire time series and all the in situ stations. The Pearson correlation and RMSE between retrieved SWE change observations and in situ stations measurements are 0.8 and 0.93 cm, respectively. The total retrieved SWE in the entire 2020–2021 time series shows an SWE error of less than 2 cm for the nine in situ stations in the scene. Additionally, the retrieved SWE using Sentinel-1 data is well correlated with lidar snow depth data, with correlation of more than 0.47. Low temporal coherence is identified as the main reason for degrading the performance of SWE retrieval using InSAR data. We also show that the performance of the phase unwrapping algorithm degrades in regions with low temporal coherence. A higher frequency such as L-band improves the temporal coherence and SWE ambiguity. SWE retrieval using C-band Sentinel-1 data is shown to be successful, but faster revisit is required to avoid low temporal coherence. Global SWE retrieval using radar interferometry will have a great opportunity with the upcoming L-band 12 d repeat-pass NASA-ISRO Synthetic Aperture Radar (NISAR) data and the future 6 d repeat-pass Radar Observing System for Europe in L-band (ROSE-L) data

Applications of SAR Interferometry in Earth and Environmental Science Research

This paper provides a review of the progress in regard to the InSAR remote sensing technique and its applications in earth and environmental sciences, especially in the past decade. Basic principles, factors, limits, InSAR sensors, available software packages for the generation of InSAR interferograms were summarized to support future applications. Emphasis was placed on the applications of InSAR in seismology, volcanology, land subsidence/uplift, landslide, glaciology, hydrology, and forestry sciences. It ends with a discussion of future research directions

Unrest at Domuyo Volcano, Argentina, detected by geophysical and geodetic data and morphometric analysis

New volcanic unrest has been detected in the Domuyo Volcanic Center (DVC), to the east of the Andes Southern Volcanic Zone in Argentina. To better understand this activity, we investigated new seismic monitoring data, gravimetric and magnetic campaign data, and interferometric synthetic aperture radar (InSAR) deformation maps, and we derived an image of the magma plumbing system and the likely source of the unrest episode. Seismic events recorded during 2017-2018 nucleate beneath the southwestern flank of the DVC. Ground deformation maps derived from InSAR processing of Sentinel-1 data exhibit an inflation area exceeding 300 km2, from 2014 to at least March 2018, which can be explained by an inflating sill model located 7 km deep. The Bouguer anomaly reveals a negative density contrast of ~35 km wavelength, which is spatially coincident with the InSAR pattern. Our 3D density modeling suggests a body approximately 4-6 km deep with a density contrast of -550 kg/m3. Therefore, the geophysical and geodetic data allow identification of the plumbing system that is subject to inflation at these shallow crustal depths. We compared the presence and dimensions of the inferred doming area to the drainage patterns of the area, which support long-established incremental uplift according to morphometric analysis. Future studies will allow us to investigate further whether the new unrest is hydrothermal or magmatic in origin.Fil: Astort, Ana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Estudios Andinos "Don Pablo Groeber". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Estudios Andinos "Don Pablo Groeber"; ArgentinaFil: Walter, Thomas R. German Research Centre for Geosciences; AlemaniaFil: Ruiz, Francisco. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto Geofísico Sismológico Volponi; ArgentinaFil: Sagripanti, Lucía. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Estudios Andinos "Don Pablo Groeber". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Estudios Andinos "Don Pablo Groeber"; ArgentinaFil: Nacif, Andres Antonio. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto Geofísico Sismológico Volponi; ArgentinaFil: Acosta, Gemma. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto Geofísico Sismológico Volponi; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Folguera Telichevsky, Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Estudios Andinos "Don Pablo Groeber". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Estudios Andinos "Don Pablo Groeber"; Argentin

Long-term monitoring of geodynamic surface deformation using SAR interferometry

Thesis (Ph.D.) University of Alaska Fairbanks, 2014Synthetic Aperture Radar Interferometry (InSAR) is a powerful tool to measure surface deformation and is well suited for surveying active volcanoes using historical and existing satellites. However, the value and applicability of InSAR for geodynamic monitoring problems is limited by the influence of temporal decorrelation and electromagnetic path delay variations in the atmosphere, both of which reduce the sensitivity and accuracy of the technique. The aim of this PhD thesis research is: how to optimize the quantity and quality of deformation signals extracted from InSAR stacks that contain only a low number of images in order to facilitate volcano monitoring and the study of their geophysical signatures. In particular, the focus is on methods of mitigating atmospheric artifacts in interferograms by combining time-series InSAR techniques and external atmospheric delay maps derived by Numerical Weather Prediction (NWP) models. In the first chapter of the thesis, the potential of the NWP Weather Research & Forecasting (WRF) model for InSAR data correction has been studied extensively. Forecasted atmospheric delays derived from operational High Resolution Rapid Refresh for the Alaska region (HRRRAK) products have been compared to radiosonding measurements in the first chapter. The result suggests that the HRRR-AK operational products are a good data source for correcting atmospheric delays in spaceborne geodetic radar observations, if the geophysical signal to be observed is larger than 20 mm. In the second chapter, an advanced method for integrating NWP products into the time series InSAR workflow is developed. The efficiency of the algorithm is tested via simulated data experiments, which demonstrate the method outperforms other more conventional methods. In Chapter 3, a geophysical case study is performed by applying the developed algorithm to the active volcanoes of Unimak Island Alaska (Westdahl, Fisher and Shishaldin) for long term volcano deformation monitoring. The volcano source location at Westdahl is determined to be approx. 7 km below sea level and approx. 3.5 km north of the Westdahl peak. This study demonstrates that Fisher caldera has had continuous subsidence over more than 10 years and there is no evident deformation signal around Shishaldin peak.Chapter 1. Performance of the High Resolution Atmospheric Model HRRR-AK for Correcting Geodetic Observations from Spaceborne Radars -- Chapter 2. Robust atmospheric filtering of InSAR data based on numerical weather prediction models -- Chapter 3. Subtle motion long term monitoring of Unimak Island from 2003 to 2010 by advanced time series SAR interferometry -- Chapter 4. Conclusion and future work

Modeling of Subsurface Scattering from Ice Sheets for Pol-InSAR Applications

Remote sensing is a fundamental tool to measure the dynamics of ice sheets and provides valuable information for ice sheet projections under a changing climate. There is, however, the potential to further reduce the uncertainties in these projections by developing innovative remote sensing methods. One of these remote sensing techniques, the polarimetric synthetic aperture radar interferometry (Pol-InSAR), is known since decades to have the potential to assess the geophysical properties below the surface of ice sheets, because of the penetration of microwave signals into dry snow, firn, and ice. Despite this, only very few studies have addressed this topic and the development of robust Pol-InSAR applications is at an early stage. Two potential Pol-InSAR applications are identified as the motivation for this thesis. First, the estimation and compensation of the penetration bias in digital elevation models derived with SAR interferometry. This bias can lead to errors of several meters or even tens of meters in surface elevation measurements. Second, the estimation of geophysical properties of the subsurface of glaciers and ice sheets using Pol-InSAR techniques. There is indeed potential to derive information about melt-refreeze processes within the firn, which are related to density and affect the mass balance. Such Pol-InSAR applications can be a valuable information source with the potential for monthly ice sheet wide coverage and high spatial resolution provided by the next generation of SAR satellites. However, the required models to link the Pol-InSAR measurements to the subsurface properties are not yet established. The aim of this thesis is to improve the modeling of the vertical backscattering distribution in the subsurface of ice sheets and its effect on polarimetric interferometric SAR measurements at different frequencies. In order to achieve this, polarimetric interferometric multi-baseline SAR data at different frequencies and from two different test sites on the Greenland ice sheet are investigated. This thesis contributes with three concepts to a better understanding and to a more accurate modeling of the vertical backscattering distribution in the subsurface of ice sheets. First, the integration of scattering from distinct subsurface layers. These are formed by refrozen melt water in the upper percolation zone and cause an interesting coherence undulation pattern, which cannot be explained with previously existing models. This represents a first link between Pol-InSAR data and geophysical subsurface properties. The second step is the improved modeling of the general vertical backscattering distribution of the subsurface volume. The advantages of more flexible volume models are demonstrated, but interestingly, the simple modification of a previously existing model with a vertical shift parameter lead to the best agreement between model and data. The third contribution is the model based compensation of the penetration bias, which is experimentally validated. At the investigated test sites, it becomes evident that the model based estimates of the surface elevations are more accurate than the interferometric phase center locations, which are conventionally used to derive surface elevations of ice sheets. This thesis therefore improves the state of the art of subsurface scattering modeling for Pol-InSAR applications, demonstrates the model-based penetration bias compensation, and makes a further research step towards the retrieval of geophysical subsurface information with Pol-InSAR

Assessment of Snow Status Changes Using L-HH Temporal-Coherence Components at Mt. Dagu, China

Multitemporal Phased Array type L-band Synthetic Aperture Radar (PALSAR) horizontally transmitted and horizontally received (HH) coherence data was decomposed into temporal-coherence, spatial-coherence, and thermal noise components. The multitemporal data spanned between February and May of 2008, and consisted of two pairs of interferometric SAR (InSAR) images formed by consecutive repeat passes. With the analysis of ancillary data, a snow increase process and a snow decrease process were determined. Then, the multiple temporal-coherence components were used to study the variation of thawing and freezing statuses of snow because the components can mostly reflect the temporal change of the snow that occurred between two data acquisitions. Compared with snow mapping results derived from optical images, the outcomes from the snow increase process and the snow decrease process reached an overall accuracy of 71.3% and 79.5%, respectively. Being capable of delineating not only the areas with or without snow cover but also status changes among no-snow, wet snow, and dry snow, we have developed a critical means to assess the water resource in alpine areas