Snow water equivalent (SWE) measurements are critical for water resource management, yet existing remote sensing methods struggle to provide accurate, large-scale estimates. This study introduces a multisensor approach combining optical snow cover data and L-band interferometric synthetic aperture radar (InSAR) to enhance SWE retrievals. By analyzing multiple optical snow cover datasets in conjunction with UAVSAR L-band InSAR data, the researchers demonstrate how these combined technologies can effectively address the uncertainties in snow measurement, improving the accuracy of SWE change estimations over snow-covered regions.
Current snow monitoring techniques, particularly those relying on satellite remote sensing, face challenges in accurately measuring snow water equivalent (SWE), especially in mountain environments where snow dynamics are complex. While radar-based L-band interferometric synthetic aperture radar (InSAR) has shown promise for SWE retrieval, its accuracy is often compromised by varying snow cover data, which can affect the interpretation of radar signals. This research explores the integration of optical snow cover data with radar-based measurements to refine SWE estimations. The study highlights the importance of choosing appropriate snow cover products and their influence on the overall SWE retrieval process.
The study (DOI: 10.34133/remotesensing.0682) , published in Journal of Remote Sensing on July 3, 2025, examines the impact of optical snow cover data on the accuracy of L-band InSAR-based SWE retrievals. Conducted by NASA's Hydrological Sciences Laboratory and other institutions, the research employs a variety of satellite-derived snow cover products, including Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat, to evaluate their effect on InSAR-based SWE change measurements over the Sierra Nevada Mountains during NASA's SnowEx 2020 campaign. The findings offer new insights into the combined use of optical and radar data for snow monitoring.
The research evaluated six optical snow cover products, including MODIS, VIIRS, Landsat, and a fused Landsat-MODIS product, to assess their impact on L-band InSAR-derived SWE change retrievals. The study was conducted over the Sierra Nevada Mountains using airborne UAVSAR L-band InSAR data from the NASA SnowEx 2020 campaign. The researchers performed a moving window analysis to quantify the variability in SWE estimates induced by different snow cover products. The results revealed that products based on MODIS and VIIRS provided SWE retrievals comparable to more complex spectral unmixing methods, while Landsat-derived snow cover data showed significant discrepancies in SWE estimates due to differences in canopy cover corrections. By comparing these datasets with a western US snow reanalysis product, the team identified the potential sources of uncertainty in L-band InSAR SWE retrievals, such as subcanopy snow detection and atmospheric phase delays. These findings emphasize the need for careful selection of snow cover data in multisensor approaches for future satellite-based SWE monitoring, particularly with the upcoming NISAR mission.
Dr. Jack Tarricone, lead researcher from NASA's Hydrological Sciences Laboratory, commented, "This study underscores the critical importance of selecting the right snow cover data for accurate SWE retrievals. Our findings show that combining optical and radar data can significantly improve the precision of SWE measurements, which are essential for water resource management in snow-dependent regions. As spaceborne radar missions like NISAR prepare for launch, this research provides valuable insights into optimizing SWE estimation techniques for broader applications."
The implications of this research are far-reaching for hydrology, climate monitoring, and water resource management. By refining the methods used to estimate SWE from space, this study offers a pathway to more accurate and timely snowpack measurements, which are essential for managing water resources in regions heavily dependent on snowmelt. The integration of optical and radar data could enhance near-real-time monitoring, facilitating better predictions for flood forecasting, drought management, and climate change analysis. As new satellite missions like NISAR come online, this multisensor approach will be pivotal in ensuring that SWE retrievals are both reliable and globally applicable.
