Investigating the Impact of Optical Snow Cover Data on L-Band InSAR Snow Water Equivalent Retrievals

No single remote sensing technique can accurately measure snow water equivalent (SWE) from space for mountain hydrology applications. To address this challenge in SWE monitoring, we evaluated a multisensor approach that leverages the strengths of both optical and radar sensors. Our study aims to understand how differences between optically derived snow cover data products propagate variability and uncertainty into interferometric synthetic aperture radar (InSAR) SWE change retrievals. We analyzed 4 airborne InSAR pairs acquired using the Uninhabited Aerial Vehicle Synthetic Aperture Radar flown over the Sierra Nevada, CA, mountains, during the National Aeronautics and Space Administration’s SnowEx 2020 campaign. We computed InSAR-based SWE changes, in combination with 6 different optically derived satellite-based fractional snow-covered area (fSCA) products used to differentiate snow-free and snow-covered terrain. We quantified the volumetric SWE change variability due to the different fSCA products using a moving window analysis and evaluated the results using the Kolmogorov–Smirnov test. Results show that the moderate-resolution (~375- to 500-m) normalized-difference-snow-index-based fSCA products provide SWE change results statistically similar to those from using more complex spectral unmixing and machine learning retrieval methods. This indicates that the readily available snow cover products from the Moderate Resolution Imaging Spectroradiometer and the Visible Infrared Imaging Radiometer Suite are adequate for an optical-radar SWE monitoring approach when there is limited cloud cover. Additionally, we found statistically significant differences between the SWE change results from the Landsat fSCA and all other fSCA data due to canopy cover correction differences. Lastly, we identified potential sources of uncertainty in L-band InSAR SWE retrievals using a western US SWE reanalysis. Future work should focus on understanding how subcanopy snow in forested regions affects the accuracy and variability of snow cover products. Furthermore, near-real-time, high-resolution cloud- and gap-filled snow cover data will be important for supporting water resource decision-making.