Optimizing InSAR and optical data fusion for tracking changes in snow water equivalent

No remote sensing technique can continually measure snow water equivalent (SWE) from space for mountain hydrologic applications. Optical sensors from Terra MODIS, Landsat OLI, and Sentinel-2A/B MSI are robust for measuring fractional snow-covered area (fSCA) at various spatial and temporal resolutions. Optical sensors are limited by cloud cover and do not provide information on SWE. Synthetic aperture radar (SAR) techniques can quantify SWE information directly, penetrate clouds, but cannot discriminate between snow-free and snow-covered areas. Addressing the SWE monitoring challenge will require a multi-sensor fusion approach that leverages the strengths of both optical and radar sensors. L-band Interferometric Synthetic Aperture Radar (InSAR) has shown promise for mapping SWE changes using multiple overpasses. With the planned launch of NASA-ISRO SAR (NISAR) and its 12-day repeat L-band InSAR, there is renewed interest in this technique. We first demonstrate the value of optical and radar data fusion by comparing SAR imagery with and without fSCA masks. We then examine the utility of various fSCA products based on their temporal and spatial resolutions. To do this, we analyze three SnowEx 2020 UAVSAR flight lines over the Sierra Nevada Mountains, CA, and fSCA products from MODIS, Landsat, Harmonized Landsat-Sentinel (HLS), and a machine learning fused Landsat/MODIS (FLM). The frequency of the daily temporal resolution from MODIS has benefits, as does the spatial resolution of both Landsat and HLS. The high spatial resolution is ideal, but cloud-free satellite overpasses don't always coincide with UAVSAR and future NISAR acquisitions. FLM combines the strengths of the various sensors providing the desired spatial and temporal resolution. We also consider the impact of spatial resolution and fractionally covered pixels on SWE retrievals. Developing near-real-time FLM data for a NISAR SWE change data product would support future needs.