SPIReS-MODIS-ParBal snow water equivalent reconstruction: Western USA, water years 2001–2021

Given the tradeoffs between spatial and temporal resolution, questions
about resolution optimality are fundamental to the study of global snow.
Answers to these questions will inform future scientific priorities and
mission specifications. Heterogeneity of mountain snowpacks drives a need
for daily snow cover mapping at the slope scale (≤ 30 m) that is unmet for
a variety of scientific users, ranging from hydrologists to the military
to wildlife biologists. But finer spatial resolution usually requires
coarser temporal or spectral resolution. Thus, no single sensor can meet
all these needs. Recently, constellations of satellites and fusion
techniques have made noteworthy progress. The efficacy of two such recent
advances is examined: 1) a fused MODIS - Landsat product with daily 30 m
spatial resolution; and 2) a harmonized Landsat 8 - Sentinel 2A/B (HLS)
product with 2–3-day temporal and 30-m spatial resolution. State-of-art
spectral unmixing techniques are applied to surface reflectance products
from 1 & 2 to create snow cover and albedo maps. Then an energy
balance model was run to reconstruct snow water equivalent (SWE). For
validation, lidar-based Airborne Snow Observatory SWE estimates were used.
Results show that reconstructed SWE forced with 30 m resolution snow cover
has lower bias, a measure of basin-wide accuracy, than the baseline case
using MODIS (463 m cell size), but higher mean absolute error, a measure
of per-pixel accuracy. However, the differences in errors may be within
uncertainties from scaling artifacts e.g., basin boundary delineation.
Other explanations are 1) the importance of daily acquisitions and 2) the
limitations of downscaled forcings for reconstruction. Conclusions are: 1)
spectrally unmixed snow cover and snow albedo from MODIS continue to
provide accurate forcings for snow models; and 2) finer spatial and
temporal resolution through sensor design, fusion techniques, and
satellite constellations are the future for Earth observations.