Canopy Adjustment and Improved Cloud Detection for Remotely Sensed Snow Cover Mapping
Maps of snow cover serve as early indicators for hydrologic forecasts and as inputs to hydrologic models that inform water management strategies. Advances in snow cover mapping have led to increasing accuracy, but unsatisfactory treatment of vegetation's interference when mapping snow has led to maps that have limited utility for water forecasting. Vegetation affects snow mapping because ground surfaces not visible to the satellite produce uncertainty as to whether the ground is snow covered. At nadir, the forest canopy obscures the satellite view below the canopy. At oblique viewing angles, the forest floor is obscured by both the canopy and the projection of tree profiles onto the forest floor. We present a canopy correction method based on Moderate Resolution Imaging Spectroradiometer satellite imagery validated with field observations that mitigates geometric and georegistration issues associated with changing satellite acquisition angles in forested areas. The largest effect from a variable viewing zenith angle on the viewable gap fraction in forested areas occurs in moderately forested areas with 30–40% tree canopy coverage. Cloud cover frequently causes errors in snow identification, with some clouds identified as snow and some snow identified as cloud. A snow-cloud identification method utilizes a time series of fractional vegetation and rock land-surface data to flag snow-cloud identification errors and improve snow-map accuracy reducing bias by 20% over previous methods. Together, these contributions to snow-mapping techniques could advance hydrologic forecasting in forested, snow-dominated basins that comprise an estimated one fifth of Northern Hemisphere snow-covered areas.