Summary Results from:

MODIS Terra Collection 6 fractional snow cover validation in mountainous terrain during spring snowmelt using Landsat TM and ETM+
As they relate to the validation of MOD10

Authors: Crawford, C.J.

Source: Hydrological Processes, Vol. 29, No. 1, 12-138, 2015

Link to: Access Publication


Daily swath MODIS Terra Collection 6 fractional snow cover (MOD10_L2) estimates were validated with two-day Landsat TM/ETM + snow-covered area estimates across central Idaho and southwestern Montana, USA. Snow cover maps during spring snowmelt for 2000, 2001, 2002, 2003, 2005, 2007, and 2009 were compared between MODIS Terra and Landsat TM/ETM + using least-squared regression. Strong spatial and temporal map agreement was found between MODIS Terra fractional snow cover and Landsat TM/ETM + snow-covered area, although map disagreement was observed for two validation dates. High-altitude cirrus cloud contamination during low snow conditions as well as late season transient snowfall resulted in map disagreement. MODIS Terra's spatial resolution limits retrieval of thin-patchy snow cover, especially during partially cloudy conditions. Landsat's image acquisition frequency can introduce difficulty when discriminating between transient and resident mountain snow cover. Furthermore, transient snowfall later in the snowmelt season, which is a stochastic accumulation event that does not usually persist beyond the daily timescale, will skew decadal snow-covered area variability if bi-monthly climate data record development is the objective. As a quality control step, ground-based daily snow telemetry snow-water-equivalent measurements can be used to verify transient snowfall events. Users of daily MODIS Terra fractional snow products should be aware that local solar illumination and sensor viewing geometry might influence fractional snow cover estimation in mountainous terrain. Cross-sensor interoperability has been confirmed between MODIS Terra and Landsat TM/ETM + when mapping snow from the visible/infrared spectrum. This relationship is strong and supports operational multi-sensor snow cover mapping, specifically climate data record development to expand cryosphere, climate, and hydrological science applications.