Synergistic Use of Remote Sensing for Snow Cover and Snow Water Equivalent Estimation
Jonathan Muñoz *
NOAA-Cooperative Remote Sensing Science and Technology Center (NOAA-CREST), City College of New York, 160 Convent Ave, NY 10031.USA.
Jose Infante
NOAA-Cooperative Remote Sensing Science and Technology Center (NOAA-CREST), City College of New York, 160 Convent Ave, NY 10031.USA.
Tarendra Lakhankar
NOAA-Cooperative Remote Sensing Science and Technology Center (NOAA-CREST), City College of New York, 160 Convent Ave, NY 10031.USA.
Reza Khanbilvardi
NOAA-Cooperative Remote Sensing Science and Technology Center (NOAA-CREST), City College of New York, 160 Convent Ave, NY 10031.USA.
Peter Romanov
NOAA-Cooperative Remote Sensing Science and Technology Center (NOAA-CREST), City College of New York, 160 Convent Ave, NY 10031.USA.
Nir Krakauer
NOAA-Cooperative Remote Sensing Science and Technology Center (NOAA-CREST), City College of New York, 160 Convent Ave, NY 10031.USA.
Al Powell
NOAA/NESDIS/Center for Satellite Applications and Research (STAR) 5200 Auth Road, WWB, Camp Springs, MD 20746, USA.
*Author to whom correspondence should be addressed.
Abstract
An increasing number of satellite sensors operating in the optical and microwave spectral bands represent an opportunity for utilizing multi-sensor fusion and data assimilation techniques for improving the estimation of snowpack properties using remote sensing. In this paper, the strength of a synergistic approach of leveraging optical, active and passive microwave remote sensing measurements to estimate snowpack characteristics is discussed and examples from recent work are given. Observations with each type of sensor have specific technical constraints and limitations. Optical sensor data has high spatial resolution but is limited to cloud free days, whereas passive microwave sensors have coarse spatial resolution and are sensitive to multiple snowpack properties. Multi-source and multi-temporal remote sensing data therefore hold great promise for moving the monitoring and analysis of snow toward estimates of a suite of snow properties at high spatial and temporal resolution.
Keywords: Snow, optical, active, passive, microwave, remote Sensing.