Using Machine Learning to Improve Snow Cover Fraction Observations

January 25, 2022

ESSIC/CISESS scientists Soni Yatheendradas and Sujay Kumar are co-authors on a paper in Journal of Hydrometeorology titled, “A novel Machine Learning-based gap-filling of fine-resolution remotely sensed snow cover fraction data by combining downscaling and regression”.

$Figure. The 6 × 6 km2 Howland forest area in Maine: (A) the true color image obtained by the EO-1 Hyperion on March 5, 2014 (DOY 64) at a spatial resolution of 30 m; and maps of (B) snow cover fraction (SNOWCF); (C) surface water cover fraction (WaterBodyCF); (D)soil cover fraction (SOILCF); (E) vegetation cover fraction (VGCF); (F) fAPARcanopy; (G) fAPARchl; (H) fAPARnon-chl.$

An Evergreen Forest Ecosystem from Satellites

November 16, 2021

ESSIC/CISESS Scientist Qingyuan Zhang has a new article to be published in International Journal of Applied Earth Observation and Geoinformation that characterizes the seasonally snow-covered Howland boreal forest ecosystem in Maine, USA with satellite images.

A snow covered hill with snowy pine trees

Snow Cover Product Comparison Article Published

April 2, 2020

Ralph Ferraro was a co-author on an article published in Advances in Meteorology titled “Intercomparison and Validation of MIRS, MSPPS, and IMS Snow Cover Products”.

Tag: snow cover

Using Machine Learning to Improve Snow Cover Fraction Observations

An Evergreen Forest Ecosystem from Satellites

Snow Cover Product Comparison Article Published

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