How Does Radar Interact with Forests: Quantifying Forest Impact on Snow Radar Remote Sensing

Thumbnail Image



Journal Title

Journal ISSN

Volume Title


The Ohio State University

Research Projects

Organizational Units

Journal Issue


Snowpack stores winter precipitation allowing for sufficient streamflow to meet spring and summer demand. Snow is an essential water source and supplies water for drinking and irrigation throughout the year. Climate change has been impacting and will continue to impact snowpack which will have far reaching consequences for food, water, and energy security, the economy, human health, biodiversity, wildfires, and climate change itself. It is more important now than ever to understand snow extent, snow properties such as depth and snow-water equivalent (SWE), and snowmelt dynamics. Despite the need for this data, snow remote sensing is not yet in a place where the questions of how snow might melt and how much water is contained within the snow can be answered. The best idea is a dual frequency dual polarization synthetic aperture radar and radiometer (SWESARR) at X and Ku band frequency to maximize sensitivity to various snow and vegetation properties. SWE calculation is especially difficult in forested area with a biomass density of greater than 100 m3/ha or a cover fraction (Cf) of greater than 30%. Most studies look at the volume scattering extinction of radar by canopies and use radiative transfer models where SWESARR readings are insufficient but neglect signal enhancement before the trees. We examine both enhancement and extinction in forested areas and propose a conceptual model against which to compare our results. We find a geometric enhancement of about 4 dB, an extinction magnitude of around -16 dB, with enhancement occurring at a distance of half the height of the trees and a signal recovery occurring at a distance of two thirds the height of the trees. Algorithms should consider both extinction and enhancement when estimating SWE to maximize accuracy.



Snow Remote Sensing, SWESARR, Forests, Snow-water equivalent, snow