How do forests affect both snow properties and the detection of snow properties by remote sensing? What forest metrics are most relevant to snow remote sensing and at what scales? What are optimal…

This is a $368,554.00 grant award from the National Aeronautics and Space Administration (NASA) to the Board of Regents of the Nevada System of Higher Education, operating through the University of Nevada, Reno (UNR). The award is focused on understanding the effects of forests on snow properties and the detection of snow properties by remote sensing, with the goal of improving snow hydrology modeling and remote sensing capabilities in forested regions.

The award does not have a set-aside designation, indicating UNR's competitive capabilities to secure this grant based on its expertise in environmental sciences, climate studies, and remote sensing research. As a 1862 Land-Grant College and state-controlled institution of higher learning, UNR has a strong track record of executing federal grants and contracts across a range of scientific and technical domains. The State of Nevada's Office of the Governor oversees the implementation of these federal awards through its subsidiary state agencies and institutions, demonstrating the state's capacity to support national priorities through specialized services and research.

How do forests affect both snow properties and the detection of snow properties by remote sensing? What forest metrics are most relevant to snow remote sensing and at what scales? What are optimal synergistic combinations of remote sensing technologies for mapping and characterizing snow in forested regions? In what ways can snow hydrology models be used to augment remote sensing capabilities where detection of snow properties is limited? These are the motivating science questions for snow remote sensing in forested regions with applications to terrestrial hydrology and land surface modeling. At present, we lack the remote sensing capacity to consistently produce accurate estimates of key snow properties such as snow-covered area (SCA), snow albedo, snow water equivalent (SWE), snowmelt status, and snow grain size in forested ecosystems. Forests obscure snow from satellite observations and concurrently modify snowpack properties. Forests modify snow accumulation through canopy interception and also influence snowpack evolution by reducing wind speed and incoming shortwave radiation, radiating longwave energy, and decreasing snow albedo. If we are to effectively detect and characterize snow in forested regions, we must first understand the abilities and limitations of optical and microwave remote sensing instruments in forest environments. Furthermore, we must examine and evaluate the synergistic capabilities of snowpack models to supply key information needed by algorithms to determine SCA, SWE etc. From optical and microwave instruments. As a member of the science definition team for the next generation clpx, nolin will focus on these snow-forest questions, working closely with other team members to identify needs, capabilities, and uncertainties in snow remote sensing in forested regions.