Detecting snow under and within trees with satellite lidar for improved climate and weather modelling

Background
Snow is the largest transient feature of the land surface. It provides drinking water to a significant fraction of the population, affects the weather, and controls plant growth and wildfires fire through water availability. Maps of snow extent are produced by a range of satellites. These are used to drive weather and hydrological forecasting and to test climate models; changing snow extent with temperature is a key metric of the accuracy of climate models' sensitivity (Mudryk et al 2020). Currently these maps are generated by passive remote sensing. Due to the mixing of energy from the ground and plants, these tend to underestimate the extent of snow in forested areas and cannot easily detect snow within trees. Snow that is caught in trees can sublime into the atmosphere, whilst snow under trees is shaded from the sun, changing the hydrology and so these processes are important for accurate forecasts (Ly et al 2019).
A new generation of lidar (laser ranging) satellites can separate out signals from the ground and canopy (Armston et al 2013). This holds the potential to map snow under trees and to estimate how much is held within trees (Russell et al 2020). These new maps could allow step changes in the accuracy of snow in climate and weather predictions. The snow caught in trees is modelled based on very limited data, so having large-scale maps would allow the first detailed test of the impact of snow in trees on weather and hydrology. Accurate maps of snow under trees would allow large scale testing of weather models, which is currently a large uncertainty in weather and climate forecast models.

Experimental plan
Building on work to determine ground and vegetation canopy reflectance from NASA's ICEsat-2 and GEDI satellite lidars (working with the NASA ICESat-2 vegetation product lead), the first step would be to determine whether the satellite lidars can measure ground reflectance accurately enough to predict sub-canopy snow cover. The primary error here is ground finding, and so any novel findings could be used to improve all other lidar data products, including height and biomass. This will be compared against ground cameras and high-resolution satellite images. An accurate method will allow ICESat-2 data to map sub-canopy snow over large areas of the Earth.
The canopy reflectance can be investigated to determine whether it can be used to measure the amount of snow held within trees. This is currently an unknown in snow modelling and may be causing large biases in the water balance (Russell et al 2021). Any large-scale observations would help improve forecasts. This can involve fieldwork to snow affected forests (Scandinavia or North America), making use of terrestrial laser scanning and snow mass measurements to monitor snow falling and being caught within trees.
Lidar has sparse temporal coverage compared to passive satellites and so ICEsat-2 will not be suitable for testing models at seasonal temporal resolutions. To achieve that, ICESat-2 data can be used to calibrate passive optical and microwave satellites to estimate sub-canopy snow through machine learning techniques, allowing large-scale mapping at high-temporal resolution (monthly to daily).
These updated maps can be used to test weather and climate models in snow-affected forests, allowing applications in climate models, hydrological forecasting and wildfire estimation. The choice of which final applications to pursue can be determined by the PhD student, with the support of the supervision team.