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Mapping snow under and within trees with satellite lidar for improved weather, climate and hydrological modelling

   School of Geosciences

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  Dr S Hancock, Dr R Essery, Dr A Ross, Dr Amy Neuenschwander  Applications accepted all year round  Competition Funded PhD Project (Students Worldwide)

About the Project


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 satellite (working with the NASA ICESat-2 vegetation product lead), the first step would be to determine whether ICESat-2 can measure ground reflectance accurately enough to predict sub- canopy snow cover. This will be compared against ground cameras and high-resolution satellite images. Once a robust workflow has been developed and tested, ICESat-2 data will be used to map sub-canopy snow over large areas of the Earth.

Once a method to robustly estimate the ground and canopy reflectance has been developed, the canopy reflectance will 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 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, 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.

 The student will be primarily based at Edinburgh, with some time spent at Leeds and potentially with project partners in the US.

This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See

Funding Notes

This 3 year 9 month long NERC SENSE CDT award will provide tuition fees (£4,500 for 2021/22), tax-free stipend at the UK research council rate (£15,609 for 2021/22), and a research training and support grant to support national and international conference travel.


Armston, J., Disney, M., Lewis, P., Scarth, P., Phinn, S., Lucas, R., Bunting, P. and Goodwin, N., 2013. Direct retrieval of canopy gap probability using airborne waveform lidar. Remote Sensing of Environment, 134, pp.24-38.
Lv, Z. and Pomeroy, J.W., 2019. Detecting intercepted snow on mountain needleleaf forest canopies using satellite remote sensing. Remote Sensing of Environment, 231, p.111222.
Mudryk, L., Santolaria-Otín, M., Krinner, G., Ménégoz, M., Derksen, C., Brutel-Vuilmet, C., Brady, M. and Essery, R., 2020. Historical Northern Hemisphere snow cover trends and projected changes in the CMIP6 multi-model ensemble. The Cryosphere, 14(7), pp.2495-2514.
Russell, M., UH Eitel, J., J Maguire, A. and E Link, T., 2020. Toward a novel laser-based approach for estimating snow interception. Remote Sensing, 12(7), p.1146.
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