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


   School of Geosciences

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  Dr S Hancock, Dr A Ross, Prof Richard Essery, Dr Amy Neuenschwander  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Steven Hancock1, Andrew Ross2, Richard Essery1, Amy Neuenschwander3

1 School of Geosciences, University of Edinburgh. 2 School of Earth and Environment, University of Leeds. 3 Center for Space Research, University of Texas at Austin and NASA ICESat-2 vegetation product lead.

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. Novel algorithms, making use of fusion with ancillary datasets and machine learning, will be needed. 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.

https://eo-cdt.org/projects/mapping-snow-under-and-within-trees-with-satellite-lidar-for-improved-weather-climate-and-hydrological-modelling/

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 and field training. All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org

Application support

As part of our ongoing EDI work, we want to widen participation for UK-domiciled underrepresented groups, which we have identified as Black, Asian and minority ethnic candidates, disabled persons and those from a disadvantaged socio-economic background. As such, we are offering prospective candidates from these groups opportunities and resources such as:

  • This year SENSE are guaranteeing interview slots for up to 50% of our previous year’s interview allocation, for BAME or disabled applicants. Gender is not one of SENSE’s underrepresented categories.
  • 1-2-1 sessions with one of our Centre Managers ([Email Address Removed]) or our EDI Champion to further advise candidates on their applications.

More information for applicants can be found on our available ProjectsHow to Apply and dedicated FAQ webpages. You will also find additional resources such as How can a PhD help me with my career?


Funding Notes

This 3 year 9 month long NERC SENSE CDT award will provide tuition fees, tax-free stipend at the UK research council rate (£17,668 for 2022/23), and a research training and support grant to support national and international conference travel. http://www.eo-cdt.org

References

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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