About the Project
This PhD project will develop a method to couple EO and soil data to forecast the risk of soil saturation excess. This will provide the first step towards identifying regions with excessive flooding risk under future climates. This research includes the use of big data from multiple in situ sources, multispectral remote satellite data, and machine learning techniques.
The project comprises four key objectives:
– Objective 1: Build a database of existing in situ soil moisture and climate data in areas at risk of saturation excess;
– Objective 2: Develop a model to predict soil water storage capacity at depth from satellite soil surface moisture data;
– Objective 3: Develop methods to increase soil water mapping measurement resolution. High-resolution digital elevation models to be included;
– Objective 4: Predict and forecast runoff risk from coupled soil, meteorological and satellite data for selected regions.
Methodology: Satellite data (SMOS, SMAP) will provide surface soil water content time histories. Soil hydraulic properties will be estimated in the first instance from literature sources, e.g. the USDA Unsaturated Soils Hydraulic Database, to test and develop model functionality. Producing a functioning model (in Python or Matlab) constitutes the first milestone. These results will be compared to in situ observations. Methods used to increase data resolution, for example those based on soil texture heterogeneity or topography, will be examined for use with Scottish soil data. SAR data (i.e. Sentinel-1) will be used to complement SMOS and SMAP information at a higher resolution (10 m). Microwave information will be compared to that derived from the use of neural networks trained to derive relationships between soil water content data and multi-spectral properties . Data used for this include high and super-high-resolution multispectral satellites (i.e. Sentinel-2 and Pleiades through the UKSA Data Procurement initiative).
Check https://eo-cdt.org/ for more information on funding and application process.
This 3 year 9 month long NERC SENSE CDT award will provide tuition fees (£4,409 for 2020/21), tax-free stipend at the UK research council rate (£15,285 for 2020/21), and a research training and support grant to support national and international conference travel.
 HM Govt. 2019. Flood and coastal risk management: long-term investment scenarios.
 Horgan, “New Civil Engineer”, 2020.
 Hosseinzadehtalaei et al. 2020. “Satellite-based data driven quantification of pluvial floods over Europe under future climatic and socioeconomic changes”, Total Environment.
 FAME project, Online. Accessed 20 April 2020.
 eSurge project, Accessed 20 April 2020.
 FAST project, Online. Accessed 20 April 2020.
 European Space Agency, SMOS, Sentinel-2 and Pleiades. Online. Accessed 20 April 2020.
 NASA, “Soil Moisture Active Passive: why it matters,” Online. Accessed 20 April 2020.
 Medina-Lopez et al. 2019. “High-resolution sea surface temperature and salinity in coastal areas worldwide from raw satellite data”, Remote Sensing.
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