Assessing floods and droughts using satellite radar data

In this novel and timely research, we aim to improve the management and protection of floodplains and arid regions by using cutting-edge satellite images to develop field-tested methodologies to monitor flood level and soil moisture on a weekly basis to inform flood and drought risk management strategies. Besides using satellite data, you will also be performing experiments with a ground radar where we will take measurements of real life flooding and droughts as well as simulate these events in the lab.

Common sense seems to put floods and droughts in diametrically opposite directions. However, this is not true, since they are both linked by a very important parameter which is soil moisture. Very arid soils are for instance less able to absorb water and strong rains can easily produce flooding. Currently this knowledge is hampered by insufficient monitoring instrumentation on the ground, especially in remote areas.

The work in this project is in the framework of the Scotland’s International Environment Centre (SIEC) [SIEC 2021], a £23M government investment to help Scotland (but also more broadly the UK and the World) to improve climate change resilience and to help deliver net zero carbon.

You will be working on 2 main test sites with different challenges. (i) The Forth Valley region (Scotland) is a thriving region in Scotland, but it is heavily affected by flooding which impacts local businesses and infrastructure. (ii) The North Rupununi, in the southern interior of Guyana, South America, supports a high terrestrial and freshwater biodiversity, which is important for conservation but also supplies local people with a range of livelihood activities, including subsistence fishing, drinking water and ecotourism. The North Rupununi is a semi-arid region characterised by low topography and seasonal flooding and has recently been the target of major agro-business interest particularly for rice cultivation. In Guyana, droughts can be severe and flooding is also connected to the risk of malaria, therefore monitoring standing water will also improve prevention of malaria outbreaks.

In this project we will use algorithms to demonstrate internationally the benefits of satellite monitoring of flooding and drought. We will use satellite Synthetic Aperture Radar (SAR), which is able to obtain images of the environment from space using microwaves. It allows us to acquire images independent of weather condition and solar illumination, which is very valuable in areas with frequent cloud cover. We will also use a cutting edge radar technology called polarimetry interferometric (Pol-InSAR). The advantage of Pol-InSAR is that we can use the polarisation and interferometric information of the radar echo to obtain more images and therefore more information about objects in the scene [ESA-PolSAR]. In addition to using satellite data, we will be carrying out extensive experiments to ground-truth the data using a ground radar which can simulate the images obtained from satellites. Additionally, we will use ground sensors (water level probes, soil moisture probes) installed in key areas by SIEC’s infrastructure.

A strong motivation for using satellite images is that we entered a new era of freely available satellite data (e.g. the ESA Sentinel constellation missions [ESA-Sentinel]). We are experiencing a rapid growth of activities in the Space industry and the Earth Observation sector.

The development work will be accompanied by large fieldwork in Scotland (and possibly one trip to Guyana). In Scotland we will make large use of the ground radar where the polarimetric radar signature of flooded and drought areas will be analysed. We will also design experiments with simulated floods and droughts in the lab. If successful, the processing stacks produced in this project will be incorporated in SIEC and feed into the Scottish Environmental Protection Agency (SEPA) flood and drought management strategy.


Deliverables: In this project, we will set up a series of methodologies that will be able to provide weekly updates of flood and drought starting from images acquired from space. Among other products we are interested in monitoring drastic changes in water quantity conditions which could help rapid intervention.

Novelty: Pol-InSAR is a cutting edge technology and is very useful to retrieve biophysical parameters of vegetation and soil [ESA-PolSAR]. However, we are in urgent need of controlled experiments on the ground, which will allow a much better understanding of the satellite signal over flooded areas (especially when this covers vegetation). Additionally, the research work carried out in this project puts flood and drought risk management at the core of the project, developing mechanisms that use satellite observation for informing policy and leading actions.

Data (satellite): Archived Pol-InSAR data are already available. Future acquisitions will be carried out synchronised to fieldwork. The datasets used will include at least the following satellite missions: ALOS-2 (Japanese Space Agency); Sentinel-1 (European Space Agency); NISAR (NASA-ISRO).

Data (ground): We will be using a ground radar built in the Stirling radar lab (based on a VNA architecture) to acquire images that emulate satellites. This can be tuned at different frequencies and acquire quad-polarimetric and interferometric data. It can be easily transported and installed on a tripod. In one of the novel experiments we will reproduce flood and drought under vegetation where we will progressively add water to a basin that can be closed containing soil and vegetation until the soil is fully saturated and the water level under vegetation increases.

Algorithm development: In this project we will develop algorithms that exploit weekly available Pol-InSAR images combined with sparse ground measurements to monitor water quantity especially when this is under vegetation.
1) We will monitor changes in water quantity under vegetation, by applying scattering models and change detectors. One of the methodologies will be based on the use of optimisations of polarimetric data [Marino et al 2014].
2) Analysis of time series. This will allow the evaluation of trends in flood/drought frequency and extent.

Project Timeline

Year 1

Preparing a literature review on the topics: SAR, soil moisture, floods, droughts. Fieldwork: Start working on ground measurements and monitoring with Pol-InSAR. Start of lab experiment with simulated flood/drought. Attending international training events. Expected submission of a journal paper on monitoring soil moisture with Pol-InSAR.

Year 2

Monitor multi-year changes in flood and drought under moderate vegetation cover. Finalise results of lab experiment. Expected submission of a journal paper on time series of Pol-InSAR data.

Year 3

Use models to evaluate the sustainability/management of human activities based on temporal trends observed. Writing up of thesis chapters. Expected submission of journal paper on sustainability assessment.

Year 3.5

Complete thesis, submission and viva.

& Skills

This is a multi-disciplinary project including topics related to (a) satellite Earth Observation; (b) physical models (electromagnetic scattering); (c) data analysis; (d) floods, droughts, wetlands; (f) programming.

The successful candidate will have the opportunity to gain valuable skills in the context of: (a) analysing and processing satellite images using Python; (b) planning and accomplishing ground radar campaigns; (c) developing analytical and empirical models to measure biophysical parameters of the environment; (d) using Geographical Information Systems (GIS) software.

The training will also include the attendance of major international training events such as the training on polarimetric SAR data, provide by ESA in Europe.

References & further reading

[ESA-PolSAR]: https://earth.esa.int/web/polsarpro/polarimetry-tutorial[ESA-Sentinel]: https://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Sentinel-1/Satellite_constellation[Marino et al 2014]: Marino, A. and Hajnsek, I. (2014). “A change detector based on an optimization with polarimetric SAR imagery”. IEEE TGRS, 52(8).[SIEC 2021] https://www.stir.ac.uk/about/scotlands-international-environment-centre/

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