Near-real time surface water extent monitoring from SLC Sentinel-1 SAR imagery:
The SMART project case study.
Cristian Silva-Perez1, Javier Ruiz-Ramos2, Armando Marino1, Andrea Berardi2,
1 University of Stirling, Scotland, UK; 2The Open University, Milton Keynes, UK.
Timely information on surface water extent constitutes a key decision support tool suitable for informed decision making. In the context of floods, it allows assessment of flood impact, effective risk and action management and prioritised investments in flood defences. With regards to natural ecosystems, such as wetlands, it allows monitoring critical hydrological dynamics that provide the appropriate conditions for rich biodiversity to thrive. The Landscape Sensor-based Monitoring Assessment using Remote Technologies (SMART) project provides a novel Synthetic Aperture Radar (SAR) based tool to establish a new global service aimed at providing surface water extent monitoring. The project will initially demonstrate potential in three sites as follows. In the Firth of Forth (Scotland), the aim is to monitor the extent of floods, heavy rainfall and fast snow melting events that cause river levels to rise resulting in surface water flooding affecting private and commercial infrastructure and disturbing transportation. The two additional test sites include Colombo urban wetlands (Sri Lanka) and North Rupununi wetlands (Guyana). The natural ecosystems in these two locations are characterised by seasonal floods and support important terrestrial and freshwater biodiversity, supplying local communities with a range of livelihood activities, including subsistence fishing and ecotourism. In these locations, a service to monitor the hydrological and ecological condition of the wetlands provide crucial information for sustainable development, particularly in the context of flooding and droughts emphasised by climate change.
This presentation will provide a comparison of the algorithms for near-real-time surface water extent monitoring implemented within SMART, using Single Look Complex (SLC) Dual-polarimetric (Dual-PolSAR) Sentinel-1 imagery. As a benchmark, we include the following two methodologies:
• Our previous work developed in  for natural wetlands monitoring based on the Cumulative Sums algorithm applied to SAR time series (SAR-CUSUM). It consists of a robust statistical and multitemporal approach to map open water and flooded vegetation areas. The algorithm extracts a dry condition reference from historical imagery and cumulates the difference between new acquisitions and this reference. This procedure highlights consecutive deviations from the dry conditions, thus enhancing any possible recurrent variation which takes place over time. Using a threshold based on the histograms of regions, open water and flooded vegetation areas can be masked out.
• A current state-of-the-art algorithm for surface soil moisture estimation  which utilises a stack of multitemporal VV backscatter intensity images. It infers reference dry and wet conditions for a test site and compares them via a normalised difference with every new SAR image acquisition. Since the results of the comparisons are normalised, it presents the estimated surface soil moisture in an intuitive 0 to 1 scale.
In SMART we also derive a series of novel algorithms that are included in this comparison as follow:
a) An expanded version of the multitemporal CuSum algorithm presented in  that includes Dual-PolSAR features such as alpha angle and entropy, derived from the dual polarimetric pixel covariance matrices.
b) A set of change detection approaches based on optimisations of covariance matrices as presented in . The PolSAR detectors have been designed to identify not only the intensity of change between images, but also the type of change expressed by the change in scattering mechanisms. For the SMART project, we adapted two of these detectors for the Dual-PolSAR case: A change detector based on the difference of covariance matrices and a detector based on the ratio of these covariance matrices. Given the difference in the interpretation for each of them, complementary information may be obtained.
c) A deep learning-based approach that uses the well-known U-NET image segmentation model  for supervised surface water extent monitoring. For this method, a training and testing dataset was created from the semi-automatic flood maps produced by the Copernicus Emergency Management Service (human-adjusted maps produced by a computer algorithm). We test different combinations of input features, including backscatter intensities and dual-PolSAR features derived from SLC Sentinel-1 imagery.
Preliminary results presented in , show a rigorous statistical flood monitoring tool that was able to demonstrate a 90% accuracy in detecting the extent of open water flooding in the Guyana demonstration site. In addition, preliminary results show that including the phase information within the SLC images improves the accuracy, especially for floods under vegetation. In the presentation, we will show the exact figures for accuracy on the algorithm comparison once the SLC data of Sentinel-1 is included in the retrieval algorithms. This considers analysis on the benefits/limitations of employing Dual-PolSAR SLC data and unsupervised/supervised learning approaches. It will also highlight the bespoke elements required to map surface water on natural ecosystems (wetlands) and other terrains (flood mapping).
An additional point of this presentation will be to introduce the easy-to-use visualisation tools developed in SMART (web and mobile mapping apps), tailored for non-specialist user communities to use the results obtained by the algorithms. This is due to the fact that the visualisation and mapping platforms are developed in close collaboration with communities affected by floods through the use of capacity-building programs. The visualisation tools are designed to include environmental and social information in order to support decision-making in relation to flooding (e.g., regarding mosquito-borne diseases, flood monitoring and planning, biodiversity conservation, infrastructure development and agriculture).
This research was supported by the SMART project, funded by the UK Space Agency’s Partnership in Innovation Development (Pin2D). The SMART consortium comprises The Open University, University of Stirling, and the Cobra Collective CIC. Sentinel-1 data were provided courtesy of ESA. Validation optical imagery were provided courtesy of Planet.
 Ruiz-Ramos, J., Marino, A., Berardi, A., Hardy, A., & Simpson, M. (2021, July). Characterization of Natural Wetlands with Cumulative Sums of Polarimetric SAR Timeseries. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 5899-5902). IEEE.
 Bauer-Marschallinger, B., Freeman, V., Cao, S., Paulik, C., Schaufler, S., Stachl, T., ... & Wagner, W. (2018). Toward global soil moisture monitoring with Sentinel-1: Harnessing assets and overcoming obstacles. IEEE Transactions on Geoscience and Remote Sensing, 57(1), 520-539.
 Marino, A., & Nannini, M. (2021). Signal Models for Changes in Polarimetric SAR Data. IEEE Transactions on Geoscience and Remote Sensing.
 Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.