HS6.5

How to cite: Kalaitzis, F., Mateo Garcia, G., and Marchisio, G.: Water monitoring with Very High Resolution satellite imagery, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10295, https://doi.org/10.5194/egusphere-egu21-10295, 2021.

Synthetic Aperture Radar (SAR) based flood maps are rapidly becoming a vital part of flood monitoring applications, since they provide unobscured observations independent of illumination or weather conditions. As water surfaces are physically smoother than microwave wavelengths, they appear dark in SAR imagery due to specular reflection, enabling the automatic delineation of flooded areas. However, in arid regions using backscatter thresholds to identify inundation results in numerous false positives, since dry and smooth desert sand appears as dark as water in SAR images. Accordingly, a novel Sentinel-1 SAR-based flood mapping algorithm S1-L1 to discern flood inundation from water lookalike surfaces in arid regions. The swath is tiled to ensure comparable land-water pixel distributions and long-term water recurrence records from optical Landsat sensors is used to classify potentially water and definitely land (DL) areas. Smooth surfaces and radar shadow regions, which exhibit backscatter lower than the median value for >50% of the preceding year, are excluded from the DL pixels to avoid thin long tailed distributions. The first percentile value of the DL distribution is selected as the water threshold for each band (VV and VH), to include the maximum possible water pixels without letting in large volumes of land pixels. A Gaussian contextual smoother is used to combine the individual layers into the binary flood mask, with a weighted combination of the layers computed based on the underlying land-use. An empirical sensitivity analysis showed that different low backscatter frequency thresholds work better in different regions, and thus, a fuzzy flood plausibility layer (FPL) is proposed as a post-processor. The FPL improves upon the current state-of-the-art sand exclusion layers (SELs) by combining distance from drainage with seasonally dark surfaces and shadows identified through annual SAR backscatter time series analysis. Additionally, known agricultural land-use areas with low values of Sentinel-2 based Soil Adjusted Vegetation Index (SAVI) are used to identify harvested croplands. S1-L1 was evaluated using (1) expert classified Sentinel-1 SAR-based flood maps and (2) with Sentinel-2 clear view coincident optical maps for the 2020 flood events in Ghana (September) and Republic of the Congo (November). S1-L1 performance is compared to (a) Otsu thresholding (liberal and conservative) and (b) a deterministic SEL  with >60% low backscatter frequency, to assess improvements over current best performing approaches for arid areas. First results demonstrated 50% false positive reductions over traditional Otsu approaches and consistent improvements of >20% in Critical Success Index values. Findings indicate that S1-L1 has the potential to efficiently differentiate between water and lookalike regions, and can facilitate more reliable SAR-based flood mapping in deserts.

How to cite: Dasgupta, A., Goodman, M., Yague Martinez, N., and Tellman, B.: Improving Operational SAR-based Flood Mapping in Arid Regions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6982, https://doi.org/10.5194/egusphere-egu21-6982, 2021.

There is no doubt that the devastating socio-economic impacts associated with floods has been increasing. According to the International Disaster Database (EM-DAT), floods represent the most frequent and most impacting, in terms of the number of people affected, among the weather-related disasters: nearly 1 billion people were affected by inundations in the last decade (2006–2015), while the overall economic damage is estimated to be more than $300 billion. Despite this evidence, and the awareness of the environmental role of rivers and their inundation, our capability to respond to and forecast floods remains relatively poor.

In this context, satellite sensors represent a highly valuable source of observation data that could fill many of the gaps, especially in remote areas and developing countries. In the last decade, with the proliferation of more satellite data and the advent of ESA’s operational Sentinel missions under the EC Copernicus open data programme, satellite images, in particular SAR, have been assisting flood disaster mitigation, response and recovery operations globally.

Although the number of state-of-the-art and innovative research studies in those areas is increasing, the full potential of remotely sensed data to enhance flood mapping has yet to be unlocked, especially the latency issue is not being sufficiently well addressed. Latency, i.e. the time between image acquisition to the flood map delivery to the person that actually needs it, is not at all in line with disaster response requirements and is, to a large extent, responsible for the slow uptake of EO-based products, such as flood maps, into an operational timeline or disaster response protocols of various potential user organizations, such as the UN World Food Programme for instance.

We call to develop a prototype or concept of a product. Specifically, a digital twin experiment should be developed first to generate a prototype AI-based algorithm that could be deployed onboard a SAR satellite to produce flood maps in real time. The mapping result, which consists of simple column/row (x/y) vector indices of flood edges in the form of a short “text message”, will be delivered to the field response teams via satellite communication technology for use within minutes, rather than many hours to days as is currently the case.

In this paper, we illustrate the concept of the proposed innovation, including the future possibility of in-orbit processing. This is in part a synthetic, proof-of-concept study, and, although the societal impact and value of the service prototype developed is clear, once successfully demonstrated, the economic value of this service as well as its market share and value can be established. At this point in time, there is no service of this type in existence. For optical/hyperspectral sensors on CubeSats, onboard AI-based processing has been trialed but not for SAR and not including a rapid flood map delivery service using AI. For instance, based on the results of an ESA-supported FDL Europe challenge, Mateo-Garcia et al. (2019) demonstrated the application of a fully convolutional neural network to prototype a GPU-based onboard flood segmentation system using degraded Sentinel-2 imagery (to mimic CubeSat capability).

How to cite: Schumann, G. J.-P., Giustarini, L., Zare, M., and Gaffinet, B.: Call to action: Pushing scientific and technological innovation to develop an efficient AI flood mapper for operational SAR satellites, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5943, https://doi.org/10.5194/egusphere-egu21-5943, 2021.

Data assimilation uses observation for updating model variables and improving model output accuracy. In this study, flood extent information derived from Earth Observation data (namely Synthetic Aperture Radar images) are assimilated into a loosely coupled flood inundation forecasting system via a Particle Filter (PF). A previous study based on a synthetic experiment has shown the validity and efficiency of a recently developed PF-based assimilation framework allowing to effectively integrate remote sensing-derived probabilistic flood inundation maps into a coupled hydrologic-hydraulic model. One of the main limitations of this recent framework based on sequential importance sampling is the sample degeneracy and impoverishment, as particles loose diversity and only few of them keep a substantial importance weight in the posterior distribution. In order to circumvent this limitation, a new methodology is adopted and evaluated: a tempered particle filter. The main idea is to update a set of state variables, namely through a smooth transition (iterative and adaptative process). To do so, the likelihood is factorized using small tempering factors. Each iteration includes subsequent resampling and mutation steps using a Monte Carlo Metropolis Hasting algorithm. The mutation step is required to regain diversity between the particles after the resampling. The new methodology is tested using synthetic twin experiments and the results are compared to the one obtained with the previous approach. The new proposed method enables to substantially improve the predictions of streamflow and water levels within the hydraulic domain at the assimilation time step. Moreover, the preliminary results show that these improvements are longer lasting. The proposed tempered particle filter also helps in keeping more diversity within the ensemble.

How to cite: Di Mauro, C., Hostache, R., Matgen, P., van Leeuwen, P. J., Nichols, N., and Blöschl, G.: Assimilation of inundation extent observations into a flood forecasting system:  a tempered particle filter for combatting degeneracy and sample impoverishment., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10878, https://doi.org/10.5194/egusphere-egu21-10878, 2021.

The study of the riverscape dynamic in lowland areas is crucial for reconstructing the morphoevolution of the drainage network, especially where human activities have always been strongly connected to the river system. Not surprisingly, the Lower Mesopotamian Plain (LMP) represents the ideal study area, being a large floodplain where the Tigris and Euphrates rivers with their distributaries deposited a large volume of sediments during the Holocene. Here, a complex drainage pattern, characterized by paleochannels, levees and crevasse splays developed, representing the expression of several fluvial avulsion processes during the time. Indeed, the presence of recent and ancient crevasse splays in a given area suggests frequent seasonal floods, but at the same time, their formation and growth represent, in the LMP, an important process that conditioned the location of several human settlements since the 6th millennium BC. In this area, about 200 examples of active and abandoned crevasse splays, with various sizes, have been recognized exclusively through a remote sensing approach. The scarce elevation ranges of the LMP represent the main challenge in the detection and mapping of the crevasse splays features (i.e., channels, levees and deposits), in addition to the definition of the floodplain extension and the anthropic impact on channel networks.

Therefore, the research aims to integrate multi-sensor remote sensing data such as optical multispectral imagery and digital elevation datasets for improving the detection and mapping of crevasse splays. Landsat 8 imagery is adopted for computing two spectral indices (NDVI and Clay Ratio) and carrying on different supervised classification methods (i.e., Mahalanobis, Maximum Likelihood, Minimum Distance and SAM). Each method has been evaluated through the computation of the confusion matrix, assessing the Overall Accuracy, K coefficient, Producer Accuracy and User Accuracy. Elevation data used in the topographic analysis to determine the local micro-relief geometry are derived from two different global DEMs available at the ground resolution of 1 arcsec (AW3D30 and GDEM2). Topographic analysis has been performed to complete and validate the supervised classification results.

The outputs successfully demonstrate the potential of the integration of multispectral imagery analysis and topographic analysis from DEM for detecting and mapping with a satisfactory detail the avulsion processes and for distinguishing their state of activity. The methodological approach is a promising technique for flood hazard and risk mapping, as well as for monitoring flood dynamics, especially within arid and semi-arid zones where flawless water management is essential for guaranteeing sustainable crops, livestock and avoiding wasting water.

How to cite: Iacobucci, G., Troiani, F., Milli, S., Piacentini, D., Mazzanti, P., Zocchi, M., and Nadali, D.: Remote sensing approach for the fluvial avulsion processes detection and mapping, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2991, https://doi.org/10.5194/egusphere-egu21-2991, 2021.

In recent years Ireland has experienced significant and unprecedented flooding events, such as groundwater floods, that extended up to hundreds of hectares during the winter flood season, lasting for weeks to months, and affecting many rural communities in Ireland. In response to the serious flooding of winter 2015-2016, specifically related to groundwater, Geological Survey Ireland (GSI) initiated a project (GWFlood, 2016-2019), in collaboration with Trinity College Dublin (TCD) and Institute of Technology Carlow (ITC), to investigate the drivers, map and numerically model the extent of groundwater flooding in Ireland. Through this project, the use of remote sensing data, Sentinel-1 satellite imagery from the European Space Agency Copernicus program, was key to overcome the practical limitations of establishing and maintaining a national field-based monitoring network. The main outputs for this project included: 1) a national historic groundwater flood map, 2) a methodology for hydrograph generation using satellite images, and 3) predictive groundwater flood maps for Ireland.

Subsequently GSI started a new project (GWClimate, 2020-2022), in collaboration with ITC, to monitor floods in Ireland using remote sensing data, to enable short-term forecasting groundwater floods at a national scale, and to evaluate the potential that climate change may have on Irish groundwater resources, both in terms of flooding and drought issues. The GWClimate project is enhancing the tools developed by GWFlood in order to deliver: 1) seasonal flood maps for Ireland, 2) near-real time satellite-based hydrographs, 3) groundwater flood forecasting tools, and 4) maps evaluating the impact of climate change in groundwater systems in Ireland. The outputs of this project will contribute to monitor and quantify the impacts of flooding in Ireland at a national scale, improve the national capacity to understand how groundwater resources respond to climatic stresses, and improve the reliability of adaptation planning and predictions in the groundwater sector.

Data and maps from GWClimate and GWFlood projects are available at: 1) https://gwlevel.ie, and 2) https://www.gsi.ie/en-ie/programmes-and-projects/groundwater/activities/groundwater-flooding/gwflood-project-2016-2019/Pages/default.aspx

How to cite: Campanyà i Llovet, J., McCormack, T., Doherty, D., Schuler, P., Kabza, M., Mullarkey, E., and Naughton, O.: Mapping, Monitoring, Forecasting and Assessing the Impact of Climate Change in Groundwater Systems in Ireland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16012, https://doi.org/10.5194/egusphere-egu21-16012, 2021.

Bathymetric data are a key parameter to assess shallow-water hydrodynamic processes. In-situ surveys provide high data quality; however, surveys are expensive and cover a limited spatial extent. To fill this gap, over recent years, the Satellite Derived Bathymetry (SDB) techniques have been developed. The present work aims to elaborate a technique to estimate bathymetric data from satellite images for intertidal zones. The method applied in this work is composed of 6 steps: (1) image querying and pre-processing is done through Google Earth Engine application (API) using Copernicus Sentinel 2A and B, product type 2A. (2) Identification of the intertidal zone for the study area by temporal variability of the Normalized Difference Water Index (NDWI). (3) Recognition of the waterline in each image by the use of an adaptive threshold technique; and assignment of the elevation for each detected waterline based on local observed tide heights. (4) Validation of the estimated bathymetry by comparison with LiDAR measurements. (5) Implementation of a SDB correction: numerical and/or statistical and, (6) assessment of the validity of SDB for hydrodynamic modelling. The SDB technique was applied to 4 different estuaries in New Zealand: Maketu, Ohiwa, Whitianga and Tauranga Harbour showing similar or better estimations in comparison to previous works using optical or synthetic aperture radar (SAR). For Tauranga Harbour, results from the statistical and dynamical corrections showed that the major error source is due to the image optical properties and environmental conditions when the image was acquired (35%). However, the tidal propagation can significantly decrease the SDB accuracy (13%). Finally, the use of the SDB in numerical simulations does not present huge differences in the predicted waterlevels in comparison to the use of survey bathymetry, showing that SDB could be potentially used for coastal flooding simulations.  

How to cite: Costa, W., Bryan, K., and Coco, G.: A waterline method to derive intertidal bathymetry from multispectral satellite images and its application to hydrodynamic modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14286, https://doi.org/10.5194/egusphere-egu21-14286, 2021.