A detailed technical description of the three state-of-the-art SAR-based GFM flood mapping algorithms - which were developed by the GFM consortium members LIST, DLR and TU Wien - is provided in the GFM Product Definition Document (PDD), together with examples of their application in an operational context. In this section, the key technical points of the three individual GFM flood mapping algorithms are summarised, and references are provided to the appropriate sections of PDD containing more detailed information.
All three of GFM flood mapping algorithms - namely GFM Flood Mapping Algorithm 1 (developed by LIST), GFM Flood Mapping Algorithm 2 (developed by DLR) and GFM Flood Mapping Algorithm 3 (developed by TU Wien) - make use of historical time-series of Sentinel-1 SAR intensity data and use topography-derived indices to refine the initial classification of water bodies. However, differences do appear in how the historical time series of intensity data are finally used to parameterize the retrieval algorithms, and how the ancillary data such as topography are used in the production system. Further differences relate to the inclusion of a region-growing step, the scale at which the thresholds are determined and applied to a grid-cell's backscatter value, and other nuances in the way the retrieval algorithms are set up.
Each GFM flood mapping algorithm takes as input Sentinel-1 image data and generates an “Observed Flood Extent” output layer, which is further refined using the “Exclusion Mask” (to mask out areas that cannot be classified, due to local conditions), and the “Reference Water Mask” (showing permanent and seasonal water bodies).
As described briefly in the following, an ensemble-based approach is then used to combine the observed flood extent maps that are generated by the three GFM flood mapping algorithms, into a “consensus maps” representing the final GFM product output layer “Observed Flood Extent”.
Similar to the GFM ensemble flood mapping algorithm, which is used to generate the output layer “Observed Flood Extent”, a GFM ensemble water mapping algorithm is used to generate the output layer “Reference Water Mask”, based on a five-year time series (“data cube”) of Sentinel-1 image data.
The (internal) availability of three separate maps for observed flood extent also provides a convenient means of identifying and addressing any shortcomings affecting a single GFM flood mapping algorithm in specific circumstances or parts of the world, due to many well-known factors like topography or environmental conditions. Accordingly, the implemented quality assurance procedures can investigate whether classification errors may be attributed to shortcomings of individual algorithms, or limits on the ability of Sentinel-1 SAR data to capture the appearance (or absence) of surface water in particular situations.
The most relevant features of the three GFM flood mapping algorithms are summarized and compared in the table below. In the following sub-sections, more details are provided on the three GFM flood mapping algorithms, and on the GFM ensemble flood mapping algorithm and ensemble water mapping algorithm, which are used, respectively, to produce the GFM Product Output Layers “Observed Flood Extent” and “Reference Water Mask” output layers.
| Algorithm (Provider) |
GFM FLOOD MAPPING ALGORITHM1 (LIST) |
GFM FLOOD MAPPING ALGORITHM2 (DLR) |
GFM FLOOD MAPPING ALGORITHM3 (TU Wien) |
| Main scientific reference | Chini et al. (2017) | Martinis et al. (2015) | Bernhard Bauer-Marschallinger et al. (2022) |
| Application domain | Automated water and flood extent mapping (pixel-based) | Automated NRT water and flood extent mapping | Automated pixel-based flood extent mapping |
| Input remote sensing data | Pair of SAR intensity images acquired from same orbit (any sensor) and model parameters derived from historical time series | Single-temporal SAR intensity data | Single SAR acquisition and model parameters derived from historical time series |
| Auxiliary data | HAND index map, exclusion layer, reference water layer, water and flood extent map computed at previous time step | HAND index exclusion map, reference water extent, DEM, optional: low backscatter exclusion mask based on S-1 time-series data | HAND index, exclusion mask, reference water map for generating the fresh flooded areas |
| Main characteristics | Scene-specific statistical modelling of backscatter distributions, systematic updating of water bodies maps using combination of change detection and region growing | Hierarchical automatic tile-based thresholding, fuzzy logic-based post classification and region growing | Classification based on backscatter probability distribution by exploiting the historical time series with consideration of backscatter seasonality. |
| Use of SAR time-series data | Yes | No, integration of a low backscatter exclusion mask based on S-1 time-series data (produced off-line) can be done optionally | Yes, parametrisation through multi-year time series |
| Use of contextual information by region-growing | Yes | Yes | No |
| Method of initialization | Statistical modelling of backscatter distributions attributed to water / no water and change / no change classes (per tile) | Hierarchical automatic tile-based thresholding using statistical modelling of class distributions | Generation of backscatter probability distribution from historical time series measurements |
| Post-classification steps | Masking of exclusion areas, distinction between water and flood extent using reference water mask | Masking of exclusion areas, distinction between water and flood extent using reference water mask | Noise reduction, mask exclusion areas, extraction new flood area compared with reference water mask |
| Generation of water likelihood values | Yes (based on Bayesian inference) | Yes (based on fuzzy logic) | Yes (based on the Bayesian posterior probability) |
| Main differentiating features | Hierarchical split-based approach (HSBA) enabling re-calibration of parameters in NRT based on most recent pair of S-1 images | Fuzzy logic-based approach enabling a post classification and region growing taking advantage of topography-derived indices in addition to SAR backscatter | Exploiting per-pixel multi-year Sentinel-1 signal history in data cube; enabling a very fast and scalable production of flood and water extent maps through pre-computed global parameters at high quality |
| Additional information | GFM flood mapping algorithm 1 | GFM flood mapping algorithm 2 | GFM flood mapping algorithm 3 |
| GFM ensemble flood and water mapping algorithms | |||
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