The RADARSAT Constellation Mission Core Applications: First Results

The Canadian RADARSAT Constellation Mission (RCM) has passed its early operation phase with the performance evaluation being currently active. This evaluation aims to confirm that the innovative design of the mission’s synthetic aperture radar (SAR) meets the expectations of intended users. In this study, we provide an overview of initial results obtained for three high-priority applications; flood mapping, sea ice analysis, and wetland classification. In our study, the focus is on results obtained using not only linear polarization, but also the adopted Compact Polarimetric (CP) architecture in RCM. Our study shows a promising level of agreement between RCM and RADARSAT-2 performance in flood mapping using dual-polarized HH-HV SAR data over Red River, Manitoba, suggesting smooth continuity between the two satellite missions for operational flood mapping. Visual analysis of coincident RCM CP and RADARSAT-2 dual-polarized HH-HV SAR imagery over the Resolute Passage, Canadian Central Arctic, highlighted an improved contrast between sea ice classes in dry ice winter conditions. A statistical analysis using selected sea ice samples confirmed the increased contrast between thin and both rough and deformed ice in CP SAR. This finding is expected to enhance Canadian Ice Service’s (CIS) operational visual analysis of sea ice in RCM SAR imagery for ice chart production. Object-oriented classification of a wetland area in Newfoundland and Labrador by fusion of RCM dual-polarized VV-VH data and Sentinel-2 optical imagery revealed promising classification results, with an overall accuracy of 91.1% and a kappa coefficient of 0.87. Marsh presented the highest user’s and producer’s accuracies (87.77% and 82.08%, respectively) compared to fog, fen, and swamp.

Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions

Deep Learning techniques have been increasingly used in flood risk management to overcome the limitations of accurate, yet slow, numerical models, and to improve the results of traditional methods for flood mapping. In this paper, we review 45 recent publications to outline the state-of-the-art of the field, identify knowledge gaps, and propose future research directions. The review focuses on the type of deep learning models used for various flood mapping applications, the flood types considered, the spatial scale of the studied events, and the data used for model development. The results show that models based on convolutional layers are usually more accurate as they leverage inductive biases to better process the spatial characteristics of the flooding events. Traditional models based on fully-connected layers, instead, provide accurate results when coupled with other statistical models. Deep learning models showed increased accuracy when compared to traditional approaches and increased speed when compared to numerical methods. While there exist several applications in flood susceptibility, inundation, and hazard mapping, more work is needed to understand how deep learning can assist real-time flood warning during an emergency, and how it can be employed to estimate flood risk. A major challenge lies in developing deep learning models that can generalize to unseen case studies and sites. Furthermore, all reviewed models and their outputs, are deterministic, with limited considerations for uncertainties in outcomes and probabilistic predictions. The authors argue that these identified gaps can be addressed by exploiting recent fundamental advancements in deep learning or by taking inspiration from developments in other applied areas. Models based on graph neural networks and neural operators can work with arbitrarily structured data and thus should be capable of generalizing across different case studies and could account for complex interactions with the natural and built environment. Neural operators can also speed up numerical models while preserving the underlying physical equations and could thus be used for reliable real-time warning. Similarly, probabilistic models can be built by resorting to Deep Gaussian Processes.

Increased Flooded Area and Exposure in the White Volta River Basin in Western Africa, Identified From Multi-source Remote Sensing Data

Accurate information on flood extent and exposure is critical for disaster management in data-scarce, vulnerable regions, such as Sub-Saharan Africa (SSA). However, uncertainties in flood extent affect flood exposure estimates. This study developed a framework to examine the spatiotemporal pattern of floods and to assess flood exposure through utilization of satellite images, ground-based participatory mapping of flood extent, and socio-economic data. Drawing on a case study in the White Volta basin in Western Africa, our results showed that synergetic use of multi-temporal radar and optical satellite data improved flood mapping accuracy (77% overall agreement with participatory mapping outputs), in comparison with existing global flood datasets (43% overall agreement for the moderate-resolution imaging spectroradiometer (MODIS) Near Real-Time (NRT) Global Flood Product). Increases in flood extent were observed according to our classified product, as well as two existing global flood products. Flood exposure estimates remain highly uncertain and sensitive to the input dataset used, with the greatest farmland and infrastructure exposure estimated using a composite flood map derived from three products, with lower exposure estimated from each flood product individually. While population exposure varied greatly depending on the population dataset used. The study shows that there is considerable scope to develop an accurate flood mapping system in SSA and thereby improve flood exposure assessment and develop mitigation and intervention plans.

The Importance of Digital Elevation Model Selection in Flood Simulation and a Proposed Method to Reduce DEM Errors: A Case Study in Shanghai

Digital Elevation Models (DEMs) play a critical role in hydrologic and hydraulic modeling. Flood inundation mapping is highly dependent on the accuracy of DEMs. Various vertical differences exist among open access DEMs as they use various observation satellites and algorithms. The problem is particularly acute in small, flat coastal cities. Thus, it is necessary to assess the differences of the input of DEMs in flood simulation and to reduce anomalous errors of DEMs. In this study, we first conducted urban flood simulation in the Huangpu River Basin in Shanghai by using the LISFLOOD-FP hydrodynamic model and six open-access DEMs (SRTM, MERIT, CoastalDEM, GDEM, NASADEM, and AW3D30), and analyzed the differences in the results of the flood inundation simulations. Then, we processed the DEMs by using two statistically based methods and compared the results with those using the original DEMs. The results show that: (1) the flood inundation mappings using the six original DEMs are significantly different under the same simulation conditions—this indicates that only using a single DEM dataset may lead to bias of flood mapping and is not adequate for high confidence analysis of exposure and flood management; and (2) the accuracy of a DEM corrected by the Dixon criterion for predicting inundation extent is improved, in addition to reducing errors in extreme water depths—this indicates that the corrected datasets have some performance improvement in the accuracy of flood simulation. A freely available, accurate, high-resolution DEM is needed to support robust flood mapping. Flood-related researchers, practitioners, and other stakeholders should pay attention to the uncertainty caused by DEM quality.

The Total Operating Characteristic from Stratified Random Sampling with an Application to Flood Mapping

The Total Operating Characteristic (TOC) measures how the ranks of an index variable distinguish between presence and absence in a binary reference variable. Previous methods to generate the TOC required the reference data to derive from a census or a simple random sample. However, many researchers apply stratified random sampling to collect reference data because stratified random sampling is more efficient than simple random sampling for many applications. Our manuscript derives a new methodology that uses stratified random sampling to generate the TOC. An application to flood mapping illustrates how the TOC compares the abilities of three indices to diagnose water. The TOC shows visually and quantitatively each index’s diagnostic ability relative to baselines. Results show that the Modified Normalized Difference Water Index has the greatest diagnostic ability, while the Normalized Difference Vegetation Index has diagnostic ability greater than the Normalized Difference Water Index at the threshold where the Diagnosed Presence equals the Abundance of water. Some researchers consider only one accuracy metric at only one threshold, whereas the TOC allows visualization of several metrics at all thresholds. The TOC gives more information and clearer interpretation compared to the popular Relative Operating Characteristic. Our software generates the TOC from a census, simple random sample, or stratified random sample. The TOC Curve Generator is free as an executable file at a website that our manuscript gives.

Flood Mapping in the Coastal Region of Bangladesh Using Sentinel-1 SAR Images: A Case Study of Super Cyclone Amphan

Floods are triggered by water overflow into drylands from several sources, including rivers, lakes, oceans, or heavy rainfall. Near real-time (NRT) flood mapping plays an important role in taking strategic measures to reduce flood damage after a flood event. There are many satellite imagery based remote sensing techniques that are widely used to generate flood maps. Synthetic aperture radar (SAR) images have proven to be more effective in flood mapping due to its high spatial resolution and cloud penetration capacity. This case study is focused on the super cyclone, commonly known as Amphan, stemming from the west Bengal-Bangladesh coast across the Sundarbans on 20 May 2020, with a wind speed between 155 -165 gusting up to 185 . The flooding extent is determined by analyzing the pre and post-event synthetic aperture radar images, using the change detection and thresholding (CDAT) method. The results showed an inundated landmass of 2146 on 22 May 2020, excluding Sundarban. However, the area became 1425 about a week after the event, precisely on 28 May 2020 . This persistency generated a more severe and intense flood, due to the broken embankments. Furthermore, 13 out of 19 coastal districts were affected by the flooding, while 8 were highly inundated, including Bagerhat, Pirojpur, Satkhira, Khulna, Barisal, Jhalokati, Patuakhali and Barguna. These findings were subsequently compared with an inundation map created with a validation survey immediately after the event and also with the disposed location using a machine learning-based image classification technique. Consequently, the comparison showed a close similarity between the inundation scenario and the flood reports from the secondary sources. This circumstance envisages the significant role of CDAT application in providing relevant information for an effective decision support system.