Automatic near-real time flood extent and duration mapping based on multi-sensor Earth Observation data

In order to support disaster management activities related to flood situations, an automatic system for near-real time mapping of flood extent and duration using multi-sensor satellite data is developed. The system is based on four fully automatic processing chains for the derivation of the inundation extent from Sentinel-1 and TerraSAR-X radar as well as from optical Sentinel-2 and Landsat data. While the systematic acquisition plan of the Sentinel-1/2 and Landsat satellites allows a continuous monitoring of inundated areas at an interval of a few days, the TerraSAR-X processing chain has to be triggered on-demand over the disaster-affected areas. Beside flood extent masks, flood duration products are generated to indicate the temporal stability and evolution of flood events. The flood monitoring system is demonstrated on a severe flood situation in Mozambique related to cyclone Idai in 2019

Improving reliability in flood mapping by generating a global seasonal reference water mask using Sentinel-1/2 time-series data

Variable intra-annual climatic and hydrologic conditions result in many regions of the world in a strong seasonality of the water extent throughout the year. This behaviour, however, is usually not reflected in satellite-based flood emergency mapping. This may lead to non-reliable representations of the flood extent and to misleading information within disaster management activities. In order to be able to separate flooding from normally present seasonal water coverage, up-to-date, high-resolution information on the seasonal water cover is crucial. In this work, we present an automatic methodology to generate a global and consistent permanent and seasonal reference water product based on high resolution Earth Observation data, specifically designed for the use within flood mapping activities. The water masks are primarily based on the time-series analysis of optical Sentinel-2 imagery, which are complemented by Sentinel-1 Synthetic Aperture Radar-based information in data scarce regions. The methodology has been developed based on data of five globally distributed study areas (Australia, Germany, India, Mozambique, and Sudan). Within this work results for Australia and India are demonstrated and are systematically compared with external reference water products. Results show, that by using the proposed product it is possible to give a more reliable picture on flood-affected areas in the frame of disaster response

Interannual comparison of historical floods through flood detection using multi-temporal Sentinel-1 SAR images, Awash River Basin, Ethiopia

Synthetic-aperture radar (SAR) data from Sentinel-1 satellites provides unprecedented opportunity to evaluate inter-annual flood characteristics, although consensus on best flood detection methods is lacking. This study compared the performance of three flood detection methods to evaluate inter-annual flood characteristics at two sites in the Awash River Basin of Ethiopia. The methods are Change Detection and Thresholding (CDAT), Normalized Difference Flood Index (NDFI) and Root of Normalized Image Difference (RNID). The reference flood map was prepared based on a field survey for the maximum extent of the 2020 flood. Inter-annual flood characteristics were evaluated in terms of flood onset, recession and frequency of occurrence over the analysis period (2017 to 2022) but with a particular focus on the 2020 extreme flood events at Borkena and Dubti sites. Findings showed that the performance of the flood detection methods significantly differed. The RNID method, which allowed manual estimation of threshold, provided the highest flood detection capability at both sites. Flood detection accuracy improved when normalizing signal backscatter intensity of S-1 in change detection method. Flood onset and recession showed noticeable difference across the sites. Findings of this study indicate the potential of the satellite remote sensing methods to evaluate the spatial and temporal characteristics of floods, but further research is needed to evaluate and improve the performance of these methods for other flood affected sites.</p

MAPPING OF URBAN FLOOD INUNDATION USING 3D DIGITAL SURFACE MODEL AND SENTINEL-1 IMAGES

Flooding in urban areas poses serious risks to citizens, infrastructures, and transportation. Precise and real-time delineation of the inundated areas is crucial for a better understanding of the extent of damage and high-risk areas and people evacuation actions. It also increases citizens' awareness that living in areas with high flood risk. Yazd city is characterized by low rainfall (&lt;70 mm/yr) and the desert climate is considered the study area of this research. This city encountered a flash flood event that was generated by severe rainfall with a depth of 75 mm in 3hr (i.e., the intensity of 25 mm/hr) on July 29, 2022. Many strategic infrastructures of this city especially the railway station were flooded, which caused heavy casualties and financial losses. This study aims to monitor the flood inundated areas of Yazd city due to this flood event using remote sensing. In this research, the Sentinel-1 polarimetric radar images and the 3D model of the Yazd city surf ace were used to delineate the flooded areas. The field information of the flooded areas and the available Sentinel-1 images during or near the occurrence time of maximum flood extension were adopted. The Convolutional Neural Network (CNN) model in combination with the 3D model of the studied area was used to identify the flooded pixels in the city of Yazd. The results showed that the adopted 3D model and CNN algorithm indicated a good ability to identify flooded areas with an accuracy of 88% and a kappa coefficient of 0.83

Green Infrastructure Mapping in Urban Areas Using Sentinel-1 Imagery

High temporal resolution of synthetic aperture radar (SAR) imagery (e.g., Sentinel-1 (S1) imagery) creates new possibilities for monitoring green vegetation in urban areas and generating land-cover classification (LCC) maps. This research evaluates how different pre-processing steps of SAR imagery affect classification accuracy. Machine learning (ML) methods were applied in three different study areas: random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB). Since the presence of the speckle noise in radar imagery is inevitable, different adaptive filters were examined. Using the backscattering values of the S1 imagery, the SVM classifier achieved a mean overall accuracy (OA) of 63.14%, and a Kappa coefficient (Kappa) of 0.50. Using the SVM classifier with a Lee filter with a window size of 5×5 (Lee5) for speckle reduction, mean values of 73.86% and 0.64 for OA and Kappa were achieved, respectively. An additional increase in the LCC was obtained with texture features calculated from a grey-level co-occurrence matrix (GLCM). The highest classification accuracy obtained for the extracted GLCM texture features using the SVM classifier, and Lee5 filter was 78.32% and 0.69 for the mean OA and Kappa values, respectively. This study improved LCC with an evaluation of various radiometric and texture features and confirmed the ability to apply an SVM classifier. For the supervised classification, the SVM method outperformed the RF and XGB methods, although the highest computational time was needed for the SVM, whereas XGB performed the fastest. These results suggest pre-processing steps of the SAR imagery for green infrastructure mapping in urban areas. Future research should address the use of multitemporal SAR data along with the pre-processing steps and ML algorithms described in this research

Monitoring of an Indonesian Tropical Wetland by Machine Learning-Based Data Fusion of Passive and Active Microwave Sensors

In this study, a novel data fusion approach was used to monitor the water-body extent in a tropical wetland (Lake Sentarum, Indonesia). Monitoring is required in the region to support the conservation of water resources and biodiversity. The developed approach, random forest database unmixing (RFDBUX), makes use of pixel-based random forest regression to overcome the limitations of the existing lookup-table-based approach (DBUX). The RFDBUX approach with passive microwave data (AMSR2) and active microwave data (PALSAR-2) was used from 2012 to 2017 in order to obtain PALSAR-2-like images with a 100 m spatial resolution and three-day temporal resolution. In addition, a thresholding approach for the obtained PALSAR-2-like backscatter coefficient images provided water body extent maps. The validation revealed that the spatial patterns of the images predicted by RFDBUX are consistent with the original PALSAR-2 backscatter coefficient images (r = 0.94, RMSE = 1.04 in average), and that the temporal pattern of the predicted water body extent can track the wetland dynamics. The PALSAR-2-like images should be a useful basis for further investigation of the hydrological/climatological features of the site, and the proposed approach appears to have the potential for application in other tropical regions worldwide

Towards Daily High-resolution Inundation Observations using Deep Learning and EO

Satellite remote sensing presents a cost-effective solution for synoptic flood monitoring, and satellite-derived flood maps provide a computationally efficient alternative to numerical flood inundation models traditionally used. While satellites do offer timely inundation information when they happen to cover an ongoing flood event, they are limited by their spatiotemporal resolution in terms of their ability to dynamically monitor flood evolution at various scales. Constantly improving access to new satellite data sources as well as big data processing capabilities has unlocked an unprecedented number of possibilities in terms of data-driven solutions to this problem. Specifically, the fusion of data from satellites, such as the Copernicus Sentinels, which have high spatial and low temporal resolution, with data from NASA SMAP and GPM missions, which have low spatial but high temporal resolutions could yield high-resolution flood inundation at a daily scale. Here a Convolutional-Neural-Network is trained using flood inundation maps derived from Sentinel-1 Synthetic Aperture Radar and various hydrological, topographical, and land-use based predictors for the first time, to predict high-resolution probabilistic maps of flood inundation. The performance of UNet and SegNet model architectures for this task is evaluated, using flood masks derived from Sentinel-1 and Sentinel-2, separately with 95 percent-confidence intervals. The Area under the Curve (AUC) of the Precision Recall Curve (PR-AUC) is used as the main evaluation metric, due to the inherently imbalanced nature of classes in a binary flood mapping problem, with the best model delivering a PR-AUC of 0.85

The use of Sentinel-1 time-Series data to improve flood Monitoring in arid areas

Due to the similarity of the radar backscatter over open water and over sand surfaces a reliable near real-time flood mapping based on satellite radar sensors is usually not possible in arid areas. Within this study, an approach is presented to enhance the results of an automatic Sentinel-1 flood processing chain by removing overestimations of the water extent related to low-backscattering sand surfaces using a Sand Exclusion Layer (SEL) derived from time-series statistics of Sentinel-1 data sets. The methodology was tested and validated on a flood event in May 2016 at Webi Shabelle River, Somalia and Ethiopia, which has been covered by a time-series of 202 Sentinel-1 scenes within the period June 2014 to May 2017. The approach proved capable to significantly improve the classification accuracy of the Sentinel-1 flood service within this study site. The Overall Accuracy increased by ~5% to a value of 98.5%, the User’s Accuracy by 25.2% to a value of 96.0%. Experimental results have shown that the classification accuracy is influenced by several parameters such as the lengths of the time-series used for generating the SEL

Monitoring surface water dynamics in the Prairie Pothole Region of North Dakota using dual-polarised Sentinel-1 synthetic aperture radar (SAR) time series

The North American Prairie Pothole Region (PPR) represents a large system of wetlands with great importance for biodiversity, water storage and flood management. Knowledge of seasonal and inter-annual surface water dynamics in the PPR is important for understanding the functionality of these wetland ecosystems and the changing degree of hydrologic connectivity between them. Optical sensors that are widely used for retrieving such information are often limited by their temporal resolution and cloud cover, especially in the case of flood events. Synthetic aperture radar (SAR) sensors can potentially overcome such limitations. However, water extent retrieval from SAR data is often impacted by environmental factors, such as wind on water surfaces. Hence, robust retrieval methods are required to reliably monitor water extent over longer time periods. The aim of this study was to develop a robust approach for classifying open water extent in the PPR and to analyse the obtained time series covering the entire available Sentinel-1 observation period from 2015 to 2020 in the hydrometeorological context. Open water in prairie potholes was classified by fusing dual-polarised Sentinel-1 data and high-resolution topographical information using a Bayesian framework. The approach was tested for a study area in North Dakota. The resulting surface water maps were validated using high-resolution airborne optical imagery. For the observation period, the total water area, the number of waterbodies and the median area per waterbody were computed. The validation of the retrieved water maps yielded producer's accuracies between 84 % and 95 % for calm days and between 74 % and 88 % for windy days. User's accuracies were above 98 % in all cases, indicating a very low occurrence of false positives due to the constraints introduced by topographical information. The observed dynamics of total water area displayed both intra-annual and inter-annual patterns. In addition to differences in seasonality between small ( 1 ha) waterbodies due to the effect of evaporation during summer, these size classes also responded differently to an extremely wet period from 2019 to 2020 in terms of the increase in the number of waterbodies and the total area covered. The results demonstrate the potential of Sentinel-1 data for high-resolution monitoring of prairie wetlands. Limitations of this method are related to wind inhibiting the correct water extent retrieval and to the rather long acquisition interval of 12 d over the PPR, which is a result of the observation strategy of Sentinel-1