The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas

Abstract

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 of significantly improving 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% and the User's Accuracy increased 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 of an Indonesian Tropical Wetland by Machine Learning-Based Data Fusion of Passive and Active Microwave Sensors

“…A popular SAR mission is the Sentinel-1, which provides C-band SAR images observed by a constellation of two satellites. Researchers have used backscatter coefficient images of vertical transmitting and vertical receiving (VV) polarization, which is included in the primary observation mode of Sentinel-1 (interferometric wide swath: IW) to map surface water [8][9][10]. In recent years, the relatively high repeat frequency achieved by this constellation (after 2016) and its data availability to users (i.e., they are provided for free by European Space Agency) have attracted attention for its use in rapid water mapping [9].…”

Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands

“…L-band radar imaging systems would provide the best potential for making these assessments [35,36,95]. Although it is encouraging to report accuracies associated with mapping inundated vegetation using TropWet equivalent to those reported by approaches that use L-band imagery [32,35] or GNSS-R [21,23,24], it should be reiterated that TropWet is limited to inundated grassland environments. However, L-band imagery archives are not currently ingested within GEE.…”

“…These approaches provide valuable tools for quantifying wetland dynamics at continental scales with important applications such as characterising greenhouse gas flux [17,18,24,25]. However, assessments of wetland extent and dynamics using these approaches tend to be generated at relatively coarse spatial resolutions (e.g., 25-36 km) and have limited applicability for informing decisions at a national or sub-national level, particularly related to more fine-scale environmental challenges such as those related to biodiversity, public health, and flood hazard.In terms of inundation mapping, perhaps the most mature area of research is the use of EO satellite imagery for mapping flood water hazards [2][3][4][5]7,[26][27][28][29][30][31][32]. Radar imagery provides one of the most reliable means of detecting flood water mainly due to the fact that this imagery is: (i) independent of cloud cover, (ii) relatively high resolution (e.g., Sentinel-1: 10 m), (iii) relatively high revisit times (e.g., Sentinel-1: 6-12 days), and (iv) there is a strong signal (low backscatter) over relatively smooth water surfaces.…”

“…In terms of inundation mapping, perhaps the most mature area of research is the use of EO satellite imagery for mapping flood water hazards [2][3][4][5]7,[26][27][28][29][30][31][32]. Radar imagery provides one of the most reliable means of detecting flood water mainly due to the fact that this imagery is: (i) independent of cloud cover, (ii) relatively high resolution (e.g., Sentinel-1: 10 m), (iii) relatively high revisit times (e.g., Sentinel-1: 6-12 days), and (iv) there is a strong signal (low backscatter) over relatively smooth water surfaces.…”

Análise da vegetação a partir de dados de sensoriamento remoto multitemporal no município de Pato Branco-PR

“…Monitorar as mudanças na superfície da Terra, usando sensoriamento remoto, é amplamente utilizado em diferentes aplicações, como: mudança de uso e cobertura do solo (SALMON et al, 2013;HUANG et al, 2019); monitoramento de desastres (VOLPI et al, 2013;MARTINIS et al, 2018); crescimento e expansão urbana (RAJA et al, 2013;WANG et al, 2018); hidrologia (DRONOVA et al, 2011;LUO et al, 2019) e mudança da vegetação (MARKOGIANNI et al, 2013;ZHAO et al, 2019).…”