FLOMPY: An Open-Source Toolbox for Floodwater Mapping Using Sentinel-1 Intensity Time Series

Abstract

A new automatic, free and open-source python toolbox for the mapping of floodwater is presented. The output of the toolbox is a binary mask of floodwater at a user-specified time point within geographical boundaries. It exploits the high spatial (10m) and temporal (6 days per orbit over Europe) resolution of Sentinel-1 GRD intensity time series and is based on four processing steps. In the first step, a selection of Sentinel-1 images related to pre-flood (baseline) state and flood state is performed. In the second step, the preprocessing of the selected images is performed in order to create a co-registered stack with all the pre-flood and flood images. In the third step, a statistical temporal analysis is performed and a t-score map that represents the changes due to a flood event is calculated. Finally, in the fourth step, a classification procedure based on the t-score map is performed to extract the final flood map. A thorough analysis based on several flood events is presented to demonstrate the main benefits, limitations and the potential of the proposed methodology. The validation was performed using Copernicus Emergency Management Service (EMS) products. In all case studies, overall accuracies were higher than 0.95 with Kappa scores higher than 0.76. We believe that the end-user community can benefit by exploiting the flood maps of the proposed methodological pipeline by using the provided open-source toolbox.

Fine-Resolution Measurement of Soil Moisture From Cumulative InSAR Closure Phase

“…Some works characterize or correct this bias, including methods outlined in [15], [18], [19], [38], [39], [43]- [45]; many of these take advantage of the full stack of interferograms or of longer temporal baselines, which empirically observe lower systematic phase biases [15]. Others invert for soil moisture explicitly [14], [16], [20], [46], using models of varying complexity. The solutions may incorporate additional information such as interferometric phase [20], coherence magnitude [14], [20], SAR backscatter [14], [47], calculations of decorrelation phase [16], [38], or meteorological information [46].…”

“…Others invert for soil moisture explicitly [14], [16], [20], [46], using models of varying complexity. The solutions may incorporate additional information such as interferometric phase [20], coherence magnitude [14], [20], SAR backscatter [14], [47], calculations of decorrelation phase [16], [38], or meteorological information [46]. External data are used to solve for the ambiguity where more than one soil moisture time series may generate the same closure phase even in the absence of noise [11], [13], [20].…”

“…External data are used to solve for the ambiguity where more than one soil moisture time series may generate the same closure phase even in the absence of noise [11], [13], [20]. The inversion algorithm may involve ordering the acquisitions by moisture level [14], [46]. This work considers the closure phase as a source of signal rather than a source of error or noise.…”

A Temporal Deep Convolutional Neural Network Model on Sentinel-1 Image Time Series for Pixel-Wise Flood Classification

“…Core flood detection approaches regardless of number of images are thresholding, change detection [5], change detection and thresholding [6], supervised [7,8], semi-supervised [9,10] and unsupervised [2] image classification based on classical machine and deep learning. Multi-temporal pixelwise time series approaches such as [11,12], despite being lightweight, do not exploit the temporal autocorrelation, as they treat the time series using statistical analysis without any learning procedure. On the other hand, sophisticated deep learning methods may be more accurate but they have a high computational complexity.…”

Towards better flood risk management using a Bayesian network approach

“…One such new methodology is the Floodwater Depth Estimation Tool (FwDET) which estimates floodwater depth based on an inundation map and a digital elevation model (DEM). Another open-source toolbox for floodwater mapping is the Flood Mapping Python toolbox (FLOMPY) [5]. This is an easy-to-use Python tool, even for less experienced users.…”