An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

open8siThe assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to moni- tor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM– DA in recent years (e.g. the Advanced SCATterometer – AS- CAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportu- nity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydro- logical model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was car- ried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i) revealing the potential provided by S1- based SM–DA for improving discharge predictions, espe- cially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spa- tial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time pe- riod, and thus should be supported by further research activ- ities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM–DA framework for flash flood risk mitigation.openCenci, Luca; Pulvirenti, Luca; Boni, Giorgio; Chini, Marco; Matgen, Patrick; Gabellani, Simone; Squicciarino, Giuseppe; Pierdicca, NazzarenoCenci, Luca; Pulvirenti, Luca; Boni, Giorgio; Chini, Marco; Matgen, Patrick; Gabellani, Simone; Squicciarino, Giuseppe; Pierdicca, Nazzaren

A Method to Automatically Detect Changes in Multitemporal Spectral Indices: Application to Natural Disaster Damage Assessment

This paper presents a new method, based on clustering and thresholding, to automatically perform binary change detection in multitemporal spectral indices. The method is denoted as Buffer-From-Cluster Approach (BFCA). To estimate the distributions of changed and unchanged pixels, as needed for the purpose of a reliable thresholding of a spectral index, a clustering algorithm is preliminarily applied to identify image objects possibly corresponding to areas where significant changes occurred. Then, a buffer zone is created around the selected cluster to identify unchanged areas surrounding changed ones. The cluster and the buffer zone are jointly analyzed to estimate the distributions of changed and unchanged pixels and to verify that they can be distinguished from each other. Finally, the results of thresholding and clustering are combined to generate the binary change map. The BFCA has been conceived to map the extent of the areas affected by a natural disaster like wildfire. To validate the proposed method, burned area maps produced by applying the BFCA to spectral indices derived from Sentinel-2 data have been compared to maps produced by the Copernicus Emergency Management Service. For testing the multi-hazard detection capability, the same kind of exercise has been carried out for a flooding test case too. The positive results of the comparison have confirmed the effectiveness of the proposed method

A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data

An automated tool for pre-operational mapping of floods and inland waters using Sentinel- 1 data is presented. The acronym AUTOWADE (AUTOmaticWater Areas DEtector) is used to denote it. The tool provides the end user (Italian Department of Civil Protection) with a continuous, near real-time (NRT) monitoring of the extent of inland water surfaces (floodwater and permanent water). It implements the following operations: downloading of Sentinel-1 products; preprocessing of the products and storage of the resulting geocoded and calibrated data; generation of the intermediate products, such as the exclusion mask; application of a floodwater/permanent water mapping algorithm; generation of the output layer, i.e., a map of floodwater/permanent water; delivery of the output layer to the end user. The open floodwater/permanent water mapping algorithm implemented in AUTOWADE is based on a new approach, denoted as buffer-from-edge (BFE), which combines different techniques, such as clustering, edge filtering, automatic thresholding and region growing. AUTOWADE copes also with the typical presence of gaps in the flood maps caused by undetected flooded vegetation. An attempt to partially fill these gaps by analyzing vegetated areas adjacent to open water is performed by another algorithm implemented in the tool, based on the fuzzy logic. The BFE approach has been validated offline using maps produced by the Copernicus Emergency Management Service. Validation has given good results with a F1-score larger than 0.87 and a kappa coefficient larger than 0.80. The algorithm to detect flooded vegetation has been visually compared with optical data and aerial photos; its capability to fill some of the gaps present in flood maps has been confirmed

Mapping Flooded Vegetation Using COSMO-SkyMed: Comparison With Polarimetric and Optical Data Over Rice Fields

The capability of COSMO-SkyMed (CSK) radar to remotely sense standing water beneath vegetation using an auto- matic algorithm working on a single image is investigated. The ob- jective is to contribute to tackle the problem of missed detection of inundated vegetation by near real-time flood mapping algorithms using SAR data. The focus is on CSK because its four-satellite con- stellation is very suitable for rapid mapping. A set of CSK observa- tions of an area in Northern Italy where many rice fields are present and recurrent artificial inundations occur were analyzed. Consid- ering that double-bounce is the key process to detect floodwater under vegetation and that polarimetry is potentially able to dis- criminate double-bounce among different scattering mechanisms, single polarization CSK observations were compared with ALOS-2 and RADARSAT-2 fully polarimetric data. Such a multifrequency and multiangle dataset helped understanding the multitemporal signature of CSK data. A set of Landsat-8 images collected un- der cloud free conditions were also used as reference. Satellite acquisitions were gathered in order to ensure both spatial over- lap among the images of the various sensors and temporal overlap along most of the rice growing season. The comparison between CSK and polarimetric data showed that at least for a slender leaf plant like rice, CSK can be able to detect the enhancement of double-bounce backscattering involving water and vertical plant stems. For some selected fields, it was found a good agreement be- tween CSK-derived floodwater maps and those produced using the normalized-difference water index derived from Landsat-8 images, as well as double-bounce detection from polarimetric data

Polarimetric SAR data for improving flood mapping: An investigation over rice flooded fields

In this paper, we investigate the role of polarimetric features to improve flood mapping in agricultural areas. Considering that the double bounce enhancement due to standing water can increase the backscatter from flooded agricultural fields, polarimetry can potentially detect this mechanism and mitigate the misdetection of algorithms based on the identification of dark areas in the image. The investigation was carried out by analysing a polarimetric and multifrequency SAR dataset (COSMO-SkyMed, Alos-2, Radarsat-2) collected over the Vercelli district in North Italy, characterized by a widespread and intense cultivation of rice crop, were the fields were routinely artificially flooded and dried according to the agricultural practice. The investigation demonstrated that the polarimetric data are able to recognize the double bounce return in areas with high backscattering. They overcome the need of a pre-flood image, otherwise required to identify a sudden increase of backscatter to be ascribed to the standing water

Polarimetric SAR data for improving flood mapping: An investigation over rice flooded fields

In this paper, we investigate the role of polarimetric features to improve flood mapping in agricultural areas. Considering that the double bounce enhancement due to standing water can increase the backscatter from flooded agricultural fields, polarimetry can potentially detect this mechanism and mitigate the misdetection of algorithms based on the identification of dark areas in the image. The investigation was carried out by analysing a polarimetric and multifrequency SAR dataset (COSMO-SkyMed, Alos-2, Radarsat-2) collected over the Vercelli district in North Italy, characterized by a widespread and intense cultivation of rice crop, were the fields were routinely artificially flooded and dried according to the agricultural practice. The investigation demonstrated that the polarimetric data are able to recognize the double bounce return in areas with high backscattering. They overcome the need of a pre-flood image, otherwise required to identify a sudden increase of backscatter to be ascribed to the standing water

Flood mapping by SAR: Possible approaches to mitigate errors due to ambiguous radar signatures

The latest generation of synthetic aperture radar (SAR) systems allows providing emergency managers with near real time flood maps characterized by a very high spatial resolution. Near real time flood detection algorithms generally search for regions of low backscatter, thus assuming that floodwater appears dark in a SAR image. It is well known that this assumption is not always valid. For instance, in urban areas, the double bounce backscattering involving ground and vertical walls produce high radar return that can be further increased by the presence of the highly reflective floodwater. In addition, even mapping bare or scarcely vegetated inundated terrains, or crops totally submerged by water can turn out to be a difficult task. In fact, in the presence of significant wind that roughens the water surface, floodwater can appear bright in SAR images. This paper proposes possible strategies to cope with flood mapping using SAR data in urban areas and in the presence of significant wind. In particular, the use of the interferometric coherence for floodwater detection in urban areas and the use of an electromagnetic model able to simulate the radar return from shallow water as function of the wind field are proposed

MONITORING RESERVOIRS' WATER LEVEL FROM SPACE FOR FLOOD CONTROL APPLICATIONS. A CASE STUDY IN THE ITALIAN ALPINE REGION

reserved8siThe objective of this research was to develop a method for water level retrieval in natural and artificial lakes. It was thought to be applied for monitoring purposes and flood control applications, especially in data-scarce environments. The method is based on a combined GIS, remote sensing and statistical modeling approach. It was tested on both optical (Landsat 8) and SAR (Cosmo-SkyMed (R)) data. The topographic information, required by the method, were obtained from freely available digital elevation models (SRTM and ASTER) to compare their performances. The Place Moulin Lake, an Alpine reservoir, was selected as study area since it represents a very challenging case study for developing the proposed methodology. The results showed that: i) the method provided reasonably accurate results when the degree of filling of the reservoir was high. ii) The accuracy of the results strongly relied on the accuracy of the topographic information. iii) The combination of Cosmo-SkyMed (R) and SRTM data provided more reliable results. Further analyses are required to evaluate the method in different environmental conditions.mixedCenci, L; Boni, G; Pulvirenti, L; Squicciarino, G; Gabellani, S; Gardella, F; Pierdicca, N; Chini, MCenci, L; Boni, G; Pulvirenti, L; Squicciarino, G; Gabellani, S; Gardella, F; Pierdicca, N; Chini,