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This paper presents MULESME, a software designed for the systematic mapping of surface soil moisture using Sentinel-1 SAR data. MULESME implements a multi-temporal algorithm that uses time series of Sentinel-1 data and ancillary data, such as a plant water content map, as inputs. A secondary software module generates the plant water content map from optical data provided by Landsat-8, or Sentinel-2, or MODIS. Each output of MULESME includes another map showing the level of uncertainty of the soil moisture estimates. MULESME was tested by using both synthetic and actual data. The results of the tests showed that root mean square error is in the range between 0.03 m 3 /m 3 (synthetic data) and 0.06 m 3 /m 3 (actual data) for bare soil. The accuracy decreases in the presence of vegetation (root mean square in the range 0.08–0.12 m 3 /m 3 ), as expected. [ABSTRACT FROM AUTHOR]

An automated tool for pre-operational mapping of floods and inland waters using Sentinel-1 data is presented. The acronym AUTOWADE (AUTOmatic Water 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A fully automated processing chain for near real-time mapping of burned forest areas using Sentinel-2 multispectral data is presented. The acronym AUTOBAM (AUTOmatic Burned Areas Mapper) is used to denote it. AUTOBAM is conceived to work daily at a national scale for the Italian territory to support the Italian Civil Protection Department in the management of one of the major natural hazards, which affects the territory. The processing chain includes a Sentinel-2 data procurement component, an image processing algorithm, and the delivery of the map to the end-user. The data procurement component searches every day for the most updated products into different archives. The image processing part represents the core of AUTOBAM and implements an algorithm for burned forest areas mapping that uses, as fundamental parameters, the relativized form of the delta normalized burn ratio and the normalized difference vegetation index. The minimum mapping unit is 1 ha. The algorithm implemented in the image processing block is validated off-line using maps of burned areas produced by the Copernicus Emergency Management Service. The results of the validation shows an overall accuracy (considering the classes of burned and unburned areas) larger than 95% and a kappa coefficient larger than 80%. For what concerns the class of burned areas, the commission error is around 1%−3%, except for one case where it reaches 25%, while the omission error ranges between 6% and 25%. [ABSTRACT FROM AUTHOR]

We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (∼ 301 × 10 3 km2) – a transitional continental-to-Mediterranean region where snow plays an important but still poorly constrained societal and ecological role. IT-SNOW provides ∼ 500 m daily maps of snow water equivalent (SWE), snow depth, bulk snow density, and liquid water content for the initial period 1 September 2010–31 August 2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel-2, MODIS (Moderate Resolution Imaging Spectroradiometer), and H SAF products, as well as maps of snow depth from the spatialization of over 350 on-the-ground snow depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in situ snow data from three focus regions (Aosta Valley, Lombardy, and Molise) show little to no mean bias compared to the former, and root mean square errors are of the typical order of 30–60 cm and 90–300 mm for in situ, measured snow depth and snow water equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak water volume in snow and annual streamflow that are in line with expectations for this mixed rain–snow region (22 % on average and 12 % median). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of water volume stored in snow across the Italian landscape at peak accumulation, which on average occurs on 4 March ± 10 d. Nearly 52 % of the mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23 %), and central Apennines (5 %). IT-SNOW is freely available at 10.5281/zenodo.7034956 and can contribute to better constraining the role of snow for seasonal to annual water resources – a crucial endeavor in a warming and drier climate. [ABSTRACT FROM AUTHOR]

We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (300k+ km²) covering a transitional continental-to-Mediterranean region where snow plays an important, but still poorly constrained societal and ecological role. IT-SNOW provides ∼500-m, daily maps of Snow Water Equivalent (SWE), snow depth, bulk-snow density, and liquid water content for the 5 period 01/09/2010 - 31/08/2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil-protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel 2, MODIS, and H-SAF products, and maps of snow depth from the spazialization of 350+ on-the-ground snow-depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in-situ snow data from three focus regions (Aosta Valley, Lombardia, and Molise) shows little to none mean bias compared to the former, and Root Mean Square Errors on the order of 30 to 60 cm and 90 to 300 mm for in-situ, measured snow depth and Snow Water Equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak SWE and annual streamflow that are in line with expectations for this mixed rain-snow region (22% on average). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of SWE across the Italian landscape at peak accumulation, which on average occurs on the 4th of March. Nearly 52% of mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23%), and central Apennines (5%). IT-SNOW is freely available with the following DOI: https://doi.org/10.5281/zenodo.7034956 (Avanzi et al., 2022b) and can contribute to better constraining the role of snow for seasonal to annual water resources - a crucial endevor in a warming and drier climate. [ABSTRACT FROM AUTHOR]

This work aimed at exploiting optical satellite data for mapping the effects of the wind storm that hit the Northeast Italy between the 27 and the 30 October 2018. During the storm, wind gusts up to 192 km/h were recorded (ARPAV, 2019). The event produced significant damages in forested areas, where several trees were flattened. The objective of this work was to implement a methodology to map the affected area in which trees were destroyed. To this end, multitemporal data acquired by the Sentinel 2 constellation (Level-2A products) were exploited. This task has been formalized as a change detection problem and from a methodological perspective it was addressed through a supervised classification approach. In particular, the adopted technique is based on the integration of two popular approaches for remote sensing image analysis, namely support vector machine and Markov random fields, and it is able to integrate in the classification process the spatial-contextual information associated with the input imagery (Moser and Serpico, 2013). A pair of Sentinel 2 images was used in the analysis: a pre-event image (dated 31/10/2017) and a post-event image (dated 15/11/2018). Before applying the above-mentioned change detection methodology, the images were pre-processed for masking out, in both scenes, all the pixels that were likely to produce false errors in at least one of them (e.g., clouds, clouds shadows, dark areas, snow). To achieve this objective, the classification masks provided in each Sentinel 2 product were used. In order to minimise the effect of the phenological cycle, from the images there were masked out all the pixels that were not classified as "Coniferous Forest" according to the CORINE Land Cover 2012 product. The rationale is that all the changes occurring in the remaining "not masked" area are due to the effect of the wind storm on the coniferous forests. Importantly, during the elaboration only the spectral bands retaining a spatial resolution of 10 m were used. Classification results were qualitatively validated by means of a visual inspection. Because of the extraordinary nature of the event, many national and international news agencies reported the news and facilitated the dissemination images and videos (e.g., collected by unmanned aerial vehicles) that were also used for validation. Findings showed that the methodology that was implemented successfully allowed the identification of the forested areas damaged by the wind storm. References:- ARPAV, 2019: http://www.arpa.veneto.it/ - Accessed: 01/10/2019- Moser, G. and Serpico, S. B.: Combining support vector machines and markov random fields in an integrated framework for contextual image classification, IEEE Transactions on Geoscience and Remote Sensing, 51, 2734–2752, 2013. [ABSTRACT FROM AUTHOR]

The proposed classification approach integrates multi-sensor, multi-source and contextual information associated with COSMO-SkyMed (CSK) and Pléiades images, thus formalizing the change-detection task in terms of a data fusion problem. Multi-temporal fusion "at the decision level" is used in this work. In particular, the change detection problem is operatively addressed through a multi-temporal supervised classification of multi-sensor CSK/Pléiades data. The method makes use of the Markov Random Field (MRF) theory to integrate the spatial context and the temporal correlation associated with the images acquired at the different dates, and is fully integrated with a region-based approach able to characterize the geometrical structures in the images through the employment of multiple segmentation maps at different scales. The approach has been applied to bespoke multi-temporal series consisting of pairs of CSK/Pléiades images that have been acquired as much as possible simultaneously, and provided by the Italian Space Agency (ASI) and the Centre National d'Études Spatiales (CNES) in the framework of CEOS (Committee on Earth Observation Satellites) Working Group on Disasters activities. The target is to monitor the changes in land cover classes. In order to exploit the potential of the change detection methodology and the spatial information of very high resolution (VHR) imagery, the proposed approach is implemented in the two following steps: - a first analysis conducted on the acquired imagery subsampled at a resolution of 10 m;- a second analysis performed on particularly interesting areas highlighted during the first step, making use of the imagery at the maximum available resolution.The target of the project is to test the potential of the proposed approach to monitor changes in the land use/land cover that can be related to:(i) the recovery phase after a natural disaster;(ii) the implementation of Disaster Risk Reduction (DRR) policies (e.g. monitoring of Sendai Framework Assessment - SFA indicators).Two case studies are presented:(i) The monitoring of the recovery phase in the aftermath of Hurricane Matthew in Haiti, which struck the Caribbean in October 2016, with a dataset composed of CSK/Pléiades pairs acquired in November 2015 (before the event), in October 2016 (few days after the event) and a year later, in October 2017.(ii) The monitoring of the surroundings of the city of Phnom Penh, Cambodia, at different time steps. The target is to monitor the changes in land cover classes related to SFA indicators. The focus is on a subset of the indicators C and D, which can be directly related to specific land cover classes. In particular C2-C, C-2FO, C-3, C-4, C-5, D2, D3, and D4.The work presented in this abstract is framed within the activity of the CEOS-DRM project (Committee on Earth Observation Satellites – Disaster Risk Management) funded by ASI. [ABSTRACT FROM AUTHOR]