We present a new methodology for rapid flood mapping exploiting Sentinel-1 synthetic aperture radar data. In particular, we propose the usage of ground range detected (GRD) images, i.e., preprocessed products made available by the European Space Agency, which can be quickly treated for information extraction through simple and poorly demanding algorithms. The proposed framework is based on two processing levels providing event maps with increasing resolution. The first level exploits classic co-occurrence texture measures combined with amplitude information in a fuzzy classification system avoiding the critical step of thresholding. The second level consists of a change-detection approach applied to the full resolution GRD product. The discussion is supported by several experiments demonstrating the potentiality of the proposed methodology, which is particularly oriented toward the end-user community.

In this paper we explore the performances and the opportunities provided by the European satellite Sentinel-1 for water resource management applications in low-income countries. The analysis is supported by a synthetic aperture radar (SAR) simulator, which allowed the quantification of the expected characteristics of Sentinel-1 products in three applications: interferometric digital elevation models (DEMs) generation, land cover mapping and estimation of water volumes retained by small reservoirs. The obtained results quantitatively show that Sentinel-1 data characteristics are fully suitable for most of the application already explored in the recent SAR literature.

Images provided by the ESA Sentinel-2 mission are rapidly becoming the main source of information for the entire remote sensing community, thanks to their unprecedented combination of spatial, spectral and temporal resolution, as well as their associated open access policy. Due to a sensor design trade-off, images are acquired (and delivered) at different spatial resolutions (10, 20 and 60 m) according to specific sets of wavelengths, with only the four visible and near infrared bands provided at the highest resolution (10 m). Although this is not a limiting factor in general, many applications seem to emerge in which the resolution enhancement of 20 m bands may be beneficial, motivating the development of specific super-resolution methods. In this work, we propose to leverage Convolutional Neural Networks (CNNs) to provide a fast, upscalable method for the single-sensor fusion of Sentinel-2 (S2) data, whose aim is to provide a 10 m super-resolution of the original 20 m bands. Experimental results demonstrate that the proposed solution can achieve better performance with respect to most of the state-of-the-art methods, including other deep learning based ones with a considerable saving of computational burden.

Microwave remote sensing has widely demonstrated its potential in the continuous monitoring of our rapidly changing planet. This review provides an overview of state-of-the-art methodologies for multi-temporal synthetic aperture radar change detection and its applications to biosphere and hydrosphere monitoring, with special focus on topics like forestry, water resources management in semi-arid environments and floods. The analyzed literature is categorized on the base of the approach adopted and the data exploited and discussed in light of the downstream remote sensing market. The purpose is to highlight the main issues and limitations preventing the diffusion of synthetic aperture radar data in both industrial and multidisciplinary research contexts and the possible solutions for boosting their usage among end-users.

Satellite data are very useful for the continuous monitoring of ever-changing environments, such as wetlands. In this study, we investigated the use of multispectral imagery to monitor the winter evolution of land cover in the Albufera wetland (Spain), using Landsat-8 and Sentinel-2 datasets. With multispectral data, the frequency of observation is limited by the possible presence of clouds. To overcome this problem, the data acquired by the two missions, Landsat-8 and Sentinel-2, were jointly used, thus roughly halving the revisit time. The varied types of land cover were grouped into four classes: (1) open water, (2) mosaic of water, mud and vegetation, (3) bare soil and (4) vegetated soil. The automatic classification of the four classes was obtained through a rule-based method that combined the NDWI, MNDWI and NDVI indices. Point information, provided by geo-located ground pictures, was spatially extended with the help of a very high-resolution image (GeoEye-1). In this way, surfaces with known land cover were obtained and used for the validation of the classification method. The overall accuracy was found to be 0.96 and 0.98 for Landsat-8 and Sentinel-2, respectively. The consistency evaluation between Landsat-8 and Sentinel-2 was performed in six days, in which acquisitions by both missions were available. The observed dynamics of the land cover were highly variable in space. For example, the presence of the open water condition lasted for around 60–80 days in the areas closest to the Albufera lake and progressively decreased towards the boundaries of the park. The study demonstrates the feasibility of using moderate-resolution multispectral images to monitor land cover changes in wetland environments.

In this paper, we present a new approach to the fusion of Sentinel 1 (S1) and Sentinel 2 (S2) data for land cover mapping. The proposed solution aims at improving methods based on Sentinel 2 data, that are unusable in case of cloud cover. This goal is achieved by using S1 data to generate S2-like segmentation maps to be used to integrate S2 acquisitions forbidden by cloud cover. In particular, we propose for the first time in remote sensing a multi-temporal W-Net approach for the segmentation of Interferometric Wide swath mode (IW) Sentinel-1 data collected along ascending/descending orbit to discriminate rice, water, and bare soil. The quantitative assessment of segmentation accuracy shows an improvement of 0.18 and 0.25 in terms of accuracy and F1-score by applying the proposed multi-temporal procedure with respect to the previous single-date approach. Advantages and disadvantages of the proposed W-Net based solution have been tested in the National Park of Albufera, Valencia, and we show a performance gain in terms of the classical metrics used in segmentation tasks and the computational time.