Higher order mode conversion via focused ion beam milled Bragg gratings in Silicon-on-Insulator waveguides

We report the first Bragg gratings fabricated by Focused Ion Beam milling in optical waveguides. We observe striking features in the optical transmission spectra of surface relief gratings in silicon-on-insulator waveguides and achieve good agreement with theoretical results obtained using a novel adaptation of the beam propagation method and coupled mode theory. We demonstrate that leaky Higher Order Modes (HOM), often present in large numbers (although normally not observed) even in nominally single mode rib waveguides, can dramatically affect the Bragg grating optical transmission spectra. We investigate the dependence of the grating spectrum on grating dimensions and etch depth, and show that our results have significant implications for designing narrow spectral width gratings in high index waveguides, either for minimizing HOM effects for conventional WDM filters, or potentially for designing devices to capitalize on very efficient HOM conversion

Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles

The final authenticated publication is available at https://doi.org/10.1109/TGRS.2018.2858004.Soil moisture is a key environmental variable, important to, e.g., farmers, meteorologists, and disaster management units. Here, we present a method to retrieve surface soil moisture (SSM) from the Sentinel-1 (S-1) satellites, which carry C-band Synthetic Aperture Radar (CSAR) sensors that provide the richest freely available SAR data source so far, unprecedented in accuracy and coverage. Our SSM retrieval method, adapting well-established change detection algorithms, builds the first globally deployable soil moisture observation data set with 1-km resolution. This paper provides an algorithm formulation to be operated in data cube architectures and high-performance computing environments. It includes the novel dynamic Gaussian upscaling method for spatial upscaling of SAR imagery, harnessing its field-scale information and successfully mitigating effects from the SAR's high signal complexity. Also, a new regression-based approach for estimating the radar slope is defined, coping with Sentinel-1's inhomogeneity in spatial coverage. We employ the S-1 SSM algorithm on a 3-year S-1 data cube over Italy, obtaining a consistent set of model parameters and product masks, unperturbed by coverage discontinuities. An evaluation of therefrom generated S-1 SSM data, involving a 1-km soil water balance model over Umbria, yields high agreement over plains and agricultural areas, with low agreement over forests and strong topography. While positive biases during the growing season are detected, the excellent capability to capture small-scale soil moisture changes as from rainfall or irrigation is evident. The S-1 SSM is currently in preparation toward operational product dissemination in the Copernicus Global Land Service.5205392

Feasibility assessment of an automated, global, satellite-based flood monitoring product for the Copernicus Emergency Management Service

This report presents the results of the work carried out by an Expert Group, which was set up by the European Commission’s Joint Research Centre (JRC), and composed of scientific experts and representatives from industry and the research community, complemented by experts from the Commission’s JRC and DG GROW, with the aim of assessing the feasibility of an operational, global, automated, satellite-based flood monitoring product, within the CEMS framework. The specific objectives of the report are the following: a) To evaluate the user requirements in relation to improving the preparedness, management and response to floods, taking into account already existing tools. b) To analyse and evaluate the feasibility, and the scientific and technical issues that should be resolved, with regard to the implementation of an operational, global, automated, satellite-based flood-monitoring product. c) To propose a feasible methodology for an automated, global satellite-based flood monitoring product, that would be suitable for inclusion as part of CEMS.JRC.E.1-Disaster Risk Managemen

Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube

Spaceborne Synthetic Aperture Radar (SAR) are well-established systems for flood mapping, thanks to their high sensitivity towards water surfaces and their independence from daylight and cloud cover. Particularly able is the 2014-launched Copernicus Sentinel-1 C-band SAR mission, with its systematic monitoring schedule featuring global land coverage in a short revisit time and a 20 m ground resolution. Yet, variable environment conditions, low-contrasting land cover, and complex terrain pose major challenges to fully automated flood monitoring. To overcome these issues, and aiming for a robust classification, we formulate a datacube-based flood mapping algorithm that exploits the Sentinel-1 orbit repetition and a priori generated probability parameters for flood and non-flood conditions. A globally applicable flood signature is obtained from manually collected wind- and frost-free images. Through harmonic analysis of each pixel’s full time series, we derive a local seasonal non-flood signal comprising the expected backscatter values for each day-of-year. From those predefined probability distributions, we classify incoming Sentinel-1 images by simple Bayes inference, which is computationally slim and hence suitable for near-real-time operations, and also yields uncertainty values. The datacube-based masking of no-sensitivity resulting from impeding land cover and ill-posed SAR configuration enhances the classification robustness. We employed the algorithm on a 6-year Sentinel-1 datacube over Greece, where a major flood hit the region of Thessaly in 2018. In-depth analysis of model parameters and sensitivity, and the evaluation against microwave and optical reference flood maps, suggest excellent flood mapping skill, and very satisfying classification metrics with about 96% overall accuracy and only few false positives. The presented algorithm is part of the ensemble flood mapping product of the Global Flood Monitoring (GFM) component of the Copernicus Emergency Management Service (CEMS)

Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube

Spaceborne Synthetic Aperture Radar (SAR) are well-established systems for flood mapping, thanks to their high sensitivity towards water surfaces and their independence from daylight and cloud cover. Particularly able is the 2014-launched Copernicus Sentinel-1 C-band SAR mission, with its systematic monitoring schedule featuring global land coverage in a short revisit time and a 20 m ground resolution. Yet, variable environment conditions, low-contrasting land cover, and complex terrain pose major challenges to fully automated flood monitoring. To overcome these issues, and aiming for a robust classification, we formulate a datacube-based flood mapping algorithm that exploits the Sentinel-1 orbit repetition and a priori generated probability parameters for flood and non-flood conditions. A globally applicable flood signature is obtained from manually collected wind- and frost-free images. Through harmonic analysis of each pixel’s full time series, we derive a local seasonal non-flood signal comprising the expected backscatter values for each day-of-year. From those predefined probability distributions, we classify incoming Sentinel-1 images by simple Bayes inference, which is computationally slim and hence suitable for near-real-time operations, and also yields uncertainty values. The datacube-based masking of no-sensitivity resulting from impeding land cover and ill-posed SAR configuration enhances the classification robustness. We employed the algorithm on a 6-year Sentinel-1 datacube over Greece, where a major flood hit the region of Thessaly in 2018. In-depth analysis of model parameters and sensitivity, and the evaluation against microwave and optical reference flood maps, suggest excellent flood mapping skill, and very satisfying classification metrics with about 96% overall accuracy and only few false positives. The presented algorithm is part of the ensemble flood mapping product of the Global Flood Monitoring (GFM) component of the Copernicus Emergency Management Service (CEMS)

Bragg Gratings in Silicon-on-insulator Waveguides using Focused Ion Beam Milling

We report Bragg grating structures fabricated by focused ion beam milling in optical waveguides, and demonstrate that they can be used as a powerful diagnostic of optical modes in very high index waveguides. We show that higher-order lossy modes, which can be present in large numbers even in single-moded silicon-on-insulator waveguides, can dramatically affect the optical transmission spectra of Bragg gratings in these waveguides, even though these modes are normally not observable. Our results not only illuminate challenges to realize practical gratings in high index waveguides, but raise the possibility of devices based on mode conversion to extremely high order modes

Bragg gratings in silicon-on-insulator waveguides by focused ion beam milling

We report Bragg grating structures fabricated by focused ion beam milling in optical waveguides, and demonstrate that they can be used as a powerful diagnostic of optical modes in very high index waveguides. We show that higher-order lossy modes, which can be present in large numbers even in single-moded silicon-on-insulator waveguides, can dramatically affect the optical transmission spectra of Bragg gratings in these waveguides, even though these modes are normally not observable. Our results not only illuminate challenges to realize practical gratings in high index waveguides, but raise the possibility of devices based on mode conversion to extremely high order modes

Data processing architectures for monitoring floods using Sentinel-1

Synthetic Aperture Radar (SAR) images acquired by Earth observation satellites often constitute the only source of information formonitoring the progression of flood events over larger regions. Particularly attractive are the SAR data acquired by the CopernicusSentinel-1 satellites because they are free and open, and combine a short revisit time with a good spatial and radiometric resolution.In this contribution, we discuss how a Sentinel-1 data processing system should be designed to optimally benefit from the denseSentinel-1 time series and advanced algorithms such as change detection or machine learning methods. This was one of the questionsaddressed by an expert group tasked by the Joint Research Centre of the European Commission to investigate the feasibility of anautomated, global, satellite-based flood monitoring product for the Copernicus Emergency Management Service. Drawing fromthe expert group report, we distinguish three broad categories of data processing architectures, namely single-image, dual-image,and data cube processing architectures. While the latter architecture is the most demanding in terms of large storage and computecapacities, it is also the most promising to derive high-quality Sentinel-1 flood maps comprised not just of the flood mask but alsoof data fields describing the retrieval uncertainty and masks showing where Sentinel-1 cannot detect floods due to physical reasons.Therefore, we recommend to use data cube processing architectures and showcase the use of the Austrian Data Cube for monitoringa small-scale flood event that occurred in Austria in November 2019