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The Global Navigation Satellite System (GNSS) is a key asset for tropospheric monitoring. Currently, GNSS meteorology relies primarily on geodetic-grade stations. However, such stations are too costly to be densely deployed, which limits the contribution of GNSS to tropospheric monitoring. In 2016, Google released the raw GNSS measurement application programming interface for smartphones running on Android version 7.0 and higher. Since nowadays there are billions of Android smartphones worldwide, utilizing those devices for atmospheric monitoring represents a remarkable scientific opportunity. In this study, smartphone GNSS data collected in Germany as part of the Application of Machine Learning Technology for GNSS IoT Data Fusion (CAMALIOT) crowdsourcing campaign in 2022 were utilized to investigate this idea. Approximately twenty thousand raw GNSS observation files were collected there during the campaign. First, a dedicated data processing pipeline was established that consists of two major parts: machine learning (ML)-based data selection and ionosphere-free Precise Point Positioning (PPP)-based Zenith Total Delay (ZTD) estimation. The proposed method was validated with a dedicated smartphone data collection experiment conducted on the rooftop of the ETH campus. The results confirmed that ZTD estimates of mm-level precision could be achieved with smartphone data collected in an open-sky environment. The impacts of observation time span and utilization of multi-GNSS observations on ZTD estimation were also investigated. Subsequently, the crowdsourced data from Germany were processed by PPP with the ionospheric delays interpolated using observations from surrounding SAPOS (Satellite Positioning Service of the German State Survey) GNSS stations. The ZTDs derived from ERA5 and an ML-based ZTD product served as benchmarks. The results revealed that an accuracy of better than 10 mm can be achieved by utilizing selected high-quality crowdsourced smartphone data. This study marks the first successful demonstration of high-precision ZTD determination with crowdsourced smartphone GNSS data and reveals success factors and current limitations. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System (GNSS) is a key asset for tropospheric monitoring. Currently, GNSS meteorology relies primarily on geodetic-grade stations. However, such stations are too costly to be densely deployed, which limits the contribution of GNSS to tropospheric monitoring. In 2016, Google released the raw GNSS measurement application programming interface for smartphones running on Android version 7.0 and higher. Since nowadays there are billions of Android smartphones worldwide, utilizing those devices for atmospheric monitoring represents a remarkable scientific opportunity. In this study, smartphone GNSS data collected in Germany as part of the Application of Machine Learning Technology for GNSS IoT Data Fusion (CAMALIOT) crowdsourcing campaign in 2022 were utilized to investigate this idea. Approximately twenty thousand raw GNSS observation files were collected there during the campaign. First, a dedicated data processing pipeline was established that consists of two major parts: machine learning (ML)-based data selection and ionosphere-free Precise Point Positioning (PPP)-based Zenith Total Delay (ZTD) estimation. The proposed method was validated with a dedicated smartphone data collection experiment conducted on the rooftop of the ETH campus. The results confirmed that ZTD estimates of mm-level precision could be achieved with smartphone data collected in an open-sky environment. The impacts of observation time span and utilization of multi-GNSS observations on ZTD estimation were also investigated. Subsequently, the crowdsourced data from Germany were processed by PPP with the ionospheric delays interpolated using observations from surrounding SAPOS (Satellite Positioning Service of the German State Survey) GNSS stations. The ZTDs derived from ERA5 and an ML-based ZTD product served as benchmarks. The results revealed that an accuracy of better than 10 mm can be achieved by utilizing selected high-quality crowdsourced smartphone data. This study marks the first successful demonstration of high-precision ZTD determination with crowdsourced smartphone GNSS data and reveals success factors and current limitations. [ABSTRACT FROM AUTHOR]

Very high resolution (VHR) satellite imagery from Google Earth and Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, this imagery is used to create detailed time-sensitive maps, e.g. for emergency response purposes, or to validate coarser resolution products such as global land cover maps. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global snapshot of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas. [ABSTRACT FROM AUTHOR]

The Zurich Flood Resilience Alliance, initiated and funded by Zurich Insurance, brings together NGO partners, academia and the private sector to help build flood resilience in communities across the globe. We highlight two innovative efforts of risk and resilience knowledge co-generation innovatively blending local knowledge with earth observation technology.Risk data is sparse in many vulnerable regions and global expert-generated models are often too coarse in terms of resolution. Participatory Vulnerability Capacity Assessment is a widely used tool to collect local risk and resilience information, yet not linked to digitised information. Combining such community-based participatory mapping processes with internet-based collaborative techniques, our approach digitises maps on disaster risk and community resources where locations of rivers, houses, infrastructure and shelters are usually hand-drawn providing critical information used by local stakeholders. Such new digital community maps, uploaded and overlaid with satellite imagery or OpenStreetMap as taken forward with collaborators in Nepal, Peru and Mexico, are richer in content, more accurate, and easier to update and share than conventional approaches. Understanding what builds resilience is fundamental to informing action, yet few systematic and large-scale efforts have systematically measured resilience.To this effect, the Alliance developed the Flood Resilience Measurement Framework for Communities (FRMC) to benchmark and track the underlying sources of resilience. The FRMC involves measuring a community's sources of resilience pre- and post-disaster, operationalised around 5 types of assets (human, natural, social, physical and financial) and their interactions. Resilience sources are qualitatively graded based on household surveys, community focus groups, expert informants, and other third-party sources. Sources are assessed by specially trained NGO experts embedded in communities, while data is collected globally via an integrated mobile and web-based system. Through an inclusive process and technology, the measurement framework and tool, rolled out globally in Latin America, Asia, Europe and the US to more than 100 communities, has helped communities and development partners make improved decisions on how to build resilience. [ABSTRACT FROM AUTHOR]