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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. Given that 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 20 000 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 millimeter-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 satellite positioning service of the German National Survey (SAPOS) 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.

The global spatial extent of croplands is a crucial input to global and regional agricultural monitoring and modeling systems. Although many new remotely-sensed products are now appearing due to recent advances in the spatial and temporal resolution of satellite sensors, there are still issues with these products that are related to the definition of cropland used and the accuracies of these maps, particularly when examined spatially. To address the needs of the agricultural monitoring community, here we have created a hybrid map of global cropland extent at a 500 m resolution by fusing two of the latest high resolution remotely-sensed cropland products: the European Space Agency's WorldCereal and the cropland layer from the University of Maryland. We aggregated the two products to a common resolution of 500 m to produce percentage cropland and compared them spatially, calculating two kinds of disagreement: density disagreement, where the two maps differ by more than 80%, and absence-presence of cropland disagreement, where one map indicates the presence of cropland while the other does not. Based on these disagreements, we selected continuous areas of disagreement, referred to in the paper as hotspots of disagreement, for manual correction by experts using the Geo-Wiki land cover application. The hybrid map was then validated using a stratified random sample based on the disagreement layer, where the sample was visually interpreted by a different set of experts using Geo-Wiki. The results show that the hybrid product improves upon the overall accuracy statistics in the areas where the underlying cropland layer from the University of Maryland was improved with the WorldCereal product, but more importantly, it represents an improved spatially explicit cropland mask for early warning and food security assessment purposes.

The last few decades have seen an explosion in the availability of remotely sensed and geospatial big data, which are defined by the 3 Vs: a large volume of data; a variety of different forms of data; and the rapid velocity of data arrival. The term big data is particularly applicable to remote sensing. The opening of the Landsat archive, the spatially and temporally rich data now available from the Sentinel satellites, and the proliferation of small satellites photographing the Earth all provide new opportunities for characterizing and monitoring the Earth’s surface. New sources of geospatial big data (as well as regular geospatial data) can also benefit the mapping and monitoring of land cover and land use. These include data from authoritative sources, e.g., data from official censuses and surveys, as well as data generated by citizens, both actively and passively. Citizen science and volunteered geographic information can provide data on land cover and use through initiatives such as OpenStreetMap (OSM), Geo-Wiki, and many other projects that involve volunteers monitoring the environment or landscape features. Mobile phones and low-cost sensors can provide new streams of information through mobile apps that facilitate data collection or that collect information in the background, as well as a variety of different sensors that are being used for environmental monitoring. Data from social media, including geotagged photographs from sites such as Flickr or street-level photographs from providers such as Google Street View and Mapillary, can be processed using computer vision and segmentation to extract information related to land cover and land use. The logical progression of this field of study is the integration of remote sensing with these different sources of geospatial data using various machine learning and data fusion approaches to create new data sets on land cover and land use. Much of the previous integration work in this area has focused on urban areas b

The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drivers vary in space and time, producing accurate spatially explicit maps with regular temporal updates is essential. Drivers can be recognized from satellite imagery but the scale of tropical deforestation makes it unfeasible to do so manually. Machine learning opens up possibilities for automating and scaling up this process. In this study, we developed and trained a deep learning model to classify the drivers of any forest loss—including deforestation—from satellite image time series. Our model architecture allows understanding of how the input time series is used to make a prediction, showing the model learns different patterns for recognizing each driver and highlighting the need for temporal data. We used our model to classify over 588 ′ 000 sites to produce a map detailing the drivers behind tropical forest loss. The results confirm that the majority of it is driven by agriculture, but also show significant regional differences. Such data is a crucial source of information to enable targeting specific drivers locally and can be updated in the future using free satellite data.

Spatially explicit information on land cover (LC) is commonly derived using remote sensing, but the lack of training data still remains a major challenge for producing accurate LC products. Here, we develop a computer vision methodology to extract LC information from photos from the Land Use-Land Cover Area Frame Survey (LUCAS). Given the large number of photographs available and the comprehensive spatial coverage, the objective is to show how the automatic classification of photos could be used to develop reference data sets for training and validation of LC products as well as other purposes. We first selected a representative sample of 1120 photos covering eight major LC types across the European Union. We then applied semantic segmentation to these photos using a neural network (Deeplabv3+) trained with the ADE20k dataset. For each photo, we extracted the original LC identified by the LUCAS surveyor, the segmented objects, and the pixel count for each ADE20k class. Using the latter as input features, we then trained a Random Forest model to classify the LC of the photo. Examining the relationship between the objects/features extracted by Deeplabv3+ and the LC labels provided by the LUCAS surveyors demonstrated how the LC classes can be decomposed into multiple objects, highlighting the complexity of LC classification from photographs. The results of the classification show a mean F1 Score of 89%, increasing to 93% when the Wetland class is not considered. Based on these results, this approach holds promise for the automated retrieval of LC information from the rich source of LUCAS photographs as well as the increasing number of geo-referenced photos now becoming available through social media and sites like Mapillary or Google Street View.

Accounting and reporting of greenhouse gas (GHG) emissions are mandatory for Parties under the Paris Agreement. Emissions reporting is important for understanding the global carbon cycle and for addressing global climate change. However, in a period of open conflict or war, military emissions increase significantly and the accounting system is not currently designed to account adequately for this source. In this paper we analyze how, during the first 18 months of the 2022/2023 full-scale war in Ukraine, GHG national inventory reporting to the UNFCCC was affected. We estimated the decrease of emissions due to a reduction in traditional human activities. We identified major, war-related, emission processes from the territory of Ukraine not covered by current GHG inventory guidelines and that are not likely to be included in national inventory reports. If these emissions are included, they will likely be incorporated in a way that is not transparent with potentially high uncertainty. We analyze publicly available data and use expert judgment to estimate such emissions from (1) the use of bombs, missiles, barrel artillery, and mines; (2) the consumption of oil products for military operations; (3) fires at petroleum storage depots and refineries; (4) fires in buildings and infrastructure facilities; (5) fires on forest and agricultural lands; and (6) the decomposition of war-related garbage/waste. Our estimate of these war-related emissions of carbon dioxide, methane, and nitrous oxide for the first 18 months of the war in Ukraine is 77 MtCO2-eq. with a relative uncertainty of ±22 % (95 % confidence interval).

The aim of this study is to present a method for estimating the pollutant load from different sources in an effort to provide improved information regarding water pollution and help control the surface water pollution, using Lai Chau city as a case study. The pollutant load was calculated in accordance with the Vietnam Environment Administration Decree No.154/2019 on the guidance for calculating the total pollutant load of river water. The pollutant sources include point sources (domestic wastewater, animal husbandry, industrial complexes and economic services) and surface sources (run-off from agricultural land uses) that generate wastes that potentially contaminate water bodies. The source locations were mapped and spatially joined with the drainage-basin map delineated from a Digital Elevation Model (DEM) to calculate the loads for the sub-basin units. Multivariate analysis then showed that the farming and domestic sources had the strongest positive loading factors for the sub-basins located in the city center and its fringe areas. Of these waste from animal husbandry account for up to 75.1% of total pollutant load. The main conclusion from the study's results is that the management approach should be changed from the total controlling mode, which is currently applied in the city, to a source specific approach based on the pollutant discharge loads and the allocated capacities.

The era of modern smartphones, running on Android version 7.0 and higher, facilitates nowadays acquisition of raw dual-frequency multi-constellation GNSS observations. This paves the way for GNSS community data to be potentially exploited for precise positioning, GNSS reflectometry or geoscience applications at large. The continuously expanding global GNSS infrastructure along with the enormous volume of prospective GNSS community data bring, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest. In addition, such large datasets cannot be managed manually anymore, leading thus to the need for fully automated and sophisticated data processing pipelines. Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) was an ESA NAVISP Element 1 project (NAVISP-EL1-038.2) with activities aiming to address the aforementioned points related to GNSS community data and their exploitation for scientific applications with the use of Machine Learning (ML). This contribution provides an overview of the CAMALIOT project with information on the designed and implemented cloud-native software for GNSS processing and ML at scale, developed Android application for retrieving GNSS observations from the modern generation of smartphones through dedicated crowdsourcing campaigns, related data ingestion and processing, and GNSS analysis concerning both conventional and smartphone GNSS observations. With the use of the developed GNSS engine employing an Extended Kalman Filter, example processing results related to the Zenith Total Delay (ZTD) and Slant Total Electron Content (STEC) are provided based on the analysis of observations collected with geodetic-grade GNSS receivers and from local measurement sessions involving Xiaomi Mi 8 that collected GNSS observations using the developed Android application. For smartphone observations, ZTD is derived in a differential manner based on a single

There is currently a lack of high-resolution pan-European information on land use management, especially in terms of how intensively and extensively cropland and grassland are managed. This is partly due to the lack of ground-based information, which is needed to downscale these types of management practices (some of which are captured in different types of agricultural censuses and surveys) as well as the inability of remote sensing to capture different kinds of land use. This type of information is needed for economic land use modelling and for assessing policy impacts, such as the latest reforms from the Common Agricultural Policy (CAP) and other European Union (EU) Green Deal targets. These types of analyses are undertaken using economic land use models such as GLOBIOM and CAPRI, which is one of the main aims of the Horizon Europe funded LAMASUS project (https://www.lamasus.eu/). This presentation will provide an overview of the ongoing developments in creating high-resolution spatially explicit layers on agricultural and grassland management for Europe to support the LAMASUS project. The proposed cropland and grassland management classes will be outlined along with the methodology for how they have been implemented using existing data layers from remote sensing, statistical data from Eurostat, the Joint Research Centre of the EU, agricultural ministries, and other sources. One of the key challenges is ensuring that the high-resolution data matches official statistics at the national (and NUTS2 level where available) so that they can be used by the economic land use models in LAMASUS. A method will be presented for how this is achieved using priors in the form of integrated layers of cropland and grassland probability created from existing high-resolution remotely sensed input layers.

Achieving the health and well-being related Sustainable Development Goals (SDGs) and the World Health Organization’s (WHO) Triple Billion Targets depends on informed decisions that are based on concerted data collection and monitoring efforts. Even though data availability has been increasing in recent years, significant gaps still remain for routine surveillance to guide policies and actions. The COVID-19 crisis has shown that more and better data and strengthened health information systems are needed to inform timely decisions that save lives. Traditional sources of data such as nationally representative surveys are not adequate for addressing this challenge alone. Additionally, the funding required to measure all health and well-being related SDG indicators and Triple Billion Targets using only traditional sources of data is a challenge to achieving efficient, timely and reliable monitoring systems. Citizen science, public participation in scientific research and knowledge production, can contribute to addressing some of these data gaps efficiently and sustainably when designed well, and ultimately, could contribute to the achievement of the health and well-being related SDGs and Triple Billion Targets. Through a systematic review of health and well-being related indicators, as well as citizen science initiatives, this paper aims to explore the potential of citizen science for monitoring health and well-being and for mobilizing action toward the achievement of health and well-being related targets as outlined in the SDG framework and Triple Billion Targets. The results demonstrate that out of 58 health and well-being related indicators of the SDGs and Triple Billion Targets covered in this study, citizen science could potentially contribute to monitoring 48 of these indicators and their targets, mostly at a local and community level, which can then be upscaled at a national level with the projection to reach global level monitoring and implementation. To integrat

The creation of crop type maps from satellite data has proven challenging and is often impeded by a lack of accurate in situ data. Street-level imagery represents a new potential source of in situ data that may aid crop type mapping, but it requires automated algorithms to recognize the features of interest. This paper aims to demonstrate a method for crop type (i.e., maize, wheat and others) recognition from street-level imagery based on a convolutional neural network using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery using the Picture Pile application. The classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for others. Given that wheat and maize are two of the most common food crops grown globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved global crop type monitoring. Challenges remain in addressing the noise aspect of street-level imagery (i.e., buildings, hedgerows, automobiles, etc.) and uncertainties due to differences in the time of day and location. Such an approach could also be applied to developing other in situ data sets from street-level imagery, e.g., for land use mapping or socioeconomic indicators.

Citizen science data are an example of a non-traditional data source that is starting to be used in the monitoring of the United Nations (UN) Sustainable Development Goals (SDGs) and for national monitoring by National Statistical Systems (NSSs). However, little is known about how the official statistics community views citizen science data, including the opportunities and the challenges, apart from some selected examples in the literature. To fill this gap, this paper presents the results from a survey of NSS representatives globally to understand the key factors in the readiness of national data ecosystems to leverage citizen science data for official monitoring and reporting, and assesses the current awareness and perceptions of NSSs regarding the potential use of these data. The results showed that less than 20% of respondents had direct experience with citizen science data, but almost 50% felt that citizen science data could provide data for SDG and national indicators where there are significant data gaps, listing SDGs 1, 5, and 6 as key areas where citizen science could contribute. The main perceived impediments to the use of citizen science data were lack of awareness, lack of human capacity, and lack of methodological guidance, and several different kinds of quality issues were raised by the respondents, including accuracy, reliability, and the need for appropriate statistical procedures, among many others. The survey was then used as a starting point to identify case studies of successful examples of the use of citizen science data, with follow-up interviews used to collect detailed information from different countries. Finally, the paper provides concrete recommendations targeted at NSSs on how they can use citizen science data for official monitoring.

The accumulation of plastic litter in marine environments is a major environmental challenge along with the difficulties in their measurement because of the massive size of the oceans and vast circulation of plastic litter, which is being addressed as part of the United Nations (UN) Sustainable Development Goals (SDGs). Citizen science, public participation in scientific research and knowledge production, represents a potential source of data for SDG monitoring and reporting of marine plastic litter, yet there has been no evidence of its use to date. Here, we show how Ghana has become the first country to integrate existing citizen science data on marine plastic litter in their official monitoring and reporting of SDG indicator 14.1.1b for the years 2016–2020, which has also helped to bridge local data collection efforts with global monitoring processes and policy agendas by leveraging the SDG framework. The results have been used in Ghana’s 2022 Voluntary National Review of the SDGs, and reported on the UN SDG Global Database, as well as helping to inform relevant policies in Ghana. In addition, here, we present a pathway that can be adopted by the relevant government authorities in other countries that have an interest in following a similar citizen science data validation and reporting process for this indicator and potentially others.

The Global Navigation Satellite System (GNSS) is a well-recognized tool to probe the Earth’s atmosphere. This contribution highlights how GNSS data collected from smartphones of voluntary contributors can be used to determine parameters of the troposphere and ionosphere. In this regard, the application of machine learning (ML) to characterize the quality of the crowd-sourced data and model atmospheric parameters is discussed. We demonstrate that in certain cases, GNSS data from smartphones can reach a precision that would allow such data to densify observations from existing geodetic infrastructures.

This chapter will provide an overview of how citizen science has contributed to the development of remotely sensed land cover and land use maps in the form of training and validation data collection using visual interpretation and field-based data collection methods. Online visual interpretation via applications such as Geo-Wiki and Collect Earth as well as in situ data collection via mobile apps are now yielding vast quantities of data that are benefiting remote sensing applications. Existing citizen-driven initiatives such as OpenStreetMap are also providing input data for generating land cover and land use maps while data quality remains a key area of concern regarding the use of citizen-generated data.

Given the urgency of addressing data gaps on marine plastic litter, timely data, effective stakeholder engagement, cross- sector collaboration, and citizen engagement are critical to advancing this feasibility study. The innovative and integrated approach outlined in this report can also support the fulfillment of SDG 14.1.1b (measurement of plastic debris density) as well as informing policy formulation and action plans. The ability to harness the aforementioned innovative data sources, involve members of the public and communities in the process, and communicate findings in accessible formats, empowers people and communities, policymakers, and various stakeholders with the insights necessary for guiding sustainable practices.

The Amazon forests act as a global reserve for carbon, have very high biodiversity, and provide a variety of additional ecosystem services. These forests are, however, under increasing pressure, coming mainly from deforestation, despite the fact that accurate satellite monitoring is in place that produces annual deforestation maps and timely alerts. Here, we present a proof of concept for rapid deforestation monitoring that engages the global community directly in the monitoring process via crowdsourcing while subsequently leveraging the power of deep learning. Offering no tangible incentives, we were able to sustain participation from more than 5500 active contributors from 96 different nations over a 6-month period, resulting in the crowd classification of 43,108 satellite images (representing around 390,000 km2). Training a suite of AI models with results from the crowd, we achieved an accuracy greater than 90% in detecting new and existing deforestation. These findings demonstrate the potential of a crowd–AI approach to rapidly detect and validate deforestation events. Our method directly engages a large, enthusiastic, and increasingly digital global community who wish to participate in the stewardship of the global environment. Coupled with existing monitoring systems, this approach could offer an additional means of verification, increasing confidence in global deforestation monitoring.

The restoration of tree cover has been placed at the top of international policy agendas, yet often, the ‘type’ of restored forests can be widely different, with consequences for biodiversity and livelihoods. Here we used a map of forest management types to assess the extent of managed forests in recent tree cover gains globally. We call on policymakers to differentiate forest management as a distinct element of reforestation targets.

Raw observations (carrier-phase and code observations) from the Global Navigation Satellite System (GNSS) can now be accessed from Android mobile phones (Version 7.0 onwards). This paves the way for GNSS data to be utilized for low-cost precise positioning or in ionospheric or tropospheric applications. This paper presents results from data collection campaigns using the CAMALIOT mobile app. In the first campaign, 116.3 billion measurements from 11,828 mobile devices were collected from all continents. Although participation decreased during the second campaign, data are still being collected globally. In this contribution, we demonstrate the potential of volunteered geographic information (VGI) from mobile phones to fill data gaps in geodetic station networks that collect GNSS data, e.g. in Brazil, but also how the data can provide a denser set of observations than current networks in countries across Europe. We also show that mobile phones capable of dual-frequency reception, which is an emerging technology that can provide a richer source of GNSS data, are contributing in a substantial way. Finally, we present the results from a survey of participants to indicate that participation is diverse in terms of backgrounds and geography, where the dominant motivation for participation is to contribute to scientific research.

Das Potenzial von Citizen Science ist über die Wissenschaft hinaus bekannt, z.B. in der Bereitstellung von Daten für amtliche Informationssysteme, bei der Schließung räumlicher und zeitlicher Datenlücken oder zur besseren Beteiligung der Öffentlichkeit am wissenschaftlichen und politischen Diskurs. Gleichzeitig wird über die Legitimität von Citizen Science debattiert, im engeren Sinne in Bezug auf Datenqualität, im weiteren Sinne hinsichtlich ihres Potenzials, bestehende Entscheidungssysteme in Frage zu stellen oder Lücken darin aufzuzeigen. In diesem Cabin Talk wird anhand von Real-Life-Szenarien durchgespielt und reflektiert, welche Potentiale bürger*innenbasierte Datenströme in der urbanen Klimawandelanpassung und Grünraumplanung haben, wie diese durch latente Macht- und Zielkonflikte zwischen beteiligten Akteur*innen beeinflusst werden und welche rechtlichen Grundlagen die Situation bestimmen.

It is becoming increasingly obvious that in order to address current global challenges and achieve the SDGs in the land-use sector, monitoring and evaluation using remote sensing technologies are essential. In particular, with the Copernicus program of the European Union, unprecedented free and open Earth observation data are becoming available. However, in order to improve our remotely sensed based machine learning models, training data in the form of in-situ or annotated land-use or land cover data which are based on the visual interpretation of aerial photographs or very high resolution satellite data are of utmost importance. Without sufficient training data, many land-use and land cover maps lack sufficient quality. The presentation will provide an overview of existing and open in-situ data in the field of land-use science. It will highlight what land-use data are currently available including data collected though crowdsourcing and the Geo-Wiki toolbox. In particular, it will provide insights into current gaps in land cover, land-use, livestock, forest as well as crop type information globally. It will draw on existing global data products such as those from the Copernicus global land monitoring service, and more recently generated products such as WorldCover and WorldCereal. Furthermore, tools to close those data gaps will be shown. The presentation will furthermore explore current obstacles and limitations to data sharing and debunk current arguments that are often put forth for not sharing in-situ data. These arguments include limited resources, quality issues, competition, as well as time constraints, etc. Specific attention will be given to the role of doners and funders in more clearly defining open and FAIR requirements for in-situ data. The presentation will close by making the audience aware of the LUCKINet consortium, which is trying to make more reference data openly accessible and to build a consistent global land-use change dataset as well as work

Dead wood, including coarse woody debris, CWD, and fine woody debris, FWD, plays a substantial role in forest ecosystem functioning. However, the amount and dynamics of dead wood in the forests of Northern Eurasia are poorly understood. The aim of this study was to develop a spatially distributed modelling system (limited to the territories of the former Soviet Union) to assess the amount and structure of dead wood by its components (including snags, logs, stumps, and the dry branches of living trees) based on the most comprehensive database of field measurements to date. The system is intended to be used to assess the dead wood volume and the amount of dead wood in carbon units as part of the carbon budget calculation of forests at different scales. It is presented using multi-dimensional regression equations of dead wood expansion factors (DWEF)—the ratio of the dead wood component volume to the growing stock volume of the stands. The system can be also used for the accounting of dead wood stock and its dynamics in national greenhouse gas inventories and UNFCCC reporting. The system’s accuracy is satisfactory for the average level of disturbance regimes but it may require corrections for regions with accelerated disturbance regimes.