Copernicus Programme Research Articles

Air Quality Estimation in Ukraine Using SDG 11.6.2 Indicator Assessment

Ukraine is an associate member of the European Union, and in the coming years, it is expected that all the data and services already used by European Union countries will become available for Ukraine. An important program, which is the basis for building European monitoring services for smart cities, is the Copernicus program. The two most important services of this program are the Copernicus Land Monitoring Service (CLMS) and the Copernicus Atmosphere Monitoring Service (CAMS). CLMS provides important information on land use in Europe. In the context of smart cities, the most valuable tool is the Urban Atlas service, which is related to local CLMS services and provides a detailed digital city plan in vector form, which is segmented into small functional areas classified by Coordinate Information on the Environment (CORINE) nomenclature. The Urban Atlas is a geospatial layer with high resolution, built for all European cities with a population of more than 100,000. It combines high-resolution satellite data, city segmentation by blocks and functional urban areas (FUAs), important city infrastructure, etc. This product is used as a basis for city planning and obtaining analytics on the most important indicators of city development, including air quality monitoring. For Ukraine, such geospatial products are not provided under the Copernicus program. In this article, FUAs are developed for Ukrainian cities using European technology. It is important to start work on this program’s implementation as early as possible so that when the first city atlas appears, Ukraine will be ready to work with it together with the European community. This requires preparing the basis for national research and training national stakeholders and consumers to use this product. To make this happen, it is necessary to have a national geospatial product that can be used as an analogue of the city atlas. In this article, the authors analyzed the existing methods of air quality assessment and the Global Sustainable Development Goal (SDG) indicator 11.6.2, “Annual mean levels of fine particulate matter (e.g., PM2.5 and PM10) in cities (population weighted)”, achieved for European cities. Based on this, indicator 11.6.2 was then evaluated for the first time in Ukraine, considering the next 5 years. For the correct use of global products for Ukraine, CAMS global satellite data and population data (Global Human Settlement Layer and NASA population data) for Ukrainian cities were validated. These studies showed a statistically significant result and, therefore, demonstrated that global products can be used to monitor air quality both at the city level and for Ukraine as a whole. The obtained results were analyzed, and the values of indicator 11.6.2 for Ukraine were compared with those for other European countries.

UTILIZATION OF SENTINEL-2 IMAGERY IN THE ESTIMATION OF PLASTICS AMONG FLOATING DEBRIS ALONG THE COAST OF MANILA BAY

Abstract. With the Philippines ranking as the third largest source of plastics that end up in the oceans, there is a need to further explore methodologies that will become an aid in plastic waste removal from the ocean. Manila Bay is a natural harbor in the Philippines that serves as the center of different economic activities. However, the bay is also threatened with plastic pollution due to increasing population and industrial activities. BASECO is one of the areas in Manila Bay where clean-up activities are focused as this is where trash accumulates. Sentinel-2 images are provided free of charge by the European Commission's Copernicus Programme. Satellite images from June 2019 to May 2020 were inspected, then cloud-free images were downloaded. After downloading and pre-processing, spectral data of different types of plastic such as shipping pouch, bubble wrap, styrofoam, PET bottle, sando bag and snack packaging that were measured by a spectrometer during a fieldwork by the Development of Integrated Mapping, Monitoring, and Analytical Network System for Manila Bay and Linked Environments (project MapABLE) were utilized in the selection of training data. Then, indices such as the Normalized Vegetation Index (NDVI), Floating Debris Index (FDI) and Plastic Index (PI) from previous studies were analyzed for further separation of classes used as training data. These training data served as an input to the two supervised classification methods, Naive Bayes and Mixture Tuned Matched Filtering (MTMF). Both methods were validated by reports and articles from Philippine agencies indicating the spots where trash frequently accumulates.

METHODS FOR IDENTIFICATION AND VISUALIZATION OF MUNICIPAL WASTE DISPOSAL SITES USING AEROSPACE DATA

With the increase of temperatures in the summer, the danger of self-ignition of landfills for household waste increases, because of the biochemical processes that take place inside them. The most recent example is the municipal landfill for non-hazardous waste near the town of Dupnitsa. The self-ignition started on July 23, 2021 and continued to smolder for almost a week, which led to a great danger of burning the area around it or poisoning the population. It is believed that one of the main reasons is the great depth of the accumulated waste. There is an urgent need to do quality control of most of the landfills for municipal waste in our country. Some of them are located next to major river arteries or international roads. Non-compliance with the requirements for maintenance, management and operation hides a serious potential for an ecological catastrophe. It is essential that stricter measures are taken and that these landfills are monitored. The aim of the present work is to reveal the possibilities and potential of aerospace data and to show different methods for processing, interpretation, and visualization. They can easily identify, map, and survey a waste disposal site. Optical images of the multispectral instrument (MSI) of the Sentinel 2 platform and radar (SAR) data from the Sentinel 1 platform of the Copernicus program of the European Space Agency were used. Thermal bands from the Landsat 5 - 7 (ETM) and Landsat 8 (OLI/TIRS) sensors of the Landsat program were used to calculate the land surface temperature. Satellite images have been orthogonized, and composite images between optical and radar data have been created for better visualization.

Sentinel-2 Recognition of Uncovered and Plastic Covered Agricultural Soil

Medium resolution satellite data, such as Sentinel-2 of the Copernicus programme, offer great new opportunities for the agricultural sector, and provide insights on soil surface characteristics and their management. Soil monitoring requires a high-quality dataset of uncovered and plastic covered agricultural soil. We developed a methodology to identify uncovered soil pixels in agricultural parcels during seedbed preparation and considered the impacts of clouds and shadows, vegetation cover, and artificial covers, such as those of greenhouses and plastic mulch films. We preserved the spatial and temporal integrity of parcels in the process and analysed spectral anomalies and their sources. The approach is based on freely available tools, namely Google Earth Engine and R Programming packages. We tested the methodology on the northern region of Belgium, which is characterised by small, fragmented parcels. We selected a period between mid-April to end-May, when active agricultural management practices leave the soil bare in preparation for the main cropping season. The spectral angle mapper was used to identify soil covered by non-plastic greenhouses or temporary soil covers, such as plastic mulch films. The effect of underlying soil on temporary covers was considered. The retrogressive plastic greenhouse index was used for detecting plastic greenhouses. The result was a high quality dataset of potential bare uncovered agricultural soil that allows further soil surface characterisation. This offered an improved understanding of the use of artificial covers, their spatial distribution, and their corresponding crops during the considered period. Artificial covers occurred most frequently in maize parcels. The approach resulted in precision values exceeding 0.9 for the detection of temporary covers and non-plastic greenhouses and a sensitivity value exceeding 0.95 for non-plastic and plastic greenhouses.

Copernicus data to boost raw material source management: Illustrations from the RawMatCop programme

Earth Observation (EO) data can become an essential tool in the transformation of a raw materials sector that aims to reconfigure its model of operation. The high demand for the mineral resources necessary for the transition to a carbon neutral and circular economy conflicts with the increasing difficulties of finding new deposits. As the sector heads towards embracing circularity and reducing the environmental impacts, a clear focus has been set on developing appropriate tools to boost the efficiency of mineral resource management, both technologically and economically. In this scenario, the Sentinel satellites of the European Copernicus program come into play. Despite being satellites considered medium resolution, they provide great temporal and spatial coverage in a continuous record, which makes them tools with great potential for the raw materials sector. However, the lack of applications in the raw materials sector suggests that these technological advances have remained underrated by sectoral actors. The RawMatCop program was designed to bridge this gap. This program, co-funded by the European Commission and EIT RawMaterials, aims to develop applications and promote the use of Copernicus data in the raw materials sector to contribute to a safe and sustainable supply of mineral resources. The presented applications can be grouped into three categories covering the whole mining cycle from exploration to exploitation and post-mining. Two of the presented case studies cover the study of primary sources including exploration of Iron Oxide Copper Gold mineralisations to identify high-potential mining areas and mapping of informal gold mining and its environmental impacts. Another project focused on secondary sources tackled data applications for grade mapping and sample optimisation in mining residues. And the forth project focused on monitoring ground stability related to mining activity. The results demonstrate the high cost-effectiveness of Sentinel 1 and 2 in extending ground-based measurements to larger areas, especially when these are hard-to-reach areas. Finally, the presented projects examine the industrial and social impacts of technological innovations, as well as contribute to the achievement of prominent European Union policy objectives and the United Nations Sustainable Development Goals.

Global time series and temporal mosaics of glacier surface velocities derived from Sentinel-1 data

Abstract. Consistent and continuous data on glacier surface velocity are important inputs to time series analyses, numerical ice dynamic modeling and glacier mass flux computations. Since 2014, repeat-pass synthetic aperture radar (SAR) data have been acquired by the Sentinel-1 satellite constellation as part of the Copernicus program of the EU (European Union) and ESA (European Space Agency). It enables global, near-real-time-like and fully automatic processing of glacier surface velocity fields at up to 6 d temporal resolution, independent of weather conditions, season and daylight. We present a new global data set of glacier surface velocities that comprises continuously updated scene-pair velocity fields, as well as monthly and annually averaged velocity mosaics at 200 m spatial resolution. The velocity information is derived from archived and new Sentinel-1 SAR acquisitions by applying a well-established intensity offset tracking technique. The data set covers 12 major glacierized regions outside the polar ice sheets and is generated in an HPC (high-performance computing) environment at the University of Erlangen-Nuremberg. The velocity products are freely accessible via an interactive web portal that provides capabilities for download and simple online analyses: http://retreat.geographie.uni-erlangen.de (last access: 6 October 2021). In this paper, we give information on the data processing and how to access the data. For the example region of Svalbard, we demonstrate the potential of our products for velocity time series analyses at very high temporal resolution and assess the quality of our velocity products by comparing them to those generated from very high-resolution TerraSAR-X SAR and Landsat-8 optical (ITS_LIVE, GoLIVE) data. The subset of Sentinel-1 velocities for Svalbard analyzed in this paper is accessible via the GFZ Potsdam Data Services under the DOI https://doi.org/10.5880/fidgeo.2021.016 (Friedl et al., 2021). We find that Landsat-8 and Sentinel-1 annual velocity mosaics are in an overall good agreement, but speckle tracking on Sentinel-1 6 d repeat acquisitions derives more reliable velocity measurements over featureless and slow-moving areas than the optical data. Additionally, uncertainties of 12 d repeat Sentinel-1 mid-glacier scene-pair velocities have less than half (< 0.08 m d−1) of the uncertainties derived for 16 d repeat Landsat-8 data (0.17–0.18 m d−1).

From parcel to continental scale – A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations

Detailed parcel-level crop type mapping for the whole European Union (EU) is necessary for the evaluation of agricultural policies. The Copernicus program, and Sentinel-1 (S1) in particular, offers the opportunity to monitor agricultural land at a continental scale and in a timely manner. However, so far the potential of S1 has not been explored at such a scale. Capitalizing on the unique LUCAS 2018 Copernicus in-situ survey, we present the first continental crop type map at 10-m spatial resolution for the EU based on S1A and S1B Synthetic Aperture Radar observations for the year 2018. Random Forest classification algorithms are tuned to detect 19 different crop types. We assess the accuracy of this EU crop map with three approaches. First, the accuracy is assessed with independent LUCAS core in-situ observations over the continent. Second, an accuracy assessment is done specifically for main crop types from farmers declarations from 6 EU member countries or regions totaling >3M parcels and 8.21Mha. Finally, the crop areas derived by classification are compared to the subnational (NUTS 2) area statistics reported by Eurostat. The overall accuracy for the map is reported as 80.3% when grouping main crop classes and 76% when considering all 19 crop type classes separately. Highest accuracies are obtained for rape and turnip rape with user and produced accuracies higher than 96%. The correlation between the remotely sensed estimated and Eurostat reported crop area ranges from 0.93 (potatoes) to 0.99 (rape and turnip rape). Finally, we discuss how the framework presented here can underpin the operational delivery of in-season high-resolution based crop mapping.

Semantic modelling of Earth Observation remote sensing

Earth Observation (EO) based on Remote Sensing (RS) is gaining importance nowadays, since it offers a well-grounded technological framework for the development of advanced applications in multiple domains, such as climate change, precision agriculture, smart urbanism, safety, and many others. This promotes the continuous generation of data-driven software facilities oriented to advanced processing, analysis and visualization, which often offer enhanced computing capabilities. Nevertheless, the development of knowledge-driven approaches is still an open challenge in remote sensing, besides they provide human experts with domain knowledge representation, support for data standardization and semantic integration of sources, which indeed enhance the construction of advanced on-top applications. To this end, the use of ontologies and web semantic technologies have shown high success in knowledge representation in many fields, in which the Earth Observation is not an exception. However, as argued by the research community, there is large room for improvement in the specific case of remote sensing, where ontologies that consider the special nature and structure of different satellital and airborne data products are demanded. This article addresses, in first instance, part of this need by proposing a semantic model for the consolidation, integration, reasoning and linking of data (and meta-data), in the context of satellital remote sensing products for EO. With this objective, an OWL ontology has been developed and an RDF repository has been generated to allow advanced SPARQL querying. Although the proposal has been designed to consider remote sensing data products in general, the current study is mainly focused on the Sentinel 2 satellite mission from the Copernicus Programme of the European Space Agency (ESA). Four different use cases are showcased to check potentials of the proposed semantic model in terms of ontology integration, federated querying, data analysis and reasoning.

Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway

The intertidal zone (ITZ) is a highly dynamic and diverse coastal ecosystem under pressure that provides important eco-services. Being periodically under water makes it challenging to monitor, and the only possibility to map it in all tidal stages is by using dense time series of observations. At high latitudes, the Sentinel-1 (S1) constellation of the European Copernicus Program consistently provides radar imagery at fixed times on a near-daily basis, independently of cloud cover and sunlight. As tides have a period of 12 h 25.2 min, 1–2 year long S1 time series are therefore able to sample the whole tidal range and, thus, map the percentage of atmospheric exposure of the ITZ, which is an important environmental parameter. Tidal reference levels of mean high/low water at spring, mean and neap tide correspond each to specific percentiles of tidal heights and inversely correspond to atmospheric exposure. The presented method maps atmospheric exposure on the basis of purely statistical analyses of Sentinel-1 time series without the need for any tidal gauge data, by extracting water lines via simple thresholding of radar backscatter percentiles images. The individual thresholds for the second, fifth, 25th, 50th, 75th, 95th, and 98th percentile image were determined by fitting the threshold contour lines to in situ water line GPS tracks collected at corresponding tidal reference levels at five locations around Tromsø in Northern Norway. They inversely correspond to atmospheric exposures of 98%, 95%, 75%, 50%, 25%, 5%, and 2%, respectively. The method was applied to the whole Tromsø Municipality resulting in an ITZ atmospheric exposure map. The validation shows that the mean low water lines at neap, mid, and spring tide were mapped with accuracies of 93%, 84%, and 64%, respectively. The overall approach should be applicable worldwide.

ENABLING AIR QUALITY MONITORING WITH THE OPEN DATA CUBE: IMPLEMENTATION FOR SENTINEL-5P AND GROUND SENSOR OBSERVATIONS

Abstract. Nowadays, the amount of open geospatial data delivered e.g. by private and public entities, such as environmental agencies, enables outstanding possibilities to any user interested in investigating real-world phenomena. However, the availability of such information presents several challenges in terms of its practical use. These are mainly connected to the heterogeneity of data sources, formats and processing tools which have to be mastered by the user to obtain the desired results. As a relevant example, air quality monitoring requires the integration of multiple data with different spatial and temporal granularities that are often distributed by more than one provider using not uniform formats and access methods. Besides traditional air pollution ground sensors observations, novel data sources have emerged. Among them, the Sentinel-5P mission of the European Copernicus Programme is one of the most recent Earth Observation platforms providing estimates of air pollutants with daily global coverage. These estimates are promising to foster air quality analysis and monitoring by complementing ground sensors observations. Therefore, the development of data handling and analysis strategies – allowing users for a smooth integration of satellite and ground sensor observations – is key to support future air quality studies. To that end, the present work investigates the use of the Open Data Cube as a single data endpoint to incorporate ground sensors and satellite observations into local air pollution analyses. A preliminary implementation is presented using the Lombardy region (Northern Italy) as a case study.

COPERNICUS AND THE ENERGY CHALLENGE

Abstract. The energy sector will drastically change in the following years; multiple agreements have been signed by countries with the purpose to reduce carbon emission and contain the global temperature increase. Besides, in the next years the energy demand will increase with the growth of the Information and Communications Technology sector. To combine these two aspects, future energy needs to be produced with renewable resources and less with fossil fuels. An opportunity to discover and plan the use of renewable energy resources are geospatial data derived from satellite acquisitions. The European Earth Observation programme Copernicus provides multiple datasets in an Open Science approach. Within this paper, multiple datasets offered by Copernicus services are presented in relation to their exploitation for the energy system analysis, with a particular attention to renewable energy. The datasets will be analysed according to their properties and possibility of usage. Additional Copernicus satellite derived data that can benefit the emerging topic of the food-energy-water nexus are finally presented to point out significant development in the energy sector which is recently claiming growing attention.

LANDSLIDE DETECTION IN CENTRAL AMERICA USING THE DIFFERENTIAL BARE SOIL INDEX

Abstract. The increasing availability of EO data from the Copernicus program through its Sentinel satellites of the medium spatial and spectral resolution has generated new applications for risk management and disaster management. The recent growth in the intensity and number of hurricanes and earthquakes has demanded an increase in the monitoring of landslides. It is necessary to monitor large areas at a detailed level, which has previously been unsatisfactory due to its reliance on the interpretation of aerial photographs and the cost of high-resolution images.Using the differential Bare Soil Index for optical imagery interpretation in combination with cloud-computing in Google Earth Engine is a novel approach. Applying this method on a recent landslide event in Oaxaca in Mexico around 62% of the landslides were detected automatically, however, there is a big potential for improvement. Including NDVI values and considering images with a higher spatial resolution could contribute to the enhancement of landslide detection, as the majority of missed events have a size smaller than half a pixel. Landslide detection in Google Earth Engine has become a promising approach for big data processing and landslide inventory creation.

EXPLORING CLOUD-BASED PLATFORMS FOR RAPID INSAR TIME SERIES ANALYSIS

Abstract. The idea of near real-time deformation analysis using Synthetic Aperture Radar (SAR) data as a response to natural and anthropogenic disasters has been an interesting topic in the last years. A major limiting factor for this purpose has been the non-availability of both spatially and temporally homogeneous SAR datasets. This has now been resolved thanks to the SAR data provided by the Sentinel-1A/B missions, freely available at a global scale via the Copernicus program of the European Space Agency (ESA). Efficient InSAR analysis in the era of Sentinel demands working with cloud-based platforms to tackle problems posed by large volumes of data. In this study, we explore a variety of existing cloud-based platforms for Multi-Temporal Interferometric SAR (MTI) analysis and discuss their opportunities and limitations.

Sentinel-1 soil moisture at 1 km resolution: a validation study

This study presents an assessment of a pre-operational soil moisture product at 1 km resolution derived from satellite data acquired by the European Radar Observatory Sentinel-1 (S-1), representing the first space component of the Copernicus program. The product consists of an estimate of surface soil volumetric water content Θ [m3/m3] and its uncertainty [m3/m3], both at 1 km. The retrieval algorithm relies on a time series based Short Term Change Detection (STCD) approach, taking advantage of the frequent revisit of the S-1 constellation that performs C-band Synthetic Aperture Radar (SAR) imaging. The performance of the S-1 Θ product is estimated through a direct comparison between 1068 S-1 Θ images against in situ Θ measurements acquired by 167 ground stations located in Europe, America and Australia, over 4 years between January 2015 and December 2020, depending on the site. The paper develops a method to estimate the spatial representativeness error (SRE) that arises from the mismatch between the S-1 Θ retrieved at 1 km resolution and the in situ point-scale Θ observations. The impact of SRE on standard validation metrics, i.e., root mean square error (RMSE), Pearson correlation (R) and linear regression, is quantified and experimentally assessed using S-1 and ground Θ data collected over a dense hydrologic network (4 − 5 stations/km2) located in the Apulian Tavoliere (Southern Italy). Results show that for the dense hydrological network the RMSE and correlation are ~0.06 m3/m3 and 0.71, respectively, whereas for the sparse hydrological networks, i.e., 1 station/km2, the SRE increases the RMSE by ~0.02 m3/m3 (70% Confidence Level). Globally, the S-1 Θ product is characterized by an intrinsic (i.e., with SRE removed) RMSE of ~0.07 m3/m3 over the Θ range [0.03, 0.60] m3/m3 and R of 0.54. A breakdown of the RMSE per dry, medium and wet Θ ranges is also derived and its implications for setting realistic requirements for SAR-based Θ retrieval are discussed together with recommendations for the density of in situ Θ observations.

EFFECTS OF GEOSPATIAL DATA SOURCES ON THE IDENTIFICATION AND CHARACTERIZATION OF BURNT AREAS IN PORTUGAL

Abstract. The work presented in this paper compares the burnt areas in continental Portugal in 2017 and 2018 mapped by three initiatives, namely the Portuguese Institute of Nature and Forests Conservation (ICNF), the Corine Land Cover (CLC) inventory of the Copernicus programme and the European Forest Fire Information System (EFFIS). Then, the Land Use Land Cover (LULC) classes affected by the 2017 burnt areas mapped by ICNF are analysed considering CLC 2018 and the 2018 LULC map produced by the Portuguese National Mapping Agency (Direção Geral do Território) – “Carta de Ocupação do Solo” (COS 2018). To enable a comparison between the classes of both LULC products, a nomenclature was selected and both CLC 2018 and COS 2018 were mapped into the chosen classes. The comparison of the burnt area’s extent showed that there are large differences in both area and levels of detail between the analysed data sources. The results regarding the LULC classes affected by the 2017 fires mapped by ICNF show large differences in terms of burnt area in each class as well as the proportion of the burnt areas associated to the classes. This analysis shows that very different results may be reached if different products are used, and therefore a large level of uncertainty is associated with the conclusions achieved with these products.

On the use of Sentinel-2 satellites and lidar surveys for the change detection of shallow bathymetry: The case study of North Carolina inlets

Mapping bathymetric change is a core task for a wide range of navigation, research, monitoring, and design applications. Satellite-derived bathymetry (SDB) can support this activity, particularly when using data from a platform, like the Sentinel-2A/B twin mission of the Copernicus programme, which provides routine and repetitive image acquisition at 10 m spatial resolution. As a result, SDB can expand on the temporal and spatial scope of more conventional mapping methods, such as lidar (or multi-beam). In this study, we use SDB, in comparison with high-resolution airborne lidar bathymetry (ALB), to quantify bathymetric changes at two inlets in North Carolina following the impacts of the devastating Hurricane Florence in September 2018. A recently developed multi-temporal SDB model that overcomes problems associated with turbidity and noise effects (whitecaps, ships, cloud shadows, etc.) is used with Sentinel-2 imagery. We identify bathymetric changes in shallow areas with navigation channels in two of the most dynamic inlets in the Outer Banks, Oregon and Hatteras. Multiple lidar surveys are used to validate the SDB method and for an assessment of accuracy and vertical uncertainty. The multi-temporal SDB products and ALB both show similar results in the erosion/accretion patterns. Comparing the change determined from the two methods, gives a median absolute error of ~0.5 m of SDB compared with ALB, with bias of ±0.2 m for depths ≤7 m; errors that are equivalent to those associated with the SDB estimated absolute depths. The composited SDB yields greater spatial coverage than lidar because it can retrieve data in areas where the lidar mission may have been constrained by turbidity or surf, and also because of the 300-km swath width. The Sentinel-2 constellation provides five-day revisit at the equator (more frequent at higher latitudes), allowing rapid construction of a bathymetric map as well as developing an archive for retrospective analysis. By implementing the multi-temporal turbidity correction, SDB based on Sentinel-2 may substantially enhance existing survey methods for change detection and support operational and recursive coastal monitoring on local to regional scales.

Multispectral Sentinel-2 and SAR Sentinel-1 Integration for Automatic Land Cover Classification

The study of land cover and land use dynamics are fundamental to understanding the radical changes that human activity is causing locally and globally and to analyse the continuous metamorphosis of landscape. In Europe, the Copernicus Program offers numerous territorial monitoring tools to users and decision makers, such as Sentinel data. This research aims at developing and implementing a land cover mapping and change detection methodology through the classification of Copernicus Sentinel-1 and Sentinel-2 satellite data. The goal is to create a versatile and economically sustainable algorithm capable of rapidly processing large amounts of data, allowing the creation of national-scale products with high spatial resolution and update frequency for operational purposes. Great attention was paid to compatibility with the main activities planned in the near future at the national and European level. In this sense, a land cover classification system consistent with the European specifications of the EAGLE group has been adopted. The methodology involves the definition of distinct sets of decision rules for each of the land cover macro-classes and for the land cover change classes. The classification refers to pixels’ spectral and backscatter characteristics, exploiting the main multi-temporal indices while proposing two new ones: the NDCI to distinguish between broad-leaved and needle-leaved trees, and the Burned Index (BI) to identify burned areas. This activity allowed for the production of a land cover map for 2018 and the change detection related to forest disturbances and land consumption for 2017–2018, reaching an overall accuracy of 83%.

Edusat: remote sensing as an educational resource. The use of data from the Copernicus program as an innovative teaching method for students, teachers, and researchers

Abstract. The intensification in recent decades of scientific evidence on climate change and on the degradation of natural systems has led to increasing public awareness about the environment. In recent times, this commitment to respecting the natural environment has emerged strongly among young people. Through various platforms, entities, and slogans, students from all over the world, and belonging to different disciplines, are coming together to defend their right to have a planet that enjoys good environmental health.The platform Edusat presented in this article aims to provide young people with empirical and quantitative learning tools to strengthen their ecology message. By means of remote sensing and through the data generated by the Copernicus program, an educational resource that analyzes the consequences of global environmental change is presented. In this context, remote sensing is a technological and transdisciplinary resource that provides young people with scientific arguments to censure the current relationship between human societies and nature.

A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey

The success rate in landslide susceptibility mapping efforts increased with the advancements in machine learning algorithms and the availability of geospatial data with high spatial and temporal resolutions. Existing data-driven susceptibility mapping models are not globally applicable due to the high variability of landslide conditioning parameters and the limitations in the availability of up-to-date and accurate data. Among numerous applications, landslide susceptibility maps are essential for site selection and health monitoring of engineering structures, such as dams, for increasing their lifetime and to prevent from disastrous events caused by the damages. In this study, landslide susceptibility mapping performance of XGBoost algorithm was evaluated in a landslide-prone area in the upper basin of Ataturk Dam, which is a prime investment located in the southeast of Turkey. The study area has a size of 2718.7 km2 with an elevation difference of ca. 2000 m and contains 27 lithological units. EU-DEM v1.1 from the Copernicus Programme was used to derive the geomorphological features. High classification accuracy with area under curve value of 0.96 could be obtained from the XGBoost algorithm. According to the results, the main factors controlling the landslides in the study area are the lithology, altitude and topographic wetness index.

Large-scale winter catch crop monitoring with Sentinel-2 time series and machine learning–An alternative to on-site controls?

The European legislative guidelines on the Common Agricultural Policy (CAP) lead to an obligation of area-wide information about cross-compliant Greening measures in EU countries. But by on-site controlling, agricultural authorities can monitor only a small portion of all registered parcels. With the Copernicus Programme, the freely available Sentinel-1 and Sentinel-2 imagery increasingly supports remote sensing-based agricultural monitoring on a large-scale. However, most prototypes lack the topic of winter catch cropping. Therefore, we developed a new machine learning method for catch crop monitoring and detection at the parcel-level. To gain training and test data for supervised machine learning, we collected winter catch crop parcel data from different years (2016–2019) and four federal states in Germany. Normalized Difference Vegetation Index (NDVI) time series were calculated for each parcel from Sentinel-2 data within the typical winter catch crop cultivation season (July-April). We revealed distinctive temporal patterns of catch cropping and developed nineteen descriptive features for automatized prediction. Then, we trained fifteen Random Forest classifiers comprising different regions and years and conducted a multi-level validation to identify the model with the highest robustness on new data. The Random Forest classifier trained with the input data from all federal states and years outperformed the other models. It reached a mean prediction accuracy of 84% for both classes (catch crop and non-catch crop) across eleven different spatio-temporal domains. Under optimal annual weather conditions it reached accuracies close to 90%. Anomalies caused by heat waves and early frost events were found to have a high influence on the phenology of catch crops and thus lead to reduced prediction accuracies. From the set of predictors, those features with the highest importance measured the correlation between the observed time series and a simulated NDVI phenological profile. We concluded that catch cropping parcels are automatically separable from parcels with other winter cultivations (e.g., winter cereals, grasslands, fallow) with Sentinel-2 NDVI time series data. Different catch crop subgroups (i.e., seed mixes) could not be differentiated by our approach due to very similar phenological profiles. Nonetheless, the used approach allows for large-scale winter catch crop monitoring and supports authorities in the selection of parcels with high demand for on-site controlling. By merging the training datasets from different federal states and years, we could overcome the typical spatial and temporal overfitting problem in machine learning. Therefore, the study’s final classifier can be reliably transferred to new datasets in Germany and other regions with similar bio-geographical conditions.