Carbon is an essential element and contributor to healthy soil conditions as well as ecological soil function and productivity. Additionally, carbon is a component of all plants and animals on the planet and is a necessary component of life. Natural vegetation serves as a significant but highly dynamic carbon sink. When vegetation is removed quicker than it can regenerate, for example by harvesting crops or timber, soil carbon is depleted. Thus, understanding the environmental effects and dynamics of loss of vegetation is a crucial prerequisite to turning our natural resource management from a carbon emitter to a carbon sink to avoid that and achieve sustainability. At the same time, the spatial distribution of soil organic carbon is also highly heterogeneous, with variations in climate, other soil characteristics, and land use/land cover affecting how our ecosystem reacts to the loss of vegetation. Thus, to effectively improve green metrics and contribute to the creation of future policies, it is required to conduct research on the changes in vegetation and their effect on soil organic carbon and provide regionally appropriate management advice. Here, in this research, our goal is to examine the "individual treatment effects" (ITE), which are a personalized or individualized effect estimation of one variable on the output, and utilize causal inference to address them.  Using the LUCAS dataset, we explore the heterogeneous treatment effect of percent tree coverage (PTC), as a parameter of the density of trees on the ground, on the soil organic carbon content in Germany. We do this by leveraging some parameters, such as climate data, land use/land cover information, and other information from the soil. We thus offer a data-driven viewpoint for focusing on sustainable behaviors and effectively increasing soil organic carbon content levels.

How to cite: Kakhani, N., Gläßle, T., Taghizadeh-Mehrjardi, R., Kebonye, N. M., and Scholten, T.: Exploring the ‘Individual Treatment Effects’ (ITE) of Vegetation with Causal Inference on Soil Organic Carbon Prediction in Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1083, https://doi.org/10.5194/egusphere-egu23-1083, 2023.

The adoption of regenerative agricultural practices is gaining traction as an approach to enhance soil health and sequester carbon to combat climate change. Several sustainability frameworks and programmes are now incentivizing producers to transition to regenerative farming. These evolving initiatives have created a need to build and operate Measurement, Reporting and Verification (MRV) platforms to track cropland practices and impacts. To help scale initiatives, we have developed an automated approach that leverages multi-source remote sensing, data science and machine learning for cost-effective, robust and transparent tracking of tillage practices. Our approach leverages time-series satellite observations from Sentinel-1 and Sentinel-2 constellations, along with ancillary data from SMAP, soils and weather. Within a hierarchical classification, these inputs are blended with dense, independent training data (i.e., “ground truth”) collected across Europe with tens of thousands of samples gathered across France, Belgium, Denmark and the UK. Training data includes observations of crop types and rotations, residue, soil disturbance and field conditions. Together, these multi-source data feed into gradient boosting and Convolutional Neural Networks to ultimately help seasonally classify tillage practices into conventional, reduced or no till at field scale for all major row crops. Withheld independent observations and data science best practices are used to tune model performance and class accuracy depending on regional schemes, residue categories and landscape practice variability. F1 score and Overall Accuracy achieve > 80% with some crop and tillage practice combinations (i.e. corn, soy, wheat conventional) > 0.9. In addition, we share lessons learnt and next challenges. With this approach, the Community of Practice can robustly track every field wall-to-wall over seasons and feed downstream applications, such as estimating Soil Organic Carbon and emissions process modelling. With these tools, and open operational data streams such as Copernucis, we can support scaling regenerative agriculture impacts and grow carbon farming initiatives and ecosystem service markets across Europe. 

How to cite: Torbick, N., Whelan, A., Synes, N., Huang, X., and Cornwell, V.: Tracking tillage practices across European croplands using multi-scale remote sensing and machine learning., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2114, https://doi.org/10.5194/egusphere-egu23-2114, 2023.

Accurate and in-time monitoring of cropping systems is critical to precision farming in order to facilitate decision-making for agronomic management and enhancing crop yield under changing climate. In this study, multi-source unmanned aerial vehicle (UAV) remote sensing observations were conducted at several key growing stages of crops at a standard wheat-maize cropping system field trials in the North China Plain from 2018 to 2020. Crop leaf area index, above-ground biomass, chlorophyll content, grain yield, and plant density were estimated using multi-source UAV remote sensing observations (including RGB, multi/hyperspectral, LiDAR, and thermal sensors) processed by machine/deep learning approaches.

In this study, we will give a comprehensive research introduction focusing on how to improve the estimation accuracy of the above crop growth variables via UAV remote sensing and machine/deep learning approaches, including three aspects:

(1) Data source and fusion, including the integration of multi-source UAV information for comprehensive maize growth monitoring, comparison of UAV-based point clouds with different densities for crop biomass estimation, and crop chlorophyll content estimation using multi-scale hyperspectral information.

(2) Optimization of UAV observation management: we will answer when is the most relevant phenological stage for maize yield estimation via high-frequent UAV observations; investigate extrapolation artefacts, validate the suitability and discuss the uncertainty of the UAV-based strategies for 'model calibration at a small site while applying these models at a large extent' for crop monitoring.

(3) Modeling improvement will give two cases to introduce improving crop biomass estimation accuracyand realize the plant density counting during the vigorous growing period employing deep learning.

How to cite: Sun, Z., Zhu, W., Eyshi Rezaei, E., Peng, J., Yu, D., and Siebert, S.: Multi-source UAV remote sensing and AI for crop growth monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12111, https://doi.org/10.5194/egusphere-egu23-12111, 2023.

Dryland forests are highly climate-sensitive are facing more frequent droughts and, consequently, increasing tree mortality, extreme wildfire events, and outbreaks of forest insects and pathogens. These changes, associated with climate change, are leading to biodiversity loss and the deterioration of related ecosystem services. Understanding the relationships between forest structure and function is essential for managing dryland forests to adapt to these changes. We studied the structure-function relationships in four dryland conifer forests distributed along a semiarid to sub-humid climatic aridity gradient. Forest structure was represented by leaf area index (LAI) and function by gross primary productivity (GPP), evapotranspiration (ET), and the derived efficiencies of water use (WUE= GPP/ET) and leaf area (LAE = GPP/LAI). The water and carbon fluxes at the ecosystem level were estimated by an empirical approach in which regression models were developed to relate multiple spectral data (VIs) derived from VENμS and Sentinel-2A satellites, combined with meteorological data, to local eddy covariance measurements from flux tower records available at three of the four study sites. The red-edge-based MERIS Terrestrial Chlorophyll Index (MTCI) from VENμS and Sentinel-2A showed strong correlations to flux tower GPP and ET measurements (R²_cal >0.91, R²_val >0.84). Using our approach, we showed that as LAI decreased with decreasing AI (dryer conditions), estimated GPP and ET decreased (R²>0.8 to LAI), while WUE (R²=0.68 to LAI) and LAE increased with decreasing AI. We propose that the higher WUE and LAE reflect an increased proportion of sun vs. shade leaves as LAI decreases. The results demonstrate the importance of high-resolution spectral and spatial data in low-density dry forests and the intricate structure-function interactions in the forests’ response to drying conditions.

How to cite: Dubinin, M. (., Osem, Y., Yakir, D., and Paz-Kagan, T.: Investigating the relationships between the leaf area index and forest functions of dryland conifer forests along an aridity gradient using VENµS and Sentinel-2 satellites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12505, https://doi.org/10.5194/egusphere-egu23-12505, 2023.

The accurate retrieval of Solar-Induced chlorophyll Fluorescence (SIF) is a pivotal target for Earth Observation since SIF can be easily monitored through optical remote sensing and provides unique information concerning the vegetation health status. Here, we propose i-φ-MaLe (metti il nome per esteso), a novel algorithm, which couples the Fourier analysis with a supervised machine learning-based procedure trained with the atmosphere-canopy radiative transfer (RT) SCOPE model.  i-φ-MaLe is the first method able to simultaneously retrieve, from the vegetation reflectance spectra, the Top Of Canopy SIF spectrum, the SIF spectrum corrected for leaf/canopy reabsorption (i.e. at photosystem level), the quantum efficiency (Fqe) and three canopy-related biophysical parameters (Leaf Area Index - LAI, Chlorophyll content - Cab and APAR) in few milliseconds. Validation procedures, based on the analysis of RT simulations, demonstrated that i-φ-MaLe, in experimental conditions (signal to noise ratio – SNR >= 500), estimates each biophysical parameter and SIF spectrum with a relative root mean square error (RRMSE) lower than 5%. In order to investigate the seasonal and daily dynamics of SIF, LAI, Cab, Fqe and APAR, the method has been also applied to field experimental data collected in the context of the AtmoFLEX and FLEXSense ESA campaigns, both at top-of-canopy (TOC) and tower (~100 meters) levels. Concerning the TOC scenario, the retrieved annual dynamic for SIF spectra has been compared with the results obtained by inversion-based methods, showing a good consistency amongthe different approaches (RRMSE ~ 10%). Moreover, SIF daily and annual dynamics, retrieved by excluding the oxygen spectral bands affected by the atmospheric reabsorption,  have been investigated for  high tower measurements. . In this context, i-φ-MaLe provided  promising results that can integrate and possibly overcome complex and computationally expensive atmospheric compensation techniques actually needed to retrieve fluorescence from oxygen absorptions bands. This study demonstrates a promising potential to exploit ground and tower spectral measurements with advanced processing algorithms, for improving our understanding on the link between canopy structure and physiological functioning of plants. Moreover, i-φ-MaLe can be straightforwardly employed to process reflectance spectra and open new perspectives in fluorescence retrieval at different scales.

How to cite: Scodellaro, R., Cesana, I., D'Alfonso, L., Bouzin, M., Collini, M., Chirico, G., Colombo, R., Miglietta, F., Celesti, M., Schuettemeyer, D., Cogliati, S., and Sironi, L.: i-φ-MaLe: a novel AI-phasor based method for a fast and accurate retrieval of multiple Solar-Induced Fluorescence metrics and biophysical parameters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14746, https://doi.org/10.5194/egusphere-egu23-14746, 2023.

The wetlands and lakes that make up more than 30% of Cambodia's terrain are home to a diverse range of resources and biodiversity. More than 46% of the population lives and works in these wetlands while 80% of the local population relies on their vital resources for sustenance such as fish, food, water and vegetables. This makes Cambodia one of the nations with the highest reliance on wetland and lake ecosystems in the world. On-going development in the region has boosted the rates of  urbanization. Urban expansion has deteriorated wetland ecosystems through land reclamation and infilling projects as well as hydrological and sediment cycle disruptions. It has also increased the demand for mined sand from the Mekong River. Mapping and monitoring the extent and distribution of wetland ecosystems in order to quantify the impact of human activities on these vital areas is critical for maintaining the ecological balance and promoting the sustainable development of an extensively eco-service dependent country such as Cambodia. In this study we combine spaceborne multispectral and radar remote sensing datasets with machine learning classification models and algorithms within the Google Earth Engine to monitor the changes observed in Cambodian wetlands through time. Our classifier is trained by comparing Sentinel 1 Synthetic Aperture Radar data to corresponding multispectral images captured from Landsat. We then use the classifier to monitor wetland extent through time from 1989 to present using merged Landsat 5 and 8 databases. With our maps and areal statistics, we identify the spatio-temporal trends and changes in wetland cover linked to climatic patterns and local anthropogenic influence connected to sand mining from the Mekong River and land infilling. In the last 15 years, about half the country’s wetlands have disappeared, with 15 out of 25 lakes near the capital completely infilled with sand that can be clearly observed with analysis of satellite data.

How to cite: Darbari, V., Christopher, H., Grigorios, V., Rodney, F., and Dan, P.: Mapping Cambodian Wetlands with Satellite Imagery and Google Earth Engine’s Machine Learning Algorithm., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6520, https://doi.org/10.5194/egusphere-egu23-6520, 2023.

In the context of global sea level rising, coasts are directly impacted. The retreat to coastlines and submersion of anthropic installations are among the major impacts. It is thus imperative to continuously monitor the coastlines status and devise the means and techniques to effectively assess their status. The United Arab Emirates (UAE) for example is a country which has a long sandy coastline. In this research, an algorithm was developed that makes use of remote sensing temporal data to assess the variability of the coastline in the UAE. The algorithm is used to automatically extract the whole coastline between 1991 and 2021 from Landsat 5 and 8 satellite images. They were selected for 1991, 2001, 2013 and 2021 because of the availability of data, and the significant changes that have been done in coastal areas due to urban development during this period.

Only the Landsat spectral bands of green and near-infrared were utilized to calculate the spectral index of detection of the coast DDWI (Direct Difference Water Index). It is the first step of the algorithm developed. Then is used an automatic threshold Otsu to differentiate the land from water. The result is filled to remove the main artifacts and a canny edge detector is used to detect the coastline. At the end of the algorithm, the result is georeferenced because it lost it during the process. The georeferenced layer is polygonised so that the remaining artifacts are easier to remove. Then, a mask layer was created including boats, clouds, etc… and it is removed from the polygonised layer to get the final extracted coastline.

The preliminary findings of this study show that the sandbanks have increased during the period of the study along the Arabian Gulf waters, suggesting that the coastline is retreating. The results showed a development of the sandbanks towards the Arabian Gulf in several places along the northern coastline but also their general retreat on the north-western one. This can be explained by the sediment settlement or the backfills that have been done to create new islands especially around Abu Dhabi city and Dubai. The creation of mangroves plantations or port infrastructures in the same place has completely changed the coastline layout of the UAE.

On the other side of the UAE, along the sea of Oman, the sandbanks have retreated, suggesting either soil erosion by water currents or advancement of the coastline. The results show no significant change at all and no sandbanks. The only changes observed are linked to the anthropic modification of the coast. While the coastline did not change, the developed algorithm detected scattered sandbanks as the coastline. This confusion likely comes from the similar reflectance of sandbanks in shallow water with the sand of the coast. A further improvement for the developed algorithm will be pursued in the future to reduce such confusions.
This methodology is applicable worldwide, but it is necessary to monitor the results for sandy areas such as the Middle East.

How to cite: Sarrau, J. and Abuelgasim, A.: Temporal variations of United Arab Emirates coastline from 1991 to 2021, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11414, https://doi.org/10.5194/egusphere-egu23-11414, 2023.

Coastal aquaculture has become one of the main sources of animal protein and plays an important role in food and nutrition supplies and security around the world. Accurately mapping aquaculture areas is the basis for its sustainable management and use, and provides important support to policy development and implementation at regional, national, and global levels. Considering the concentrated and densely distributed characteristics of aquacultures, it is difficult to distinguish the dikes between aquacultures and identify small-scale aquacultures using medium-and low-resolution SAR images. GaoFen-3 (GF-3) is the first launched full-polarimetric C-band SAR satellite of China at metre-level resolution. This study aims to use a novel combination model to extract coastal aquacultures in the Yancheng coastal wetland, China on the basis of GF-3 Fully Polarimetric SAR Imagery. Polarimetric decomposition algorithms were applied to extract polarimetric scattering features and feature optimization was applied based on the separability index. To separate adjacent and even adhering ponds into individual aquaculture objects, we proposed a novel model that integrated two UNet++ subnetworks with the marker-controlled watershed (MCW) segmentation strategy to obtain more refined coastal aquaculture results. The accuracy assessment results demonstrated a considerable performance, with F1 greater than 95%, IoU greater than 90%, and insF1 higher than 85%. The experimental results indicate that the proposed algorithm achieved fairly high accuracy in aquaculture extraction and can effectively improve the boundary quality of individual aquacultures.

How to cite: Yu, J., He, X., Motagh, M., Yang, P., and Xu, J.: Coastal Aquaculture mapping using a novel combination model of GF-3 Fully Polarimetric SAR Imagery: A case study of Yancheng coastal wetland, China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14979, https://doi.org/10.5194/egusphere-egu23-14979, 2023.

The 2021 eruptive activity at Mt Etna was characterized by 57 paroxysmal events at the South-East Crater, the most active among its four summit craters. These episodes of Strombolian activity and high lava fountains fed lava flows towards East, South, and South-West and caused ashfall in the surroundings of the volcano. In 2022 the SEC gave rise to only two paroxysms in February and effusive activity in May-June and since November (still ongoing). Although the impacted area does not include permanent infrastructures it is of high tourist attraction. Hence, timely mapping of each lava flow field was mandatory for hazard mitigation. The high frequency of the 2021 paroxysms, up to two events in 24 hours, forced us to implement a multidisciplinary approach based on various remote sensing techniques, with different spatial resolutions and revisiting time. In particular, several satellite images were processed, depending on data availability and weather conditions. Data acquired by Sentinel-2 MSI, Skysat, Landsat-8 OLI, and TIRS allowed us to map the lava flow fields at a spatial resolution ranging from 0.5 to 90 meters. High-spatial resolution (from 4.5 up to 55 cm) DEMs and orthomosaics were also realized elaborating the visible and thermal images acquired through Unmanned Aerial Systems (UASs) surveys. Moreover, data acquired by the thermal cameras of the Istituto Nazionale di Geofisica e Vulcanologia permanent network were re-projected into the topography for analyzing the lava flow field evolution at 5-meter spatial resolution. These multi-platform remote sensing data allowed for mapping the lava flows and compiling a geodatabase reporting the main geometrical parameters (e.g. length, area, average thickness, and volume). The resulting multi-sensor methodology enabled, for the first time on Etna, to timely and accurately characterize frequently occurring effusive events.

How to cite: Proietti, C., De Beni, E., Cantarero, M., and Ricci, T.: Timely mapping and quantification of volcanological parameters: the 2021-2022 Etna lava flows, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15311, https://doi.org/10.5194/egusphere-egu23-15311, 2023.

Campi Flegrei caldera (CFc; Southern Italy) is the archetype for volcanic risk occurring within a highly anthropized area. CFc was mostly shaped by the collapse following the Neapolitan Yellow Tuff eruption (NYT) ~15 ky BP, which generated about 50 km³ of volcanic products mainly deposited via huge pyroclastic flows. More than 50 eruptions from several volcanic centers were generated within the caldera after the NYT, with last eruption occurring in 1538 (Monte Nuovo eruption). Since the 1950s, CFc experiences a long-term unrest in the Pozzuoli area. After the 1969-72 and 1982-84 episode, uplift started again in 2004, raising serious concern to population and authorities also because of the recurrent seismic activity and the persistence of fumarolic emissions fed by the underlying hydrothermal system.

In this context, thermal monitoring of the caldera is a strategic issue for volcanic forecasting, considering that several areas are prone to the opening of volcanic vents. The large size of the onshore portion of the CFc (90 km²), the difficulties to access several anomalous sites and the huge degree of anthropization make the direct assessment of ground-thermal anomalies and the realization of periodic measurement campaigns very difficult, time-consuming and unsafe.

Here we report on a detailed airborne thermal mapping from a flight made on 15 April 2022 at ~6.30 am (local time). Thermal acquisition was performed with a wide-array broadband thermal sensor in conjunction with optical imagery in Red-Green-Blue bands via a 150 MP camera. The sensor platform and the aircraft ¾  including logistic facilities, agreements with military airports and authorizations to fly ¾  are a strategic asset of the BENECON. The high resolution of thermal mapping (instrumental accuracy: 0.05 °C on temperature; pixel size: 0.45m x 0.45m) in conjunction with real-time acquisition of optical images allows a straightforward discrimination of natural ground anomalies from thermal emissions and spots due to anthropic activities. Ground thermal anomalies related to volcanic-hydrothermal activity and associated with the caldera unrest are concentrated in the well-known Solfatara and Pisciarelli sites, whereas minor features are detected on the relief bordering the western side of Agnano plain, inside the Astroni crater and on the southern flank of Monte Nuovo, in line with results from existing ground surveys. At Solfatara and Pisciarelli, the shape of measured thermal anomalies matches that of CO₂ fluxes interpolated from literature data. The consistency between heat fluxes computed from airborne-detected ground temperatures and soil CO₂ fluxes (e.g., in the order of 100 MW for the Solfatara crater) confirms that steam condensation from hydrothermal activity is presently the dominant engine responsible for endogenous heat release at CFc.

The fast execution of the airborne survey, the rapid data processing and post-processing and the capability of detecting the most subtle anomaly prompt for periodic surveys of the CFc thermal flux aimed at 1) the tracking of existing anomalies 2) the rapid detection of new thermal features and 3) the building of time-series. Integration with optical and, in perspective, hyperspectral VNIR images foster an unprecedented capability to monitor the ongoing volcanic unrest.

How to cite: Gambardella, C., Moretti, R., Ciaburro, G., Martimucci, D., Marconi, F., and Parente, R.: Thermal features and heat budget of Campi Flegrei unrest caldera from fast, high-resolution airborne mapping, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17477, https://doi.org/10.5194/egusphere-egu23-17477, 2023.

The characterization of the eruption source parameters (EPS) of explosive eruptions is of vital importance to prevent damages, mitigate environmental impact and reduce aviation risks.  We consider highly explosive eruptions with a Volcanic Explosive Index (VEI) greater than 3. During these eruptions, a great number of volcanic particles are ejected into the atmosphere where they can remain suspended for several weeks. Satellite passive sensors can be adopted to monitor volcanoes due to their high spatial and temporal resolution. 

In this work we combine the Microwave (MW) and Millimetre-wave (MMW) observations with Thermal-InfraRed (TIR) radiometric data from Low Earth Orbit (LEO) satellites to have a complete characterization of the volcanic clouds. MW-MMW passive sensors are adopted to detect larger volcanic particles (i.e. size bigger than 20 µm) by working at lower frequencies. TIR observations are employed to study smaller particles due to the sensor settings which work at smaller wavelengths. We describe new physical-statistical methods together with machine learning techniques aiming at detecting and retrieving volcanic clouds masses of 2015 Calbuco, 2014 Kelud as well as other eruptions having high explosive activities worldwide. Concerning the detection, we compare the well-known split-window methods with a machine learning algorithm named Random Forest (RF). This work highlights how the machine learning model is suitable to automatically identify tephra contaminated pixels by combining different spectral information (i.e. MW-MMW and TIR) coming from different satellite platforms. Indeed, we used data coming from: Advanced Technology Microwave Sounder (ATMS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors on board the Suomi-NPP LEO satellite; Microwave Humidity Sounder (MHS) and Advanced Very High Resolution Radiometer (AVHRR) sensors on board the Metop series.  In terms of retrieval, the new developed Radiative Transfer Model Algorithm (RTM_A) is designed to estimate the total columnar content (TCC) and in turn the mass, for both MW-MMW and TIR observations. The synthetic BTs (simulated by RTM_A) are linked with the observed BTs to retrieve the volcanic clouds features. In this respect, two minimization techniques, the Maximum Likelihood Estimation (MLE) and the Neural Network (NN) architecture, are also compared and discussed. Results show a good comparison of the mass obtained using the MLE and NN methods for all the analysed bands but also with previous studies on the deposit as well as other validated satellite retrieval methods. 

In conclusion, this work shows how the machine learning model can be an effective tool for volcanic cloud detection and how the synergic use of the TIR and MW-MMW observations can give more accurate estimates of the near source volcanic cloud.

How to cite: Romeo, F., Mereu, L., Corradini, S., Merucci, L., and Scollo, S.: Volcanic cloud detection and retrieval by micro-millimetre-waves and thermal-infrared satellite observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12720, https://doi.org/10.5194/egusphere-egu23-12720, 2023.

The Geohazard Supersites and Natural Laboratories (GSNL) is an initiative of the Group of Earth Observation (GEO) that has started in 2007 with the Frascati declaration, in which the GeoHazards Community of Practice recommended to: “... stimulate international and intergovernmental effort to monitor and study selected reference (geologic hazards) sites, by establishing open access to relevant datasets according to GEO principles, to foster collaboration between various partners and end users”. Since the beginning the main idea has been the improvement of the hazard assessment by combining space- and ground-based datasets provided by the Space Agencies and the research institutions managing the in-situ observation systems, respectively.

According to the definition of Supersite, since the early stage of the GSLN initiative, Mt. Etna has been identified as one of the Supersites due to its almost continuous eruptive activity, the great amount of satellite and in-situ data available, and the advanced in-situ multi-parametric observing systems. Officially, Mt. Etna is a Permanent Supersite since 2014. The Space Agencies provide quotas of SAR and high-spatial resolution optical multispectral satellite data and INGV offers geophysical, geochemical, and volcanological data. The data are accessible via an open access platform implemented in the framework of the EC FP7 MED-SUV project, and is going to be integrated in the EPOS research infrastructure.

During the past few decades, Mt. Etna has erupted almost every year offering the optimal conditions to apply the Supersite concept; thus here we report some relevant examples of the integrated use of the space-and ground-based data applied to Mt. Etna’s eruptions, highlighting how such complementarity improved the monitoring of the eruptive events and the assessment of the associated hazards.

How to cite: Puglisi, G., Bonforte, A., Buongiorno, M. F., Cacciola, L., Guglielmino, F., Ganci, G., Musacchio, M., Scollo, S., Reitano, D., Silvestri, M., and Spampinato, L.: Integration between space- and ground-based observations in areas prone to volcanic hazard: the experience of Mt. Etna Supersite, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12310, https://doi.org/10.5194/egusphere-egu23-12310, 2023.

The European Ground Motion Service (EGMS) allows a wide spectrum of users to access ground motion data over 30 European countries for free, under the Copernicus data policy. The EGMS aims to serve for various applications, of which geohazards are probably the primary target. Also, the Service establishes a baseline for studies dedicated to localised deformation affecting buildings and infrastructure in general.

The EGMS is the result of a massive computational effort to process thousands of Sentinel-1 images and derive three levels of products: (a) basic, i.e. line of sight (LOS) velocity maps in ascending and descending orbits referred to a local reference point; (b) calibrated, i.e. LOS velocity maps calibrated with a geodetic reference network and (c) ortho, i.e. components of motion (horizontal and vertical) anchored to the reference geodetic network. The EGMS is implemented under the responsibility of the European Environment Agency in the frame of the Copernicus Programme.

The EGMS baseline (2016-2020) and the first annual update (2016-2021) were made available to users in the summer of 2022 and in the first quarter of 2023, respectively. The EGMS products are displayed in the EGMS Explorer (https://egms.land.copernicus.eu/), where users can investigate the data in a 3D web interface, explore the temporal behaviour of ground motion through time series and download one or multiple data tiles. External web map services can be imported in the EGMS Explorer to ease the interpretation of the interferometric measurements.

The strategy to update satellite interferometric time series is a hot topic for wide area processing services. So that, one of the goals of this presentation is to show the EGMS update strategy, which should guarantee the best trade-off between the identification of new coherent targets and motion areas and the continuity of the Service in terms of technical implementation and noise level.

The expected wide usage of the EGMS products required to setup a validation system that has two goals: verify the usability of the data for the expected range of applications and assess the quality of the products with respect to service requirements. Validation is based on seven activities performed in sixteen different countries and in several validation sites, which are representative for thematic applications (e.g. mining-induced ground motion) in different environments of Europe. To guarantee reproducibility of results, the validation data (e.g. levelling or corner reflectors time series, landslide databases) will be made available to users according to licensing conditions. The results of the validation exercise will be available to users in Q3 2023.

How to cite: Solari, L., Balasis-Levinsen, J., and Andersen, H. S.: European Ground Motion Service: production status and validation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14315, https://doi.org/10.5194/egusphere-egu23-14315, 2023.

Passive RFID tags are opening new capabilities of monitoring in geoscience [1], applied to landslide [2-3], snowpack [4] or riverine pebble monitoring. This study investigate the ability to localize passive RFID tags (working at 868 MHz) from the air in harsh conditions met in natural areas outdoors. The tags are localized with the synthetic aperture radar method (SAR)  with a mobile reader from above, installed on a rail and equiped with a differential GNSS. The tags are localized either directly on the ground, under a vegetal cover, or under a snow cover, and the localization accuracy is evaluated in each case. This technique opens the possibility to monitor ground displacement even under snow or vegetal coverage, that challenge most of existing displacement measurement techniques.

Related studies on the topic :

[1] Le Breton, M., Liébault, F., Baillet, L., Charléty, A., Larose, É., Tedjini, S., 2022. Dense and long-term monitoring of earth surface processes with passive RFID—a review. Earth-Science Reviews 234, 104 225. https://doi.org/10.1016/j.earscirev.2022.104225

[2] Charléty, A., Le Breton, M., Larose, E., Baillet, L., 2022. 2D Phase-Based RFID Localization for On-Site Landslide Monitoring. Remote Sensing 14, 3 577. https://doi.org/10.3390/rs14153577

[3] Le Breton, M., Baillet, L., Larose, E., Rey, E., Benech, P., Jongmans, D., Guyoton, F., Jaboyedoff, M., 2019. Passive radio-frequency identification ranging, a dense and weather-robust technique for landslide displacement monitoring. Engineering Geology 250, 1–10. https://doi.org/10.1016/j.enggeo.2018.12.027

[4] Le Breton, M., Larose, É., Baillet, L., Lejeune, Y., van Herwijnen, A., 2022. Monitoring snowpack SWE and temperature using RFID tags as wireless sensors. https://doi.org/10.5194/egusphere-2022-761

How to cite: Le Breton, M., Charléty, A., Grunbaum, N., Larose, É., and Baillet, L.: SAR localization of passive RFID tags under snow and vegetation using a mobile reader antenna, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15413, https://doi.org/10.5194/egusphere-egu23-15413, 2023.

Southeast Asian lakes provide several ecosystem services and are an important natural resource for water supplies, industry, agriculture, shipping, fishing, and recreation. It has been demonstrated that they are highly vulnerable to anthropogenic and climate threats. Recent scientific findings clearly demonstrated that climate change has already significantly affected the SEA region and that these impacts will continue and expand as the pace of climate change accelerates. However, a deep understanding of "if" and "how" climate change as well as intensification of land uses may exacerbate those impacts on such vulnerable ecosystems across the whole region is lacking.

To contribute towards filling some of the existing knowledge gaps, in a renowned data scarce region, we present the results of a 3-year-long interdisciplinary research project entitled Climate Change Risk Assessment for Southeast Asian Lakes (CCRASEAL), led by the Asian Institute of Technology and funded by the Asia Pacific Network for Global Research (APN).

We present new insights on: i) historical, remote sensing derived, yearly land use changes from 1992 to 2021 estimated at basin scale across whole mainland SEA; ii) historical and future changes in climate respectively for the periods 1970-2006 and 2007-2100 using different downscaled CORDEX-SEA climate data at lake level; iii) detected and assessed climate and land use long-term trends and their coupled impacts on both monthly runoff at multi-basin scale level and lake surface areas of more than 700 water bodies. Finally, we detected and assessed the satellite-derived Lake Surface Water Temperature (LSWT) trends, an essential climate variable (ECV), within defined historical and future scenarios and across whole mainland SEA.

To achieve our results, we used and integrated multi-source and multi-resolution datasets made of satellite derived water and land products along with available climatic CORDEX-SEA climate datasets. Furthermore, we used a combination of conventional remote sensing, GIS, machine- and deep learning based processing approaches. In our studies we analysis possible spatial and temporal linkages between observed alterations to multiple-threats, to understand “if”, “when”, “how” and “where” climate and land use changes had affected and will affect SEA lakes.

Results have been validated using, when available, ground-based observation collected at national and regional scales.

How to cite: Virdis, S. G. P., Kongwarakom, S., Shrestha, S., Neng, L. J., Padedda, B. M., Phoesri, T., and Moe, A. C.: Are Southeast Asian lakes impacted by changes in climate and land-use? A historical and future scenario analysis., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17455, https://doi.org/10.5194/egusphere-egu23-17455, 2023.

High spatial resolution land surface temperature (LST) (<= 100 m) has a considerable significance for small scale studies like agricultural applications and urban heat island studies. Originally developed for optical data, spatiotemporal fusion methods, such as the widely used Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), are gradually becoming promising approaches to generate high resolution thermal variables but still have shortcomings, such as an invalid assumption in thermal fields and the accumulation of systematic biases. Hence, we proposed a variant of the ESTARFM algorithm, referred as the unbiased ESTARFM (ubESTARFM), aiming to better accommodate the spatiotemporal approach to thermal studies. We evaluated the results derived from our method and the typical ESTARFM against both in-situ LST and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST over a continental scale of Australia. The results show that the ubESTARFM has a bias of 2.55 K, unbiased RMSE (ubRMSE) of 2.57 K, and Pearson correlation coefficient (R) of 0.95 against the in-situ LST over 11290 samples at 12 sites, all of which are significantly better than that of the ESTARFM, with a bias of 4.73 K, ubRMSE of 3.80 K and R of 0.92. In the cross-satellite comparison, the ubESTARFM LST has a bias of -1.69 K, ubRMSE of 2.00 K, and R of 0.70 over 43 near clear-sky scenes, while the ESTARFM LST has a bias of 1.79 K, ubRMSE of 2.68 K, and R of 0.59. Overall, the ubESTARFM is able to avoid the accumulation of systematic bias, considerably reduce the deviation of uncertainty, and maintain a good level of correlation with validation datasets compared to the typical ESTARFM algorithm. It is a promising method to integrate reliable numeric values from coarse resolution LST and spatial heterogeneity from fine resolution LST, and may be further coupled with energy balance or radiative transfer models to better enable farm- or regional-scale water management strategy or decision making.

How to cite: Yu, Y., Renzullo, L., Tian, S., and Malone, B.: An unbiased spatiotemporal fusion approach to generate daily 100 m spatial resolution land surface temperature over a continental scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1501, https://doi.org/10.5194/egusphere-egu23-1501, 2023.

Across Europe there are 2.5 million potentially contaminated sites due to natural and anthropogenic activities. In this regard, phytoremediation approaches are need as a cost-effective and ecosystem-friendly technique to rehabilitate soil compared to conventional methods. Hyperspectral imaging provides an ideal method to improve and monitor existing bioremediation methods, using hyperaccumulator plants. In our study, the hyperaccumulator plant Brassica juncea showed a high tolerance to the accumulation of Cu, Zn and Ni. Hyperspectral measurements were conducted with a HySpex VNIR-SWIR hyperspectral sensor (408-2500 nm) in-situ and in the laboratory. To monitor and optimize the process of accumulation with hyperspectral imaging, we calculated different vegetation indices, related to metal-induced plant stress, such as TCARI/OSAVI, Chlorophyll Vegetation Index (CVI), Red-Edge Stress Vegetation Index (RSVI), Normalized Pigments Chlorophyll Index (NPCI), Red-Edge Inflection Point (REIP) and Disease Water Stress Index (DWSI), using various pre-processing steps (raw, smoothed and brightness corrected data). In addition, the relation between the different indices and the measured heavy metal content in the samples were tested with a multivariate technique using Partial Least Squares Regression (PLSR). Our results revealed, even with no pre-processed image data, changes in chlorophyll- and red-egde-related indices with increasing PTE concentration. With hyperspectral imaging we are already able to monitor differences of the PTE accumulation within the hyperaccumulator plant Brassica juncea.

How to cite: Kästner, F., Küster, T., Feilhauer, H., and Sut-Lohmann, M.: Identification and remediation-related monitoring of potential toxic elements (PTE) in the hyperaccumulator plant Brassica juncea with hyperspectral imaging., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8857, https://doi.org/10.5194/egusphere-egu23-8857, 2023.

Here, we report results paving the way toward a new method for retrieving surface albedo at 355 nm from lidar surface returns (LSR) of Aeolus. We found that averaged monthly LSR estimates at 2.5 x 2.5 grid clearly varied depending on the land type with the signal strength descending as follows: snow, arid areas, vegetation, water surfaces. Most importantly, given the difference in the instrumental setup, Aeolus LSR exhibited unexpectedly high agreement with Lambertian Equivalent Reflectance from TROPOMI and GOME-2 with correlation coefficients (r) of ~0.87 at global scales for median estimates at the study period (r = 0.91 for TROPOMI-GOME-2) and regional estimates for 37 selected areas (r > 0.90) where the agreement is driven by land surfaces with lower agreement over water due to inherently different physics of Aeolus LSR. Aeolus LSR showed superior sensitivity to the change of land type from vegetation to arid, compared to GOME-2 or TROPOMI as indicated by the highest negative agreement between Aeolus LSR, compared to GOME-2 or TROPOMI. We anticipate that our results will lay the foundation for the multiyear surface UV albedo climatology during the entire Aeolus lifetime.

How to cite: Labzovskii, L., van Zadelhoff, G.-J., Tilstra, G., De Kloe, J., and Donovan, D.: New method for retrieval surface UV albedo from Lidar Surface Returns (LSR) of Aeolus, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14908, https://doi.org/10.5194/egusphere-egu23-14908, 2023.

Volcanic ash presents a challenge for the aviation industry. 3D information is needed to be able to back-calculate dose – this is a key parameter in managing airspace. To recreate the ash cloud, multiangle observations are required – deal to perform visual and infrared observations. Other mission objectives using the can also be realised, for example, as volcanic ash clouds are the primary target, there is the possibility to map new magma extrusions, lava and pyroclastic flow movements. Thermal infrared data has also previously been used to observe volcanic cycles and better understand their behaviour. The visual images required for 3D construction of ash clouds can be used to create digital elevation models of terrain around volcanos which have application in disaster management and planning, and forest fires may also be included as targets of opportunity.

A CubeSat mission - Pointable Radiometer for Observing Volcanic Emissions (PROVE) Pathfinder - is proposed to monitor the ash cloud using both thermal infrared and visual cameras. The resulting 2U payload consists of a thermal infrared camera (FLIR Tau 2 with a 50mm lens) and a visual camera (a narrow field of view Basler ace ac5472-5gc with a Kowa LM75HC lens). Alongside this, a payload computer to communicate with the cameras and store data was selected (the BeagleBone Black Enhanced Industrial) with a custom PCB providing connections to the instruments and bus. The software to operate the payload takes the form of a custom scheduler for an imaging pass, sending commands to the camera systems (and to the bus) to take the required multiangle images for ash cloud reconstruction.

The engineering model of the payload is currently being tested at the European Space Agency’s CubeSat Support Facility with the support of the Education Office of the European Space Agency under the educational Fly Your Satellite! Test Opportunities programme. The team are undertaking a month of environmental testing including vibration and thermal vacuum tests. The aim of the testing campaign is to qualify the payload for launch.

How to cite: Watson, M. and Hunter, T. and the PROVE Team: The Student-Led Design and Testing of an Imaging CubeSat Payload, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16434, https://doi.org/10.5194/egusphere-egu23-16434, 2023.