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In a recent paper, we extended a previous study on the solar solar influence to the generation of the March 2012 heatwave in the northeastern USA. In the present study we check the possible relationship of solar activity with the early March 2012 bad weather in northeast Thrace, Greece. To this end, we examined data from various remote sensing instrumentation monitoring the Sun (SDO satellite), Interplanetary space (ACE satellite), the Earth's magnetosphere (Earth-based measurements, NOAA-19 satellite), the top of the clouds (Terra and Aqua satellites), and the near ground atmosphere. Our comparative data analysis suggests that: (i) the winter-like weather (rainfall, fast winds, decreased temperature) in Thrace started on 6 March 2012, the same day as the heatwave started in USA, (ii) during the March 2012 winter-like event in Thrace (6–15 March), the ACE satellite recorded enhanced fluxes of solar energetic particles (SEPs), while SOHO and PAMELA recorded solar protons at very high energies (>500 MeV), (iii) Between 3–31 March, the temperature in Alexandoupoli and the ACE/EPAM solar high energy (1.88–4.70 MeV) proton flux were strongly anticorelated (r = −0.75, p = 0.5). (iv) Thrace experienced particularly intense cyclonic circulation, during periods of magnetic storms on 8–10 and 12–13 March, which occurred after the arrival at ACE of two interplanetary shock waves, on March 8 and March 11, respectively, (v) at the beginning of the two above mentioned periods large atmospheric electric fields were recorded, with values ranging between ~−2000 V/m and ~1800 V/m on 8 March, (vi) the winter-like weather on 8–10 March 2012 occurred after the detection of the main SEP event related with a coronal mass ejection released in interplanetary space as a result of intense solar flare activity observed by SDO on 7 March 2012, (vi) the 8–10 March weather was related with a deep drop of ~63 °C in the cloud top temperature measured by MODIS/Terra, which favors strong precipitation. Finally, we analyzed data from the electric power network in Thrace (~41°N) and we found, for the first time sudden voltage changes of ~3.5 kV in the electric grid in Greece, during the decay phase of the March 2012 storm series. We discuss the winter-like March 2012 event in Thrace regarding the influence of solar cosmic rays on the low troposphere mediated by positive North Atlantic Oscillation (NAO). Finally, we infer that the novel finding of the geomagnetic effects on the electric power grid in Thrace may open a new window into space weather applications research. [ABSTRACT FROM AUTHOR]

Coastal managers, policymakers, and scientists use shoreline accretion/erosion trends to determine the coastline's historical evolution and generate models capable of predicting future changes. Different solutions have been developed to obtain shoreline positions from Earth observation data in recent years, the so-called Satellite-Derived Shorelines (SDS). Most of the methodologies available in the literature use multispectral optical satellite imagery. This paper proposes two new methods for shoreline mapping at the subpixel level based on PRISMA hyperspectral imagery. The first one analyses the spectral signatures along defined beach profiles. The second method uses techniques more commonly applied to multispectral image analysis, such as Spectral Unmixing algorithms and Spatial Attraction Models. The results obtained with both methodologies are validated on three Mediterranean microtidal beaches located in two different countries, Italy and Greece, using image-based ground truth shorelines manually photointerpreted and digitised. The obtained errors are around 6 and 7 m for the first and second methods, respectively. These results are comparable to the errors obtained from multispectral data. The paper also discusses the capability of the two methods to identify two different shoreline proxies. [ABSTRACT FROM AUTHOR]

One of the main applications of the gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite data is their combination with local gravity anomalies for geoid and gravity field modeling purposes. The aim of the present paper was the determination of an improved geoid model for the wider Hellenic area, using original GOCE SGG data filtered to retain only useful signals inside the measurement bandwidth (MBW) of the satellite. The filtered SGGs, originally at the satellite altitude, were projected to a mean orbit (MO) and then downward continued to the Earth's surface (ES) in order to be combined with local gravity anomalies. For the projection to an MO, grids of disturbing gravity gradients from a global geopotential model (GGM) were used, computed per 1 km from the maximum satellite altitude to that of the MO. The downward continuation process was then undertaken using an iterative Monte Carlo (MC) simulated annealing method with GGM gravity anomalies on the ES used as ground truth data. The final geoid model over the wider Hellenic area was estimated, employing the remove–compute–restore method and both Fast Fourier Transform (FFT) and Least Squares Collocation (LSC). Gravity-only, GOCE-only and combined models using local gravity and GOCE data were determined and evaluation of the results was carried out against available GNSS/levelling data in the study area. From the results achieved, it was concluded that even when FFT is used, so that a combined grid of local gravity and GOCE data is used, improvements to the differences regarding GNSS/levelling data by 14.53% to 27.78% can be achieved. The geoid determination with LSC was focused on three different areas over Greece, with different characteristics in the topography and gravity variability. From these results, improvements from 14.73%, for the well-surveyed local data of Thessaly, to 32.88%, over the mountainous area of Pindos, and 57.10% for the island of Crete for 57.10% were found. [ABSTRACT FROM AUTHOR]

Data recordings of the movement of vehicles can be enriched with heterogeneous and multimodal data beyond latitude, longitude, and timestamp and enhanced with complementary segmentations, constituting a semantic trajectory. Semantic Web (SW) technologies have been extensively used for the semantic integration of heterogeneous and multimodal movement-related data, and for the effective modeling of semantic trajectories, in several domains. In this paper, we present an integrated solution for the engineering of cultural heritage semantic trajectories generated from unmanned aerial vehicles (UAVs) and represented as knowledge graphs (KGs). Particularly, this work is motivated by, and evaluated based on, the application domain of UAV missions for documenting regions/points of cultural heritage interest. In this context, this research work extends our previous work on UAV semantic trajectories, contributing (a) an updated methodology for the engineering of semantic trajectories as KGs (STaKG), (b) an implemented toolset for the management of KG-based semantic trajectories, (c) a refined ontology for the representation of knowledge related to UAV semantic trajectories and to cultural heritage documentation, and (d) the application and evaluation of the proposed methodology, the developed toolset, and the ontology within the domain of UAV-based cultural heritage documentation. The evaluation of the integrated UAV solution was achieved by exploiting real datasets collected during three UAV missions to document sites of cultural interest in Lesvos, Greece, i.e., the UNESCO-protected petrified forest of Lesvos Petrified Forest/Geopark, the village of Vrissa, and University Hill. [ABSTRACT FROM AUTHOR]

Agricultural land extent and change information is needed to assess food security, the effectiveness of land use policy, and both environmental and societal impacts. This information is especially valuable in biodiversity hotspots such as the Mediterranean region, where agricultural land expansion can result in detrimental effects such as soil erosion and the loss of native species. There has also been a growing concern that changing agricultural extent in fire-prone regions of the Mediterranean may increase fire risk due to accumulation of fuel in abandoned areas. In this study, we assessed the extent and change of agricultural land in Southern Greece from 1986 to 2020 using a combined European Land Use/Cover Area frame Survey (LUCAS) and Landsat time series approach. The LUCAS data and Landsat spectral-temporal metrics were used to train a random forest classifier, which was used to classify arable land and permanent agriculture (e.g., olive orchards, vineyards) at annual time steps. A post-processing step was taken to reduce spurious landcover class transitions using transition likelihoods and annual class membership likelihoods. A validation dataset consisting of 2666 samples, identified via a stratified random sampling approach and high-resolution imagery and time series analysis, were used to evaluate stable and change strata accuracies. Overall accuracies were greater than 70% and strata-specific accuracies were highly variable between stable and change strata. The results show that southern Greece has experienced a recent gain in arable land (~12,000 ha from ~2009–2020) and a much larger gain in permanent agriculture (>115,000 ha from ~1993–2020). Arable land loss mainly occurred from 1987 to ~2002 when extent decreased by 15,000 ha, of which 66% was abandoned. The semi-automated approach described in this paper provides a promising approach for monitoring agricultural land change and enabling assessments of agriculture policy effectiveness and environmental impacts. [ABSTRACT FROM AUTHOR]

In order to improve the prediction accuracy of ionospheric total electron content (TEC), a combined intelligent prediction model (MMAdapGA-BP-NN) based on a multi-mutation, multi-cross adaptive genetic algorithm (MMAdapGA) and a back propagation neural network (BP-NN) was proposed. The model combines the international reference ionosphere (IRI), statistical machine learning (SML), BP-NN, and MMAdapGA. Compared with the IRI, SML-based, and other neural network models, MMAdapGA-BP-NN has higher accuracy and a more stable prediction effect. Taking the Athens station in Greece as an example, the root mean square errors (RMSEs) of MMAdapGA-BP-NN in 2015 and 2020 are 2.84TECU and 0.85TECU, respectively, 52.27% and 72.13% lower than the IRI model. Compared with the single neural network model, the MMAdapGA-BP-NN model reduced RMSE by 28.82% and 24.11% in 2015 and 2020, respectively. Furthermore, compared with the neural network optimized by a single mutation genetic algorithm, MMAdapGA-BP-NN has fewer iterations ranging from 10 to 30. The results show that the prediction effect and stability of the proposed model have obvious advantages. As a result, the model could be extended to an alternative prediction scheme for more ionospheric parameters. [ABSTRACT FROM AUTHOR]

Currently, the global shift towards green energy is at the forefront of efforts introducing a new era, thus rendering exploration for critical raw materials essential. To this purpose, the utilization of advanced machine learning methods in remote sensing has emerged as a rapid and cost-effective approach. This study proposes a new methodology, utilizing Sentinel-2 satellite data, to distinguish ferronickel (Fe-Ni-) laterite from bauxite across pre-mining, mining, and post-mining occurrences worldwide. Both ores contain mineral raw materials such as nickel, iron, cobalt, and alumina and their discrimination is generally macroscopically challenging, especially when their locations are often in geographical proximity. The proposed method is based on Support Vector Machines (SVM) classification using spectral signatures of known Fe-Ni-laterite and bauxite-bearing pixels in Greece, Cuba, and Jamaica. The highest classification accuracies are obtained by combining b12 with b6 or b7 spectral bands. Comparisons with specific ore mineralogies show that b6 and b7 are strongly linked to the ferric phase, while b12 is mainly associated with the argillic mineralogies, the latter probably being the key discriminating factor between the two ores. From laboratory chemical analyses, we also establish that b12 and b6 or b7 are strongly associated with Al2O3 and Fe2O3 content correspondingly. The proposed method is accurate, it has reduced prospection costs, and it can facilitate the initial screening of broad areas by automatically characterizing whether an ore is bauxite or Fe-Ni-laterite. This underscores the methodology's significance in ore differentiation and exploration within the context of green energy endeavors. [ABSTRACT FROM AUTHOR]

The rich and valuable underwater cultural heritage present in the Mediterranean is often overlooked, if not completely unknown, due to the inherent difficulties in using physical approaches. The iblueCulture project was created to bridge that gap by introducing a real-time texturing and streaming system. The system captures video streams from eight underwater cameras and manipulates it to texture and colorize the underwater cultural heritage site and its immediate surroundings in a virtual reality environment. The system can analyze incoming data and, by detecting newly introduced objects in sight, use them to enhance the user experience (such as displaying a school of fish as they pass by) or for site security. This system has been installed in some modern and ancient shipwrecks in Greece and was used for in situ viewing. It can also be modified to work remotely, for example, in museums or educational institutions, to make the sites more accessible and raise public awareness. It can potentially be used in any underwater site, both for presentation and education, as well as for monitoring and security purposes. [ABSTRACT FROM AUTHOR]

Frequency-domain electromagnetic induction (FDEMI) methods are frequently used in non-invasive, area-wise mapping of the subsurface electromagnetic soil properties. A crucial part of data analysis is the geophysical inversion of the data, resulting in either conductivity and/or magnetic susceptibility subsurface distributions. We present a novel 1D stochastic optimization approach that combines dimension-adapting reversible jump Markov chain Monte Carlo (MCMC) with artificial bee colony (ABC) optimization for geophysical inversion, with specific application to frequency-domain electromagnetic induction (FDEMI) data. Several solution models of simplified model geometry and a variable number of model knots, which are found by the inversion method, are used to create re-sampled resulting average models. We present synthetic test inversions using conductivity models based on 14 direct-push (DP) EC logs from Greece, Italy, and Germany, as well as field data applications using multi-coil FDEMI devices from three sites in Azerbaijan and Germany. These examples show that the method can effectively lead to solutions that resemble the known DP input models or image reasonable stratigraphic and archaeological features in the field data. Neighboring 1D solutions on field data examples show high coherence along profiles even though each 1D inversion is independently handled. The computational effort for one 1D inversion is less than 120,000 forward calculations, which is much less than usually needed in MCMC inversions, whereas the resulting models show more plausible solutions due to the dimension-adapting properties of the inversion method. [ABSTRACT FROM AUTHOR]

Two microwave transponders have been operating in west Crete and Gavdos to calibrate international satellite radar altimeters at the Ku-band. One has been continuously operating for about 8 years at the CDN1 Cal/Val site in the mountains of Crete, and the other at the GVD1 Cal/Val site on Gavdos since 11 October 2021. This ground infrastructure is also supported at present by four sea-surface Cal/Val sites operating, some of them for over 20 years, while two additional such Cal/Val sites are under construction. This ground infrastructure is part of the European Space Agency Permanent Facility for Altimetry Calibration (PFAC), and as of 2015, it has been producing continuously a time series of range biases for Sentinel-3A, Sentinel-3B, Sentinel-6 MF, Jason-2, Jason-3, and CryoSat-2. This work presents a thorough examination of the transponder Cal/Val responses to understand and determine absolute biases for all satellite altimeters overflying this ground infrastructure. The latest calibration results for the Jason-3, Copernicus Sentinel-3A and -3B, Sentinel-6 MF, and CryoSat-2 radar altimeters are described based on four sea-surface and two transponder Cal/Val sites of the PFAC in west Crete, Greece. Absolute biases for Jason-3, Sentinel-6 MF, Sentinel-3A, Sentinel-3B, and CryoSat-2 are close to a few mm, determined using various techniques, infrastructure, and settings. [ABSTRACT FROM AUTHOR]

This study focuses on the suspended sediment delivery of 17 rivers and streams of various sizes to the sea over a wide geographical area covering most of the Greek peninsula, utilizing two Moderate Resolution Imaging Spectroradiometer (MODIS) products. Equal-area polygons ("plume" polygons), were delineated at the mouths of each selected river. These polygons were utilized to estimate the suspended sediment load of each river through the application of suspended sediment indices, ratios, and masks. To achieve this, 669 Level 1B MODIS images (MOD02) and their corresponding MODIS cloud products (MOD35) were downloaded and processed for a 10-water-year period (2004–2014). During this period of 669 days, there were 58 flood events (episodes) ranging in duration from 5 to 45 days. Relative atmospheric correction was applied to the images based on four selected bright invariant areas (PIFs) scattered along mainland Greece. The second product used in this study was MOD09Q1, an atmospherically corrected 8-day composite processed for the entire record period (2000–2019). Suspended sediment indices, ratios, and masks were developed using all three visible channels and near-infrared (NIR) for the MOD02 dataset, while only Red and Near-InfraRed (NIR) channels were available from the MOD09Q dataset. The resulting rankings from the remote sensing analysis were compared with the predictions of soil loss models, and the outcomes were largely consistent. While the remote sensing results can be considered as a type of experimental data or measurements, they come with inherent limitations. These include infrequent access to cloud-free data on stormy days, the influence of wind and currents, and the potential impact of dust storms originating from Africa, among others. On the other hand, soil loss models are sensitive to the parameter values used, and in some cases, the uncertainties are significant. Hence, the ranking derived from remote sensing can serve as a calibration of the models, particularly for the BQART model, which provides information on the catchment's sink capacity. An index of "sediment productivity per square kilometer and mm of rainfall" was developed. This index can be considered a "sediment delivery ratio" and is crucial for accurately quantifying the phenomenon. [ABSTRACT FROM AUTHOR]

Open and analysis-ready data, as well as methodological and technical advancements have resulted in an unprecedented capability for observing the Earth's land surfaces. Over 10 years ago, Landsat time series analyses were inevitably limited to a few expensive images from carefully selected acquisition dates. Yet, such a static selection may have introduced uncertainties when spatial or inter-annual variability in seasonal vegetation growth were large. As seminal pre-open-data-era papers are still heavily cited, variations of their workflows are still widely used, too. Thus, here we quantitatively assessed the level of agreement between an approach using carefully selected images and a state-of-the-art analysis that uses all available images. We reproduced a representative case study from the year 2003 that for the first time used annual Landsat time series to assess long-term vegetation dynamics in a semi-arid Mediterranean ecosystem in Crete, Greece. We replicated this assessment using all available data paired with a time series method based on land surface phenology metrics. Results differed fundamentally because the volatile timing of statically selected images relative to the phenological cycle introduced systematic uncertainty. We further applied lessons learned to arrive at a more nuanced and information-enriched vegetation dynamics description by decomposing vegetation cover into woody and herbaceous components, followed by a syndrome-based classification of change and trend parameters. This allowed for a more reliable interpretation of vegetation changes and even permitted us to disentangle certain land-use change processes with opposite trajectories in the vegetation components that were not observable when solely analyzing total vegetation cover. The long-term budget of net cover change revealed that vegetation cover of both components has increased at large and that this process was mainly driven by gradual processes. We conclude that study designs based on static image selection strategies should be critically evaluated in the light of current data availability, analytical capabilities, and with regards to the ecosystem under investigation. We recommend using all available data and taking advantage of phenology-based approaches that remove the selection bias and hence reduce uncertainties in results. [ABSTRACT FROM AUTHOR]

In the westernmost region of the rapidly widening Corinth rift, Greece, extensive development of roads, bridges and other human infrastructure has caused continuous environmental change over the past twenty years. River networks, the land surface and the coastal environment, have been altered, especially in the areas corresponding to deltaic fans. In this paper we use earth observation systems that have captured these environmental changes, particularly medium (Landsat TM and ETM+) and high (Quickbird) resolution satellite images, to identify environmental changes between the periods 1992, 2000, 2002, and 2005. Six pseudo-color multi-temporal images in different spectral areas were created in order to detect changes to the terrestrial and coastal environment caused mainly by direct or indirect human impact. This methodology provided new data for quantifying significant alterations in the environment on different scales. In many cases this revealed their sequence during the time of observation. [ABSTRACT FROM AUTHOR]

The images of the Sentinel-2 constellation can help the verification process of farmers' declarations, providing, among other things, accurate spatial explicit maps of the agricultural land cover. The aim of the study is to design, develop, and evaluate two deep learning (DL) architectures tailored for agricultural land cover and crop type mapping. The focus is on a detailed class scheme encompassing fifteen distinct classes, utilizing Sentinel-2 imagery acquired on a monthly basis throughout the year. The study's geographical scope covers a diverse rural area in North Greece, situated within southeast Europe. These architectures are a Temporal Convolutional Neural Network (CNN) and a combination of a Recurrent and a 2D Convolutional Neural Network (R-CNN), and their accuracy is compared to the well-established Random Forest (RF) machine learning algorithm. The comparative approach is not restricted to simply presenting the results given by classification metrics, but it also assesses the uncertainty of the classification results using an entropy measure and the spatial distribution of the classification errors. Furthermore, the issue of sampling strategy for the extraction of the training set is highlighted, targeting the efficient handling of both the imbalance of the dataset and the spectral variability of instances among classes. The two developed deep learning architectures performed equally well, presenting an overall accuracy of 90.13% (Temporal CNN) and 90.18% (R-CNN), higher than the 86.31% overall accuracy of the RF approach. Finally, the Temporal CNN method presented a lower entropy value (6.63%), compared both to R-CNN (7.76%) and RF (28.94%) methods, indicating that both DL approaches should be considered for developing operational EO processing workflows. [ABSTRACT FROM AUTHOR]

Forest fires are a seasonal phenomenon in Greece, reoccurring annually and causing adverse impacts on both human-made and natural environments. Our case study focuses on the devastating fire that took place in July 2018 in the second-housing area of Mati, East Attica. In this research, we propose a simple and effective approach that combines the deformation trend obtained from the Permanent Scatterer Interferometry (PSI) analysis with the burn severity assessment aiming to identify and classify potential ground-burn hazard zones. To maximize the number of measuring points, we employ a weighted full-graph PSI approach. Additionally, we calculate the burn severity by comparing Sentinel-2 satellite images captured before and after the event. The resulting datasets are reclassified on a scale from 1 to 5, and the proposed equation yields the final product. Numerous high and very high hazard zones have been identified using this methodology. The research findings reveal the proximity between these hazard zones and the stream network. Overall, the proposed method offers valuable insights for the post-fire monitoring and management of urban and peri-urban landscapes in the affected areas. [ABSTRACT FROM AUTHOR]

The Archaic Acropolis of Europos (region of Central Macedonia, Greece) is an important archaeological site, which was inhabited since the 7th century BC up to the 4th century AD. In the lowland area, south of the acropolis and a short distance away, archaeologists speculate that the extensive ancient cemetery of the acropolis, which is a result of its long-term habitation, was located there. In an effort to locate marks that will support this view, WingtraOne GEN II, one of today's leading Uncrewed Aircraft Systems, was used to collect RGB and multispectral images. After the production of the necessary digital surface models and orthophotomosaics, index maps related to the crops were created. The total of the products allowed the visual identification of 123 marks, which are probably attributed to hitherto unknown covered ancient tombs. In addition, marks of unknown covered ditches of long length were found in the plain area, and on the outskirts of the acropolis marks of its possible wall and the base of one of its towers. [ABSTRACT FROM AUTHOR]

In the last thirty years, Synthetic Aperture Radar interferometry (InSAR) and the Global Navigation Satellite System (GNSS) have become fundamental space geodetic techniques for mapping surface deformations due to tectonic movements. One major limiting factor to those techniques is the effect of the troposphere, as surface velocities are of the order of a few mm yr−1, and high accuracy (to mm level) is required. The troposphere introduces a path delay in the microwave signal, which, in the case of GNSS Precise Point Positioning (PPP), can nowadays be partly removed with the use of specialized mapping functions. Moreover, tropospheric stratification and short wavelength spatial turbulences produce an additive noise to the low amplitude ground deformations calculated by the (multitemporal) InSAR methodology. InSAR atmospheric phase delay corrections are much more challenging, as opposed to GNSS PPP, due to the single pass geometry and the gridded nature of the acquired data. Thus, the precise knowledge of the tropospheric parameters along the propagation medium is extremely useful for the estimation and correction of the atmospheric phase delay. In this context, the PaTrop experiment aims to maximize the potential of using a high-resolution Limited-Area Model for the calculation and removal of the tropospheric noise from InSAR data, by following a synergistic approach and integrating all the latest advances in the fields of remote sensing meteorology (GNSS and InSAR) and Numerical Weather Forecasting (WRF). In the first phase of the experiment, presented in the current paper, we investigate the extent to which a high-resolution 1 km WRF weather re-analysis can produce detailed tropospheric delay maps of the required accuracy, by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is initially operated with varying parameterization, with GNSS measurements providing a benchmark of real atmospheric conditions. Subsequently, the final WRF daily re-analysis run covers an extended period of one year, based on the optimum model parameterization scheme demonstrated by the parametric analysis. The two datasets (predicted and observed) are compared and statistically evaluated, in order to investigate the extent to which meteorological parameters that affect ZTD can be simulated accurately by the model under different weather conditions. Results demonstrate a strong correlation between predicted and observed ZTDs at the 19 GNSS stations throughout the year (R ranges from 0.91 to 0.93), with an average mean bias (MB) of –19.2 mm, indicating that the model tends to slightly underestimate the tropospheric ZTD as compared to the GNSS derived values. With respect to the seasonal component, model performance is better during the autumn period (October–December), followed by spring (April–June). Setting the acceptable bias range at ±23 mm (equal to the amplitude of one Sentinel-1 C-band phase cycle when projected to the zenithal distance), it is demonstrated that the model produces satisfactory results, with a percentage of ZTD values within the bias margin ranging from 57% in summer to 63% in autumn. [ABSTRACT FROM AUTHOR]

Potential evapotranspiration (PET) is a particularly important parameter for understanding water interactions and balance in ecosystems, while it is also crucial for assessing vegetation water requirements. The accurate estimation of PET is typically data demanding, while specific climatic, geographical and local factors may further complicate this task. Especially in city environments, where built-up structures may highly influence the micrometeorological conditions and urban green sites may occupy limited spaces, the selection of proper PET estimation approaches is critical, considering also data availability issues. In this study, a wide variety of empirical PET methods were evaluated against the FAO56 Penman–Monteith benchmark method in the environment of two Mediterranean urban green sites in Greece, aiming to investigate their accuracy and suitability under specific local conditions. The methods under evaluation cover all the range of empirical PET estimations: namely, mass transfer-based, temperature-based, radiation-based, and combination approaches, including 112 methods. Furthermore, 15 locally calibrated and adjusted models have been developed based on the general forms of the mass transfer, temperature, and radiation equations, improving the performance of the original models for local application. Among the 127 (112 original and 15 adjusted) evaluated methods, the radiation-based methods and adjusted models performed overall better than the temperature-based and the mass transfer methods, whereas the data-demanding combination methods received the highest ranking scores. The adjusted models seem to give accurate PET estimates for local use, while they might be applied in sites with similar conditions after proper validation. [ABSTRACT FROM AUTHOR]

Image registration is among the most popular and important problems of remote sensing. In this paper we propose a fully unsupervised, deep learning based multistep deformable registration scheme for aligning pairs of satellite imagery. The presented method is based on the expression power of deep fully convolutional networks, regressing directly the spatial gradients of the deformation and employing a 2D transformer layer to efficiently warp one image to the other, in an end-to-end fashion. The displacements are calculated with an iterative way, utilizing different time steps to refine and regress them. Our formulation can be integrated into any kind of fully convolutional architecture, providing at the same time fast inference performances. The developed methodology has been evaluated in two different datasets depicting urban and periurban areas; i.e., the very high-resolution dataset of the East Prefecture of Attica, Greece, as well as the high resolution ISPRS Ikonos dataset. Quantitative and qualitative results demonstrated the high potentials of our method. [ABSTRACT FROM AUTHOR]

Land subsidence in coastal and delta cities often results in infrastructure and residential building damages, while also increasing the area's flooding vulnerability. The coastal cities of Messolonghi and Aitolikon are typical examples, as they are built on top of old stream deposits near the coast. In the last several years, the gradual subsidence of the sites, combined with the impact of climate change, resulted in multiple floods. The rush of seawater over the lowlands has also been reported. Persistent scatterer interferometry (PSI) is a remote-sensing technique that can provide a reliable and cost-effective solution, as it can be used to identify and monitor soil displacements. In this study, a novel parallelized PSI (P-PSI) processing chain, developed by the Operational Unit Center for Earth Observation Research and Satellite Remote Sensing (BEYOND) of the National Observatory of Athens, as well as the Copernicus EGMS product were used to identify these displacements. The results were examined in correlation with other potential factors such as the overexploitation of the underground water, the natural compaction of the clay soil layers, the primary and secondary consolidation due to the external construction loading, the oxidation of the organic soils, tidal gauge data, precipitation data, and ground truth data. In Messolonghi, various deformation rates were recorded, with maximum mean values of −5 mm/year in the eastern part, whereas in Aitolikon, the maximum values were around −4.5 mm/year. The displacements were mostly attributed to the primary consolidation due to the building loads. Deformation patterns and their correlation with precipitation could also be witnessed. It was evident that the increased precipitation rates and sea level rise played a leading role in the constant flooding. [ABSTRACT FROM AUTHOR]

In-situ infrared soil spectroscopy is prone to the effects of ambient factors, such as moisture, shadows, or roughness, resulting in measurements of compromised quality, which is amplified when multiple sensors are used for data collection. Aiming to provide accurate estimations of common physicochemical soil properties, such as soil organic carbon (SOC), texture, pH, and calcium carbonates based on in-situ reflectance captured by a set of low-cost spectrometers operating at the shortwave infrared region, we developed an AI-based spectral transfer function that maps fields to laboratory spectra. Three test sites in Cyprus, Lithuania, and Greece were used to evaluate the proposed methodology, while the dataset was harmonized and augmented by GEO-Cradle regional soil spectral library (SSL). The developed dataset was used to calibrate and validate machine learning models, with the attained predictive performance shown to be promising for directly estimating soil properties in-situ, even with sensors with reduced spectral range. Aiming to set a baseline scenario, we completed the exact same modeling experiment under laboratory conditions and performed a one-to-one comparison between field and laboratory modelling accuracy metrics. SOC and pH presented an R2 of 0.43 and 0.32 when modeling the in-situ data compared to 0.63 and 0.41 of the laboratory case, respectively, while clay demonstrated the highest accuracy with an R2 value of 0.87 in-situ and 0.90 in the laboratory. Calcium carbonates were also attempted to be modeled at the studied spectral region, with the expected accuracy loss from the laboratory to the in-situ to be observable (R2 = 0.89 for the laboratory and 0.67 for the in-situ) but the reduced dataset variability combined with the calcium carbonate characteristics that are spectrally active in the region outside the spectral range of the used in-situ sensor, induced low RPIQ values (less than 0.50), signifying the importance of the suitable sensor selection. [ABSTRACT FROM AUTHOR]

This study aims to present and analyze the time series of the snow parameters focusing on representative geographical areas of the Eastern Mediterranean (i.e., Greece and Italy) and to examine their seasonal variability, in terms of region and geography. The satellite retrievals were firstly validated against in-situ retrievals for 67 common days, with a mean bias equal to −0.018 cm, with a near-Gaussian distribution, showing the good performance of the satellite snow detection. The satellite-based analysis resulted in increasing trends of snow water equivalent, attributed to the enhanced values between 2000 and 2009; however, decreasing trends are found starting from 2010 until now of −1.79 × 10−17 and −2.31 × 10−18 over the two representative areas of Greece (e.g., Thessaloniki and Kozani). A similar pattern is found for the snow water equivalent in the Italian study area, with a decreasing trend of −4.45 × 10−18. The presented results contribute to a better understanding of the spatial snow distribution and the snow coverage seasonality that could be crucial for the long-term groundwater management, by combining snow data trends from in-situ data and satellite statistics. [ABSTRACT FROM AUTHOR]

Model-based soil erosion studies have increased in number, given the availability of geodata and the recent technological advances. However, their accuracy remains rather questionable since the scarcity of field records hinders the validation of simulated values. In this context, this study aims to present a method for measuring sediment deposition at a typical Mediterranean catchment (870 ha) in Greece through high spatial resolution field measurements acquired by an Unmanned Aerial Vehicle (UAV) survey. Three-dimensional modeling is considered to be an emerging technique for surface change detection. The UAV-derived point cloud comparison, applying the Structure-from-Motion (SfM) technique at the Platana sediment retention dam test site, quantified annual topsoil change in cm-scale accuracy (0.02–0.03 m), delivering mean sediment yield of 1620 m3 ± 180 m3 or 6.05 t ha−1yr−1 and 3500 m3 ± 194 m3 or 13 t ha−1yr−1 for the 2020–2021 and 2021–2022 estimation. Moreover, the widely applied PESERA and RUSLE models estimated the 2020–2021 mean sediment yield at 1.12 t ha−1yr−1 and 3.51 t ha−1yr−1, respectively, while an increase was evident during the 2021–2022 simulation (2.49 t ha−1yr−1 and 3.56 t ha−1yr−1, respectively). Both applications appear to underestimate the net soil loss rate, with RUSLE being closer to the measured results. The difference is mostly attributed to the model's limitation to simulate gully erosion or to a C-factor misinterpretation. To the authors' better knowledge, this study is among the few UAV applications employed to acquire high-accuracy soil loss measurements. The results proved extremely useful in our attempt to measure sediment yield at the cm scale through UAV-SfM and decipher the regional soil erosion and sediment transport pattern, also offering a direct assessment of the retention dams' life expectancy. [ABSTRACT FROM AUTHOR]

Modeling cotton plant growth is an important aspect of improving cotton yields and fiber quality and optimizing land management strategies. High-throughput phenotyping (HTP) systems, including those using high-resolution imagery from unmanned aerial systems (UAS) combined with sensor technologies, can accurately measure and characterize phenotypic traits such as plant height, canopy cover, and vegetation indices. However, manual assessment of plant characteristics is still widely used in practice. It is time-consuming, labor-intensive, and prone to human error. In this study, we investigated the use of a data-processing pipeline to estimate cotton plant height using UAS-derived visible-spectrum vegetation indices and photogrammetric products. Experiments were conducted at an experimental cotton field in Aliartos, Greece, using a DJI Phantom 4 UAS in five different stages of the 2022 summer cultivation season. Ground Control Points (GCPs) were marked in the field and used for georeferencing and model optimization. The imagery was used to generate dense point clouds, which were then used to create Digital Surface Models (DSMs), while specific Digital Elevation Models (DEMs) were interpolated from RTK GPS measurements. Three (3) vegetation indices were calculated using visible spectrum reflectance data from the generated orthomosaic maps, and ground coverage from the cotton canopy was also calculated by using binary masks. Finally, the correlations between the indices and crop height were examined. The results showed that vegetation indices, especially Green Chromatic Coordinate (GCC) and Normalized Excessive Green (NExG) indices, had high correlations with cotton height in the earlier growth stages and exceeded 0.70, while vegetation cover showed a more consistent trend throughout the season and exceeded 0.90 at the beginning of the season. [ABSTRACT FROM AUTHOR]

Mapping water stress in vineyards, at the parcel level, is of significant importance for supporting crop management decisions and applying precision agriculture practices. In this paper, a novel methodology based on aerial Shortwave Infrared (SWIR) data is presented, towards the estimation of water stress in vineyards at canopy scale for entire parcels. In particular, aerial broadband spectral data were collected from an integrated SWIR and multispectral instrumentation, onboard an unmanned aerial vehicle (UAV). Concurrently, in-situ leaf stomatal conductance measurements and supplementary data for radiometric and geometric corrections were acquired. A processing pipeline has been designed, developed, and validated, able to execute the required analysis, including data pre-processing, data co-registration, reflectance calibration, canopy extraction and water stress estimation. Experiments were performed at two viticultural regions in Greece, for several vine parcels of four different vine varieties, Sauvignon Blanc, Merlot, Syrah and Xinomavro. The performed qualitative and quantitative assessment indicated that a single model for the estimation of water stress across all studied vine varieties was not able to be established (r2 < 0.30). Relatively high correlation rates (r2 > 0.80) were achieved per variety and per individual variety clone. The overall root mean square error (RMSE) for the estimated canopy water stress was less than 29 mmol m−2 s−1, spanning from no-stress to severe canopy stress levels. Overall, experimental results and validation indicated the quite high potentials of the proposed instrumentation and methodology. [ABSTRACT FROM AUTHOR]

The evolution of the high-quality 3D archaeological representations from niche products to integrated online media has not yet been completed. Digital archives of the field often lack multimodal data interoperability, user interaction and intelligibility. A web-based cultural heritage archive that compensates for these issues is presented in this paper. The multi-resolution 3D models constitute the core of the visualization on top of which supportive documentation data and multimedia content are spatial and logical connected. Our holistic approach focuses on the dynamic manipulation of the 3D scene through the development of advanced navigation mechanisms and information retrieval tools. Users parse the multi-modal content in a geo-referenced way through interactive annotation systems over cultural points of interest and automatic narrative tours. Multiple 3D and 2D viewpoints are enabled in real-time to support data inspection. The implementation exploits front-end programming languages, 3D graphic libraries and visualization frameworks to handle efficiently the asynchronous operations and preserve the initial assets' accuracy. The choice of Greece's Meteora, UNESCO world site, as a case study accounts for the platform's applicability to complex geometries and large-scale historical environments. [ABSTRACT FROM AUTHOR]

The ML 5.8 earthquake that hit the island of Crete on 27 September 2021 is analysed with InSAR (Interferometry from Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) data. The purpose of this work is to create a model with sufficient detail for the geophysical processes that take place in several kilometres below the earth's surface and improve our ability to observe active tectonic processes using geodetic and seismic data. InSAR coseismic displacements maps show negative values along the LOS of ~18 cm for the ascending orbit and ~20 cm for the descending one. Similarly, the GNSS data of three permanent stations were used in PPK (Post Processing Kinematic) mode to (i) estimate the coseismic shifts, highlighting the same range of values as the InSAR, (ii) model the deformation of the ground associated with the main shock, and (iii) validate InSAR results by combining GNSS and InSAR data. This allowed us to constrain the geometric characteristics of the seismogenic fault and the slip distribution on it. Our model, which stands on a joint inversion of the InSAR and GNSS data, highlights a major rupture surface striking 214°, dipping 50° NW and extending at depth from 2.5 km down to 12 km. The kinematics is almost dip-slip normal (rake −106°), while a maximum slip of ~1.0 m occurred at a depth of ca. 6 km. The crucial though indirect role of inherited tectonic structures affecting the seismogenic crustal volume is also discussed suggesting their influence on the surrounding stress field and their capacity to dynamically merge distinct fault segments. [ABSTRACT FROM AUTHOR]

Reality capture technologies such as Structure-from-Motion (SfM) photogrammetry have become a state-of-the-art practice within landslide research workflows in recent years. Such technology has been predominantly utilized to provide detailed digital products in landslide assessment where often, for thorough mapping, significant accessibility restrictions must be overcome. UAV photogrammetry produces a set of multi-dimensional digital models to support landslide management, including orthomosaic, digital surface model (DSM), and 3D point cloud. At the same time, the recognition of objects depicted in images has become increasingly possible with the development of various methodologies. Among those, Geographic Object-Based Image Analysis (GEOBIA) has been established as a new paradigm in the geospatial data domain and has also recently found applications in landslide research. However, most of the landslide-related GEOBIA applications focus on large scales based on satellite imagery. In this work, we examine the potential of different UAV photogrammetry product combinations to be used as inputs to image segmentation techniques for the automated extraction of landslide elements at site-specific scales. Image segmentation is the core process within GEOBIA workflows. The objective of this work is to investigate the incorporation of fully 3D data into GEOBIA workflows for the delineation of landslide elements that are often challenging to be identified within typical rasterized models due to the steepness of the terrain. Here, we apply a common unsupervised image segmentation pipeline to 3D grids based on the superpixel/supervoxel and graph cut algorithms. The products of UAV photogrammetry for two landslide cases in Greece are combined and used as 2D (orthomosaic), 2.5D (orthomosaic + DSM), and 3D (point cloud) terrain representations in this research. We provide a detailed quantitative comparative analysis of the different models based on expert-based annotations of the landscapes and conclude that using fully 3D terrain representations as inputs to segmentation algorithms provides consistently better landslide segments. [ABSTRACT FROM AUTHOR]

Upwelling mantle plumes often instigate extensional stress within the continental crust of Earth. When stress exceeds crustal strength, extensional structures develop, reducing the effective stress and trigger magmatic processes at the crust–mantle boundary. However, such processes and their relationship to the formation of many surface structures remain poorly characterized on Mars. We identified a series of extensional structures in the southern highlands of Mars which collectively resemble continental rift zones on Earth. We further characterized these extensional structures and their surrounding region (area of ~1.8 M km2) by determining the surface mineralogy and bulk regional geochemistry of the terrain. In turn, this constrains their formation and yields a framework for their comparison with extensional structures on Earth. These terrains are notable for olivine and high-Ca pyroxene with a high abundance of potassium and calcium akin to alkali basalts. In the case of Mars, this Earth-like proto-plate tectonic scenario may be related to the plume-induced crustal stretching and considering their distribution and temporal relationship with the Hellas basin, we conclude that the plume is impact-induced. Overall, the findings of this work support the presence of mantle plume activity in the Noachian, as suggested by thermal evolution models of Mars. [ABSTRACT FROM AUTHOR]

Contributing to the United Nations 2030 Sustainable Development Goals (SDGs) within Target 11.4 "Strengthen efforts to protect and safeguard the world's cultural and natural heritage", it is critical to monitor the spatial and temporal stabilities of cultural heritages. The study of the interactive relationship between earthquakes and the protection of cultural heritages needs to be strengthened. On 27 September 2021, the destructive Mw 5.9 Arkalochori earthquake occurred ~25 km away from the city of Heraklion (Greece) where the Heraklion City Wall (HCW), a representative cultural heritage of Greece and Europe, was located. This offered a proper case to investigate the shortcomings aforementioned. Here, we intend to set up and answer the following three questions (Whether, Where and What, 3Ws): Whether there were impacts on the HCW caused by the Arkalochori earthquake? Where did the maximum deformation occur? What was the relationship between seismic deformation between the epicenter and the HCW over time? We performed two-dimensional (2D) InSAR measurements for both co-seismic and post-seismic deformations using the ascending and descending Sentinel-1A SAR images. The spatial-temporal characteristics of Up–Down (UD) and East–West (EW) were revealed. The 2D co-seismic deformation field showed that the near-filed deformations were dominating compared with the deformations at the HCW, the UD deformation was mainly featured with subsidence with a maximum value of ~21 cm, the EW deformation was ~9 cm westward and ~10 cm eastward. The time-series measurements showed that: (1) temporally, the HCW responded quickly to the Arkalochori earthquake, and the accumulative deformations at the seven different bastions of the HCW showed the same trend as the near-field area over time. (2) Spatially, the closer to the Mw 5.9 epicenter, the larger the deformations that occurred. (3) The EW and UD deformation trends of the HCW that were consistent with the Mw 5.9 epicenter were interrupted at the middle time spot (22 January 2022), indicating the influence of another earthquake sequence consisting of eight earthquakes with magnitudes larger than 3.5 that happened on 16–18 January 2022. Respectively, to summarize and address the aforementioned 3Ws based on the post-seismic analysis accomplished by the MSBAS method, the Arkalochori earthquake did affect the HCW; besides, the influences of the ~13 km earthquake sequence were also detected; the nearest part to the epicenter suffered the most; the deformation trends of the HCW were approximately the same with the epicenter area of the Arkalochori earthquake both in the UD and EW directions. [ABSTRACT FROM AUTHOR]

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

This paper looks at the periodic land use/cover (LUC) changes that occurred in Attica, Greece from 1991 to 2016. During this period, land transformations were mostly related to the artificial LUC categories; therefore, the aim was to map LUC with a high thematic resolution aimed at these specific categories, according to their density and continuity. The classification was implemented using the Random Forests (RF) machine learning algorithm and the presented methodological framework involved a high degree of automation. The results revealed that the majority of the expansion of the built-up areas took place at the expense of agricultural land. Moreover, mapping and quantifying the LUC changes revealed three uneven phases of development, which reflect the socioeconomic circumstances of each period. The discontinuous low-density urban fabric started to increase rapidly around 2003, reaching 7% (from 2.5% in 1991), and this trend continued, reaching 12% in 2016. The continuous as well as the discontinuous dense urban fabric, almost doubled throughout the study period. Agricultural areas were dramatically reduced to almost half of what they were in 1991, while forests, scrubs, and other natural areas remained relatively stable, decreasing only by 3% in 25 years. [ABSTRACT FROM AUTHOR]

In this study we present a pre-operational forecasting assimilation system of different types of aerosols. This system has been developed within the chemistry-transport model of Météo-France, MOCAGE, and uses the assimilation of the Aerosol Optical Depth (AOD) from MODIS (Moderate Resolution Imaging Spectroradiometer) onboard both Terra and Aqua. It is based on the AOD assimilation system within the MOCAGE model. It operates on a daily basis with a global configuration of 1 ∘ × 1 ∘ (longitude × latitude). The motivation of such a development is the capability to predict and anticipate extreme events and their impacts on the air quality and the aviation safety in the case of a huge volcanic eruption. The validation of the pre-operational system outputs has been done in terms of AOD compared against the global AERONET observations within two complete years (January 2018–December 2019). The comparison between both datasets shows that the correlation between the MODIS assimilated outputs and AERONET over the whole period of study is 0.77, whereas the biases and the RMSE (Root Mean Square Error) are 0.006 and 0.135, respectively. The ability of the pre-operational system to predict extreme events in near real time such as the desert dust transport and the propagation of the biomass burning was tested and evaluated. We particularly presented and documented the desert dust outbreak which occurred over Greece in late March 2018 as well as the wildfire event which happened on Australia between July 2019 and February 2020. We only presented these two events, but globally the assimilation chain has shown that it is capable of predicting desert dust events and biomass burning aerosols which happen all over the globe. [ABSTRACT FROM AUTHOR]

Inland water resources are facing increasing quantitative and qualitative pressures, deriving from anthropogenic causes and the ongoing climate change. The monitoring of reservoirs is essential for sustainable management and preparation against water scarcity and extreme events, such as droughts. This research, relying on the Sentinel-2 and 3 missions, attempts to demonstrate the efficiency of combining remotely sensed water level and water area estimations, in order to estimate the water storage variation of Yliki reservoir. The case study is conducted in one of the few sufficiently monitored reservoirs in Greece, enabling a direct comparison of the proposed methodology results with in situ observations. Moreover, this research work proposes a weekly time interval for pairing level and area estimations, instead of shorter time intervals. The results strongly demonstrate the efficiency of remote sensing in the production of empirical level–area–storage (L–A–S) curves. Correlation to in situ monitored storage- and satellite-derived water level, area stand for 98.81% and 99.27% respectively. Water storage variation is estimated and compared to the observed time series, resulting in an RMSE of 1.28% of the reservoir capacity and a correlation of 96.14%. The empirical L–S relationship underestimates storage, while the A–S relationship overestimates storage when compared to the existing L–A–S curve. [ABSTRACT FROM AUTHOR]

The wider Athens metropolitan area serves as an interesting setting for conducting geodetic studies. On the one hand, it has a complex regional geotectonic characteristic with several active and blind faults, one of which gave the deadly Mw 5.9 Athens earthquake on September 1999. On the other hand, the Greek capital is heavily urbanized, and construction activities have been taking place in the last few decades to address the city’s needs for advanced infrastructures. This work focuses on estimating ground velocities for the wider Athens area in a period spanning two decades, with an extended spatial coverage, increased spatial sampling of the measurements and at high precision. The aim is to deliver to the community a reference geodetic database containing consistent and robust velocity estimates to support further studies for modeling and multi-hazard assessment. The analysis employs advanced persistent scatterer interferometry methods, covering Athens with both ascending and descending ERS-1, ERS-2 and Envisat Synthetic Aperture Radar data, forming six independent interferometric stacks. A methodology is developed and applied to exploit track diversity for decomposing the actual surface velocity field to its vertical and horizontal components and coping with the post-processing of the multi-track big data. Results of the time series analysis reveal that a large area containing the Kifisia municipality experienced non-linear motion; while it had been subsiding in the period 1992–1995 (−12 mm/year), the same area has been uplifting since 2005 (+4 mm/year). This behavior is speculated to have its origin on the regional water extraction activities, which when halted, led to a physical restoration phase of the municipality. In addition, a zoom in the area inflicted by the 1999 earthquake shows that there were zones of counter-force horizontal movement prior to the event. Further analysis is suggested to investigate the source and tectonic implications of this observation. [ABSTRACT FROM AUTHOR]

Multi-sensor prospecting is a fast-emerging paradigm in archaeological geophysics. Given suitable ground conditions for navigation, sensor arrays drastically increase efficiency in data collection. In particular, geomagnetic prospecting benefits from this development. Despite these advancements, data processing still lacks a best-practice approach. Conventional processing methods developed for gridded data has been challenged by sensor arrays "roaming" in the landscape. In realization of the issue, the Innovative Geophysical Approaches for the Study of Early Agricultural Villages of Neolithic Thessaly (IGEAN) Project explored various innovative techniques for the betterment of the multi-sensor geomagnetic data processing. As a result, a modular pipeline is produced with minimal user intervention. In addition to standard steps, such as data clipping, various other algorithms have been introduced. This pipeline is tested over 20 Neolithic settlements in Thessaly, Greece, three of which are presented here in detail. The proposed workflow provides drastic improvements over raw data. As a result of these improvements, the IGEAN project revealed astonishing details on architectural elements, settlement enclosures, and paleolandscapes, changing completely the existing perspective of the Neolithic habitation in Thessaly. [ABSTRACT FROM AUTHOR]

This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher's discriminant analysis (rkFDA) machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel) versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877). Both methods outperformed the standard Normalized Difference Water Index (NDWI) thresholding (OA = 94.63, K = 0.818) by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the "not flooded" class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models). This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study offers important information towards exploring the possibilities of the use of such data to map other significant flood events from space in an economically viable way. [ABSTRACT FROM AUTHOR]

Helike, the Achaean Dodecapolis capital, in the Corinth Gulf, Greece, was, according to historical sources, devastated and disappeared from sight during an earthquake followed by sea invasion on to land in 373/372 B.C. A marine remote sensing survey, which was carried out to examine a landslide following a 6.2 R earthquake in 1995, that affected the coastal and near-shore delta plain zone on which Helike stood, accompanied by onshore borehole data, enabled us to postulate the geological processes leading to the Helike catastrophe. Helike was initially leveled during a 6 to 6.7 R earthquake; it is postulated to have then submerged following a translational landslide caused by liquefaction. This Helike catastrophe model is consistent with historical sources and current views regarding the expected geological hazards magnitude in the Gulf. [ABSTRACT FROM AUTHOR]

We studied the kinematic behaviour of active landslides at several localities in the area of Panachaikon Mountain, Achaia (Peloponnese, Greece) using Sentinel (C-band) InSAR time series analysis. We processed LiCSAR interferograms using the SBAS tool, and we obtained average displacement maps for the period 2016–2021. We found that the maximum displacement rate of each landslide is located at about the center of it. The average E-W velocity of the Krini landslide is ~3 cm/year (toward the east) and 0.6 cm/year downward. The line-of-sight (LOS) velocity of the landslide (descending orbit) compares well to a co-located GNSS station within (±) 3 mm/yr. Our results also suggest a correlation between rainfall and landslide motion. For the Krini landslide, a cross-correlation analysis of our data suggests that the mean time lag was 13.5 days between the maximum seasonal rainfall and the change in the LOS displacement rate. We also found that the amount of total seasonal rainfall controls the increase in the displacement rate, as 40–550% changes in the displacement rate of the Krini landslide were detected, following to a seasonal maximum of rainfall values at the nearby meteorological station of Kato Vlassia. According to our results, it seems that large part of this mountainous region of Achaia suffers from slope instability that is manifested in various degrees of ground displacement greatly affecting its morphological features and inhabited areas. [ABSTRACT FROM AUTHOR]

Given the great importance of lakes in Earth's environment and human life, continuous water quality (WQ) monitoring within the frame of the Water Framework Directive (WFD) is the most crucial aspect for lake management. In this study, Earth Observation (EO) data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) sensors have been combined with co-orbital in situ measurements from 50 lakes located in Greece with the main objective of delivering robust WQ assessment models. Correlation analysis among in situ co-orbital WQ data (Chlorophylla, Secchi depths, Total phosphorus-TP-) contributed to distinguishing their inter-relationships and improving the WQ models' accuracy. Subsequently, stepwise multiple regression analysis (MLR) of the available TP and Secchi depth datasets was implemented to explore the potential to establish optimal quantitative models regardless of lake characteristics. Then, further MLR analysis concerning whether the lakes are natural or artificial was conducted with the basic aim of generating different remote sensing derived models for different types of lakes, while their combination was further utilized to assess their trophic status. Correlation matrix results showed a high and positive relationship between TP and Chlorophyll-a (0.85), whereas high negative relationships were found between Secchi depth with TP (−0.84) and Chlorophyll-a (−0.83). MLRs among Landsat data and Secchi depths resulted in 3 optimal models concerning the assessment of Secchi depth of all lakes (Secchigeneral; R = 0.78; RMSE = 0.24 m), natural (Secchinatural; R = 0.95; RMSE = 0.14 m) and artificial (Secchiartificial; R = 0.62; RMSE = 0.1 m), with reliable accuracy. Study findings showed that TP-related MLR analyses failed to deliver a statistically acceptable model for the reservoirs; nevertheless, they delivered a robust TPgeneral (R = 0.71; RMSE = 1.41 mg/L) and TPnatural model (R = 0.93; RMSE = 1.43 mg/L). Subsequently, trophic status classification was conducted herein, calculating Carlson's Trophic State Index (TSI) initially throughout all lakes and then oriented toward natural-only and artificial-only lakes. Those three types of TSI (general, natural, artificial) were calculated based on previously published satellite-derived Chlorophyll-a (Chl-a) assessment models and the hereby specially designed WQ models (Secchi depth, TP). The higher deviation of satellite-derived TSI values in relation to in situ ones was detected in reservoirs and shallower lakes (mean depth < 5 m), indicating noticeable divergences among natural and artificial lakes. All in all, the study findings provide important support toward the perpetual WQ monitoring and trophic status prediction of Greek lakes and, by extension, their sustainable management, particularly in cases when ground truth data is limited. [ABSTRACT FROM AUTHOR]

The devastating series of fire events that occurred during the summers of 2007 and 2009 in Greece made evident the need for an operational mechanism to map burned areas in an accurate and timely fashion to be developed. In this work, Système pour l'Observation de la Terre (SPOT)-4 HRVIR images are introduced in an object-based classification environment in order to develop a classification procedure for burned area mapping. The development of the procedure was based on two images and then tested for its transferability to other burned areas. Results from the SPOT-4 HRVIR burned area mapping showed very high classification accuracies (∼0.86 kappa coefficient), while the object-based classification procedure that was developed proved to be transferable when applied to other study areas. [ABSTRACT FROM AUTHOR]

Greece is a high risk Mediterranean country with respect to wildfires. This risk has been increasing under the impact of climate change, and in summer 2007 approximately 200,000 ha of vegetated land were burnt. The SEVIRI sensor, on board the Meteosat Second Generation (MSG) geostationary satellite, is the only spaceborne sensor providing five and 15-minute observations of Europe in 12 spectral channels, including a short-wave infrared band sensitive to fire radiative temperature. In August 2007, when the bulk of the destructive wildfires started in Greece, the receiving station, operated by the Institute for Space Applications and Remote Sensing, provided us with a time series of MSG-SEVIRI images. These images were processed in order to test the reliability of a real-time detection and tracking system and its complementarity to conventional means provided by the Fire Brigade. EUMETSAT's Active Fire Monitoring (FIR) image processing algorithm for fire detection and monitoring was applied to SEVIRI data, then fine-tuned according to Greek conditions, and evaluated. Alarm announcements from the Fire Brigade's archives were used as ground truthing data in order to assess detection reliability and system performance. During the examined period, MSG-SEVIRI data successfully detected 82% of the fire events in Greek territory with less than 1% false alarms. [ABSTRACT FROM AUTHOR]

Monitoring of active faults in urban areas is of great importance, providing useful information to assess seismic hazards and risks. The present study concerns the monitoring of the potential ground deformation caused by the active tectonism in the cities of Patras and Pyrgos in Western Greece. A PS interferometric analysis technique was applied using a rich data-set of ERS-1 & 2 SLC images. The results of the interferometric analysis were compared with the tectonic maps of the two cities. Patras show clearer uplift-subsidence results due to the more distinct fault pattern and intense deformation compared to the Pyrgos area, where more diffused deformation is observed, with no significant displacements on the surface. [ABSTRACT FROM AUTHOR]

Tropical-like cyclone (TLC or medicane) Ianos formed during mid-September 2020 over the Southern Mediterranean Sea, and, during its mature stage on days 17–18, it affected southern Italy and especially Greece and its Ionian islands, where it brought widespread disruption due to torrential rainfall, severe wind gusts, and landslides, causing casualties. This study performs a sensitivity analysis of the mature phase of TLC Ianos with the WRF model to different microphysics parameterization schemes and initial and boundary condition (IBC) datasets. Satellite measurements from the Global Precipitation Measurement Mission-Core Observatory (GPM-CO) dual-frequency precipitation radar (DPR) and the Advanced Scatterometer (ASCAT) sea-surface wind field were used to verify the WRF model forecast quality. Results show that the model is most sensitive to the nature of the IBC dataset (spatial resolution and other dynamical and physical differences), which better defines the primary mesoscale features of Ianos (low-level vortex, eyewall, and main rainband structure) when using those at higher resolution (~25 km versus ~50 km) independently of the microphysics scheme, but with the downside of producing too much convection and excessively low minimum surface pressures. On the other hand, no significant differences emerged among their respective trajectories. All experiments overestimated the vertical extension of the main rainbands and display a tendency to shift the system to the west/northwest of the actual position. Especially among the experiments with the higher-resolution IBCs, the more complex WRF microphysics schemes (Thompson and Morrison) tended to outperform the others in terms of rain rate forecast and most of the other variables examined. Furthermore, WSM6 showed a good performance while WDM6 was generally the least accurate. Lastly, the calculation of the cyclone phase space diagram confirmed that all simulations triggered a warm-core storm, and all but one also exhibited axisymmetry at some point of the studied lifecycle. [ABSTRACT FROM AUTHOR]

Stem biomass is a fundamental component of the global carbon cycle that is essential for forest productivity estimation. Over the last few decades, Light Detection and Ranging (LiDAR) has proven to be a useful tool for accurate carbon stock and biomass estimation in various biomes. The aim of this study was to investigate the potential of multispectral LiDAR data for the reliable estimation of single-tree total and barkless stem biomass (TSB and BSB) in an uneven-aged structured forest with complex topography. Destructive and non-destructive field measurements were collected for a total of 67 dominant and co-dominant Abies borisii-regis trees located in a mountainous area in Greece. Subsequently, two allometric equations were constructed to enrich the reference data with non-destructively sampled trees. Five different regression algorithms were tested for single-tree BSB and TSB estimation using height (height percentiles and bicentiles, max and average height) and intensity (skewness, standard deviation and average intensity) LiDAR-derived metrics: Generalized Linear Models (GLMs), Gaussian Process (GP), Random Forest (RF), Support Vector Regression (SVR) and Extreme Gradient Boosting (XGBoost). The results showcased that the RF algorithm provided the best overall predictive performance in both BSB (i.e., RMSE = 175.76 kg and R2 = 0.78) and TSB (i.e., RMSE = 211.16 kg and R2 = 0.65) cases. Our work demonstrates that BSB can be estimated with moderate to high accuracy using all the tested algorithms, contrary to the TSB, where only three algorithms (RF, SVR and GP) can adequately provide accurate TSB predictions due to bark irregularities along the stems. Overall, the multispectral LiDAR data provide accurate stem biomass estimates, the general applicability of which should be further tested in different biomes and ecosystems. [ABSTRACT FROM AUTHOR]

The frequent occurrence of large and high-intensity wildfires in the Mediterranean region poses a major threat to people and the environment. In this context, the estimation of dead fine fuel moisture content (DFMC) has become an integrated part of wildfire management since it provides valuable information for the flammability status of the vegetation. This study investigates the effectiveness of a physically based fuel moisture model in estimating DFMC during severe fire events in Greece. Our analysis considers two approaches, the satellite-based (MODIS DFMC model) and the weather station-based (AWSs DFMC model) approach, using a fuel moisture model which is based on the relationship between the fuel moisture of the fine fuels and the water vapor pressure deficit (D). During the analysis we used weather station data and MODIS satellite data from fourteen wildfires in Greece. Due to the lack of field measurements, the models' performance was assessed only in the case of the satellite data by using weather observations obtained from the network of automated weather stations operated by the National Observatory of Athens (NOA). Results show that, in general, the satellite-based model achieved satisfactory accuracy in estimating the spatial distribution of the DFMC during the examined fire events. More specifically, the validation of the satellite-derived DFMC against the weather-station based DFMC indicated that, in all cases examined, the MODIS DFMC model tended to underestimate DFMC, with MBE ranging from −0.3% to −7.3%. Moreover, in all of the cases examined, apart from one (Sartis' fire case, MAE: 8.2%), the MAE of the MODIS DFMC model was less than 2.2%. The remaining numerical results align with the existing literature, except for the MAE case of 8.2%. The good performance of the satellite based DFMC model indicates that the estimation of DFMC is feasible at various spatial scales in Greece. Presently, the main drawback of this approach is the occurrence of data gaps in the MODIS satellite imagery. The examination and comparison of the two approaches, regarding their operational use, indicates that the weather station-based approach meets the requirements for operational DFMC mapping to a higher degree compared to the satellite-based approach. [ABSTRACT FROM AUTHOR]

Mean optical and microphysical aerosol properties of long-range transported biomass burning (BB) particles and mixtures are presented from a 9-year (2011–2019) data set of multiwavelength Raman lidar data, obtained by the EOLE lidar over the city of Athens (37.58° N, 23.47° E), Greece. We studied 34 aerosol layers characterized as: (1) smoke; (2) smoke + continental polluted, and (3) smoke + mixed dust. We found, mainly, small-sized aerosols with mean backscatter-related (355 nm/532 nm, 532 nm/1064 nm) values and Ångström exponent (AE) values in the range 1.4–1.7. The lidar ratio (LR) value at 355 nm was found to be 57 ± 10 sr, 51 ± 5 sr, and 38 ± 9 sr for the aerosol categories (1), (2), and (3), respectively; while at 532 nm, we observed LR values of 73 ± 11 sr, 59 ± 10 sr, and 62 ± 12 for the same categories. Regarding the retrieved microphysical properties, the effective radius (reff) ranged from 0.24 ± 0.11 to 0.24 ± 0.14 μm for all aerosol categories, while the volume density (vd) ranged from 8.6 ± 3.2 to 20.7 ± 14.1 μm−3cm−3 with the higher values linked to aerosol categories (1) and (2); the real part of the refractive index (mR) ranged between 1.49 and 1.53, while for the imaginary part (mI), we found values within 0.0108 i and 0.0126 i. Finally, the single scattering albedo (SSA) of the propped particles varied from 0.915 to 0.936 at all three wavelengths (355–532–1064 nm). The novelty of this study is the provision of typical values of BB aerosol properties from the UV to the near IR, which can be used in forecasting the aerosol climatic effects in the European region. [ABSTRACT FROM AUTHOR]

In this work, the effect that two basic air quality indexes, aerosols and tropospheric NO2, exert on surface solar radiation (SSR) is studied, along with the effect of liquid and ice clouds over 16 locations in Greece, in the heart of the Eastern Mediterranean. State-of-the-art satellite-based observations and climatological data for the 15-year period 2005–2019, and a radiative transfer system based on a modified version of the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model are used. Our SSR simulations are in good agreement with ground observations and two satellite products. It is shown that liquid clouds dominate, with an annual radiative effect (RE) of −36 W/m2, with ice clouds (−19 W/m2) and aerosols (−13 W/m2) following. The radiative effect of tropospheric NO2 is smaller by two orders of magnitude (−0.074 W/m2). Under clear skies, REaer is about 3–4 times larger than for liquid and ice cloud-covered skies, while RENO2 doubles. The radiative effect of all the parameters exhibits a distinct seasonal cycle. An increase in SSR is observed for the period 2005–2019 (positive trends ranging from 0.01 to 0.52 W/m2/year), which is mostly related to a decrease in the aerosol optical depth and the liquid cloud fraction. [ABSTRACT FROM AUTHOR]

Mapping burned areas using satellite imagery has become a subject of extensive research over the past decades. The availability of high-resolution satellite data allows burned area maps to be produced with great detail. However, their increasing spatial resolution is usually not matched by a similar increase in the temporal domain. Moreover, high-resolution data can be a computational challenge. Existing methods usually require downloading and processing massive volumes of data in order to produce the resulting maps. In this work we propose a method to make this procedure fast and yet accurate by leveraging the use of a coarse resolution burned area product, the computation capabilities of Google Earth Engine to pre-process and download Sentinel-2 10-m resolution data, and a deep learning model trained to map the multispectral satellite data into the burned area maps. For a 1500 ha fire our method can generate a 10-m resolution map in about 5 min, using a computer with an 8-core processor and 8 GB of RAM. An analysis of six important case studies located in Portugal, southern France and Greece shows the detailed computation time for each process and how the resulting maps compare to the input satellite data as well as to independent reference maps produced by Copernicus Emergency Management System. We also analyze the feature importance of each input band to the final burned area map, giving further insight about the differences among these events. [ABSTRACT FROM AUTHOR]

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites. [ABSTRACT FROM AUTHOR]