Space and Planetary Science, Earth-Surface Processes, Computers in Earth Sciences
244
Scopus Publications
10210
Scholar Citations
52
Scholar h-index
153
Scholar i10-index
Scopus Publications
Combining ENMAP Hyperspectral Imaging and Machine Learning for Land Use/Cover Classification Evniki Nikolaou-Alavanou, George P. Petropoulos, Kleomenis Kalogeropoulos Remote Sensing in Earth Systems Sciences, 2026 Imaging spectroscopy enables detailed recording of the Earth’s surface and ecosystems’ properties at a global level. Due to the specifications of the hyperspectral sensors, it becomes possible to utilize the hyperspectral data in a multitude of applications, among which the mapping, monitoring and understanding of land use and land cover (LULC). The Environmental Mapping and Analysis Program (EnMAP) is a German satellite mission that aims to monitor and characterize the Earth’s surface on a global scale providing high-quality data with the advantage of open access. This study explores the capabilities of the EnMAP satellite in LULC mapping. The goal of the study is to leverage the advantages and potential of state-of-the-art machine learning algorithms, in particular of Support Vector Machine (SVM) and Random Forest (RF), in combination with EnMAP’s hyperspectral data, for mapping LULC. The study area is characterized as a typical Mediterranean environment. The thematic LULC maps accuracy was assessed using statistical methods and further analysis was conducted through McNemar’s statistical test to evaluate the statistical significance of the differences in the results. All in all, the SVM algorithm proved to be more accurate than RF algorithm, with an overall accuracy of 91% compared to 86%, respectively. The McNemar’s test confirmed the higher accuracy of the SVM algorithm’s results, at least this was the case in our study. The study’s findings highlighted EnMAP’s hyperspectral data promising role of in the field of LULC classification. This study adds valuable information towards evaluating EnMAP satellite data in producing thematic maps in typical Mediterranean environments and allows for comparisons with similar data sets in other areas or with data from other sensors in similar environments.
Expanding the Ts–VI feature space for retrieving new parameters characterising the water and carbon cycle: proof-of-concept of a new methodological framework and its validation at selected FLUXNET sites George P. Petropoulos, Spyridon E. Detsikas, Christina Lekka Science of the Total Environment, 2026 Land Surface Interactions (LSI) have an important role in shaping the complex dynamics of climate-related processes across varying spatiotemporal scales. Therefore, the accurate quantification of exchanges of turbulent fluxes and greenhouse gas is essential for the efficient management of natural resources and the adaptation to climate change mitigation strategies. Toward this direction, this study introduces a novel framework for deriving mass, energy and carbon flux directly from the Ts – VI (land surface temperature – vegetation index) feature space. In particular, herein is provided the first validation of the “analytical triangle” using Landsat-8 and the latest version of the SimSphere SVAT model, while also introduced the proof-of-concept of a new scheme that extends the application of the “analytical” triangle to retrieving new variables characterising land surface interactions. In particular, the study applied the “analytical triangle” concept, which is also proposed to be extended to include the retrievals of instantaneous maps of [O₃] and O₃ flux, GPP, [CO₂], and water use efficiency (WUE). A first evaluation of the proof-of-concept methodological framework proposed herein was performed at 3 sites belonging to the FLUXNET ground global network, representative of three biomes. The analysis yielded satisfactory RMSDs, with the best performance for GPP (6.268 μmol CO₂ m −2 s −1 ), followed by WUE (8.116 kg m −3 ), [O₃] (8.270 ppb), O₃ flux (5.42 μmol mol −1 ), and [CO₂] (59.91 μmol mol −1 ). Overall, the most accurate predictions, with the lowest average errors were observed at cropland sites, especially for GPP predictions ( R = 0.93) without requiring extensive model calibration, demonstrates the method's potential for future practical applications. All in all, the findings showed a great capacity of the “analytical triangle” and the newly proposed scheme in particular in supporting improved monitoring of key parameters characterising land-atmosphere interactions.
RegreSSM: A novel software tool for downscaling the SMAP L3 soil moisture operational product utilizing the Ts/VI feature space and Sentinel-3 data George P. Petropoulos, Spyridon E. Detsikas, Vasileios Anagnostopoulos, Christina Lekka Environmental Modelling and Software, 2026 Herein we present RegreSSM, a software tool that enables the downscaling of SMAP L3 Surface Soil Moisture (SSM) operational product from 36 km to 1 km by the fusion of optical and thermal data retrieved from Sentinel-3 platform. The downscaling method is based on the well-established properties of the Ts/VI feature space. Most of the existing soil moisture downscaling methods are computationally complex, require advanced expertise, and lack standalone tools suitable for operational or non-expert use. To address these limitations, this study proposes a simple and accessible framework for generating high-resolution SSM maps using only land surface temperature and vegetation cover as inputs. The tool has been developed in python programming language as a stand-alone application and can be executed in any operational system. The application offers automated and reproducible workflows for spatiotemporal matching and processing of SMAP L3 SSM products and Sentinel-3 dataset. The software tool's practical application is demonstrated over the Iberian Peninsula, where validation of the SMAP L3 product performed for all calendar year 2022 using in-situ observations from the REMEDHUS operational network stations. Results showed a satisfactory retrieval of SSM with a small average bias of 0.01 m 3 /m 3 , a MAD of 0.06 m 3 /m 3 , a RMSD of 0.07 m 3 /m 3 , and a satisfactory R 2 of 0.63, confirming the ability of the proposed downscaling framework and RegreSSM to retrieve SSM at the 1 km spatial resolution. Results obtained herein were also compared to the validation metrics reported for operational RS-based SSM products, with typically reported uncertainty of 0.04 m 3 /m 3 . The availability of RegreSSM to the SSM users' community consists an important step towards the standardization of downscaling procedures as well as bridging the spatial gap of existing operational SM products to the requirements of the fine-scale applications. It also contributes towards advancing the deployment of geo-processing tools utilizing the synergies between state-of-the-art methods and RS data available today from the most sophisticated satellites in orbit. • An open-source Python tool for SMAP operational product automated downscaling. • Spatiotemporal fusion of Sentinel-3 optical/thermal data for generating 1 km soil moisture products. • Practical demonstration of REGRESSM in REMEDHUS network in-situ data.
Use of geoinformation technologies to study climate risk and socio-economic vulnerability in coastal Arctic regions: state of the art, challenges & future perspectives Maria Polychronaki, George P. Petropoulos, Niki Evelpidou Remote Sensing Applications Society and Environment, 2026 This literature review critically evaluates how geoinformation technologies, specifically Geographic Information Systems (GIS) and Earth Observation Indices (EO indices), are applied to develop and implement socio-economic vulnerability indices to assess climate change impacts across coastal and coastal adjacent Arctic regions. While these areas currently face increasing environmental pressures, their vulnerability is also shaped by socio-economic conditions, governance mechanisms and community resilience. Based on the analysis of 40 peer-reviewed studies published between 2008 and 2025, this review systematically categorizes research approaches by data sources, indicator types, spatial scale and geospatial methodologies, following a structured analytical framework. The results reveal a dominant reliance on quantitative, infrastructure-based indicators, with comparatively limited integration of adaptive capacity, governance structures, or cultural dimensions. GIS-based overlays and Digital Elevation Model (DEM) analyses are the most frequently used tools, while participatory and combined frameworks are overlooked. The European Arctic emerges as particularly underrepresented in policy-relevant spatial vulnerability assessments. Addressing a critical gap in scholarship regarding climate risk and socio-economic vulnerability in the Arctic, this review, offers a focused synthesis of how geospatial technologies have been used to construct and apply socio-economic vulnerability indices for coastal Arctic climate risk assessment It also provides a roadmap for future research by highlighting overlooked regions, methodological biases and opportunities for advancing more inclusive, scalable and policy-relevant assessments to support adaptation planning in the rapidly transforming Arctic. • Examination of geoinformation technology use in socio-economic climate vulnerability assessments of coastal Arctic regions. • Emphasizing the widespread reliance on infrastructure-related indicators, reflecting their ease of geospatial integration and compatibility with EO indices and GIS tools. • Identification of gaps in adaptive capacity, cultural and governance integration. • Recommendation of more standardized, participatory and locally grounded approaches. • A structured, and critical synthesis of how geoinformatics have been applied to assess socio-economic vulnerability to climate change across coastal Arctic regions.
Analysis of wind data consistency derived from wind profile and L-band sounding radar based on ICC Fang Pang, Yu Long Han, Yi Pan, Ming Yi Gu, Yansong Bao, George P. Petropoulos International Journal of Remote Sensing, 2026 The Intraclass Correlation Coefficient (ICC) is commonly used to assess the consistency and reliability of different measurement methods for the same quantitative variable. The accuracy and reliability of multi-method measurement results, including the consistency in evaluation, are critical in meteorological research. In this study, vertical profile data from wind profiler radar and L-band radar at the Beihai National Meteorological Observatory in Guangxi, collected in 2022, were evaluated with ICC methodology to assess the consistency between the wind measurements. The results indicate that: (1) the overall ICC of the u component in the wind profile radar and the L-band radiosonde radar’s wind measurement data is 0.946, while the ICC of the v component is 0.908, demonstrating a very high degree of consistency in the wind component measurements; (2) values for different seasons were all greater than 0.88, with the consistency being higher in spring and summer compared to autumn and winter; (3) the ICC values at 08:00 and 20:00 were both greater than 0.91, showing nearly identical consistency between the two radars; (4) the ICC for wind speeds greater than 7.1 m·s−1 and below 7.1 m·s−1 were both above 0.85, with a higher consistency observed at higher wind speeds; (5) the ICC values both under precipitation and non-precipitation conditions were greater than 0.9, with no significant difference in the consistency degree between the two radar datasets; (6) the ICC values under varying mean relative humidity conditions were all greater than 0.83, indicating a high degree of consistency, and the consistency was significantly lower when the mean relative humidity was below 60% compared to when it was above 60%; (7) the ICC values under different detection altitudes were all greater than 0.88 with a high degree of consistency. Notably, the agreement above 2,000 metres is significantly stronger than that below this altitude.
Coastal Vulnerability Assessment in Svalbard region of the Arctic exploiting geoinformation technologies and a cloud-based platform Christos Kyrveis, George P. Petropoulos, Danai Varvatsouli Remote Sensing Applications Society and Environment, 2025 Monitoring coastal vulnerability is becoming increasingly important, due to climate-driven phenomena, such as sea level rise, glacier melt, and coastal changes, accelerate erosion and land loss, threatening infrastructure and ecosystems. In this study, a comprehensive methodology was developed and applied in part of Svalbard, Norway, using the Coastal Vulnerability Index (CVI). The approach is based on an automated workflow developed in the Google Earth Engine (GEE) platform, enabling automated and accurate extraction of coastlines from Landsat satellite data for the period 2014–2023. Additionally, geospatial analysis tools including ArcGIS and DSAS employed to calculate parameters such as shoreline change rates. The results revealed spatial variations in vulnerability and dynamic changes in the coastline, providing valuable information for coastal zone management in one of the most vulnerable environments on the planet. Specifically, approximately 4% of the coastline was classified with high vulnerability, 29% with moderate, 53% with low and 14% with very low. The proposed methodology is expected to be an effective tool for researchers and decision-makers, enhancing adaptation planning to the impacts of climate change in Arctic coastal areas.
CVIc: A web platform for automated Coastal Vulnerability Index-based assessment Alexandros Liaskos, George P. Petropoulos, Niki Evelpidou, Spyridon E. Detsikas Environmental Modelling and Software, 2025 Intensifying climate change impacts, such as Sea-Level Rise (SLR), floods, extreme weather events and coastal erosion, threaten ecosystems, infrastructure, and human communities at a global scale, making vulnerability assessments a crucial prerequisite for identifying areas necessitating urgent and effective actions. The Coastal Vulnerability Index (CVI) is a widely used index-based methodology for such assessments; yet its implementation often relies on complex, manual workflows across multiple proprietary desktop Geographic Information Systems (GIS) software. Existing approaches limit accessibility, lack transparency, hinder reproducibility, and are frequently time-consuming. To address these challenges, CVIc (Coastal Vulnerability Index Compiler) is presented herein as a novel, open-source, and open-access geoprocessing web application for the computation of the CVI. CVIc provides an end-to-end dynamic workflow, guiding users from shoreline data import to the application of various standardized indices (CVI, ICVI). CVIc is deployed as a website (https://alexandrosliaskos.github.io/CVIc/) and features interactive tools for shoreline digitization, segmentation, parameter value assignment, and visualization and export of results. The only input requirements are a shoreline Shapefile input or a GeoTIFF image for digitization, and the knowledge of the spatial distribution of the parameter values for the area under study. By leveraging IndexedDB for browser-based data storage, CVIc operates without server-side dependencies, ensuring data privacy, protection and large-scale dataset processing. To our knowledge, this consists the first web solution of its kind, as its streamlined approach into a unified and user-friendly platform makes this type of analysis more feasible to researchers and coastal practitioners, while providing policymakers with more accessible and robust data for decision-making. Its open-source nature enables community-driven advancements, and the simple User Interface (UI) and map components mark it as appropriate for educational purposes. • CVIc is as a novel, open-source, and open-access geoprocessing web application for the computation of the CVI. • CVIc provides an end-to-end dynamic workflow, guiding users from shoreline data import to the application of various standardized indices. • CVIc is deployed as a website ( https://cvic-456409.web.app/ ) and features interactive tools for data input and export • The new software tool addresses important challenges concerning the coastal vulnerability computation
Remote Sensing of Evapotranspiration from Croplands Trent W. Biggs, Pamela L. Nagler, Anderson Ruhoff, Triantafyllia Petsini, Michael Marshall, Stefanie Kagone, Gabriel B. Senay, George P. Petropoulos, Camila Abe, Edward P. Glenn Remote Sensing Handbook Volume V Six Volume Set Water Hydrology Floods Snow and Ice Wetlands and Water Productivity Second Edition, 2024
Soil moisture monitoring using unmanned aerial system Ruodan Zhuang, Salvatore Manfreda, Yijian Zeng, Zhongbo Su, Eyal Ben Dor, George P. Petropoulos Unmanned Aerial Systems for Monitoring Soil Vegetation and Riverine Environments, 2023
Using PlanetScope imagery and GEOBIA to map urban green spaces Evangelos A. Dosiadis, George P. Petropoulos, Ana-Maria Popa, Ionut Sandric, Antigoni Faka, Diana Andrea Onose, Prashant K. Srivastava Earth Observation in Urban Monitoring Techniques and Challenges, 2023
The International Soil Moisture Network: Serving Earth system science for over a decade Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, Roberto Sabia Hydrology and Earth System Sciences, 2021
Introduction to GPS/GNSS technology Amit Kumar, Shubham Kumar, Preet Lal, Purabi Saikia, Prashant K. Srivastava, George P. Petropoulos GPS and Gnss Technology in Geosciences, 2021
GNSS and UAV in archeology Michalis Vidalis-Kelagiannis, Kleomenis Kalogeropoulos, Grigoris Grigorakakis, Nikolaos Stathopoulos, George P. Petropoulos, Andreas Tsatsaris, Christos Chalkias GPS and Gnss Technology in Geosciences, 2021
Remote sensing of actual evapotranspiration from croplands Teixeira, Antônio De C., Hernandez, Fernando B. T. H. [UNESP], Scherer-Warren, Morris, Andrade, Ricardo G., Leivas, Janice F., et al. Remote Sensing of Water Resources Disasters and Urban Studies, 2015
Satellite Remote Sensing of Surface Soil Moisture Antônio de Castro, Fernando Hernandez, H Lopes, Morris Scherer-Warren, Luís Bassoi Remote Sensing of Energy Fluxes and Soil Moisture Content, 2013
Retrievals of turbulent heat fluxes and surface soil water content by remote sensing Advances in Environmental Remote Sensing Sensors Algorithms and Applications, 2011
Analysis of wind data consistency derived from wind profile and L-band sounding radar based on ICC F Pang, YL Han, Y Pan, MY Gu, Y Bao, GP Petropoulos International Journal of Remote Sensing 47 (10), 4328-4353 , 2026 2026
A Geospatial Database for Monitoring Arctic Coastline Dynamics: Mapping Shoreline Change in Iceland and Svalbard S Kapsokefalou, P Gartagani, E Plessas, C Nakos, I Kafouris, NG Tselos, ... EGU26 , 2026 2026
Assessing the effect of different ground sampling distances for drone-based mapping of fractional cover: a case study from a vineyard field in Northern Greece GN Tselos, SE Detsikas, GP Petropoulos, K Grigoriadis, V Polychronos, ... EGU26 , 2026 2026
Evaluating different methodological approaches for very high spatial resolution mapping of agricultural areas exploiting UAV data: a case study from Greek agricultural site P Charisoulis, GP Petropoulos, SE Detsikas, E Volianaki, A Litke EGU26 , 2026 2026
Detecting vineyards using multispectral UAV imagery and artificial intelligence: A case study from Northern Greece C Asimakopoulos, GP Petropoulos, G Saitis, SE Detsikas, N Evelpidou, ... EGU26 , 2026 2026
Litter detection and mapping from the combined use of multispectral UAV imagery and Deep Learning: A case study from Greece C Mitsopoulou, GP Petropoulos, SE Detsikas, C Lekka, K Grigoriadis, ... EGU26 , 2026 2026
Assessing Socio-Economic Impacts of Climate Change in the Arctic through Geoinformatics: the contribution of EO-PERSIST project M Starakis, N Myofa, E Volianaki, GN Tselos, K Petropoulou, SE Detsikas, ... EGU26 , 2026 2026
Combining ENMAP Hyperspectral Imaging and Machine Learning for Land Use/Cover Classification E Nikolaou-Alavanou, GP Petropoulos, K Kalogeropoulos Remote Sensing in Earth Systems Sciences 9 (1), 19 , 2026 2026
predicting the spatiotemporal variability of shortwave incoming C Lekka, SE Detsikas, GP Petropoulos Agricultural Applications of Earth Observation, 47 , 2026 2026
Expanding the Ts–VI feature space for retrieving new parameters characterising the water and carbon cycle: proof-of-concept of a new methodological framework and its validation … GP Petropoulos, SE Detsikas, C Lekka Science of The Total Environment 1015, 181377 , 2026 2026
Health geography insights into air quality dynamics during COVID-19 lockdown: A case study in Athens, Greece using Sentinel-5P and Google Earth Engine E Gkatsi, GP Petropoulos, K Kalogeropoulos Health Geography, 155-174 , 2026 2026
Use of geoinformation technologies to study climate risk and socio-economic vulnerability in coastal Arctic regions: state of the art, challenges & future perspectives M Polychronaki, GP Petropoulos, N Evelpidou Remote Sensing Applications: Society and Environment 41, 101917 , 2026 2026
Coastal vulnerability mapping of the fjords in the Romsdal area, Norway using geoinformatics M Giannakoudakis, SE Detsikas, GP Petropoulos, K Tsanakas, GN Tselos, ... Satellite Remote Sensing for Forest and Environmental Monitoring, 515-532 , 2026 2026
Mapping Arctic impervious surface changes with Earth observation and machine learning GP Petropoulos, K Dermosinoglou, SE Detsikas, K Kalogeropoulos, ... Data-Driven Earth Observation for Disaster Management, 679-695 , 2026 2026
RegreSSM: A novel software tool for downscaling the SMAP L3 soil moisture operational product utilizing the Ts/VI feature space and Sentinel-3 data GP Petropoulos, SE Detsikas, V Anagnostopoulos, C Lekka Environmental Modelling & Software, 106836 , 2025 2025 Citations: 1
Developing state of the art web cartography tools to evaluate the socioeconomic impact of climate change in the Arctic GN Tselos, SE Detsikas, K Petropoulou, A Litke, GP Petropoulos Abstracts of the ICA 10, 285 , 2025 2025
Developing state of the art geoinformation tools to enable an improved understanding of land surface interactions: the input of LISTEN-EO project GP Petropoulos, SE Detsikas, C Lekka Abstracts of the ICA 10, 228 , 2025 2025
A Local Spectral Library from the Kopaida Plain, Greece, for Rapid Soil Property SE Detsikas, G Petropoulos MDPI , 2025 2025
Coastal vulnerability assessment in Norway: a geoinformation-based approach C Asimakopoulos, GP Petropoulos, A Karkani, N Evelpidou, SE Detsikas Earth Science Informatics 18 (4), 555 , 2025 2025
A Geoinformation-Based Approach for Mapping Coastal Vulnerability in Sweden E Achmakidou, GP Petropoulos, A Karkani, N Evelpidou, SE Detsikas, ... Water 17 (20), 3027 , 2025 2025
MOST CITED SCHOLAR PUBLICATIONS
Surface soil moisture retrievals from remote sensing: Current status, products & future trends GP Petropoulos, G Ireland, B Barrett Physics and Chemistry of the Earth, Parts a/b/c 83, 36-56 , 2015 2015 Citations: 609
The International Soil Moisture Network: serving Earth system science for over a decade W Dorigo, I Himmelbauer, D Aberer, L Schremmer, I Petrakovic, L Zappa, ... Hydrology and Earth System Sciences Discussions 2021, 1-83 , 2021 2021 Citations: 528
A review of Ts/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture G Petropoulos, TN Carlson, MJ Wooster, S Islam Progress in Physical Geography 33 (2), 224-250 , 2009 2009 Citations: 375
Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery GP Petropoulos, C Kalaitzidis, KP Vadrevu Computers & Geosciences 41, 99-107 , 2012 2012 Citations: 330
Hyperspectral remote sensing in precision agriculture: Present status, challenges, and future trends P Singh, PC Pandey, GP Petropoulos, A Pavlides, PK Srivastava, ... Hyperspectral remote sensing, 121-146 , 2020 2020 Citations: 262
Land use/land cover in view of earth observation: Data sources, input dimensions, and classifiers—A review of the state of the art PC Pandey, N Koutsias, GP Petropoulos, PK Srivastava, E Ben Dor Geocarto International 36 (9), 957-988 , 2021 2021 Citations: 259
A new synergistic approach for monitoring wetlands using Sentinels-1 and 2 data with object-based machine learning algorithms A Whyte, KP Ferentinos, GP Petropoulos Environmental Modelling & Software 104, 40-54 , 2018 2018 Citations: 256
Co-Orbital Sentinel 1 and 2 for LULC mapping with emphasis on wetlands in a mediterranean setting based on machine learning A Chatziantoniou, GP Petropoulos, E Psomiadis Remote Sensing 9 (12), 1259 , 2017 2017 Citations: 216
Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model Y Bao, L Lin, S Wu, KAK Deng, GP Petropoulos International journal of applied earth observation and geoinformation 72, 76-85 , 2018 2018 Citations: 202
Burnt area delineation from a uni-temporal perspective based on Landsat TM imagery classification using Support Vector Machines GP Petropoulos, C Kontoes, I Keramitsoglou International Journal of Applied Earth Observation and Geoinformation 13 (1 … , 2011 2011 Citations: 194
A comparison of spectral angle mapper and artificial neural network classifiers combined with Landsat TM imagery analysis for obtaining burnt area mapping GP Petropoulos, KP Vadrevu, G Xanthopoulos, G Karantounias, ... Sensors 10 (3), 1967-1985 , 2010 2010 Citations: 174
Actual evapotranspiration in drylands derived from in-situ and satellite data: Assessing biophysical constraints M García, I Sandholt, P Ceccato, M Ridler, E Mougin, L Kergoat, L Morillas, ... Remote Sensing of Environment 131, 103-118 , 2013 2013 Citations: 167
Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping GP Petropoulos, K Arvanitis, N Sigrimis Expert systems with Applications 39 (3), 3800-3809 , 2012 2012 Citations: 166
Exploring the relationships between post-fire vegetation regeneration dynamics, topography and burn severity: A case study from the Montane Cordillera Ecozones of Western Canada G Ireland, GP Petropoulos Applied Geography 56, 232-248 , 2015 2015 Citations: 164
Towards improved spatio-temporal resolution soil moisture retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations M Piles, GP Petropoulos, N Sánchez, Á González-Zamora, G Ireland Remote Sensing of Environment 180, 403-417 , 2016 2016 Citations: 162
Landscape transform and spatial metrics for mapping spatiotemporal land cover dynamics using Earth Observation data-sets SK Singh, PK Srivastava, S Szabó, GP Petropoulos, M Gupta, T Islam Geocarto international 32 (2), 113-127 , 2017 2017 Citations: 160
Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS ® S Lamine, GP Petropoulos, SK Singh, S Szabó, NEI Bachari, ... Geocarto international 33 (8), 862-878 , 2018 2018 Citations: 148
Determining the use of Sentinel-2A MSI for wildfire burning & severity detection C Amos, GP Petropoulos, KP Ferentinos International journal of remote sensing 40 (3), 905-930 , 2019 2019 Citations: 131
Examining the capability of supervised machine learning classifiers in extracting flooded areas from Landsat TM imagery: a case study from a Mediterranean flood G Ireland, M Volpi, GP Petropoulos Remote sensing 7 (3), 3372-3399 , 2015 2015 Citations: 130
Geoinformation technologies in support of environmental hazards monitoring under climate change: An extensive review A Tsatsaris, K Kalogeropoulos, N Stathopoulos, P Louka, K Tsanakas, ... ISPRS International Journal of Geo-Information 10 (2), 94 , 2021 2021 Citations: 127
