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Peer Reviewed International Journals

2022:

  1. Zhu, L., Bao, Y., Lu, Q., Fan, S.; Petropoulos, G. P.; Mao, J., Li, Y., Li, X., (2022): "A Method for Retrieving Thermodynamic Atmospheric Profiles Using Microwave Radiometers of Meteorological Observation Networks," in IEEE Transactions on Geoscience and Remote Sensing, doi:10.1109/TGRS.2022.3208939 [IF: 8.125]

  2. Wu, Y., Bao, J., Liu, Z., Bao Y., Petropoulos, G.P. (2022):Investigation of the Sensitivity of Microwave Land Surface Emissivity to Soil Texture in MLEM. Remote Sensing. 14(13):3045. https://doi.org/10.3390/rs14133045 [IF: 5.349]

  3. Li M., Wu Y., Bao Y., Liu B., Petropoulos, G. P.  (2022): Near-Surface NO2 Concentration Estimation by Random Forest Modeling and Sentinel-5P and Ancillary Data. Remote Sensing. 14(15):3612. https://doi.org/10.3390/rs14153612 [IF: 5.349]

  4. Popa ,A.M., Onose ,D.A., Sandric ,I.C., Dosiadis ,E.A., Petropoulos ,G.P., Gavrilidis ,A.A., Faka ,A. (2022): Using GEOBIA and Vegetation Indices to Assess Small Urban Green Areas in Two Climatic Regions. Remote Sensing., 14(19):4888. https://doi.org/10.3390/rs14194888 [IF: 5.349]

  5. ingh, R.; Srivastava, P.K.; Petropoulos, G.P.; Shukla, S.; Prasad, R. (2022): Improvement of the “Triangle Method” for Soil Moisture Retrieval Using ECOSTRESS and Sentinel-2: Results over a Heterogeneous Agricultural Field in Northern India. Water 2022, 14, 3179. https://doi.org/10.3390/w14193179

  6. Moradizadeh, M. P. K. Srivastava & G. P. Petropoulos (2022): Synergistic evaluation of passive microwave and optical/IR data for modelling vegetation transmissivity towards improved soil moisture retrieval. Sensors MDPI, 22, 1354-66, https://doi.org/10.3390/s22041354 [IF: 3.576]

  7. Mehmood, K. S. Mushtag, Y. Bao, S. Sadia-Bibi, M. Yaseen, M. A. Khan, M. M. Abrar, Z. Ulhassan, S. Fahad & G. P. Petropoulos (2022): The impact of COVID-19 pandemic on air pollution: a global research framework, challenges and future perspectives. Environmental Science and Pollution, https://doi.org/10.1007/s11356-022-19484-5in press, [IF: 4.223]

  8. Sandric I., R. Irmia, G. P. Petropoulos, A. Anand, P.K. Srivastava, A. Pesolanu, I. Faraslis, D. Stateras & D. Kalivas (2022): Tree’s detection and heath assessment from ultra-high resolution UAV imagery and deep learning. Geocarto International, https://www.tandfonline.com/doi/full/10.1080/10106049.2022.2036824, in press, [IF: 4.889]

  9. Markogianni, V., D. Kalivas, G.P. Petropoulos & E. Dimitriou (2020): Modelling of Greek lakes water quality using Earth Observation in the framework of the water framework directive (WFD). Remote Sensing, MDPI, 14, 739-770, https://www.mdpi.com/2072-4292/14/3/739  [IF: 4.848]

​
​2021:

  1. Carlson, T.N, A. A. Person, T.J. Canish & G. P. Petropoulos (2021): A Downloable Soil vegetation Atmosphere Transfer (SVAT) model for Teaching and Research, Bulletin of the American Meteorological Society, in press, https://doi.org/10.1175/BAMS-D-20-0296.1  [IF: 8.766]

  2. Gupta, A., P. K. Srivastava, G. P. Petropoulos & P. Singh (2021): Statistical Unfolding Approach to Understand Influencing Factors for Taxol Content Variation in High Altitude Himalayan Region. Forests, MDPI, https://www.mdpi.com/1999-4907/12/12/1726 [IF: 2.634]

  3. Dorigo, W., Himmelbauer, et al…(2021): The international Soil Moisture network: serving Earth system science for over a decade. Hydrology Earth System Science, 25, 5749-5804, https://hess.copernicus.org/articles/25/5749/2021  [IF: 5.748]

  4. Howells, D.O., G. P. Petropoulos, P. K. Srivastava & D. Triantakonstantis (2021): Exploring the potential of SCAT-SAR SWI for soil moisture retrievals at selected COSMOS-UK sites. International Journal of Remote Sensing, 42 (23), 9146-9160, https://doi.org/10.1080/01431161.2021.1988185   [IF: 2.899]

  5. Srivastava, P.K. , G. P. Petropoulos,R. Prasad & D. Triantakonstantis (2021): Random Forests with Bagging and Genetic Algorithms coupled with least trimmed squares regression for soil moisture deficit using SMOS satellite soil moisture. ISPRS International Journal of Geo-Information MDPI, 10, 507-520, https://doi.org/10.3390/ijgi10080507 [IF: 2.976]

  6. Wang, X., B. Yang, Y. Bao, G. P. Petropoulos, H. Liu & B. Hu (2021): Seasonal trends in clouds and radiation over the Arctic areas from satellite observations during 1982 to 2019. Remote Sensing MDPI, 13, 3201-3219, https://doi.org/10.3390/rs13163201  [IF: 4.848]

  7. Mehmood, K., Y.Bao, R. Abbas, Saifullah, , H. G. P. Petropoulos, Raza Ahmad, M. M. Abrar, A. Mustafa, A. Abdalla, K. Lasaridi & S. Fahad (2021): Pollution characteristics and human health risk assessments of toxic metals and particle pollutants via soil and air using geoinformation in urbanised city of Pakistan. Environmental Science & Pollution, doi: https://doi.org/10.1007/s11356-021-14436-x , in press [IF: 4.223]

  8. Srivastava, P.K. R. K. Pradhan, G. P. Petropoulos, V. Pandey, M. Gupta, A. Yaduvanshi, W. Jaafar, R. K. Mall & A. K. Sahai (2021): Long-term trend analysis of precipitation and extreme events over Kosi river basin in India. Water MDPI, 13, 1695-1703 doi: https://doi.org/10.3390/w13121695 [IF: 3.103]

  9. Anand, A. R. K. M. Malhi, P.K. Srivastava, P. Singh, A. N. Mudaliar, G. P. Petropoulos, & C. S. Kiramn (2021): Optimal band characterisation in reformation of hyperspectral indices for species diversity estimation. Physics & Chemistry of the Earth, pp: 1030-40, doi: https://doi.org/10.1016/j.pce.2021.103040 [IF: 2.712]

  10. Hu, J., Y. Bao, J. Liu, H. Liu, G. P. Petropoulos, P. Katsafados, L. Zhu & X. Cai (2021): Temperature and relative humidity profile retrieval from Fengyun-3D/HIRAS in the Arctic Region. Remote Sensing MDPI, (13), 1884-2004, https://www.mdpi.com/2072-4292/13/10/1884   [IF: 4.848]

  11. Srivastava, P. K., M. Gupta, U. Singh, R. Prasad, P. C. Pandey, A.S. Raghubanshi & G. P. Petropoulos, (2021): Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data. Environment, Development and Sustainability, 23, pp5504-5519, doi: https://doi.org/10.1007/s10668-020-00827-6  [IF: 3.219]

  12. Maurya, S., P.K. Srivastava, A. Yaduvanshi, G. P. Petropoulos,, L. Zhuo & R.K. Mall (2021): Soil erosion in future scenario using CMIP5 models and Earth Observation datasets. Journal of Hydrology, [in press] [IF: 5.722]

  13. Mehmood, K., Y.Bao, G. P. Petropoulos, R. Abbas, M. M. Abrar, Saifullah, A. Mustafa, A. S. Shah Saud, M. Ahmad, I. Hussain & S. Fahadl (2021): Investigating connections between COVID-19 pandemic, air pollution and community interventions for Pakistan employing geoinformation technologies, Chemosphere, 272, doi: https://doi.org/10.1016/j.chemosphere.2021.129809 [IF: 7.086]

  14. Tsatsaris, A.,K. Kalogeropoulos, N. Stathopoulos, P. Louka, K. Tsanakas, D. E. Tsesmelis, V. Krassanakis, G. P. Petropoulos, , V. Pappas & C. Chalkias (2021): Geoinformation Technologies in Support of Environmental Hazards Monitoring under Climate Change: An Extensive Review. ISPRS International Journal of Geo-Information, 10 (94) 1-32, doi: https://doi.org/10.3390/ijgi10020094 [IF: 2.889]

  15. Mehmood, K., Y.Bao, M.Abrar, G. P. Petropoulos, Saifullah,.A. Soban, S. Saud, Z. A. Khan, S.M. Khan & S. Fahad (2021): Spatiotemporal variability of COVID-19 pandemic in relation to air pollution, climate and socioeconomic factors in Pakistan, Chemosphere, 272, doi: https://doi.org/10.1016/j.chemosphere.2021.1295840045-6535 [IF: 7.086]

  16. Malhi, R.K.M., M. K. Pandey, A. Anand, P.K. Srivastava, G.P. Petropoulos, P. Singh, G. Sandhya Kiran & B. K. Bhattarcharya (2021): Band selection algorithms for foliar trait retrieval using AVIRIS-NG: a comparison of feature based attribute evaluators, Geocarto International, DOI: 10.1080/10106049.2020.1870167 [IF: 4.889]

  17. Pandey, V.; P.K., Srivastava, K., Singh, G. P. Petropoulos, S Mall, R.K. (2021): Drought Identification and Trend Analysis Using Long-Term CHIRPS Satellite Precipitation Product in Bundelkhand, India. Sustainability, 13, 1042. https:// doi.org/10.3390/su13031042 [IF: 3.251]

  18. Al-Hajri, S., G. P. Petropoulos, M., & V. Markogianni (2020): Seasonal variation of key environmental parameters in the Sea of Oman using EO data and GIS. Environment, Development and Sustainability, doi.org/10.1007/s10668-020-00860-5 [IF: 1.930].

  19. Gupta, D. K., P. K. Srivastava, A. Singh, G. P. Petropoulos, N. Stathopoulos & R. Prasad (2021): SMAP soil moisture product assessment over Wales, UK, using observations from the WSMN ground monitoring network. Sustainability MDPI, 13, 6019-6028, doi: https://doi.org/10.3390/su13116019 [IF: 3.251]

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2020:

  1. Al-Hajri, S. M., & V. Markogianni (2020): Seasonal variation of key environmental parameters in the Sea of Oman using EO data and GIS. Environment, Development and Sustainability, doi.org/10.1007/s10668-020-00860-5 [IF: 1.930].

  2. Anand, A., P.C. Pandey, , A. Pavlides, P.K. Srivastava, J. K. Sharma & R. K. M. Malhi (2020): Use of Hyperion for Mangrove Forest Carbon Stock Assessment in Bhitarkanika Forest Reserve: A Contribution Towards Blue Carbon Initiative. Remote Sensing MDPI, 12, 597; doi:10.3390/rs12040597 [IF: 4.509].

  3. Cai. X., Y. Bao, , F. Lu, Q. Lu, L. Zhu & Y. Wu (2020): Temperature and Humidity Profile Retrieval from FY4-GIIRS Hyperspectral Data Using Artificial Neural Networks. Remote Sensing MDPI, 12, 1872-1896, doi:10.3390/rs12111872 [IF: 4.509].

  4. Fragou, S., K. Kalogeropoulos, N. Stathopoulos, P. Louka, P.K. Srivastava, S. Karpouzas, D.P. Kalivas & (2020): Quantifying Land Cover Changes in a Mediterranean Environment Using Landsat TM and Support Vector Machines. Forests MDPI, 11, 750-769, doi:10.3390/f11070750 [IF: 2.221].

  5. Kalogeropoulos, K., N. Stathopoulos, A. Psaroginanis, E. Pissias, P. Louka, & C. Chalkias (2020): An Integrated GIS-Hydro Modeling Methodology for Surface Runoff Exploitation via Small-Scale Reservoirs. Water MDPI, 12, 3182-3200, doi:10.3390/w12113182. [IF: 2.544].

  6. Louka, P., I. Papanikolaou, , K. Kalogeropoulos & N. Stathopoulos (2020): Identifying Spatially Correlated Patterns between Surface Water and Frost Risk Using EO Data and Geospatial Indices. Water MDPI, 12, 700; doi:10.3390/w12030700 [IF: 2.544].

  7. Malhi, R.K. M., A. Anand, P.K. Srivastava, G. Sandhya Kiran, & C. Chalkias (2020): An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas. ISPRS International Journal of Geoinformation MDPI, 9, 530-550, doi:10.3390/ijgi9090530. [IF: 2.239].

  8. Markogianni, V., D. Kalivas, & E. Dimitriou (2020): Estimating Chlorophyll-a of Inland Water Bodies in Greece Based on Landsat Data. Remote Sensing MDPI, 12, 2087-2109, doi:10.3390/rs12132087. [IF: 4.509].

  9. Petropoulos, G.P. & D. Hristopulos (2020): Retrievals of key biophysical parameters at mesoscale from the Ts/VI scatterplot domain. Geocarto International, https://doi.org/10.1080/10106049.2020.1821099 [IF: 3.789].

  10. Petropoulos, G.P., Maltese, A., Carlson, T.N., Provenzano, G., Pavlides, A., Ciraolo, G., Hristopulos, D., Capodici, F., Chlakias, C., Dardanelli, G. & S. Manfreda (2020): Exploring the use of UAVs with the simplified “triangle” technique for Soil Water Content and Evaporative Fraction retrievals in a Mediterranean setting. International Journal of Remote Sensing, 42 (5), doi.org/10.1080/01431161.2020.1841319 [IF: 2.976].

  11. Petropoulos, G.P., Sandric, I., Hristopulos, D. and T.N., Carlson, (2020): Evaporative fluxes and Surface Soil Moisture Retrievals in a Mediterranean setting from Sentinel-3 and the "simplified triangle".  Remote Sensing MDPI, 12(19), 3192; https://doi.org/10.3390/rs12193192 [IF: 4.509].

  12. Srivastava, P.K., M. Gupta, U. Singh, R. Prasad, P. C. Pandey, A.S. Raghubanchi & G (2020): Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data. Environment, Development and Sustainability, doi.org/10.1007/s10668-020-00827-6 [IF: 1.930].

  13. Suman, S. P.K. Srivastava, , D. K. Pandey & P. O’Neil (2020): Appraisal of SMAP Operational Soil Moisture Product from a Global Perspective. Remote Sensing MDPI, 12, 1977-2101, doi:10.3390/rs12121977 [IF: 4.118].

  14. Wu, Y., M. Li, Y. Bao & (2020): Cross-Validation of Radio-Frequency-Interference Signature in Satellite Microwave Radiometer Observations over the Ocean. Remote Sensing MDPI, 12, 3433-3463, doi:10.3390/rs12203433. [IF: 4.509].

  15. Zhu, L. Y. Bao, , P. Zhang, F. Lu, Q. Lu, Y. Wu & D. Xu (2020): Temperature and Humidity Profiles Retrieval in a Plain Area from Fengyun-3D/HIRAS Sensor Using a 1D-VAR Assimilation Scheme. Remote Sensing MDPI, 12, 435; doi:10.3390/rs12030435 [IF: 4.509].

 
2019:

  1. Deng, K.A.K., S. Lamine, A. Pavlides, G.P. Petropoulos, P.K. Srivastava, Y. Bao, D. Hristopulos & V. Anagnostopoulos (2019): Operational Soil Moisture from ASCAT in Support of 2 Water Resources Management. Remote Sensing MDPI, [in press], [IF: 3.406]

  2. Bao, Y. L. Zhu, Q. Guan, Y. Guan, Q. Lu, G.P. Petropoulos, H. Che, G. Ali, Y. Dong, Z. Tang, Y. Gu, W. Tang & Y. Hou (2019): Assessing the impact of Chinese FY-3/MERSI AOD Data Assimilation on Air Quality Forecasts: Sand Dust Events in Northeast China, Atmospheric Environment, S1352-2310(19)30118-9, DOI: 10.1016/j.atmosenv.2019.02.026  [in press], [IF: 3.708]

  3. Lamine, S., G.P. Petropoulos, P.A. Brewer, N. I, Bachari, P.K> Srivastava, K. Manevski, C. Kalaitzidis & M. G. Macklin (2019): Heavy Metal soil Contamination Detection Using Combined Geochemistry and Field Spectroradiometry in the United Kingdom. Sensors MDPI, 19, 762, doi:10.3390/s19040762 [IF: 2.475]

  4. Wu, Y., B. Qian, Y. Bao, M. Li, G.P. Petropoulos, X. Liu & L. Li (2019): Microwave land emissivity over the Qinghai-Tibetan plateau using FY-3B MWRI measurements. Remote Sensing MDPI, 11, 2206, 1-16, doi:10.3390/rs11192206 [IF: 4.118].

  5. Shao, M. Y. Bao, G.P. Petropoulos & H. Zhang (2019): A two-season impact study of radaitive forced tropospheric response to stratospheric initial conditions inferred from satellite radiance assimilation. Climate MDPI, 7, 114, 1-11, doi:10.3390/cli7090114 [IF: 1.950] .

  6. Pandey, P. C., N. Koutsias, G.P. Petropoulos, P.K. Srivastava & E.B. Dor (2019): Land Use/Land Cover in view of Earth Observation: Data Sources, Input Dimensions and Classifiers -a Review of the State of the Art". Geocarto International, [in press], [IF: 2.365].

  7. Wu, Y., B. Qian, Y. Bao, M. Li, G.P. Petropoulos, X. Liu & L. Li (2019): Detection and analysis of C-band radio frequency Interference in AMSR2 data over land. Remote Sensing MDPI, 11, 1228, 1-19, doi:10.3390/rs11101228 [IF: 4.118].

  8. Bridges, J. G.P. Petropoulos & N. Clerici (2019): Immediate Change in Organic Matter and Plant available nutrients of Haplic Luvisol soils following different experimental burning intensities in Damak Forest, Hungary (2019). Forests MDPI, 10(5), 453
    DOI: 10.3390/f10050453 [IF: 2.116].

  9. Deng, K.A.K., S. Lamine, A. Pavlides, G.P. Petropoulos, Y. Bao, P.K. Srivastava, & Y. Guan (2019): Large Scale Operational Soil Moisture Mapping from Passive MW Radiometry: SMOS product evaluation in Europe & USA. International Journal of Applied Earth Observation & Geoinformation, 80, 206-217, DOI: 10.1016/j.jag.2019.04.015
    [IF: 4.846].

  10. Dawson, R., G.P. Petropoulos, L. Toulios & P.K. Srivastava (2019): Mapping and Monitoring of the Land Use/Cover Changes in the Wider Area of ltanos, Crete, Using Very High Resolution EO Imagery With Specific Interest in Archaeological Sites. Environment, Development and Sustainability, [in press], DOI: 10.1007/s10668-019-00353-0 [IF: 1.676].

  11. Srivastava, P.K., P. C. Pandey, G.P. Petropoulos, N. K. Kourgialas, S. Pandley & U. Singh (2019): GIS and remote sensing aided information for soil moisture estimation: A comparative study of interpolation technique. Resources MDPI, 8(2), 70.
    DOI: 10.3390/resources8020070 [IF: 2.600].

  12. Cass, A., G.P. Petropoulos, K.P. Ferentinos, A. Pavlides & P.K. Srivastava (2019): Exploring the synergy between Landsat and ASAR towards improving thematic mapping accuracy of optical EO data. Applied Geomatics, 1-12, DOI: 10.1007/s12518-019-00258-7, [IF:1.200].

  13. Carlson, T.N. & G.P. Petropoulos (2019): A New Method for Estimating of Evapotranspiration and Surface Soil Moisture from Optical and Thermal Infrared Measurements: The Simplified Triangle. International Journal of Remote Sensing, 40(20), 7716-7729, DOI: 10.1080/01431161.2019.1601288 [IF: 2.493].

  14. Deng, K.A.K., S. Lamine, A. Pavlides, G.P. Petropoulos, P.K. Srivastava, Y. Bao, D. Hristopulos & V. Anagnostopoulos (2019): Operational Soil Moisture from ASCAT in Support of 2 Water Resources Management. Remote Sensing MDPI, 11(5), 579, DOI: 10.3390/rs11050579 [IF: 4.118].

  15. Bao, Y. L. Zhu, Q. Guan, Y. Guan, Q. Lu, G.P. Petropoulos, H. Che, G. Ali, Y. Dong, Z. Tang, Y. Gu, W. Tang & Y. Hou (2019): Assessing the impact of Chinese FY-3/MERSI AOD Data Assimilation on Air Quality Forecasts: Sand Dust Events in Northeast China, Atmospheric Environment, S1352-2310(19)30118-9, DOI: 10.1016/j.atmosenv.2019.02.026, [IF: 4.012].

  16. Lamine, S., G.P. Petropoulos, P.A. Brewer, N. I, Bachari, P.K. Srivastava, K. Manevski, C. Kalaitzidis & M. G. Macklin (2019): Heavy Metal soil Contamination Detection Using Combined Geochemistry and Field Spectroradiometry in the United Kingdom. Sensors MDPI, 19, 762, doi:10.3390/s19040762 [IF: 3.031].

  17. Srivastava, P.K., G.P. Petropoulos, M. Gupta, S.K. Singh, T. Islam & D. Loka (2019): Deriving Forest Fire Probability Maps From the Fusion of Visible/Infrared Satellite Data and Geospatial Data Mining. Modeling Earth Systems and Environment, [in press]. DOI 10.1007/s40808-018-0555-5 [IF: 0.830].

​
2018:

  1. Brown, R.A, G. P. Petropoulos & K. Ferentinos (2018): Appraisal of the Sentinel-1 & 2 use in a large-scale wildfire assessment: A case study from Portugal’s fires of 2017. Applied Geography, 100, 78-89 [IF: 3.117]

  2. Amos, C, G.P. Petropoulos & K.  P. Feredinos (2018):Determining the use of sentinel-2A MSI for wildfire burning and severity detection. International Journal of Remote Sensing, in press [IF: 1.782]

  3. Srivastava, P.K., G.P. Petropoulos, M. Gupta, S.K. Singh, T. Islam & D. Loka (2019): Deriving Forest Fire Probability Maps From the Fusion of Visible/Infrared Satellite Data and Geospatial Data Mining. Modeling Earth Systems and Environment, [in press].

  4. Petropoulos, G.P., P.K. Srivastava, K.P. Feredinos & D. Hristopoulos (2018): Evaluating the capabilities of optical/TIR imagine sensing systems for quantifying soil water content. Geocarto Internationnal, in press [1.759]

  5. Banerjee, R., P.K. Srivastava, A.W.G. Pike & G. P. Petropoulos (2018): Identification of painted rock-shelter sites using GIS integrated with a Decision Support system and Fuzzy Logic. International Journal of Geo-Information, 7, 326-386, doi:10.3390/ijgi7080326 [IF: 1.723].

  6. Evans, A., S. Lamine, D. Kalivas & G.P. Petropoulos (2018):Exploring the Potential of EO data and GIS for Ecosystem Health Modelling in Response to Wildfire: a Case Study in Central Greece. Environmental Engineering & Management. [in press], [IF: 1.096]

  7. Markogianni, V., D. Kalivas, G. P. Petropoulos & E. Dimitriou (2018): An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators. Remote Sensing MDPI,10, 1-22, doi:10.3390/rs10071018 [IF: 3.406]

  8. Colson, D., G.P. Petropoulos & K. Ferentinos (2018):Exploring the Potential of Sentinels-1 & 2 of the Copernicus Mission in Support of Rapid and Cost-effective Wildfire Assessment. International Journal of Applied Earth Observation & Geoinformation, 73, 262-276, doi.org/10.1016/j.jag.2018.06.011 [IF: 3.930]

  9. Bao, Y., L. Lin, S. Wu, K.A.K. Deng & G.P. Petropoulos(2018): Surface Soil Moisture Retrievals Over Partially Vegetated Areas From the Synergy of Sentinel-1 & Landsat 8 Data Using a Modified Water-Cloud Model. International Journal of Applied earth Observation & Geoinformation, 72, 76-85, /doi.org/10.1016/j.jag.2018.05.026 [IF: 4.003]

  10. Whyte, A., K. Fredinos & G.P. Petropoulos (2018): A New Synergistic Approach for Monitoring Wetlands Using Sentinels -1 and 2 data With Object-based Machine Learning Algorithms. Environmental Modelling & Software, 104, 40-57, doi.org/10.1016/j.envsoft.2018.01.023 [IF:4.177]. 

  11. Petropoulos, G.P., P.K. Srivastava, M. Piles & S. Pearson (2018): EO-based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management. Sustainability MDPI, 10, 181-1-20, doi:10.3390/su10010181 [IF: 2.075] 

  12. Lamine, S. G.P. Petropoulos, S.K. Singh, S. Szabo, N Bachari, P.K. Srivastava & S. Suman (2018): Quantifying Land Use/land Cover Spatio-temporal Landscape Pattern Dynamics from Hyperion Using SVMs Classifier and FRAGSTATS. Geocarto International, 33:8, 862-878, doi.org/10.1080/10106049.2017.1307460 [IF: 1.370]

  13.  Srivastava, P.K., G.P. Petropoulos, M. Gupta, S.K. Singh, T. Islam & D. Loka (2018):Deriving Forest Fire Probability Maps From the Fusion of Visible/Infrared Satellite Data and Geospatial Data Mining. Modeling Earth Systems and Environment. DOI 10.1007/s40808-018-0555-5.

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2017:

  1. Chatziantoniou, A. G.P. Petropoulos & E. Psomiadis (2017): Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning. Remote Sensing, 9, pp: 1-18, doi.org/10.1080/10106049.2017.1307460 [IF: 3.244] 

  2. Anagnostopoulos, V. & Petropoulos, G.P. (2017): A Modernized Version of a 1D Soil Vegetation Atmosphere Transfer model for Use in Land Surface Interactions Studies. Environmental Modelling & Software, 90 pp. 147-156. doi.org/10.1016/j.envsoft.2017.01.004 [ IF: 4.207]

  3. Srivastava, P.K., T. Islam, G.P. Petropoulos & M. Gupta (2017): WRF-RDM: Prognostic Approach for Discharge Prediction in Ungauged Catchment. European Water, 75, pp: 129-132, [IF: 1.325] 

  4. Petropoulos, G.P. & Jon P. McCalmont (2017): An Operational In-situ Soil Moisture and Soil Temperature Monitoring Network for West Wales, UK: The WSMN network. Sensors,17, pp:1481-1491, doi:10.3390/s17071481  [IF: 2.475]  

  5. Srivastava, P.K., D. Han, A. Yaduvanshi, G. P. Petropoulos, S. K. Singh, R. K. Mall & R. Prasad (2017): Reference Evapotranspiration Retrievals From a Mesoscale Model Based Weather Variables for Soil Moisture Deficit Estimation. Sustainability, 9, 1971-88, doi:10.3390/su9111971 [IF: 1.789]

​

2016:

  1. Srivastava, P.K., T. Islam, S. K. Singh, G.P. Petropoulos, M. Gupta & Q. Dai (2016): Arabian Sea Level Rise Forecasting Using Exponential Smoothing State Space Models and ARIMA using TOPEX and Jason Satellite Radar Altimeter Data. Meteorological Applications, [IF: 1.852]

  2. Islam, T., P.K. Srivastava, G.P. Petropoulos (2016): Uncertainty Quantification in the Infrared Surface Emissivity Model (ISEM). Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 9 (12), 5888-5892, doi: 10.1109/JSTARS.2016.2557303 [IF: 3.026]

  3. Islam, T., P.K. Srivastava, D. Kumar, G.P. Petropoulos, Q. Dai & L. Zhuo (2016): Satellite Radiance Assimilation Using a 3DVAR Assimilation System for Hurricane Sandy Forecasts. Natural Hazards, doi: 10.1007/s11069-016-2221-4. [IF: 1.719]

  4. Karamesouti, M., G.P. Petropoulos, I.D. Papanikolaou, O. Kairis & K. Kosmas (2016): An Evaluation of the PESERA and RUSLE in Predicting Erosion Rates at a Mediterranean Site Before and After a Wildfire: Comparison & implications. Geoderma, 261, pp:44-58, doi:org/10.1016/j.geoderma.2015.06.02.  [IF: 2.509]

  5. Petropoulos, G.P., G. Ireland, S. Lamine, N. Ghilain, V. Anagnostopoulos, M.R. North, P.K. Srivastava & H. Georgopoulou (2016): Evapotranspiration Estimates from SEVIRI to Support Sustainable Water Management. Journal of Applied Earth Observation & Geoinformation, 49, 175-187, doi.org/10.1016/j.jag.2016.02.006 [IF: 3.470]

  6. Islam, T., Srivastava, P. K., Petropoulos, G.P., Singh, S.K., (2016): Reduced Major Axis Approach for Correcting GPM/GMI Radiometric Biases to Coincide With Radiative Transfer Simulation. Journal of Quantitative Spectroscopy and Radiative Transfer, 168, pp:40-45, doi: doi.org/10.1016/j.jqsrt.2015.08.016. [IF: 2.645]

  7. Petropoulos, G.P. & V. Anagnostopoulos (2016): SEVIRI PrePro: A Novel Software Tool for the Pre-processing of SEVIRI Geostationary Orbit EO Data Products. Environmental Modelling & Software, doi.org/10.1016/j.envsoft.2016.03.015 [in press], [IF: 4.420]

  8. Srivastava, P.K., Han, D., Islam, T., Petropoulos,G.P., Gupta, M. & Q. Dai (2016): Seasonal evaluation of Evapotranspiration fluxes from MODIS Satellite and Mesoscale Model Downscaled Global Reanalysis Datasets. Theoretical and Applied Climatology, pages 1-14, DOI 10.1007/s00704-015-1430-1 [IF: 3.709]

  9. Piles, M., G.P. Petropoulos, N. Sanchez, A. González-Zamora & G. Ireland  (2016): Towards Improved Spatio-Temporal Resolution Soil Moisture Retrievals From the Synergy of SMOS & MSG SEVIRI Spaceborne Observations. Remote Sensing of Environment, 180, pp:403-471, doi.org/10.1016/j.rse.2016.02.048 [IF: 6.397]

  10. Singh, S.K., P.K. Srivastava, S. Szabo, G.P. Petropoulos, M. Gupta & T. Islam (2016): Landscape Transform and Spatial Metrics for Mapping Spatiotemporal Land Cover Dynamics Using Earth Observation Datasets. Geocarto International, 2016, pp1-16, doi:10.1080/10106049.2015.1130084 [IF: 1.370]

​

2015:

  1. Ireland, G., G.P. Petropoulos, T.N. Carlson & S. Purdy (2015): Addressing the Ability of a Land Biosphere Model to Predict Key Biophysical Vegetation Characterisation Parameters With Global Sensitivity Analysis.  Environmental Modelling & Software, 65, 94-107, doi.org/10.1016/j.envsoft.2014.11.010 [IF: 4.420]

  2. North, M. R., Petropoulos, G.P., Rentall, D.V., Ireland, G.I. & J.P. McCalmont (2015): Appraising the capability of a land biosphere model as a tool in modelling land surface interactions: results from its validation at selected European ecosystems. Earth Surface Dynamics Discussions, 6, pp:217-265, DOI: doi:10.5194/esdd-6-217-2015 [IF: 2.771] 

  3. Petropoulos, G.P., G. Ireland & P.K. Srivastava (2015):Evaluation of the Soil Moisture Operational Estimates from SMOS in Europe: Results Over Diverse Ecosystems. IEEE Sensors, DOI:: 10.1109/JSEN.2015.2427657  [IF: 1.852]

  4. Ireland, G., Volpi, M. & G.P., Petropoulos (2015): Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood. Remote Sensing 7, 3372-3399; doi:10.3390/rs70303372 [IF: 3.180]

  5. Ireland, G. & G.P. Petropoulos (2015): Exploring the Relationships Between Post-fire Vegetation Regeneration Dynamics, Topography and Burn Severity: a case study from the Montane Cordillera Ecozones of Western Canada. Applied Geography, 56, 232-248, doi.org/10.1016/j.apgeog.2014.11.016 [IF: 2.494]

  6. 32. Petropoulos, G.P., G. Ireland H. Griffiths, M.C. Kennedy, P. Ioannou-Katidis, & D.K. P. Kalivas (2015): Extending the Global Sensitivity Analysis of the SimSphere model in the Context of its Future Exploitation by the Scientific Community. Water MDPI, 7, 2101-2141. DOI: 10.3390/w7052101 [IF: 1.428]

  7. Petropoulos, G.P., G. Ireland, A. Cass & P.K. Srivastava (2015): Performance Assessment of the SEVIRI Evapotranspiration Operational Product: Results Over Diverse Mediterranean Ecosystems.  IEEE Sensors, [in press], DOI 10.1109/JSEN.2015.2390031  [IF: 1.852] 

  8. Petropoulos, G.P., D.P. Kalivas, H.M. Griffiths & P. Dimou (2015): Remote Sensing and GIS analysis for Mapping Spatio-temporal Changes of Erosion and Deposition of two Mediterranean River Deltas: The Case of the Axios and Aliakmonas Rivers, Greece. International Journal of Applied Earth Observation & Geoinformation, 35, 217-228, doi: 10.1016/j.jag.2014.08.004 [IF: 3.470]

  9. Petropoulos, G.P., Ireland, G. & B. Barrett (2015): Surface Soil Moisture Retrievals from Remote Sensing: Current Status, Products & Future Trends. Physics and Chemistry of the Earth. DOI: 10.1016/j.pce.2015.02.009. [IF: 1.477]

  10. Islam, T., P.K. Srivastava, G.P. Petropoulos & S. Singh (2015): Variational Bayes and the Principal Component Analysis Coupled With Bayesian Regulation Back Propagation Network to Retrieve Total Precipitable Water (TPW) From GCOM-W1/AMSR2. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (10), pp: 4819-4824, doi: 10.1109/JSTARS.2015.2447532. [IF: 3.026]

  11. Srivastava, P. K., G.P. Petropoulos, M. Gupta, S. K. Singhe & S. Mukherjeef (2015): WRF Dynamical Downscaling and Bias Correction Schemes for NCEP Estimated Hydro-meteorological Variables. Water Resources Management. DOI: 10.1007/s11269-015-0940-z [IF: 2.600]

  12. Said, Y.A, G.P. Petropoulos & P.K. Srivastava (2015):Assessing the Influence of Atmospheric and Topographic Correction on Burnt Scars Identification from High Resolution. Natural Hazards, DOI 10.1007/s11069-015-1792-9 [IF: 1.719] 

  13. Petropoulos, G.P., North, M. R., Ireland, G., Srivastava, P.K., & D.V., Rendall (2015): Quantifying the Prediction Accuracy of a 1-D SVAT Model at a Range of Ecosystems in the USA and Australia: Evidence Towards its Use as a Tool to Study Earth’s System Interactions. Geoscientific Models Development, DOI: 10.5194/gmdd-8-2437-2015. [IF: 6.081]

  14. Petropoulos, G.P., D.P. Kalivas, H. Georgopoulou, & P.K. Srivastava (2015): Urban Vegetation Cover Extraction from Hyperspectral Remote Sensing Imagery & GIS Spatial Analysis Techniques: the Case of Athens, Greece. Journal of Applied Remote Sensing, 9, 1-18, 0091-3286/2015. [IF: 1.183]

​

2014:

  1. Petropoulos, G.P., G. Ireland, P.K. Srivastava & P. Ioannou-Katidis (2014): An Appraisal of Soil Moisture Operational Estimates Accuracy From SMOS MIRAS Using Validated In-situ Observations Acquired at a Mediterranean Environment. International Journal of Remote Sensing, 35 (13), 5239-5250, doi: 10.1080/2150704X.2014.933277. [IF: 1.652]

  2. Petropoulos, G.P., H.M. Griffiths & D. Kalivas (2014):Quantifying Spatial and Temporal Vegetation Recovery Dynamics Following a Wildfire Event in a Mediterranean Landscape Using EO Data and GIS. Applied Geography, 50, 120-131, doi: 10.1016/j.apgeog.2014.02.006.  [IF: 2.565]

  3. Scott, D., Petropoulos, G.P., Moxley, J & H. Malcolm (2014): Quantifying the Physical Composition of Urban Morphology Throughout Wales Based on the Time Series (1989-2011) Analysis of Landsat TM/ETM+ Images and Supporting GIS data. Remote Sensing, 6, 11731-11752; doi:10.3390/rs61211731 [IF: 3.180] 

  4. Petropoulos, G.P., H.M. Griffiths, T.N. Carlson, P. Ioannou-Katidis & T. Holt (2014): SimSphere Model Sensitivity Analysis Towards Establishing its Use for Deriving Key Parameters Characterising Land Surface Interactions. Geoscientific Model Development, 7, 1873-1887, doi: 10.5194/gmd-7-1873-2014.  [IF: 6.086] 

​

2013:

  1. Henke, J. & G.P. Petropoulos (2013): A GIS-based Exploration of the Relationships Between Human Health, Social Deprivation and Ecosystem Services for Wales, UK. Applied Geography, 45, 77-88, doi: http://dx.doi.org/10.1016/j.envsoft.2013.07.010. [IF: 2.494]

  2. Petropoulos, G.P., H.M Griffiths & S. Tarantola (2013): Sensitivity Analysis of the SimSphere SVAT Model in the Context of EO-based Operational Products Development. Environmental Modelling and Software, 49, 166-179, doi: /10.1016/j.envsoft.2013.07.010. [IF: 4.420]

  3. Kalivas, D., Petropoulos, G.P., Athanasiou, I. & V. Kollias (2013): An Intercomparison of Burnt Area Estimates Derived From Key Operational Products: Analysis of Greek Wildland Fires 2005-2007. Non-linear Processes in Geophysics, 20, 1-13, doi: 10.5194/npg-20-1-2013. [IF: 1.692]

  4. Petropoulos, G.P., Partsinevelos, P. & Z. Mitraka (2013):Change Detection of Surface Mining Activity and Reclamation Based on a Machine Learning Approach of Multi-temporal Landsat TM Imagery. Geocarto International, 1-20, doi: DOI:10.1080/10106049.2012.706648. [IF: 0.897]

  5. Volpi, M., G.P. Petropoulos & M. Kanevski (2013): Flooding Extent Cartography With Landsat TM Imagery and Regularized Kernel Fisher's Discriminant Analysis. Computers and Geosciences, 57, 24-31, doi: 10.1016/j.cageo.2013.03.009. [IF: 2.054]

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2012:

  1. Elatawneh, A., C. Kalaitzidis, Petropoulos, G.P. & T. Schneider (2012): Evaluation of Diverse Classification Approaches for Land Use/Cover Mapping in a Mediterranean Region Utilizing Hyperion Data. International Journal of Digital Earth, 1-23, doi: 10.1080/17538947.2012.671378. [IF: 3.291]

  2. Petropoulos, G.P., K. Arvanitis & N. Sigrimis (2012): Hyperion Hyperspectral Imagery Analysis Combined With Machine Learning Classifiers for Land Use/Cover Mapping. Expert Systems with Applications, 39, 3800-3809, doi:10.1016/j.eswa.2011.09.083. [IF: 2.240]

  3. Petropoulos, G.P., C. C. Kontoes  &. I. Keramitsoglou (2012):Land Cover Mapping With Emphasis to Burnt Area Delineation Using Co-orbital ALI and Landsat TM Imagery. International Journal of Applied Earth Observation and Geoinformation, 18, 344-355, doi: 10.1016/j.jag.2012.02.004. [IF: 3.470]

  4. Petropoulos, G.P., K. P. Vadrevu & C. Kalaitzidis (2012): Spectral Angle Mapper and Object-based classification Combined With Hyperspectral Remote Sensing Imagery for Obtaining Land use/cover Mapping in a Mediterranean region. Geocarto International, 28 (2), pp 1-16, doi: 10.1080/10106049.2012.668950. [IF: 0.897]

  5. Manevski, K., Manakos, I., Petropoulos, G.P. & C. Kalaitzidis (2012): Spectral Discrimination of Mediterranean Maquis and Phrygana Vegetation: Results From a Case Study in Greece. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,   5 (2), 604-616, doi: 10.1109/JSTARS.2012.2190044.  [IF: 3.026]

  6. Petropoulos, G.P., C. Kalaitzidis & K. P. Vadrevu (2012):Support Vector Machines and Object-Based Classification for Obtaining Land Use/Cover Cartography from Hyperion Hyperspectral Imagery. Computers and Geosciences, 41, 99-107, doi:10.1016/j.cageo.2011.08.019. [IF: 2.054]

​

2011:

  1. Pediaditi, K., M. Stanojevic, C. Kouskouna, M. Karydas, D. Zianis,  G. P. Petropoulos, & N. Boretos (2011): A Decision Support System for Assessing and Managing Environment Risk Cross Borders. Earth Science Informatics, 4, 107-115, doi:10.1007/s12145-011-0081-8. [IF: 0.743]

  2. Pediaditi, K., F., Buono, Petropoulos, G.P., C., Bogliotti,  E., Nurlu, G., Ladisa,  & F., Pompigna (2011). A Decision Support System-based Procedure for the Evaluation and Monitoring of Protected Areas Sustainability for the Mediterranean Region. Earth Systems Science, 120 (5), 949-961. [IF: 1.040]

  3. Petropoulos, G.P, C. Kontoes & I. Keramitsoglou (2011): Burnt Area Delineation From a Uni-temporal Perspective Based on Landsat TM Imagery Classification Using Support Vector Machines.  International Journal of Applied Earth Observation and Geoinformation, vol 13, pp. 70-80, doi:10.1016/j.jag.2010.06.008. [IF: 3.470]

  4. Knorr, W., I. Pytharoulis, G.P. Petropoulos & N. Gobron (2011): Combined Use of Weather Forecasting and Satellite Remote Sensing Information for Fire Risk, fire and fire impact monitoring. Computational Ecology and Software, 1 (2), 112-120. 

  5. Manevski, K., I. Manakos, Petropoulos, G.P. &C. Kalaitzidis (2011): Discrimination of Common Mediterranean Plant Species Using Field Spectroradiometry. International Journal of Applied Earth Observation and Geoinformation, 13, 922-933, doi:10.1016/j.jag.2011.07.001. [IF: 3.470]

​

2010:

  1. Ganas, A., Lagios, E., Petropoulos, G. & B., Psiloglou (2010):Thermal Imagine of Nissyros Volcano (Aegean Sea) using ASTER data: Estimation of Heat Flux. International Journal of Remote Sensing, 31 (15), 4033-4047, doi: 10.1080/01431160903140837. [IF: 1.652]

  2. Petropoulos, G., K.P. Vadrevu, G. Xanthopoulos, G., Karantounias & M. Scholze (2010): A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined With Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping. Sensors, 10, 1967-1985, doi:10.3390/s100301967. [IF: 2.245]

  3. Petropoulos, G., W. Knorr, M. Scholze, L. Boschetti & G., Karantounias (2010): Combining ASTER Multispectral Imagery Analysis and Support Vector Machines for Rapid and Cost-Effective Post-Fire Assessment: A Case Study From the Greek Fires of 2007. Natural Hazards and Earth Systems Science, 10, 305-317, doi:10.5194/nhess-10-305-2010. [IF: 1.735]

​

2009:

  1. Petropoulos, G., Wooster, M. J., M. Kennedy, Carlson, T.N. & M. Scholze (2009c): A Global Sensitivity Analysis Study of the 1d SimSphere SVAT Model Using the GEM SA Software. Ecological Modelling, 220 (19), 2427-2440, doi:10.1016/j.ecolmodel.2009.06.006. [IF: 2.321]

  2. Petropoulos, G., Carlson, T.N, Wooster, M. J. & S., Islam (2009): A Review of Ts/VI Remote Sensing Based Methods for the Retrieval of Land Surface Fluxes and Soil Surface Moisture Content. Progress in Physical Geography, 33 (2), 1-27. [IF: 2.612]

  3. Petropoulos, G., Carlson, T.N. & M. J., Wooster (2009): An Overview of the Use of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) Model for the Study of Land-Atmosphere Interactions. Sensors, 2009, 9(6), 4286-4308, doi:10.3390/s90604286. [IF: 2.245]

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