George P. Petropoulos
@hua.gr
Department of Geogrpahy
Harokopio University of Athens
RESEARCH, TEACHING, or OTHER INTERESTS
Space and Planetary Science, Earth-Surface Processes, Computers in Earth Sciences
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
George P. Petropoulos
Elsevier BV
Neelam Dahiya, Gurwinder Singh, Dileep Kumar Gupta, Kleomenis Kalogeropoulos, Spyridon E. Detsikas, George P. Petropoulos, Sartajvir Singh, and Vishakha Sood
Elsevier BV
Cinzia Albertini, Andrea Gioia, Vito Iacobellis, George P. Petropoulos, and Salvatore Manfreda
Elsevier BV
Bingli Wang, Wei Cheng, Yansong Bao, Shudong Wang, George P. Petropoulos, Shuiyong Fan, Jiajia Mao, Ziqi Jin, and Zihui Yang
MDPI AG
This study investigates the impacts of the joint assimilation of ground-based microwave radiometer (MWR) and FY-3D microwave sounder (MWTS-2/MWHS-2) observations on the analyses and forecasts for precipitation forecast. Based on the weather research and forecasting data assimilation (WRFDA) system, four experiments are conducted in this study, concerning a heavy precipitation event in Beijing on 2 July 2021, and 10-day batch experiments were also conducted. The key study findings include the following: (1) Both ground-based microwave radiometer and MWTS-2/MWHS-2 data contribute to improvements in the initial fields of the model, leading to appropriate adjustments in the thermal structure of the model. (2) The forecast fields of the experiments assimilating ground-based microwave radiometer and MWTS-2/MWHS-2 data show temperature and humidity performances closer to the true fields compared with the control experiment. (3) Separate assimilation of two types of microwave radiometer data can improve precipitation forecasts, while joint assimilation provides the most accurate forecasts among all the experiments. In the single-case, compared with the control experiment, the individual and combined assimilation of MWR and MWTS-2/MWHS-2 improves the six-hour cumulative precipitation threat score (TS) at the 25 mm level by 57.1%, 28.9%, and 38.2%, respectively. The combined assimilation also improves the scores at the 50 mm level by 54.4%, whereas individual assimilations show a decrease in performance. In the batch experiments, the MWR_FY experiment’s TS of 24 h precipitation forecast improves 28.5% at 10 mm and 330% at 25 mm based on the CTRL.
George P. Petropoulos, Spyridon E. Detsikas, Kleomenis Kalogeropoulos, and Andrew Pavlides
MDPI AG
Knowledge on the spatiotemporal patterns of surface energy balance parameters is crucial for understanding climate system processes. To this end, the assimilation of Earth Observation data with land biosphere models has shown promising results, but they are still hampered by several limitations related to the spatiotemporal resolution of EO sensors and cloud contamination. With the recent developments on Unmanned Aerial Vehicles (UAVs), there is a great opportunity to overcome these challenges and gain knowledge of surface energy balance parameters at unprecedented resolutions. The present study examines, for the first time, the ability of an inversion-modeling scheme, the so-called “analytical triangle” method, to retrieve estimates of surface energy fluxes and soil surface moisture (SSM) at high spatial resolution using UAV data. A further aim of our study was to examine the representativeness of the SSM estimates for the SM measurements taken at different depths. The selected experimental site is an agricultural site of citrus trees located near the city of Palermo on 30 July 2019. The results of comparisons showed that the sensible and latent heat fluxes from UAV were consistent with those measured from the ground, with absolute differences in comparison to ground measurements being 5.00 Wm−2 for the latent heat (LE) flux and 65.02 Wm−2 for H flux, whereas for the daytime fluxes H/Rn and LE/Rn were 0.161 and 0.012, respectively. When comparing analytical triangle SSM estimates with SM measurements made at different depths, it was found that there was a gradual increase in underestimation with increasing measurement depth. All in all, this study’s results provide a credible demonstration of the significant potential of the technique investigated herein as a cost-effective and rapid solution for estimating key parameters characterizing land surface processes. As those parameters are required by a wide range of disciplines and applications, utilization of the investigated technique in research and practical applications is expected to be seen in the future.
George P. Petropoulos, Vasileios Anagnostopoulos, Christina Lekka, and Spyridon E. Detsikas
Elsevier BV
George P. Petropoulos, Triantafyllia Petsini, and Spyridon E. Detsikas
MDPI AG
Climate change is increasingly affecting components of the terrestrial cryosphere with its adverse impacts in the Arctic regions of our planet are already well documented. In this context, it is regarded today as a key scientific priority to develop methodologies and operational tools that can assist towards advancing our monitoring capabilities and improving our decision-making competences in Arctic regions. In particular, the Arctic coasts are the focal point in this respect, due to their strong connection to the physical environment, society, and the economy in such areas. Geoinformation, namely Earth Observation (EO) and Geographical Information Systems (GISs), provide the way forward towards achieving this goal. The present review, which to our knowledge is the first of its kind, aims at delivering a critical consideration of the state-of-the-art approaches exploiting EO datasets and GIS for mapping the Arctic coasts properties. It also furnishes a reflective discussion on the scientific gaps and challenges that exist that require the attention of the scientific and wider community to allow exploitation of the full potential of EO/GIS technologies in this domain. As such, the present study also serves as a valuable contribution towards pinpointing directions for the design of effective policies and decision-making strategies that will promote environmental sustainability in the Arctic regions.
Spyridon E. Detsikas, George P. Petropoulos, Kleomenis Kalogeropoulos, and Ioannis Faraslis
MDPI AG
Land use/land cover (LULC) is a fundamental concept of the Earth’s system intimately connected to many phases of the human and physical environment. LULC mappings has been recently revolutionized by the use of high-resolution imagery from unmanned aerial vehicles (UAVs). The present study proposes an innovative approach for obtaining LULC maps using consumer-grade UAV imagery combined with two machine learning classification techniques, namely RF and SVM. The methodology presented herein is tested at a Mediterranean agricultural site located in Greece. The emphasis has been placed on the use of a commercially available, low-cost RGB camera which is a typical consumer’s option available today almost worldwide. The results evidenced the capability of the SVM when combined with low-cost UAV data in obtaining LULC maps at very high spatial resolution. Such information can be of practical value to both farmers and decision-makers in reaching the most appropriate decisions in this regard.
George P. Petropoulos, Athina Georgiadi, and Kleomenis Kalogeropoulos
MDPI AG
Sentinel-2 data are crucial in mapping flooded areas as they provide high spatial and spectral resolution but under cloud-free weather conditions. In the present study, we aimed to devise a method for mapping a flooded area using multispectral Sentinel-2 data from optical sensors and Geographical Information Systems (GISs). As a case study, we selected a site located in Northern Italy that was heavily affected by flooding events on 3 October 2020, when the Sesia River in the Piedmont region was hit by severe weather disturbance, heavy rainfall, and strong winds. The method developed for mapping the flooded area was a thresholding technique through spectral water indices. More specifically, the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) were chosen as they are among the most widely used methods with applications across various environments, including urban, agricultural, and natural landscapes. The corresponding flooded area product from the Copernicus Emergency Management Service (EMS) was used to evaluate the flooded area predicted by our method. The results showed that both indices captured the flooded area with a satisfactory level of detail. The NDWI demonstrated a slightly higher accuracy, where it also appeared to be more sensitive to the separation of water from soil and areas with vegetation cover. The study findings may be useful in disaster management linked to flooded-area mapping and area rehabilitation mapping following a flood event, and they can also valuably assist decision and policy making towards a more sustainable environment.
George P. Petropoulos and Christina Lekka
MDPI AG
Soil–Vegetation–Atmosphere Transfer (SVAT) models are a promising avenue towards gaining a better insight into land surface interactions and Earth’s system dynamics. One such model developed for the academic and research community is the SimSphere SVAT model, a popular software toolkit employed for simulating interactions among the layers of vegetation, soil, and atmosphere on the land surface. The aim of the present review is two-fold: (1) to deliver a critical assessment of the model’s usage by the scientific and wider community over the last 15 years, and (2) to provide information on current software developments implemented in the model. From the review conducted herein, it is clearly evident that from the models’ inception to current day, SimSphere has received notable interest worldwide, and the dissemination of the model has continuously grown over the years. SimSphere has been used so far in several applications to study land surface interactions. The validation of the model performed worldwide has shown that it is able to produce realistic estimates of land surface parameters that have been validated, whereas detailed sensitivity analysis experiments conducted with the model have further confirmed its structure and architectural coherence. Furthermore, the recent inclusion of novel functionalities in the model, as outlined in the present review, has clearly resulted in improving its capabilities and in opening up new opportunities for its use by the wider community. SimSphere developments are also ongoing in different aspects, and its use as a toolkit towards advancing our understanding of land surface interactions from both educational and research points of view is anticipated to grow in the coming years.
Yang Huang, Yansong Bao, George P. Petropoulos, Qifeng Lu, Yanfeng Huo, and Fu Wang
MDPI AG
Precipitation is the basic component of the Earth’s water cycle. Obtaining high-resolution and high-precision precipitation data is of great significance. This paper establishes a precipitation retrieval model based on a random forest classification and regression model during the day and at night with FY-4B/AGRI Level1 data on China from July to August 2022. To evaluate the retrieval effect of the model, the GPM IMERG product is used as a reference, and the retrieval results are compared against those of the FY-4B/AGRI operational precipitation product. In addition, the retrieval results are analyzed according to different underlying surfaces. The results showed that compared with the FY-4B/AGRI operational precipitation product, the retrieval model can better identify precipitation and capture precipitation areas of light rain, moderate rain, heavy rain and torrential rain. Among them, the probability of detection (POD) of the day model increased from 0.328 to 0.680, and the equitable threat score (ETS) increased from 0.252 to 0.432. The POD of the night model increased from 0.337 to 0.639, and the ETS score increased from 0.239 to 0.369. Meanwhile, the precipitation estimation accuracy of the day model increased by 38.98% and that of the night model increased by 40.85%. Our results also showed that due to the surface uniformity of the ocean, the model can identify precipitation better on the ocean than on the land. Our findings also indicated that for the different underlying surfaces of the land, there is no significant difference in each evaluation index of the model. This is a strong argument for the universal applicability of the model. Notably, the results showed that, especially for more vegetated areas and areas covered by water, the model is capable of estimating precipitation. In conclusion, the precipitation retrieval model that is proposed herein can better determine precipitation regions and estimate precipitation intensities compared with the FY-4B/AGRI operational precipitation product. It can provide some reference value for future precipitation retrieval research on FY-4B/AGRI.
Ioannis Lemesios and George P. Petropoulos
Elsevier BV
Loukas Kouvaras and George P. Petropoulos
MDPI AG
The present study proposes a technique for automated tree crown detection and segmentation in digital images derived from unmanned aerial vehicles (UAVs) using a machine learning (ML) algorithm named Detectron2. The technique, which was developed in the python programming language, receives as input images with object boundary information. After training on sets of data, it is able to set its own object boundaries. In the present study, the algorithm was trained for tree crown detection and segmentation. The test bed consisted of UAV imagery of an agricultural field of tangerine trees in the city of Palermo in Sicily, Italy. The algorithm’s output was the accurate boundary of each tree. The output from the developed algorithm was compared against the results of tree boundary segmentation generated by the Support Vector Machine (SVM) supervised classifier, which has proven to be a very promising object segmentation method. The results from the two methods were compared with the most accurate yet time-consuming method, direct digitalization. For accuracy assessment purposes, the detected area efficiency, skipped area rate, and false area rate were estimated for both methods. The results showed that the Detectron2 algorithm is more efficient in segmenting the relevant data when compared to the SVM model in two out of the three indices. Specifically, the Detectron2 algorithm exhibited a 0.959% and 0.041% fidelity rate on the common detected and skipped area rate, respectively, when compared with the digitalization method. The SVM exhibited 0.902% and 0.097%, respectively. On the other hand, the SVM classification generated better false detected area results, with 0.035% accuracy, compared to the Detectron2 algorithm’s 0.056%. Having an accurate estimation of the tree boundaries from the Detectron2 algorithm, the tree health assessment was evaluated last. For this to happen, three different vegetation indices were produced (NDVI, GLI and VARI). All those indices showed tree health as average. All in all, the results demonstrated the ability of the technique to detect and segment trees from UAV imagery.
Nitesh Awasthi, Jayant Nath Tripathi, George P. Petropoulos, Pradeep Kumar, Abhay Kumar Singh, Kailas Kamaji Dakhore, Kripan Ghosh, Dileep Kumar Gupta, Prashant K. Srivastava, Kleomenis Kalogeropoulos,et al.
MDPI AG
This study involved an investigation of the long-term seasonal rainfall patterns in central India at the district level during the period from 1991 to 2020, including various aspects such as the spatiotemporal seasonal trend of rainfall patterns, rainfall variability, trends of rainy days with different intensities, decadal percentage deviation in long-term rainfall patterns, and decadal percentage deviation in rainfall events along with their respective intensities. The central region of India was meticulously divided into distinct subparts, namely, Gujarat, Daman and Diu, Maharashtra, Goa, Dadra and Nagar Haveli, Madhya Pradesh, Chhattisgarh, and Odisha. The experimental outcomes represented the disparities in rainfall distribution across different districts of central India with the spatial distribution of mean rainfall ranges during winter (2.08 mm over Dadra and Nagar Haveli with an average of 24.19 mm over Odisha), premonsoon (6.65 mm over Gujarat to 132.89 mm over Odisha), monsoon (845.46 mm over Gujarat to 3188.21 mm over Goa), and post-monsoon (30.35 mm over Gujarat to 213.87 mm over Goa), respectively. Almost all the districts of central India displayed an uneven pattern in the percentage deviation of seasonal rainfall in all three decades for all seasons, which indicates the seasonal rainfall variability over the last 30 years. A noticeable variation in the percentage deviation of seasonal rainfall patterns has been observed in the following districts: Rewa, Puri, Anuppur, Ahmadabad, Navsari, Chhindwara, Devbhumi Dwarka, Amreli, Panch Mahals, Kolhapur, Kandhamal, Ratnagiri, Porbandar, Bametara, and Sabar Kantha. In addition, a larger number of rainy days of various categories occurred in the monsoon season in comparison to other seasons. A higher contribution of trace rainfall events was found in the winter season. The highest contributions of very light, light rainfall, moderate, rather high, and high events were found in the monsoon season in central India. The percentage of various categories of rainfall events has decreased over the last two decades (2001–2020) in comparison to the third decade (1991–2000), according to the mean number of rainfall events in the last 30 years. This spatiotemporal analysis provides valuable insights into the rainfall trends in central India, which represent regional disparities and the potential challenges impacted by climate patterns. This study contributes to our understanding of the changing rainfall dynamics and offers crucial information for effective water resource management in the region.
Christina Lekka, George P. Petropoulos, and Spyridon E. Detsikas
Elsevier BV
Cinzia Albertini, Andrea Gioia, Vito Iacobellis, Salvatore Manfreda, and George P. Petropoulos
Elsevier
Georgios Gkatzios, George P. Petropoulos, Spyridon E. Detsikas, and Prashant K. Srivastava
Elsevier
Ioannis Lemesios, Spyridon E. Detsikas, and George P. Petropoulos
Elsevier
Avgoustina I. Davri, George P. Petropoulos, Spyridon E. Detsikas, Kleomenis Kalogeropoulos, and Antigoni Faka
Elsevier
Spyridon E. Detsikas, Triantafyllia Petsini, and George P. Petropoulos
Elsevier
George P. Petropoulos, Vassilia Karathanassi, Kleanthis Karamvasis, Aikaterini Dermosinoglou, and Spyridon E. Detsikas
Elsevier
George P. Petropoulos, Spyridon E. Detsikas, Ioannis Lemesios, and Rahul Raj
Informa UK Limited
Triantafyllia Petsini and George P. Petropoulos
Informa UK Limited
ABSTRACT The present study provides a thorough evaluation of the SMOS surface soil moisture (SSM) product in a typical Mediterranean setting in Greece. For this purpose, a total of 4 agricultural sites were used for which co-orbital in-situ measurements from ground SSM sensors were available for year 2020. In this context, the effect of topographical and geomorphological features, land use/cover and the satellite orbit type and the Radio Frequency Interference (RFI) were also examined. A series of statistical metrics were computed, which allowed evaluating the agreement between the 2 datasets. In overall, results showed a reasonable agreement in specific land use/cover types between the SMOS product and the corresponding in-situ measurements obtained from the 0–5 cm soil moisture layer. In most cases, Root Mean Square Difference (RMSD) was close to 0.15 m3 m−3 (minimum 0.126 m3 m−3). Tomato and vineyard showed a satisfactory agreement compared to walnut and cotton crops. The autumn period had the highest agreement for tomato crop. The effect of RFI was also quite noticeable, as after the exclusion of pixels with high RFI, statistical agreement was noticeably improved. This study is, to our knowledge, one of the few that investigates in a Greek setting the accuracy of the SMOS product. The study results can contribute to the understanding of the practical value of the SMOS product in agricultural and arid/semi-arid Mediterranean environments while support efforts ongoing globally aiming at improving the SMOS SSM product accuracy.