@article{MTMT:34407342,
	title = {Riverine Microplastic Quantification: A Novel Approach Integrating Satellite Images, Neural Network, and Suspended Sediment Data as a Proxy},
	url = {https://m2.mtmt.hu/api/publication/34407342},
	author = {Mohsen Abdelsadek Metwaly, Ahmed and Kovács, Ferenc and Kiss, Tímea},
	doi = {10.3390/s23239505},
	journal-iso = {SENSORS-BASEL},
	journal = {SENSORS},
	volume = {23},
	unique-id = {34407342},
	abstract = {Rivers transport terrestrial microplastics (MP) to the marine system, demanding cost-effective and frequent monitoring, which is attainable through remote sensing. This study aims to develop and test microplastic concentration (MPC) models directly by satellite images and indirectly through suspended sediment concentration (SSC) as a proxy employing a neural network algorithm. These models relied upon high spatial (26 sites) and temporal (198 samples) SSC and MPC data in the Tisza River, along with optical and active sensor reflectance/backscattering. A feedforward MLP neural network was used to calibrate and validate the direct models employing k-fold cross-validation (five data folds) and the Optuna library for hyperparameter optimization. The spatiotemporal generalization capability of the developed models was assessed under various hydrological scenarios. The findings revealed that hydrology fundamentally influences the SSC and MPC. The indirect estimation method of MPC using SSC as a proxy demonstrated higher accuracy (R2 = 0.17–0.88) than the direct method (R2 = 0–0.2), due to the limitations of satellite sensors to directly estimate the very low MPCs in rivers. However, the estimation accuracy of the indirect method varied with lower accuracy (R2 = 0.17, RMSE = 12.9 item/m3 and MAE = 9.4 item/m3) during low stages and very high (R2 = 0.88, RMSE = 7.8 item/m3 and MAE = 10.8 item/m3) during floods. The worst estimates were achieved based on Sentinel-1. Although the accuracy of the MPC models is moderate, it still has practical applicability, especially during floods and employing proxy models. This study is one of the very initial attempts towards MPC quantification, thus more studies incorporating denser spatiotemporal data, additional water quality parameters, and surface roughness data are warranted to improve the estimation accuracy.},
	year = {2023},
	eissn = {1424-8220},
	orcid-numbers = {Kovács, Ferenc/0000-0001-7944-8921; Kiss, Tímea/0000-0002-2597-5176}
}

@article{MTMT:33785558,
	title = {Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery},
	url = {https://m2.mtmt.hu/api/publication/33785558},
	author = {Mohsen Abdelsadek Metwaly, Ahmed and Kiss, Tímea and Kovács, Ferenc},
	doi = {10.1007/s11356-023-27068-0},
	journal-iso = {ENVIRON SCI POLLUT R},
	journal = {ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH},
	volume = {30},
	unique-id = {33785558},
	issn = {0944-1344},
	abstract = {Despite the substantial impact of rivers on the global marine litter problem, riverine litter has been accorded inadequate consideration. Therefore, our objective was to detect riverine litter by utilizing middle-scale multispectral satellite images and machine learning (ML), with the Tisza River (Hungary) as a study area. The Very High Resolution (VHR) images obtained from the Google Earth database were employed to recognize some riverine litter spots (a blend of anthropogenic and natural substances). These litter spots served as the basis for training and validating five supervised machine-learning algorithms based on Sentinel-2 images [Artificial Neural Network (ANN), Support Vector Classifier (SVC), Random Forest (RF), Naïve Bays (NB) and Decision Tree (DT)]. To evaluate the generalization capability of the developed models, they were tested on larger unseen data under varying hydrological conditions and with different litter sizes. Besides the best-performing model was used to investigate the spatio-temporal variations of riverine litter in the Middel Tisza. According to the results, almost all the developed models showed favorable metrics based on the validation dataset (e.g., F1-score; SVC: 0.94, ANN: 0.93, RF: 0.91, DT: 0.90, and NB: 0.83); however, during the testing process, they showed medium (e.g., F1-score; RF:0.69, SVC: 0.62; ANN: 0.62) to poor performance (e.g., F1-score; NB: 0.48; DT: 0.45). The capability of all models to detect litter was bounded to the pixel size of the Sentinel-2 images. Based on the spatio-temporal investigation, hydraulic structures (e.g., Kisköre Dam) are the greatest litter accumulation spots. Although the highest transport rate of litter occurs during floods, the largest litter spot area upstream of the Kisköre Dam was observed at low stages in summer. This study represents a preliminary step in the automatic detection of riverine litter; therefore, additional research incorporating a larger dataset with more representative small litter spots, as well as finer spatial resolution images is necessary.},
	year = {2023},
	eissn = {1614-7499},
	pages = {67742-67757},
	orcid-numbers = {Kiss, Tímea/0000-0002-2597-5176; Kovács, Ferenc/0000-0001-7944-8921}
}

@misc{MTMT:33677694,
	title = {A műholdas belvíztérképezés alapjai, lehetőségek és korlátok.},
	url = {https://m2.mtmt.hu/api/publication/33677694},
	author = {Tobak, Zalán and Kovács, Ferenc and Van Leeuwen, Boudewijn},
	unique-id = {33677694},
	year = {2023},
	orcid-numbers = {Tobak, Zalán/0000-0002-4960-2198; Kovács, Ferenc/0000-0001-7944-8921; Van Leeuwen, Boudewijn/0000-0002-1117-5872}
}

@article{MTMT:33186517,
	title = {Plot-level field monitoring with Sentinel-2 and PlanetScope data for examination of sewage sludge disposal impact},
	url = {https://m2.mtmt.hu/api/publication/33186517},
	author = {Kovács, Ferenc and Ladányi, Zsuzsanna},
	doi = {10.5937/gp26-37964},
	journal-iso = {GEOGRAPHICA PANNONICA},
	journal = {GEOGRAPHICA PANNONICA},
	volume = {26},
	unique-id = {33186517},
	issn = {0354-8724},
	abstract = {Agricultural use of sewage sludge is one of the means of sustainable environmental management. In order to monitor the short-term effects of sludge disposal a multi-year, high-resolution data collection was planned on arable land in south-eastern Hungary. Data acquisition was applied at the highest temporal and spatial resolution using Sentinel-2 and PlanetScope satellite imagery observing the vegetation period based on vegetation indices (EVI, NDVI) from 2016 to 2021. There were statistical differences in the case of sunflower and maize biomass productions but the spatial and statistical deviations between the affected and non-affected areas of sludge disposal were generally not significant. The sensitivity of EVI in the dense vegetation period and its applicability might be emphasized in a comparative analysis.},
	year = {2022},
	eissn = {1820-7138},
	pages = {241-257},
	orcid-numbers = {Kovács, Ferenc/0000-0001-7944-8921; Ladányi, Zsuzsanna/0000-0003-0397-6423}
}

@{MTMT:32852509,
	title = {Sediment discharge estimation of lowland rivers using Sentinel-2 images and machine learning algorithms},
	url = {https://m2.mtmt.hu/api/publication/32852509},
	author = {Mohsen Abdelsadek Metwaly, Ahmed and Kovács, Ferenc and Kiss, Tímea},
	booktitle = {State of geomorphological research in 2022},
	unique-id = {32852509},
	year = {2022},
	pages = {54-55},
	orcid-numbers = {Kovács, Ferenc/0000-0001-7944-8921; Kiss, Tímea/0000-0002-2597-5176}
}

@article{MTMT:32851694,
	title = {Remote Sensing of Sediment Discharge in Rivers Using Sentinel-2 Images and Machine-Learning Algorithms},
	url = {https://m2.mtmt.hu/api/publication/32851694},
	author = {Mohsen Abdelsadek Metwaly, Ahmed and Kovács, Ferenc and Kiss, Tímea},
	doi = {10.3390/hydrology9050088},
	journal-iso = {HYDROLOGY-BASEL},
	journal = {HYDROLOGY},
	volume = {9},
	unique-id = {32851694},
	year = {2022},
	eissn = {2306-5338},
	orcid-numbers = {Kovács, Ferenc/0000-0001-7944-8921; Kiss, Tímea/0000-0002-2597-5176}
}

@misc{MTMT:32623390,
	title = {A szőlőtermő talajok vizsgálata a talajerózió és az agrokemikáliák használatának összefüggésében},
	url = {https://m2.mtmt.hu/api/publication/32623390},
	author = {Babcsányi, Izabella and Fehér, Zsolt Zoltán and Kovács, Ferenc and Lemarchand, Damien and Tobak, Zalán and Barta, Károly and Pham, Thi Ha Nhung and Manaljav, Samdandorj and Juhász, Szabolcs and Balling, Péter and Farsang, Andrea},
	unique-id = {32623390},
	year = {2022},
	orcid-numbers = {Babcsányi, Izabella/0000-0003-0581-6343; Kovács, Ferenc/0000-0001-7944-8921; Tobak, Zalán/0000-0002-4960-2198; Balling, Péter/0000-0001-9833-6319; Farsang, Andrea/0000-0002-7873-5256}
}

@article{MTMT:32777835,
	title = {A felszínközeli vízkészletek monitoringjának lehetőségei a szélsőséges vízháztartási helyzetek (aszály, belvíz) értékelésének szolgálatában},
	url = {https://m2.mtmt.hu/api/publication/32777835},
	author = {Barta, Károly and Van Leeuwen, Boudewijn and Szatmári, József and Blanka, Viktória and Kovács, Ferenc and Ladányi, Zsuzsanna and Mezősi, Gábor and Rakonczai, János and Sipos, György and Szilassi, Péter and Tobak, Zalán and Fiala, Károly and Benyhe, Balázs and Fehérváry, István},
	journal-iso = {LÉGKÖR},
	journal = {LÉGKÖR: AZ ORSZÁGOS METEOROLÓGIAI INTÉZET SZAKMAI TÁJÉKOZTATÓJA},
	volume = {66},
	unique-id = {32777835},
	issn = {0133-3666},
	year = {2021},
	pages = {4-12},
	orcid-numbers = {Van Leeuwen, Boudewijn/0000-0002-1117-5872; Szatmári, József/0000-0002-7896-3363; Blanka, Viktória/0000-0001-6364-109X; Kovács, Ferenc/0000-0001-7944-8921; Ladányi, Zsuzsanna/0000-0003-0397-6423; Sipos, György/0000-0001-6224-2361; Szilassi, Péter/0000-0003-0051-6739; Tobak, Zalán/0000-0002-4960-2198; Fehérváry, István/0000-0002-2519-2008}
}

@article{MTMT:32487825,
	title = {Sediment Transport Dynamism in the Confluence Area of Two Rivers Transporting Mainly Suspended Sediment Based on Sentinel-2 Satellite Images},
	url = {https://m2.mtmt.hu/api/publication/32487825},
	author = {Mohsen Abdelsadek Metwaly, Ahmed and Kovács, Ferenc and Mezősi, Gábor and Kiss, Tímea},
	doi = {10.3390/w13213132},
	journal-iso = {WATER-SUI},
	journal = {WATER},
	volume = {13},
	unique-id = {32487825},
	year = {2021},
	eissn = {2073-4441},
	pages = {3132-3161},
	orcid-numbers = {Kovács, Ferenc/0000-0001-7944-8921; Kiss, Tímea/0000-0002-2597-5176}
}

@misc{MTMT:32288723,
	title = {Sediment Dynamism in the Confluence Zone of the Tisza and Maros Rivers Based on Sentinel-2 Satellite Imageries},
	url = {https://m2.mtmt.hu/api/publication/32288723},
	author = {Mohsen Abdelsadek Metwaly, Ahmed and Kovács, Ferenc and Kiss, Tímea},
	unique-id = {32288723},
	year = {2021},
	orcid-numbers = {Kovács, Ferenc/0000-0001-7944-8921; Kiss, Tímea/0000-0002-2597-5176}
}