@article{MTMT:34539323,
	title = {Integrating the Sentinel-1, Sentinel-2 and Topographic data into soybean yield modelling using Machine Learning},
	url = {https://m2.mtmt.hu/api/publication/34539323},
	author = {Amankulova, Khilola and Farmonov, Nizom and Omonov, Khasan and Abdurakhimova, Mokhigul and Mucsi, László},
	doi = {10.1016/j.asr.2024.01.040},
	journal-iso = {ADV SPACE RES},
	journal = {ADVANCES IN SPACE RESEARCH},
	volume = {73},
	unique-id = {34539323},
	issn = {0273-1177},
	year = {2024},
	eissn = {1879-1948},
	pages = {4052-4066},
	orcid-numbers = {Amankulova, Khilola/0000-0001-6562-5616; Mucsi, László/0000-0002-5807-3742}
}

@article{MTMT:34500549,
	title = {Effectiveness of machine learning and deep learning models at county-level soybean yield forecasting},
	url = {https://m2.mtmt.hu/api/publication/34500549},
	author = {Farmonov, Nizom and Amankulova, Khilola and Khan, Shahid Nawaz and Abdurakhimova, Mokhigul and Szatmári, József and Khabiba, Tukhtaeva and Makhliyo, Radjabova and Khodicha, Meiliyeva and Mucsi, László},
	doi = {10.15201/hungeobull.72.4.4},
	journal-iso = {HUNG GEOGR BULL (2009-)},
	journal = {HUNGARIAN GEOGRAPHICAL BULLETIN (2009-)},
	volume = {72},
	unique-id = {34500549},
	issn = {2064-5031},
	abstract = {Crop yield forecasting is critical in modern agriculture to ensure food security, economic stability, and effective resource management. The main goal of this study was to combine historical multisource satellite and environmental datasets with a deep learning (DL) model for soybean yield forecasting in the United States’ Corn Belt. The following Moderate Resolution Imaging Spectroradiometer (MODIS) products were aggregated at the county level. The crop data layer (CDL) in Google Earth Engine (GEE) was used to mask the data so that only soybean pixels were selected. Several machine learning (ML) models were trained by using 5 years of data from 2012 to 2016: random forest (RF), least absolute shrinkable and selection operator (LASSO) regression, extreme gradient boosting (XGBoost), and decision tree regression (DTR) as well as DL-based one-dimensional convolutional neural network (1D-CNN). The best model was determined by comparing their performances at forecasting the soybean yield in 2017–2021 at the county scale. The RF model outperformed all other ML models with the lowest RMSE of 0.342 t/ha, followed by XGBoost (0.373 t/ha), DTR (0.437 t/ha), and LASSO (0.452 t/ha) regression. However, the 1D-CNN model showed the highest forecasting accuracy for the 2018 growing season with RMSE of 0.280 t/ha. The developed 1D-CNN model has great potential for crop yield forecasting because it effectively captures temporal dependencies and extracts meaningful input features from sequential data.},
	year = {2023},
	eissn = {2064-5147},
	pages = {383-398},
	orcid-numbers = {Szatmári, József/0000-0002-7896-3363; Mucsi, László/0000-0002-5807-3742}
}

@article{MTMT:34026258,
	title = {Comparison of PlanetScope, Sentinel-2, and landsat 8 data in soybean yield estimation within-field variability with random forest regression},
	url = {https://m2.mtmt.hu/api/publication/34026258},
	author = {Amankulova, Khilola and Farmonov, Nizom and Akramova, Parvina and Tursunov, Ikrom and Mucsi, László},
	doi = {10.1016/j.heliyon.2023.e17432},
	journal-iso = {HELIYON},
	journal = {HELIYON},
	volume = {9},
	unique-id = {34026258},
	year = {2023},
	eissn = {2405-8440},
	orcid-numbers = {Mucsi, László/0000-0002-5807-3742}
}

@article{MTMT:33741208,
	title = {Evaluating the performance of multi-temporal synthetic-aperture radar imagery in land-cover mapping using a forward stepwise selection approach},
	url = {https://m2.mtmt.hu/api/publication/33741208},
	author = {Mucsi, László and Bui, Dang Hung},
	doi = {10.1016/j.rsase.2023.100975},
	journal-iso = {REMOTE SENS APPLIC SOC ENVIRON},
	journal = {REMOTE SENSING APPLICATIONS : SOCIETY AND ENVIRONMENT},
	volume = {30},
	unique-id = {33741208},
	abstract = {Radar images are a supplement or alternative to optical images, especially in tropical regions where cloud cover is a challenge. The present study evaluated the performance of using multi-temporal synthetic-aperture radar images in land-cover mapping in Binh Duong province, Vietnam. Two experimental cases were investigated: Case 1 used only multi-temporal radar images, whereas Case 2 used a combination of multi-temporal radar images and one optical image. A set of 24 Sentinel-1 images and one Landsat-8 image acquired in 2020 were processed. A forward stepwise selection approach based on a random forest algorithm and a six-class classification scheme were used to determine the best combination of images. In Case 1, the 16-date combination gained the best result with an overall accuracy (OA) of 76.6%. Considering the trade-off between efficiency and cost, the seven-date combination (OA = 76.1%) could be the optimal integration. Compared to using single-date radar images, the OA in Case 1 was improved by 9.5%, and the producer's and user's accuracies were improved by 4.68%–33.33%. Meanwhile, in Case 2, the combination of one optical and seven radar images gave the best result (OA = 83.7%). It had at least 7.1% higher OA than Case 1. However, its OA was at least 2.4% lower, and the producer's and user's accuracies of most classes were reduced when compared to using only a single Landsat-8 image. Overall, the findings of this study confirmed the effectiveness of using multi-temporal radar images in land-cover mapping. However, the effectiveness of the combination of radar and optical images needs further elucidation.},
	year = {2023},
	eissn = {2352-9385},
	orcid-numbers = {Mucsi, László/0000-0002-5807-3742; Bui, Dang Hung/0000-0002-7879-6585}
}

@article{MTMT:33722735,
	title = {Sunflower Crop Yield Prediction by advanced statistical modeling using Satellite-derived Vegetation Indices and Crop Phenology},
	url = {https://m2.mtmt.hu/api/publication/33722735},
	author = {Amankulova, Khilola and Farmonov, Nizom and Mukhtorov, Uzbekkhon and Mucsi, László},
	doi = {10.1080/10106049.2023.2197509},
	journal-iso = {GEOCAR INT},
	journal = {GEOCARTO INTERNATIONAL},
	volume = {38},
	unique-id = {33722735},
	issn = {1010-6049},
	year = {2023},
	eissn = {1752-0762},
	orcid-numbers = {Amankulova, Khilola/0000-0001-6562-5616; Mucsi, László/0000-0002-5807-3742}
}

@article{MTMT:33697697,
	title = {Combining PlanetScope and Sentinel-2 images with environmental data for improved wheat yield estimation},
	url = {https://m2.mtmt.hu/api/publication/33697697},
	author = {Farmonov, Nizom and Amankulova, Khilola and Szatmári, József and Urinov, Jamol and Narmanov, Zafar and Nosirov, Jakhongir and Mucsi, László},
	doi = {10.1080/17538947.2023.2186505},
	journal-iso = {INT J DIGIT EARTH},
	journal = {INTERNATIONAL JOURNAL OF DIGITAL EARTH},
	volume = {16},
	unique-id = {33697697},
	issn = {1753-8947},
	year = {2023},
	eissn = {1753-8955},
	pages = {847-867},
	orcid-numbers = {Amankulova, Khilola/0000-0001-6562-5616; Szatmári, József/0000-0002-7896-3363; Mucsi, László/0000-0002-5807-3742}
}

@article{MTMT:33630993,
	title = {Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms},
	url = {https://m2.mtmt.hu/api/publication/33630993},
	author = {Farmonov, Nizom and Amankulova, Khilola and Szatmári, József and Sharifi, Alireza and Abbasi-Moghadam, Dariush and Mirhoseini Nejad, Seyed Mahdi and Mucsi, László},
	doi = {10.1109/JSTARS.2023.3239756},
	journal-iso = {IEEE J-STARS},
	journal = {IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING},
	volume = {16},
	unique-id = {33630993},
	issn = {1939-1404},
	year = {2023},
	eissn = {2151-1535},
	pages = {1576-1588},
	orcid-numbers = {Szatmári, József/0000-0002-7896-3363; Sharifi, Alireza/0000-0001-7110-7516; Abbasi-Moghadam, Dariush/0000-0003-2228-0595; Mirhoseini Nejad, Seyed Mahdi/0000-0002-0454-8626; Mucsi, László/0000-0002-5807-3742}
}

@article{MTMT:33291883,
	title = {Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches},
	url = {https://m2.mtmt.hu/api/publication/33291883},
	author = {Mirhoseini Nejad, S.Mahdi and Abbasi-Moghadam, Dariush and Sharifi, Aireza and Farmonov, Nizom and Amankulova, Khilola and Mucsi, László},
	doi = {10.1109/JSTARS.2022.3223423},
	journal-iso = {IEEE J-STARS},
	journal = {IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING},
	volume = {16},
	unique-id = {33291883},
	issn = {1939-1404},
	year = {2023},
	eissn = {2151-1535},
	pages = {254-266},
	orcid-numbers = {Abbasi-Moghadam, Dariush/0000-0003-2228-0595; Sharifi, Aireza/0000-0001-7110-7516; Mucsi, László/0000-0002-5807-3742}
}

@article{MTMT:33033249,
	title = {Time-series analysis of Sentinel-2 satellite images for sunflower yield estimation},
	url = {https://m2.mtmt.hu/api/publication/33033249},
	author = {Amankulova, Khilola and Farmonov, Nizom and Mucsi, László},
	doi = {10.1016/j.atech.2022.100098},
	journal-iso = {SMART AGRICULT TECHN},
	journal = {SMART AGRICULTURAL TECHNOLOGY},
	volume = {3},
	unique-id = {33033249},
	year = {2023},
	eissn = {2772-3755},
	orcid-numbers = {Mucsi, László/0000-0002-5807-3742}
}

@{MTMT:33548357,
	title = {Urban land use and land cover mapping using high spatial and temporal resolution satellite images},
	url = {https://m2.mtmt.hu/api/publication/33548357},
	author = {Mucsi, László},
	booktitle = {GeoMATES '22 International Congress on Geomathematics in Earth- and Environmental Sciences},
	unique-id = {33548357},
	year = {2022},
	pages = {46-46},
	orcid-numbers = {Mucsi, László/0000-0002-5807-3742}
}