TY - JOUR AU - Amankulova, Khilola AU - Farmonov, Nizom AU - Omonov, Khasan AU - Abdurakhimova, Mokhigul AU - Mucsi, László TI - Integrating the Sentinel-1, Sentinel-2 and Topographic data into soybean yield modelling using Machine Learning JF - ADVANCES IN SPACE RESEARCH J2 - ADV SPACE RES VL - 73 PY - 2024 IS - 8 SP - 4052 EP - 4066 PG - 15 SN - 0273-1177 DO - 10.1016/j.asr.2024.01.040 UR - https://m2.mtmt.hu/api/publication/34539323 ID - 34539323 LA - English DB - MTMT ER - TY - JOUR AU - Farmonov, Nizom AU - Amankulova, Khilola AU - Khan, Shahid Nawaz AU - Abdurakhimova, Mokhigul AU - Szatmári, József AU - Khabiba, Tukhtaeva AU - Makhliyo, Radjabova AU - Khodicha, Meiliyeva AU - Mucsi, László TI - Effectiveness of machine learning and deep learning models at county-level soybean yield forecasting JF - HUNGARIAN GEOGRAPHICAL BULLETIN (2009-) J2 - HUNG GEOGR BULL (2009-) VL - 72 PY - 2023 IS - 4 SP - 383 EP - 398 PG - 16 SN - 2064-5031 DO - 10.15201/hungeobull.72.4.4 UR - https://m2.mtmt.hu/api/publication/34500549 ID - 34500549 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Amankulova, Khilola AU - Farmonov, Nizom AU - Akramova, Parvina AU - Tursunov, Ikrom AU - Mucsi, László TI - Comparison of PlanetScope, Sentinel-2, and landsat 8 data in soybean yield estimation within-field variability with random forest regression JF - HELIYON J2 - HELIYON VL - 9 PY - 2023 IS - 6 PG - 17 SN - 2405-8440 DO - 10.1016/j.heliyon.2023.e17432 UR - https://m2.mtmt.hu/api/publication/34026258 ID - 34026258 LA - English DB - MTMT ER - TY - JOUR AU - Mucsi, László AU - Bui, Dang Hung TI - Evaluating the performance of multi-temporal synthetic-aperture radar imagery in land-cover mapping using a forward stepwise selection approach JF - REMOTE SENSING APPLICATIONS : SOCIETY AND ENVIRONMENT J2 - REMOTE SENS APPLIC SOC ENVIRON VL - 30 PY - 2023 PG - 11 SN - 2352-9385 DO - 10.1016/j.rsase.2023.100975 UR - https://m2.mtmt.hu/api/publication/33741208 ID - 33741208 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Amankulova, Khilola AU - Farmonov, Nizom AU - Mukhtorov, Uzbekkhon AU - Mucsi, László TI - Sunflower Crop Yield Prediction by advanced statistical modeling using Satellite-derived Vegetation Indices and Crop Phenology JF - GEOCARTO INTERNATIONAL J2 - GEOCAR INT VL - 38 PY - 2023 IS - 1 PG - 20 SN - 1010-6049 DO - 10.1080/10106049.2023.2197509 UR - https://m2.mtmt.hu/api/publication/33722735 ID - 33722735 LA - English DB - MTMT ER - TY - JOUR AU - Farmonov, Nizom AU - Amankulova, Khilola AU - Szatmári, József AU - Urinov, Jamol AU - Narmanov, Zafar AU - Nosirov, Jakhongir AU - Mucsi, László TI - Combining PlanetScope and Sentinel-2 images with environmental data for improved wheat yield estimation JF - INTERNATIONAL JOURNAL OF DIGITAL EARTH J2 - INT J DIGIT EARTH VL - 16 PY - 2023 IS - 1 SP - 847 EP - 867 PG - 21 SN - 1753-8947 DO - 10.1080/17538947.2023.2186505 UR - https://m2.mtmt.hu/api/publication/33697697 ID - 33697697 LA - English DB - MTMT ER - TY - JOUR AU - Farmonov, Nizom AU - Amankulova, Khilola AU - Szatmári, József AU - Sharifi, Alireza AU - Abbasi-Moghadam, Dariush AU - Mirhoseini Nejad, Seyed Mahdi AU - Mucsi, László TI - Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms JF - IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING J2 - IEEE J-STARS VL - 16 PY - 2023 SP - 1576 EP - 1588 PG - 13 SN - 1939-1404 DO - 10.1109/JSTARS.2023.3239756 UR - https://m2.mtmt.hu/api/publication/33630993 ID - 33630993 LA - English DB - MTMT ER - TY - JOUR AU - Mirhoseini Nejad, S.Mahdi AU - Abbasi-Moghadam, Dariush AU - Sharifi, Aireza AU - Farmonov, Nizom AU - Amankulova, Khilola AU - Mucsi, László TI - Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches JF - IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING J2 - IEEE J-STARS VL - 16 PY - 2023 SP - 254 EP - 266 PG - 13 SN - 1939-1404 DO - 10.1109/JSTARS.2022.3223423 UR - https://m2.mtmt.hu/api/publication/33291883 ID - 33291883 LA - English DB - MTMT ER - TY - JOUR AU - Amankulova, Khilola AU - Farmonov, Nizom AU - Mucsi, László TI - Time-series analysis of Sentinel-2 satellite images for sunflower yield estimation JF - SMART AGRICULTURAL TECHNOLOGY J2 - SMART AGRICULT TECHN VL - 3 PY - 2023 PG - 10 SN - 2772-3755 DO - 10.1016/j.atech.2022.100098 UR - https://m2.mtmt.hu/api/publication/33033249 ID - 33033249 LA - English DB - MTMT ER - TY - CHAP AU - Mucsi, László ED - Hatvani, István Gábor ED - Erdélyi, Dániel ED - Fedor, Ferenc TI - Urban land use and land cover mapping using high spatial and temporal resolution satellite images T2 - GeoMATES '22 International Congress on Geomathematics in Earth- and Environmental Sciences PB - MTA Pécsi Akadémiai Bizottság (MTA PAB) CY - Pécs SN - 9789637068140 PY - 2022 SP - 46 EP - 46 PG - 1 UR - https://m2.mtmt.hu/api/publication/33548357 ID - 33548357 LA - English DB - MTMT ER -