| ITU_GGC16 The combined global gravity field model including GRACE & GOCE data up to degree and order 280 O Akyilmaz, A Ustun, C Aydin, N Arslan, S Doganalp, C Guney, H Mercan, ... | 22 | 2016 |
| Bridging the gap between GRACE and GRACE-FO missions with deep learning aided water storage simulations M Uz, KG Atman, O Akyilmaz, CK Shum, M Keleş, T Ay, B Tandoğdu, ... Science of the Total Environment 830, 154701, 2022 | 19 | 2022 |
| Machine learning based downscaling of GRACE-estimated groundwater in Central Valley, California V Agarwal, O Akyilmaz, CK Shum, W Feng, TY Yang, E Forootan, ... Science of the Total Environment 865, 161138, 2023 | 16 | 2023 |
| ITU_GRACE16 The global gravity field model including GRACE data up to degree and order 180 of ITU and other collaborating institutions O Akyilmaz, A Ustun, C Aydin, N Arslan, S Doganalp, C Guney, H Mercan, ... GFZ Data Services, 2016 | 10 | 2016 |
| High resolution gravity field determination and monitoring of regional mass variations using low-earth orbit satellites O Akyilmaz, A Ustun, C Aydın, N Arslan, S Doganalp, C Guney, H Mercan, ... ICGEM, GFZ Data Services, 2016 | 5 | 2016 |
| High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021 M Uz, O Akyılmaz, CK Shum, KG Atman, S Olgun, Ö Güneş Scientific Data 11 (1), 71, 2024 | 2 | 2024 |
| GPS verileri yardımıyla GRACE uydularının duyarlı yörüngelerinin belirlenmesi, doğrulanması ve enterpolasyonu M Uz Fen Bilimleri Enstitüsü, 2016 | 1 | 2016 |
| Machine Learning approach to study groundwater depletion in aquifers V Agarwal, O Akyılmaz, CK Shum, W Feng, UK Haritashya, M Uz, ... AGU23, 2023 | | 2023 |
| Satellite-based tools enabling India forest sustainability Y Zuo, CK Shum, S Das, Y Jia, O Akyilmaz, Y Zhang, Y Tang, M Uz, ... XXVIII General Assembly of the International Union of Geodesy and Geophysics …, 2023 | | 2023 |
| Deep Learning-aided Temporal Downscaling of Satellite GravimetryTerrestrial Water Storage Anomalies Across the Contiguous United States (CONUS) M Uz, O Akyılmaz, C Shum EGU General Assembly Conference Abstracts, EGU-632, 2023 | | 2023 |
| Geodesy as the Sentinel for Climate-induced Hazards Monitoring and Response CK Shum, Y Zhang, Y Jia, Y Ding, J Guo, O Akyılmaz, M Uz, C Zhang, ... AGU Fall Meeting Abstracts 2022, G16A-07, 2022 | | 2022 |
| Satellite Observation-Based Climate and Natural Hazards Monitoring CK Shum, Y Jia, Y Zhang, J Guo, R Qin, O Akyilmaz, E Forootan, M Uz 44th COSPAR Scientific Assembly. Held 16-24 July 44, 87, 2022 | | 2022 |
| Investigation of systematic errors in GRACE temporal gravity field solutions using the Improved Energy Balance Approach M Uz, O Akyılmaz, J Kusche, C Shum, A Üstün, Y Zhang EGU General Assembly Conference Abstracts, 11504, 2020 | | 2020 |
| Assessment of GRACE L1B RL03 Data Products for Temporal Gravity Field Solutions through Improved Energy Balance Approach. M Uz, K Shang, O Akyilmaz, C Shum, J Guo, A Ustun, Y Zhang Geophysical Research Abstracts 21, 2019 | | 2019 |
| On spline and polynomial interpolation of low earth orbiter data: GRACE example M Uz, A Ustun EGU General Assembly Conference Abstracts, EPSC2016-7350, 2016 | | 2016 |
| Global gravity field recovery from low-low satellite-to-satellite tracking with enhanced spatiotemporal resolution using deep learning paradigm M Uz Graduate School, 0 | | |
| Deep Learning-Aided High-Resolution Temporal Gravity Field Simulations: Monthly global mass grids and Spherical Harmonics from 1994 to 2021 M Uz, O Akyılmaz, CK Shum, K Atman, S Olgun, Ö Güneş | | |
