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The German government aims to convert its energy system to renewable energy by 2045. This requires a comprehensive understanding of land eligibility for renewables, particularly land-intensive ground-mounted photovoltaics (GM-PV). Federal states must set aside at least 2% of their land for renewable energy. This target value was derived using a top-down energy demand approach. Georeferenced land use data can be used to make bottom-up estimates. This study investigates how the choice of data source influences the bottom-up evaluation of land eligibility for GM-PV installations in Germany. This study evaluates the quality of data sources and their applicability for GM-PV scenario assessment by comparing the official data source Basis-DLM as the reference with the open-access data sources OpenStreetMap (OSM), Corine Land Cover (CLC), and Copernicus Emergency Management Service (CEMS). The intersection over union (IoU) and Matthews correlation coefficient (MCC) methods were used to analyse the differences in land use and eligibility due to the quality of the data sources and to compare their accuracy. The study's results show the crucial role of data source selection in estimating the potential for GM-PV in Germany. The results indicate that open-access data overestimate land eligibility by 4.0% to 4.5% compared to the official Basis-DLM data. Spatial similarities and discrepancies between the OSM, CEMS CLC, and Basis-DLM land uses were identified. The CLC data exhibit higher consistency with Basis-DLM. These findings emphasise the importance of selecting the appropriate data source depending on the purpose and the use of official data sources for accurate and spatially differentiated decision-making and project planning at different scales. Open-access data sources can be applied for initial orientation and large-scale rough assessment as they balance data accuracy and accessibility. [ABSTRACT FROM AUTHOR]

The International GNSS Service analysis centers provide orbit products of GPS satellites with weekly, daily, and sub-daily latency. The most frequent ultra-rapid products, which include 24 h of orbits derived from observations and 24 h of orbit predictions, are vital for real-time applications. However, the predicted part of the ultra-rapid orbits is less accurate than the estimated part and has deviations of several decimeters with respect to the final products. In this study, we investigate the potential of applying machine-learning (ML) and deep-learning (DL) algorithms to further enhance physics-based orbit predictions. We employed multiple ML/DL algorithms and comprehensively compared the performances of different models. Since the prediction errors of the physics-based propagators accumulate with time and have sequential characteristics, specific sequential modeling algorithms, such as Long Short-Term Memory (LSTM), show superiority. Our approach shows promising results with average improvements of 47% in 3D RMS within the 24-h prediction interval of the ultra-rapid products. In the end, we applied the orbit predictions improved by LSTM to kinematic precise point positioning and demonstrated the benefits of LSTM-improved orbit predictions for positioning applications. The accuracy of the station coordinates estimated based on these products is improved by 16% on average compared to those using ultra-rapid orbit predictions. [ABSTRACT FROM AUTHOR]