Synthetic Aperture Radar (SAR) backscatter measurements are an indispensable data source, offering day-night, all-weather Earth insights at a high spatial resolution. Most SAR sensors operate at X-, C-, and L-band frequencies, which are well-suited for monitoring Earth's water cycle and land surface properties. Consequently, numerous applications in climate research, disaster management, and sustainable resource management rely on SAR backscatter data to enable rapid mapping of floods, snow, soil moisture, vegetation, and many other bio-geophysical variables.In recent decades, the intensification of natural disasters due to climate change has led to a significant increase in demand for low-latency, high-resolution SAR backscatter data. The growing data volumes and required processing resources are pushing both data providers and users to their limits. Additionally, SAR sensors acquire data in a side-looking geometry, complicating interpretability and pre-processing, particularly for co-locating and radiometrically adjusting measurements. These challenges highlight the need for efficient, streamlined processing pipelines and coordinated efforts to provide standardised Analysis-Ready Data (ARD). This doctoral research is motivated by these needs, and aims to develop new concepts to advance the generation of analysis-ready backscatter data over land.The main data source used in this study is Ground Range Detected (GRD), Interferometric Wide (IW) swath SAR data from Sentinel-1 --- the flagship mission of ESA's open-data Copernicus programme --- which is employed to examine existing quality standards and processing frameworks. A key attribute of Sentinel-1 is its exceptional orbital stability, which enables the application of interferometry. However, an underexplored aspect is how  Sentinel-1's orbital stability influences different layers in SAR pre-processing workflows that generate sigma nought backscatter and Radiometric Terrain-Corrected (RTC) gamma nought backscatter.To assess this influence, a new lightweight and efficient SAR pre-processing engine named wizsard has been developed, which runs up to 50 times faster than version 8 of SeNtinel's Application Platform (SNAP). wizsard offers the flexibility to perform a Monte Carlo simulation with fluctuating orbital state vectors as input and parameter deviations at various workflow steps as output. Core parameters such as incidence angles, Local Contributing Areas (LCA) and SAR terrain masks exhibit negligible variance after simulation, making them suitable for offline generation for each relative orbit. Decoupling these parameters from the SAR pre-processing workflows, using them as external data sources, and generating persistent geolocation look-up tables results in performance improvements of up to 25% for sigma nought backscatter and 40% for RTC gamma nought backscatter.The current Committee on Earth Observation Satellites (CEOS) ARD standard (CEOS-ARD) for Normalised Radar Backscatter (NRB), based on RTC gamma nought backscatter, mitigates systematic, terrain-related viewing geometry effects but does not account for the scattering characteristics of Earth's land surface. Analysis of multi-year RTC gamma nought backscatter datacubes reveals a systematic incidence angle dependency of RTC gamma nought backscatter across different land cover types. This dependency can be described by a linear model with a single slope parameter and can be corrected by normalising backscatter to a reference incidence angle. Due to Sentinel-1's limited set of incidence angles and its observation scheme, appropriate normalisation is restricted to certain regions. However, building on the correlation between specific temporal backscatter statistics and the slope parameter, a novel machine learning approach using a Feed-Forward Neural Network (FFNN) has been developed. This approach unlocks the capability to estimate the slope parameter and to normalise RTC gamma nought backscatter on a global scale. Uninfluenced by land surface scattering characteristics and terrain geometry, the resulting NRB composites provide a robust foundation for land cover and land use mapping as well as bio-geophysical parameter retrieval.The concepts presented in this dissertation equip data providers and users with the knowledge necessary to efficiently generate ARD NRB products over land. The insights gained throughout this research lay a strong foundation for building reliable, low-latency, and high-quality products and services. With the upcoming launches of next-generation high-resolution SAR missions such as NISAR and ROSE-L, the results of this thesis mark an important step toward preparing to crunch petabytes of standardised ARD SAR backscatter data.