## 4 Discussion
### The Need for Integration of New and High-Resolution RS Datasets and High Quality Reference Data  
As the analysis in Table 2 of possibilities and remaining challenges of RS on small-scale agricultural and cropping systems in West Africa shows, there is a clear need for (i) integrating various sensor data and existing data sources, (ii) employing very high-resolution data, (iii) discovering new data sources, and (iv) basing the methods on high-quality reference data [29]. 1. Many studies have already employed multiple sensors in their analyses to address issues such as sparse data coverage, cloud cover, or the need to combine UAV data with satellite imagery [134]. This highlights the importance of integrating various sensors and satellite data to overcome some of the limitations related to the spatial-temporal-radiometric resolution restrictions. The topic of data fusion is extensively discussed in the recent literature [23; 190; 197]. Data fusion in RS offers substantial advantages by combining datasets of different modalities and resolutions to maximize their utility. For example, very high-resolution imagery can be fused with lower-resolution but more temporally frequent data, enabling precise field mapping while capturing dynamic processes such as crop growth. Advanced methods, such as deep learning, further enhance these applications, making data fusion a powerful tool for agricultural monitoring. However, challenges such as scalability for regional studies, high computational demands, and data compatibility issues limit its broader adoption, especially in resource-constrained regions. Despite these challenges, data fusion remains essential for addressing the complex needs of small-scale agriculture in West Africa [23; 190; 197]. In addition, several studies [67; 99; 106] point out that radar data are underutilized, a finding supported by the analysis of this review, which shows that only 8.8% of the reviewed articles employed radar data. Moreover, incorporating textural feature data or canopy height information retrieved from RS data have shown to enhance the results [29; 78; 98; 193]. * More than 70% of the studies use spatial resolutions higher than 50 m, with over 50% using resolutions higher than 30 m. This clearly illustrates the importance of the spatial resolution in addressing the variety, heterogeneity, small and irregularly shaped fields, and varied crop calendars of small-holder farming systems. The wide range of farming practices and resource availability (e.g., fertilizer, irrigation) emphasize the need for high temporal resolution in satellite data collection [7; 110]. This is further supported by the prevalence of time-series analyses and studies with multiple observations in a single year, showing the importance of time resolution for retrieving information through RS data. To effectively link household food security to satellite data, high resolution in both time and space is crucial [7; 47]. The rapid advancement of methods, especially in artificial intelligence, combined with the increasing availability of high-resolution spaceborne data are expected to noticeably influence the trends of future satellite-based research. These innovations enable the extraction of more nuanced and accurate insights from vast datasets, improving applications like crop monitoring, land use classification, and yield prediction. As big data analyses continue to evolve, challenges such as processing efficiency, model scalability, and the integration of multi-model datasets may be mitigated, paving the way for more robust, scalable, and actionable insights in satellite-based research [198]. To overcome the limitations, future research could require smaller and more cost-effective platforms, such as CubeSats, which function as a unified system or constellation. They provide higher spatial resolution well below 10 m, daily revisit times, and present substantial opportunities when paired with data fusion to assess remaining challenges [199]. * [PERSON] et al.