## 5 Discussion and Conclusions
### Data Sources  
Data is the principal critical factor for success in machine-learning- and deep-learning-based work. Crop mapping and yields are commonly estimated from vegetation indices (e.g., NDVI), crop biophysical variables (e.g., Fraction of Photosynthetically Active Radiation), representing green biomass, and the dynamics of a vegetation index over time (e.g., green-up rate or senescence). Using multi-temporal satellite images covering several moments of cropping seasons may cover different features relevant to the research outcomes. Additionally, available climate forecasting data spanning each growing season increases the prediction capabilities of remote sensing data for yield prediction problems. Atmospheric predictions can even outperform those based on observational data [78]. The reason is that atmospheric predictions use not only observational climate data but also other data relevant to predict the climate during the growing season (e.g., climate change, climate connections between the region and other parts of the globe).  
The research work revisited in this article pinpoints several limitations offering opportunities for improvement in future work. Firstly, current tools cannot predict crop yields based on open-access high-resolution data. Even with the 10 m spatial resolution of Sentinel-2 A/B datasets, their geographical availability still needs to be improved. Additionally, the temporal resolution of the five-day revisit time may still be low for some objectives. Secondly, cloud cover contaminates Sentinel data over more than half of the earth's surface throughout the year. Synthetic Aperture Radar (SAR) captures cloud-free data and is sensitive to the crop plant structure, geometry and water content, but provides higher spatial resolutions. Finally, the increase in temporal and spatial resolution demands increased computational power to enable effective and efficient exploitation. Thus, the importance of computational cost analysis will increase along with imagery resolution. Adaptive temporal resolution, for example, can be exploited for high-resolution images when computational constraints are involved. Crop growth sensitivities to climatic events vary with the growth stage [79]. For example, the grain formation process is more sensitive to drought and heat stress than the vegetative.  
Countries with smallholder-dominated croplands present additional prediction challenges. The spatial and temporal mismatches between satellite data and smallholder fields, and the lack of high-quality labels required to train machine learning classifiers, are problems still being solved [76; 81]. Other identified challenges are the high spatial and temporal variability within and between fields and the tendency to intergrade with the surrounding vegetation. UAVs offer the potential for new insights into relative plant performance in terms of phenotypic traits and abiotic stress experiments [77]. UAVs offer high spatial resolution data at a smallholder cropland scale. In addition to crop prediction and mapping, these data can be explored for crop monitoring activities--e.g., crop density analysis, irrigation scheduling, and detection and diagnosis of diseases, an emergent area with vast potential in the future.