Population growth and the increasing demand for food demand more efficient agricultural production systems. Conventional monitoring methods, based primarily on manual field inspections, require many human and financial resources, limiting their effectiveness and long-term sustainability for crop yield optimization. The development of sensor technologies and the availability of satellite-based datasets have significantly advanced remote sensing research applied to agriculture. This review aims to analyze the current state of satellite and unmanned aerial vehicle (UAV)-based platforms in remote sensing for crop monitoring, with a particular focus on methodological challenges. While the use of UAVs has grown, satellite imagery remains more prevalent due to the lack of standardization in UAV data acquisition and processing, which hinders cross-study comparability and large-scale applications. To address this, the establishment of technical guidelines ensuring data quality and consistency is essential. The development of standardized image databases is proposed as a critical step for training robust and transferable predictive models across different crops and regions. Field validation remains crucial to ensure the accuracy and applicability of remote sensing-derived products under real agricultural conditions. The integration of satellite and UAV-based sensors is proposed as a strategy to enable precise crop condition monitoring and support data-driven agricultural decision-making.