Global crop monitoring has emerged as a critical component of sustainable agriculture and food security under increasing climatic variability and population growth. Traditional ground-based surveys and desktop Geographic Information Systems face substantial limitations in scalability, timeliness, and global coverage. Cloud-native geospatial platforms represent a transformative approach by leveraging distributed computing infrastructures, petabyte-scale data repositories, and advanced analytics to enable near real-time monitoring of agricultural systems at planetary scales. This article reviews the architectures, data integration frameworks, and analytical capabilities of cloud-native platforms specifically designed for crop monitoring applications. Key technologies examined include satellite remote sensing systems (optical, synthetic aperture radar, and hyperspectral), unmanned aerial vehicle sensors, Internet of Things devices, and their integration within cloud environments such as Google Earth Engine, Amazon Web Services, and Microsoft Azure. The article analyzes big geospatial data processing frameworks, artificial intelligence and machine learning algorithms for crop analytics, and decision support tools enabling yield prediction, crop health assessment, and climate resilience monitoring. Major applications spanning precision agriculture, food security early warning, and sustainable resource management are critically evaluated. The review concludes by addressing challenges related to data volume management, computational costs, validation uncertainty, and equitable access, while highlighting future directions including digital twin technologies, edge-cloud computing synergies, and autonomous monitoring systems for next-generation agricultural informatics.