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Timely and accurate crop acreage information is essential for food security and the informed decision-making by governmental bodies and stakeholders in the agro-economic system. Surveys and fieldwork are expensive and time consuming, and the information is usually only released after the cropping season. Remote sensing technology is inexpensive, scales well for large areas, and offers the opportunity to gather within-season information. However, environmental and technical constraints complicate model applications to larger scales. For example, clouds and snow introduce data gaps, and the weather-driven crop development creates highly variable spectral-temporal signatures. This study determines the earliest possible month that permits the accurate classification of the major crops in the CONUS using gap-filled data, transfer learning, and cloud computing technology. The resulting within-season, annual crop classifications are generated with precision rates of up to 80%, presented as annual crop maps and county-level area statistics. The crop classification products presented in this work are unique, in that they were created with shorter records for the first time, using data within a concise time-range (i.e. a few months) and at a 30-meter spatial resolution, while covering continental scale. This novel prospect allows for potential new applications in forecasting regional crop productions. [ABSTRACT FROM AUTHOR]