The population growth and consequent global rise in food demand require increasingly efficient agricultural solutions, in what is commonly called digital agriculture. Among promising initiatives, the use of remotely sensed data combined with machine learning algorithms enables handling faster agricultural operations with lower associated cost. One of the most important activities in digital agriculture is crop identification, which is fundamental for managing the inventory of a farm by producers and governmental authorities, and has been addressed by several prior works. In this article, we explore crop identification from a scalability perspective using the premise that data trained at a set of labeled geo-referenced regions in the agricultural pole at central western Brazil may be used for identifying crops at the entire municipality area. We propose to use convolutional long-short term memory networks for identifying crop types using a public labeled training set, and then apply the trained model for estimating crop area in a larger area involving the entire municipality unlabeled data. Our results were evaluated against governmental census data and report evidences that the tested crop identification network is able to successfully estimate crop area from much larger unlabeled data for different crop types.