In the agricultural ecosystem, it is necessary to grasp the differences of soil fertility and crop growth in time and space. With the rapid development of remote sensing and its popularization and application in social production practice, remote sensing has become a new way to obtain farmland information. At present, remote sensing means in the field of agroecosystem monitoring mainly include satellite remote sensing and unmanned aerial remote sensing. It can monitor and manage the agro-ecosystem environment in real time by acquiring remote sensing images. It can monitor the growth and identification of crops, pests, and diseases, and water supply, analysis timely, and effectivity for the spatial information provided by a feedback processing. But its performance needs to be improved. In this paper, the imaging methods and main features of remote sensing images are introduced in order to analyze the characteristics of agricultural ecosystem targets in remote sensing images. Then, based on object-oriented classification technology, a series of farmland images are preprocessed, segmented, and sparsely represented. Processing operations are used to study how to transform crop observation into crop and noncrop discrimination in remote sensing data. The research shows that the effective acquisition of crop image area and the remote sensing monitoring of crop farmland area can be achieved by processing the crop field map image obtained by remote sensing.