Remote sensing is of unique advantages in quickly obtaining and analyzing information such as crop types, planting areas, and yields duo to its rapid, macroscopic, non-destructive and objective observing characteristics. The crop spatial distribution map, planting area, and yield information extracted or interpreted by remote sensing can serve many agricultural applications such as resource supervision, information census, insurance and investment, and precision agriculture. The research status, problems and future potential research directions of crop type identification and yield estimation using remote sensing were summarized. Firstly, the research status of crop type identification was summarized from aspects of identification features and classification models. In view of the core problem of the lack of crop-wised identification feature knowledge, deep learning methods were proposed to be used to collaboratively learn the feature of “temporal-spatial-spectrum” in the process of crop growth, and finally a knowledge graph for crop remote sensing identification was constructed, so as to solve the problems, identification accuracy and identification efficiency, that affected current crop type identification using remotely sensed imagery. Secondly, by summarizing characteristics of three types of crop yield estimation models (i.e., empirical statistical model, remote sensing photosynthesis model and crop growth model), highly integrating crop growth model and deep learning methods were proposed to forecast crop yield which may be a valuable potential solution in the future, under the circumstance of the popularization of high spatial, high spectral, and high temporal-resolution data and the development of deep learning technology. Because crop growth model was of strong mechanism and deep learning methods were capable of learning complex problems. In the future, crop growth models can be used for point-scale simulation to drive deep learning methods to build yield forecasting model in complex scenarios, and finally a yield estimation model was achieved which used growth mechanism as constraints and deep learning model as spatial extrapolation.