Agricultural lands account for nearly half of the global land area, and changes in agricultural land use directly affect food security, water security, ecological security, and climate change. Remote sensing is the main means for acquiring agricultural land use information. In recent years, the free opening of medium-resolution remote sensing data such as Landsat, Sentinel, and China's GaoFen satellites has opened unprecedented opportunities for extraction of agricultural land use information. A series of promising research progress has been made. This review paper analyzes the state of the art of agricultural land use information extraction from four aspects:cropland, crop type, agricultural planting system, and agricultural land management. We found that: (1) The products of cropland mapping have been improved from the past coarse resolution (500~1000 m) to a higher spatial resolution of 10~30 m in the past decade. The global and regional cropland layers have been well established; but there is a need to track historical cropland changes, especially to identify the key turning points, by making full use of the existing remote sensing data (data fusion and satellite constellation approaches). (2) Existing crop type mapping efforts have been mostly carried out by combining ground survey data with satellite remote sensing (mainly Landsat and Sentinel-2). It has been operationalized in North America and Europe, but the ability to monitor crop planting areas needs to be strengthened in other countries including China. Also, the early season monitoring capacity of crop type mapping needs to be improved; (3) Existing studies on tracking agricultural planting systems are mainly concentrated in Eastern Europe (e.g., the abandonment after the breakup of the Soviet Union). In China, cropland abandonment, rotation, and fallow are also common in the recent decade, due to economic and policy factors; however, existing studies are lacking on this issue. (4) in terms of the agricultural land management, encouraging progress has been made on the regional products of irrigation, but the reliability and accuracy of the products need to be improved. New technologies, including the emerging multiple sources of remote sensing data so-called remote sensing big data, deep learning algorithms, and cloud computing platforms (e.g., Google Earth Engineand Amazon Web Services) provide unprecedented opportunities for future agricultural land use information extraction, which will rely on (1) the fusion of multi-source data to form remote sensing big data with higher spatial, spectral, and temporal resolutions, (2) coupling of intelligent methods such as machine learning and deep learning algorithms with expert knowledge-based methods considering geographical and phenological information, and (3) the application of cutting-edge technologies such as remote sensing cloud computing platforms.