Abstract: Forestry remote sensing has entered the stage of quantitative inversion of parameters after air-photo interpretation, satellite visual interpretation and quantitative estimation of forest volume. Under the background of strong demands of remote sensing from forestry on operational monitoring and accuracy improvement, quantitative remote sensing is gradually integrated with forestry remote sensing. It has gradually matured in talent teams, theoretical models, data sources and application methods for the quantitative studies in forestry remote sensing. This paper puts forward the concept and framework of quantitative remote sensing of forestry (QRSF), and points out the key scientific problems: (1) how to adapt remote sensing interpretation, modeling and inversion to complex forest conditions; (2) how to improve the accuracy of parameter inversion; (3) how to enrich forestry remote sensing data sources; (3) how to develop highly intelligent and automated information extraction algorithm on remote sensing data. On the basis of introducing quantitative remote sensing models and general inversion methods suitable for forestry, the application methods of hyperspectral, thermal infrared, lidar and microwave remote sensing data sources in forestry are expounded. In the future, QRSF will make breakthroughs in the unified modeling of full-band data, information fusion mechanism, physical model inversion and large-scale data fusion.