With the rapid development of the Earth observation technologies, remote sensing plays an increasingly important role in the applications of global change, ecological environment, territorial resources, natural disasters, national defense, smart city, and other applications. Accompanied by the development and improvement for the theories and applications of the quantitative remote sensing, there are still many unprecedented challenges. Because of the Earth complexity of the surface and the limitation of the remote sensing information, the quantitative applications of remote sensing generally are hampered by ill-posed inversion, scale effect, and other problems. Especially for cloudy and hilly regions, sufficiently influenced by the cloud, topography, and spatial heterogeneity, the quantitative applications of remote sensing becomes more difficult. Based on the analysis of the application status and the challenges faced by optical and microwave remote sensing, this paper reviews the theory and approaches applied for cloudy and hilly regions from the perspective of remote sensing data, preprocessing, and the quantitative theory and approaches, which include object-oriented inversion strategy, synergy of active and passive remote sensing, time series modeling, topographic correction of the forward model, and inversion of weak sensitive parameters. In addition, specific application examples are presented based on the recent research and practice achieved by the team of the authors, including continuous change detection of land cover, forest fire risk assessment, drought monitoring, and soil moisture retrieval under vegetation cover from active and passive remote sensing in the southwest China.