With the availability of the increased amount of remotely sensed data, quantitative remote sensing is in a period of rapid development. This paper reviews the recent development of the quantitative remote sensing of land surface from the two main aspects: inversion methodology and generation of the remote sensing data products. Because the number of environment variables in the atmosphere and land surface system is much larger than that of remote sensing observations, the nature of remote sensing inversion is an ill posed inversion problem. After reviewing the machine learning methods（e.g. artificial neural network, support vector regression, multivariate adaptive regression splines） and their applications, we mainly focus on seven regularization methods for overcoming the ill posed inversion problem: using multi-source data, a prior knowledge, constrained optimization, spatial and temporal constraints, integration of multiple inversion algorithms,data assimilation, and scaling. Another significant feature of the quantitative remote sensing development is satellite observations are transformed into different geophysical and geochemical parameters, namely remote sensing high-level products, for the user community by the data providers（e.g., data acenters）. This paper mainly introduces the latest development of the Global LAnd Surface Satellite（GLASS）products produced by Beijing Normal University, and the research and the development of the Climate Data Record for climate studies.

quantitative remote sensing;inversion;regularization;machine learning methods;GLASS products;climate data records