Land data and products from satellite observations are critically important for various environmental modeling and applications. Because of the failure of Landsat7 ETM+ and launch delay of Landsat Data Continuity Mission (LDCM), gaps exist in acquiring highresolution (about 30m) satellite data. There also may be a gap in acquiring moderateresolution data (1km) between Terra/Aqua and NPP missions. Moreover, in spite of multi-year efforts, moderate-resolution high-level land products still retain high uncertainties. One of the best solutions to improve various land products is to integrate multiple products from different satellites using different inversion algorithms. This is exactly the issue being addressed by the Committee on Earth Observation Satellites (CEOS) Land Surface Imaging Virtual Constellation Working Group. China has recently launched multiple environment and resource satellites and expects to launch more in the near future. The US Earth science community, however, is not yet quite familiar with the Chinese satellite data and products, and has difficulty in accessing them. We have interacted extensively with Chinese scientists and participated in their algorithm development and product generation. In this study, we will 1) continue to actively participate in Chinese satellite programs 2) Inter-compare and validate Chinese satellite data products to determine the uncertainties 3) evaluate the instrument algorithms and jointly work with Chinese colleagues to improve them if needed 4) archive and distribute data products at the University of Maryland and 5) extensively advertise available data and products. This proposal is built on our existing strong connections with Chinese Earth science community and is a direct response of the NASA call for Earth Science U.S. Participating Investigator (USPI). It is a cost-effective strategy for us to access the enormous amount of additional satellite data products for possibly filling in US data gaps, improving NASA land products and supporting a variety of applications.