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Leaf area index (LAI) is a key surface parameter that describes the structure of vegetation and plays an important role in Earth system process modeling. In this paper, a new set of LAI products (MERSI GLOBCARBON LAI) has been developed based on the GLOBCARBON LAI algorithm and one year of FY-3A/MERSI land surface reflectance data. MERSI GLOBCARBON LAI has been inter-compared and validated over mainland China against MODIS land surface reflectance (LSR) derived LAI (using the same algorithm) and field LAI measurements. MERSI GLOBCARBON LAI and MODIS GLOBCARBON LAI show continuous and smooth LAI distributions at the start and end of the growing season. For most areas in China, the two LAI products agree well. The temporal variation in MERSI GLOBCARBON LAI and MODIS GLOBCARBON LAI consistently follows the growing season. The largest LAI difference occurs during July, when MERSI shows a much higher frequency of retrievals than does MODIS. Through validation of LAI retrievals with field measurements, our study demonstrates that LAI derived from MERSI and MODIS land surface reflectance products have comparable accuracy. MODIS top-of-atmosphere simple ratio (MODIS TOA SR) is related to MERSI TOA SR with linear correlation coefficients greater than 0.6. After atmospheric correction, the correlation coefficient increases from 0.69 to 0.75 over cropland and from 0.82 to 0.93 over grassland. However, atmospheric correction can still give rise to substantial differences in the reflectance data between the two sensors. Furthermore, different land cover types and different terrain relief have contrasting influences on the atmospheric correction, and these influences reduce the agreement between the two LAI products. This study shows the great potential of FY-3A/MERSI data for global LAI retrieval.