The leaf area index(LAI)estimation from remotely sensed data is one of hotspots in quantitative remote sensing of vegetation.Monitoring the spatial and temporal changes of LAI is very significant for carbon cycle of terrestrial ecosystem,global changes and other related studies.The paper selected ten 50 km!50 km sampling regions as our study area,including five forest regions,three crop regions and two grassland regions.The several parameters, such as leaf area index(LAI),canopy density,biomass,were measured in these regions.Taking leaf area index as a case,this study applied the partial least-squares regression method to build the estimation model of LAI combining remote sensing with in situ data and considering topographic effects for different vegetation types.Then,the crossvalidation approach was used to test model accuracy.The results indicated that the forest LAI inversion models taking topographic effects(altitude,aspect and slope)into accout is superior to those that topographic effects were not considered(R~2 increased from 0.30~0.75 to 0.50~0.80;RMSE decreased from 0.52~0.93 to 0.48~0.89 m~2/ m~2).For all vegetation types,the model validation R~2 and RMSE changed between 0.40~0.80,0.22~ 0.89 m~2/m~2,respectively.The method regarding LAI estimation from remotely sensed observations developed in this paper can help to understand topographic effects on LAI retrieval,and further provide scientific proof for monitoring vegetation growth status over mountain areas.