Uncertainties in validation cause errors in the validation of remote sensing products. Therefore

these uncertainties must be minimized to increase the accuracy of remote sensing validation. This study aims to summarize and analyze three main uncertainty sources in the remote sensing validation of Leaf Area Index（ LAI）. The three sources are ground-based measurement uncertainty

validation model uncertainty

and scale effects. This study also proposes a method to minimize each uncertainty.Field measurement and satellite observation errors can minimize the effect of LAI measurement errors. The uncertainties in the sampling

field measurement

and LAI model are minimized to measure LAI and obtain LAI map.The scale mismatch errors among ground-based LAI

fine-scale LAI map

and coarse-scale LAI map are primarily estimated by three methods to reduce the uncertainties. The Taylor expansion of the transfer function as one of the three methods is emphasized to compute the scale bias

which is a function of intra-pixel spatial heterogeneity and the degree of transfer function non-linearity. The coarse-scale LAI reference map

which can be compared with LAI remote sensing products

is obtained from fine-scale LAI aggregation after minimizing the uncertainties.An obvious scale mismatch can be observed between the ground-based LAI and coarse-scale remote sensing LAI in the case of land surface heterogeneity. A fine-scale LAI map should be used as a bridge between the ground-based LAI and coarse-scale r emote sensing LAI. The error information in field measurement and satellite observation can be used to derive the site-specific r elationship and reduce the uncertainties in the fine-scale LAI reference map. The scaling bias caused by the nonlinearity of the Normalized Difference Vegetation Index（ NDVI） compensates for the scaling bias between LAI and NDVI. This bias can be c orrected through Taylor expansion.The uncertainties in the validation of LAI remote sensing products are significant. The three main uncertainties

namely

ground-based measurement uncertainty

validation model uncertainty

and scale effects

can be minimized to show a rigorous and reliable validation of LAI remote sensing products. For relatively homogeneous land surfaces

the ground-based measurements are enough to represent the sample plot

and the scale effects of the measurements and the model can be ignored. For heterogeneous land surfaces

the scale effects can be ignored if the model is linear. However

the uncertainties caused by the spatial representation for model and ground measurements should be considered. All three types of uncertainties should be considered if the model is non-linear.