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This  paper  presents  the  results  of  validation  of  the  MODIS  LAI  product  developed  at  the  Space  Research Institute of the Russian Academy of Sciences (the IKI MODIS LAI product). Validation was performed  over  a  sparse  spruce-birch  mixed  forest  stand  (~6.7  km2),  located  off  the  southern  slopes  of the Khibiny mountains in the central part of the Kola Peninsula, Russia. Field measurements were performed  in  June  2019.  Validation  was  performed  according  to  the  methodology  to  scale  local  point  measurements  to  the  low  resolution  of  MODIS  data  (230  m)  via  the  intermediate  link  —  high  reso-lution  Sentinel-2  MSI  data  (10  m).  Special  feature  of  this  work  is  that  local  measurements  of  forest  stand were performed using UAV technology. Such an approach is especially efficient in terms of map-ping spatial variability of canopy structure at large areas. Using photogrammetric techniques, the UAV data  were  processed  to  create  3D  model  of  canopy,  and  convert  it  to  map  of  canopy  closure.  Using  UAV-based estimate of canopy closure and Sentinel NDVI (Normalized Difference Vegetation Index) data  and  applying  statistical  and  semi-empirical  methods  Sentinel  LAI  map  was  derived  to  serve  as  a  baseline  for  MODIS  LAI  product  accuracy  assessment.  Mean  calculated  product  value  at  the  study  region  was  LAI  =  1.4  and  product  accuracy  7.1  %  (RMSE  =  43.1  %).  We  also  report  that  the  IKI  MODIS  LAI  product  may  have  low  data  coverage  at  the  northern  high  latitude  of  Russia:  over  the  extent  of  validation  site  the  coverage  was  ~40  %  in  2019.  Key  reason  is  a  (potentially)  excessive  cloud  shadow filtering at low solar zenith angles at high northern latitudes. We also highlight the fact that the sparse  northern  forests  have  a  well-developed  two-level  structure  (1  —  trees  and  2  —  unified  under-story levels, which includes forest undergrowth, grass-shrub and moss-lichen), which needs to be taken into account in the future research on the LAI algorithm development and product validation.