The spaceborne light detection and ranging （LiDAR）， as a novel active remote sensing technology， offers possibilities for global diurnal research. In this study， global sea surface chlorophyll-a （Chla） concentrations were inverted using satellite data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations （CALIPSO）. A feedforward neural network model based on LiDAR data （FNN-LID） was developed to reconstruct a long-term diurnal dataset of sea surface pCO₂ in the Arctic Ocean. Subsequently， verification and analysis were conducted on the polar sea surface Chla concentrations and sea surface pCO₂ based on active remote sensing. The results demonstrated that the inversion products generated by this algorithm exhibit high data quality and exhibit favorable consistency with both other passive remote sensing products and buoy observations. Moreover， these products effectively fill data gaps during polar winters. Along the Arctic Ocean， margin seas significantly influenced by terrestrial sources consistently display high sea surface Chla concentrations. The spatial distribution of sea surface pCO₂ in the Arctic Ocean manifests meridional variations， with marked seasonal fluctuations， even higher than 80 μatm. Over the past two decades， the Arctic Ocean has consistently acted as a carbon dioxide sink， while areas with substantial sea ice decline such as the East Siberian Sea and Kara Sea exhibit pronounced increases in sea surface pCO₂.