Spatially continuous estimates of forest canopy height at national to global scales are critical for quantifying forest carbon storage, understanding forest ecosystem processes, and developing forest management and restoration policies to mitigate global climate change. Spaceborne light detection and ranging (lidar) platforms, especially the Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) Advanced Topographic Laser Altimeter System (ATLAS), can measure forest canopy height in discrete footprints globally. In this study, we developed a novel neural network guided interpolation (NNGI) method to map forest canopy height by fusing GEDI, ICESat-2 ATLAS, and Sentinel-2 images. To evaluate the performance of the proposed NNGI method, we generated a 30-m forest canopy height product across China for the year 2019. More than 140 km² of drone-lidar data were collected across the country to train and validate the NNGI method. The results showed that the average forest canopy height across China is 15.90 m with a standard deviation of 5.77 m. We evaluated the interpolated forest canopy height product of China by over 1,100,000 GEDI validation footprints (R²=0.55, RMSE=5.32 m), about 33 km² drone-lidar validation data (R²=0.58, RMSE=4.93 m), and over 59,000 field plot measurements (R²=0.60, RMSE=4.88 m). Benefiting from the interpolation-based mapping strategy, the resulting product had almost no saturation effect in areas with taller forest canopies. The high accuracy of the forest canopy height product for China demonstrates the feasibility of the proposed NNGI method for monitoring spatially continuous forest canopy height at national to global scales by integrating multi-platform spaceborne lidar data and optical images, enabling opportunities to provide more accurate quantification of terrestrial carbon storage and better understanding of forest ecosystem processes.