Accurate estimation of forest biomass is critical for modeling the carbon cycle and mitigating climate changes. Integration of multi-spectral satellite data and airborne LiDAR data can accurately estimate the biomass. However

the application of this strategy is limited in subtropical forests

particularly in China. In this study

a novelapproach was assessed using one strip of LiDAR point cloud and "wallto-wall" Landsat OLI free multi-spectral data combined with field-measured plot data to generate a low-cost and high-accuracy forest biomass map in a subtropical secondary forest in southeast China. Sixty square plots（30 m ×30 m） were established across the study site. First

the OLI data were processed by atmospheric and geometric correction

and LiDAR point clouds were extracted from the raw full-waveform LiDAR data. Second

fivesets of OLI and three sets of LiDAR metrics were extracted

and correlation analysis was performed with the field estimates of above-and below-ground biomass foroptimal metrics selection. Third

the "LiDAR biomass model" was fitted to LiDAR metrics extracted from the strip of LiDAR point cloud and the field plots within the strip. The "LiDAR-OLI biomass model" was fitted tothe OLI metrics and forest biomass estimated by the LiDAR data. Finally

the performance of the predictive models and the accuracy of the cross-validation results were evaluated through comparison with the accuracy assessment results of the "OLI biomass model." [Result] The"LiDAR-OLI biomass model"（R2 of above-and below-ground biomass estimation=0.69 and 0.56

respectively） exhibited improved performance than the "OLI biomass model"（R2 of above-and below-ground biomass estimation=0.69 and 0.56

respectively）. The relative biases of above-and below-ground biomass estimation increased by 14% and 15%

respectively. The mean differences in the cross-validation results for the "LiDAR-OLI biomass model"（mean differences in above-and below-ground biomass estimation=-12.9 and-0.15

respectively） were more accurate than the "OLI model"（mean differences in above-and below-ground biomass estimation=-18.99 and-0.33

respectively）. The ranges of above-and below-ground biomasses were 49.9—214.6 and 15.6—59.0 t·hm-2

respectively

in the entire study site. Moreover

the spatial distributions of above-and below-ground biomasses were similar to each other. Forests with high biomass were located in valleys and flat areas

whereas those with low biomass were located in the mountain ridge. This study provides an experimental basis for estimation of medium-scale forest parameters by synergizing active and passive remote sensing technologies. The study also explores the technological route of using one strip of LiDAR point cloud and "wall-to-wall" Landsat OLI free multi-spectral data for biomass mapping. These methods are relatively inexpensive and exhibit potential in supporting management and policies for addressing carbon stocks and understanding the effect of subtropical forest ecosystems under climate changes in China and elsewhere.