The Digital Elevation Model(DEM)is one of the most important data sources for various scientific studies and applications.Currently,one important data source for large-scale DEM generation originates from the TerraSAR-X add-on for digital elevation measurement(TanDEM-X)mission,which provides bistatic interferometric Synthetic Aperture Radar(InSAR)data with high spatial resolution(12 m)at the global scale.However,in forest areas,the retrieval of the subcanopy topography using TanDEM-X InSAR data still faces notable challenges because of the effects of the forest scattering process on InSAR height measurements and the limited penetration capability of X-band's signals,causing the measured elevation to be between the ground surface and the top of the tree canopy.Although SAR signals with long wavelength has strong penetrability in the forest layer,subcanopy topography still cannot be measured due to the volume scattering effect from tree canopies or trunks.In addition,the missing space-borne PolInSAR or TomoSAR data pose another limitation for subcanopy topography estimation.In this study,a new method to extract subcanopy topography over forested areas is proposed.The method uses a combination of TanDEM-X DEM and Sentinel-2 multispectral data.TanDEM-X DEM and the multiband data of Sentinel-2 are regarded as the input variables,while the high-precision ground elevation data was considered as the target variable.Subsequently,the random forest fitting method is used to construct the subcanopy topography estimation predictive model.According to the obtained model,we can extract a large-scale subcanopy topography over the areas without reference data.Results show that the subcanopy topography derived via the proposed method has an RMSE of 3.7 and 7.78 m for the two forest sites,representing an improvement of approximately 76％and 63％,respectively,in comparison with the original TanDEM-X DEM.The experimental results also show that the resultant subcanopy topography can maintain more detailed topographic information.All these findings indicate that based on publicly available data,the proposed method has great potential for extracting large-scale subcanopy topography at high spatial resolutions.
维普
万方数据
