We propose a method for mapping above-ground biomass (AGB) (Mg ha⁻¹) in boreal forests based predominantly on Landsat 8 images and on canopy height models (CHM) generated using interferometric synthetic aperture radar (InSAR) from the Shuttle Radar Topographic Mission (SRTM) and the TanDEM-X mission. The original SRTM digital elevation model (DEM) was corrected by modelling the respective effects of landform and land cover on its errors and then subtracted from a TanDEM-X DSM to produce a SAR CHM. Among all the landform factors, the terrain curvature had the largest effect on SRTM elevation errors, with a r² of 0.29. The NDSI was the best predictor of the residual SRTM land cover error, with a r² of 0.30. The final SAR CHM had a RMSE of 2.45 m, with a bias of 0.07 m, compared to a lidar-based CHM. An AGB prediction model was developed based on a combination of the SAR CHM, TanDEM-X coherence, Landsat 8 NDVI, and other vegetation indices of RVI, DVI, GRVI, EVI, LAI, GNDVI, SAVI, GVI, Brightness, Greenness, and Wetness. The best results were obtained using a Random forest regression algorithm, at the stand level, yielding a RMSE of 26 Mg ha⁻¹ (34% of average biomass), with a r² of 0.62. This method has the potential of creating spatially continuous biomass maps over entire biomes using only spaceborne sensors and requiring only low-intensity calibration.

我们提出了一种主要基于Landsat 8图像和基于穿梭雷达地形学的干涉合成孔径雷达（InSAR）生成的冠层高度模型（CHM）绘制寒带森林中地上生物量（AGB）（Mg ha ^-1）的方法任务（SRTM）和TanDEM-X任务。原始SRTM数字高程模型（DEM）通过对地形和土地覆被各自的误差建模来校正，然后从TanDEM-X DSM中减去以生成SAR CHM。在所有地形因素中，地形曲率对SRTM高程误差的影响最大，ar ²为0.29。NDSI是残留SRTM土地覆被误差的最佳预测指标，其中ar ²为0.30。与基于激光雷达的CHM相比，最终的SAR CHM的RMSE为2.45 m，偏差为0.07 m。基于SAR CHM，TanDEM-X相干性，Landsat 8 NDVI和RVI，DVI，GRVI，EVI，LAI，GNDVI，SAVI，GVI，亮度，绿色和湿润。在林分水平上使用随机森林回归算法可获得最佳结果，其RMSE为26 Mg ha ^-1（平均生物量的34％），ar ²为0.62。该方法具有仅使用星载传感器即可创建整个生物群落的空间连续生物量图的潜力，并且仅需要低强度校准即可。