Remote sensing-based forest aboveground biomass (AGB) estimation has been extensively explored in the past three decades, but how to effectively combine different sensor data and modeling algorithms is still poorly understood. This research conducted a comparative analysis of different datasets (e.g., Landsat Thematic Mapper (TM), ALOS PALSAR L-band data, and their combinations) and modeling algorithms (e.g., artificial neural network (ANN), support vector regression (SVR), Random Forest (RF), k-nearest neighbor (kNN), and linear regression (LR)) for AGB estimation in a subtropical region under non-stratification and stratification of forest types. The results show the following: (1) Landsat TM imagery provides more accurate AGB estimates (root mean squared error (RMSE) values in 27.7-29.3 Mg/ha) than ALOS PALSAR (RMSE values in 30.3-33.7 Mg/ha). The combination of TM and PALSAR data has similar performance for ANN and SVR, worse performance for RF and KNN, and slightly improved performance for LR. (2) Overestimation for small AGB values and underestimation for large AGB values are major problems when using the optical (e.g., Landsat) or radar (e.g., ALOS PALSAR) data. (3) LR is still an important tool for AGB modeling, especially for the AGB range of 40-120 Mg/ha. Machine learning algorithms have limited effects on improving AGB estimation overall, but ANN can improve AGB modeling when AGB values are greater than 120 Mg/ha. (4) Forest type and AGB range are important factors that influence AGB modeling performance. (5) Stratification based on forest types improved AGB estimation, especially when AGB was greater than 160 Mg/ha, using the LR approach. This research provides new insight for remote sensing-based AGB modeling for the subtropical forest ecosystem through a comprehensive analysis of different source data, modeling algorithms, and forest types. It is critical to develop an optimal AGB modeling procedure, including the collection of a sufficient number of sample plots, extraction of suitable variables and modeling algorithms, and evaluation of the AGB estimates.

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Forest stand age plays a crucial role in determining the terrestrial carbon source or sink strength and reflects major disturbance information. Forests in China have changed drastically in recent decades, but quantification of spatially explicit forest age at national level has been lacking to date. This study generated a national map of forest age at 1 km spatial resolution using the remotely sensed forest height and forest type data in 2005, as well as relationships between age and height retrieved from field observations. These relationships include biomass as an intermediate parameter for major forest types in different regions of China. Biomass-height and age-biomass relationships were well fitted using field observations, with respective R 2 values greater than 0.60 and 0.71 (P < 0.01), indicating the viability of age-height relationships developed for age estimation in China. The resulting map was evaluated by comparison with national, provincial, and county forest inventories. The validation had high regional (R 2 = 0.87, 2-8 years errors in six regions), provincial (R 2 = 0.53, errors less than 10 years and consistent age structure in most provinces), and plot (R 2 values of 0.16À0.32, P < 0.01) agreement between map values and inventory-based estimates. This confirms the reliability and applicability of the age-height approach demonstrated in this study for quantifying forest age over large regions. The map reveals a large spatial heterogeneity of forest age in China: old in southwestern, northwestern, and northeastern areas, and young in southern and eastern regions.

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