Accurately predicting the vegetation index (VI) of the Yangtze River Basin and analyzing its spatiotemporal trends are essential for assessing vegetation dynamics and providing recommendations for environmental resource management in the region. This study selected the key climate factors most strongly correlated with three vegetation indexes (VI): the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and kernel Normalized Difference Vegetation Index (kNDVI). Historical VI and climate data (2001–2020) were used to train, validate, and test a CNN-BiLSTM-AM deep learning model, which integrates the strengths of Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention Mechanism (AM). The performance of this model was compared with CNN-BiLSTM, LSTM, and BiLSTM-AM models to validate its superiority in predicting the VI. Finally, climate simulation data under three Shared Socioeconomic Pathway (SSP) scenarios (SSP1-1.9, SSP2-4.5, and SSP5-8.5) were used as inputs to the CNN-BiLSTM-AM model to predict the VI for the next 20 years (2021–2040), aiming to analyze spatiotemporal trends. The results showed the following: (1) Temperature, precipitation, and evapotranspiration had the highest correlation with VI data and were used as inputs to the time series VI model. (2) The CNN-BiLSTM-AM model combined with the EVI achieved the best performance (R² = 0.981, RMSE = 0.022, MAE = 0.019). (3) Under all three scenarios, the EVI over the next 20 years showed an upward trend compared to the previous 20 years, with the most significant growth observed under SSP5-8.5. Vegetation in the source region and the western part of the upper reaches increased slowly, while significant increases were observed in the eastern part of the upper reaches, middle reaches, lower reaches, and estuary. The analysis of the predicted EVI time series indicates that the vegetation growth conditions in the Yangtze River Basin will continue to improve over the next 20 years.

Forest aboveground biomass (AGB) products serve as essential references for research on carbon cycle and climate change. However, significant uncertainties exist regarding forest AGB products and their evaluation methods. This study aims to evaluate AGB products in the context of discrepancies in plot size and product scales, while also investigate the applicability of large-scale AGB products at a regional level. The National Aeronautics and Space Administration (NASA)’s Global Ecosystem Dynamics Investigation (GEDI) and the European Space Agency (ESA)’s Climate Change Initiative (CCI) biomass data were evaluated using sample plots from the National Forest Inventory (NFI). The study was conducted in Jilin Province, located in Northeast China, which is predominantly covered by natural forests. Spatial representativeness evaluation indicators for sample plots were established, followed by a comprehensive representativeness assessment and the selection of sample plots based on the criteria importance through the intercriteria correlation (CRITIC) method. Additionally, the study conducted an overall evaluation of the products, as well as evaluations across different biomass ranges and various forest types. The results indicate that the accuracy metrics demonstrated improved performance when using representative plots compared to all plots, with the R² increasing by 15.38%. Both products demonstrated optimal accuracy and stability in the 50–150 Mg/ha range. GEDI and CCI biomass data indicated an overall underestimation, with biases of −25.68 Mg/ha and −83.95 Mg/ha, respectively. Specifically, a slight overestimation occurred in the <50 Mg/ha range, while a gradually increasing underestimation was observed in the ≥50 Mg/ha range. This study highlights the advantages of spatial representativeness analysis in mitigating evaluation uncertainties arising from scale mismatches and enhancing the reliability of product evaluation. The accuracy trends of AGB products offer significant insights that could facilitate improvements and enhance their application.