The dynamic monitoring and analysis of wetland vegetation play important roles in revealing the change, restoration and reconstruction of the ecosystem environment. The increasing availability of high spatial-temporal resolution remote sensing data provides an unprecedented opportunity for wetland dynamic monitoring and change detection. Using the reconstructed dense monthly Landsat time series, this study focuses on the continuous monitoring of vegetation dynamics in Dongting Lake wetland, south China, in the last two decades (2000–2019) by using the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) method. Firstly, the flexible spatiotemporal data fusion (FSDAF) model is applied to blend Landsat and moderate-resolution imaging spectroradiometer (MODIS) images on the basis of the input image pair selection strategy named “cross-fusion” to generate the monthly time-series normalized difference vegetation index (NDVI) with the spatial resolution of 30 m. Then, the abrupt changes, trend, and seasonality of the vegetation in the study area as well as the uncertainties of change detection are estimated by the BEAST method. Results show that there is a close relationship between the ground true data and the estimated changepoints. A high overall accuracy (OA) of 87.37% and Kappa coefficient of 0.85 were achieved by the proposed framework. Additionally, the temporal validation got the interval intersection of 86.57% and the absolute difference of mean interval length of 6.8 days. All of the results demonstrate that the vegetation changes in the Dongting Lake wetland varied spatially and temporally in the last two decades, because of extreme weathers and anthropogenic factors. The presented approach can accurately identify the vegetation changes and time of disturbance in both the spatial and temporal domains, and also can retrieve the evolution process of wetland vegetation under the influence of climate changes and human activities. Therefore, it can be used to reveal potential causes of the degradation and recovery of wetland vegetation in subtropical areas.

Estimating the net primary production (NPP) of vegetation is essential for eco-environment conservation and carbon cycle research. Remote sensing techniques, combined with algorithm models, have been proven to be promising methods for NPP estimation. High-precision and real-time NPP monitoring in heterogeneous areas requires high spatio-temporal resolution remote sensing data, which are not easy to acquire by single remote sensors, especially in cloudy weather. This study proposes to fuse images of different sensors to provide high spatio-temporal resolution data for NPP estimation in cloud-prone areas. Firstly, the time series Normalized Difference Vegetation Index (NDVI) with a spatial resolution of 30 m and a temporal resolution of 16 days, are obtained by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Then, the time series NDVI data, combined with meteorological data are input into an improved Carnegie–Ames–Stanford Approach (CASA) model for NPP estimation. This method is validated by a case study of a heavily urbanized area, in the middle reaches of the Yangtze River in China. The results indicate that the NPP estimated by the fused NDVI data has more detailed spatial information than by using the MODIS data. The results show a strong correlation between the actual Landsat8 NDVI and the fused NDVI images, which means that the accuracy of synthetic NDVI images (a 16 day interval and a 30 m resolution) is reliable, and it can provide superior inputs for accurate estimations of a NPP time series. The correlation coefficient (R) and root mean square error between the NPP, based on the fused NDVI and the measured NPP, are 0.66 and 14.280 g C/(m²·yr), respectively, indicating a good consistency. The small discrepancy is caused by the uncertainties of fused NDVI, measurement errors, conversion errors, and other factors in the CASA model. In this study, we achieved NPP with high spatial and temporal resolutions, which can provide higher accuracies of NPP data for analyzing the carbon cycling heavily urbanized areas, compared with similar studies using mono-temporal NPP data. The spatio-temporal fusion technique is an effective way of generating high spatio-temporal resolution images from different sensors, thereby providing enough data for NPP monitoring in urbanized areas.

Accurate estimation and monitoring of forest aboveground biomass (AGB) are essential for understanding carbon dynamics, managing forest resources, and guiding environmental policies. However, the spatial and temporal patterns, dynamics, and driving factors of forest AGB in China over recent decades remain insufficiently understood, hindering ecosystem analysis and forest management strategies. This study combines multi-source remote sensing data with residual neural networks (ResNets) to develop the first 30 m resolution annual China Forest AGB dataset (1985–2023) with uncertainty quantification. Validation results confirm the robustness of the ResNets model, achieving an R2 of 0.91, RMSE of 16.49 Mg ha−1, and Bias of 0.50 Mg ha−1 against GEDI footprint AGBD, and an R2 of 0.63, RMSE of 68.26 Mg ha−1, and Bias of −19.87 Mg ha−1 against independent multi-year ground survey data. The dataset reveals a notable increase in China's average forest aboveground biomass density (AGBD) from 95.74 ± 11.30 Mg ha−1 in 1985 to 122.69 ± 13.94 Mg ha−1 in 2023. During this period, total forest aboveground carbon (AGC) stock rose from 5.50 ± 0.23 PgC to 13.97 ± 0.87 PgC, establishing China's forests as a significant carbon sink over the past four decades, with a net carbon sink of 0.22 ± 0.01 PgC yr−1, offsetting 11.5 %–14.9 % of China's annual fossil fuel and industrial carbon dioxide emissions. Forest growth contributed 65.1 % (5.75 PgC) of the total AGC increase, while forest expansion accounted for 34.9 % (3.09 PgC). This dataset provides critical information for forest carbon accounting in China and offers valuable insights for climate change mitigation, ecosystem conservation, and sustainable land management. The dataset is available at https://doi.org/10.5281/zenodo.12620984 (Part I: 1985–1993, Cai et al., 2025a), https://doi.org/10.5281/zenodo.12637101 (Part II: 1994–2001, Cai et al., 2025b), https://doi.org/10.5281/zenodo.12655492 (Part III: 2002–2008, Cai et al., 2025c), https://doi.org/10.5281/zenodo.12658255 (Part IV: 2009–2015, Cai et al., 2025d), https://doi.org/10.5281/zenodo.12742210 (Part V: 2016–2021, Cai et al., 2025e), https://doi.org/10.5281/zenodo.12747329 (Part VI: 2022-2023, Cai et al., 2025f).