## 5 Conclusions  
In this study, an artificial neural network (ANN) model, which offers the advantage of effectively handling complex nonlinear relationships, identifying patterns and trendswithin data, and overcoming limitations associated with inaccurate assumptions or oversimplifications commonly observed in traditional methods, was developed to generate a fraction of absorbed photosynthetically active radiation (FPAR) data. It extends the MODIS product to 1980-2018, from a previous range of 2002-2018, which provides the possibility of direct use of upcoming MODIS products along with our dataset. This new data set was applied to investigate the spatiotemporal dynamics of Chinese terrestrial ecosystems from 1980 to 2018. Results suggest that vegetation growth has increased significantly and has sped up in the most recent decade, with a specific turning point around 2010. The turning point could be attributed to the monsoon climate change indicated by a warming hiatus around 2010. However, though additional research is essential to quantify the contribution from both natural environmental change and human activities, this study provides valuable insights into the spatiotemporal dynamics of FPAR in China and the underlying drivers of these changes. Due to some uncertainties still existing in the new FPAR data, some additional methods could be developed to improve data quality. However, this study not only provides a long-term dataset with lots of potential applications, but also provides a methodological framework for quality improvement of other data in the future, which can be used in further exploration of vegetation dynamic change and carbon stock estimation with longer time series and finer spatial resolution. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs15153707/s1](https://www.mdpi.com/article/10.3390/rs15153707/s1)]([https://www.mdpi.com/article/10.3390/rs15153707/s1](https://www.mdpi.com/article/10.3390/rs15153707/s1)), Figure S1: The land cover and land use in 2005 for the terrestrial ecosystems in China and its nine regions. The nine regions are Northwest China (NW), Inner Mongolia (IM), Northeast China (NE) Tibetan Plateau (TP), North China (NC), Southwest China (SW), Central China (CC), Southeast China (SE), and South China (SC); Figure S2: The framework to produce the Artificial Neural Networks (ANN)-based FPAR dataset over Chinese terrestrial ecosystems from 1980 to 2018 in this study; Figure S3: Using the FPAR product of MODIS (MCD15A2) as a benchmark, the structural similarity index (SSIM) was applied to evaluate the spatiotemporal consistency of the ANN-based FPAR developed in this study (a,c,e), and the FPAR from the GLASS (b,d,f) through the data on the day 225 of year 2005 (a,b), 2010 (c,d), and 2015 (e,f); Figure S4: The frequency of the structural similarity index (SSIM). The SSIM were calculated for the ANN-based FPAR developed in this study (FPARANN) and the FPAR from the GLASS (FPARBNU) by comparing with the FPAR product of MODIS (MCD15A2) as a benchmark through the data on the day 225 of year 2005, 2010, and 2015, respectively; Figure S5: The trends quantified by the slope of linear regression for the FPAR with the corresponding year for each decade in 1980s (a), 1990s (b), 2000 (c) and 2010s (d) respectively; Figure S6: The coefficient of determination (\(R^{2}\)) from random forest (RF) regressions with 1000 replications.