## 1 Introduction  
Vegetation dynamics are of critical importance for understanding terrestrial ecosystem changes and underlying driving factors from climate change and human activities [1]. Global vegetation was found to display a decadal shift from an increasing (greening) trend to decreasing (browning) during the period 1982-2013 [1], though overall it was found to be greening during the period of 1982-2009 [2]. Vegetation decadal changes are very important and can indicate altered ecosystem functions, such as carbon uptake [3]. Therefore, further study is essential to explore spatiotemporal changes in vegetation productivity trends and ecosystem functions over a longer period [4, 5, 6]. However, finer, more consistent and longer-term observations were not available until the present, though many satellite-based data products (Table 1) have been widely applied. The Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is a biophysical parameter used in production efficiency models (PEM) to estimate vegetation productivity and carbon fluxes [7; 8]. Compared to other vegetation parameters, such as Gross Primary Production (GPP), FPAR is a more direct indicator of vegetation response to climate change. Some FPAR products have already been developed and widely applied; for example, \(\rm FPAR_{\rm MCD15A2}\), \(\rm GIMMS_{\rm 3g}\), and \(\rm FPAR_{\rm BNU}\)[9; 10; 11]. However, problems with these data include the short time horizon covered or low spatial resolution. For instance, \(\rm GIMMS3g\) has a temporal resolution of 15 days but a spatial resolution of \(1/12^{\circ}\) from 1982 to 2015 [12; 13], while the \(\rm FPAR_{\rm BNU}\) provides a long-term product at different spatial resolutions of 5 km and 1 km from AVHRR and MODIS, respectively [9; 10; 11]. The \(\rm FPAR_{\rm MCD15A2}\) was generated using a radiative-transfer model and a backup method based on relationships between FPAR and NDVI [9] and performs well despite a slight overestimation according to an accuracy assessment using ground-based measurements over Europe [10]. Though having a higher spatial resolution of 1 km and even 500 m, it was produced only from 2000 to the present [14]. Longer-term data are essential to explore vegetation changes over time and discover the underlying driving influences of climate change and human activity [15; 16], which is important for understanding the heterogeneity of ecosystems. Therefore, improving the spatial and temporal resolution of FPAR data is essential for advancing our understanding of ecosystem functioning, particularly in the context of a changing climate [17]. Therefore, there is a continuing data demand for long-term, finer spatial resolution. The need for a dataset with a 1 km resolution over a period of close to 40 years has been noted [17]. A spatiotemporal data fusion algorithm is a promising approach for building time series data with higher spatiotemporal resolution [18; 19; 20]. As one of the machine learning algorithms, Artificial Neural Network (ANN) has been widely used as a powerful tool for processing complex linear and nonlinear relationships between inputs and outputs without demanding statistical assumptions [21; 22; 23; 24]. In addition, it has been considered a more effective and better method than linear regression which was always applied to calibrate data against other data [25]. As a data-driven approach, ANN can effectively capture intricate relationships and patterns in large datasets and is flexible for incorporating multiple explanatory variables [26; 27]. By combining the advantages of different FPAR datasets, particularly spatial resolution and length of observations, ANN algorithms have great potential to provide accurate and precise estimations of FPAR, which can be used for ecological assessments and carbon cycle simulations in terrestrial ecosystems [28]. This paper, therefore, first constructs a long-time series FPAR dataset through an ANN algorithm with the Chinese terrestrial ecosystem as an example.