## 1 Introduction  
Recently, remote sensing has been used to continuously monitor and manage the ecological changes of plants in time and space [1, 2]. Compared with basic measurement methods, remote sensing has the characteristics of real-time, dynamic, and large-scale and can obtain a large area of soil and crop information. Remote sensing is widely used in crop area, growth, yield estimation, and soil moisture monitoring [3, 4, 5, 6, 7]. The Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is the ratio of Photosynthetically Active Radiation (PAR) absorbed by the green part of the vegetation canopy to the total PAR at the top of the canopy [8, 9]. The methods for estimating FPAR based on remote sensing mainly include the empirical statistical model and physical model. The empirical statistical model is widely used due to its simplicity, minimal parameters, and high computational efficiency. However, this model is less feasible than a more complex physical model, which involves more parameters inferred from the perspective of energy balance. There are several global FPAR data sets, such as the Moderate Resolution Imaging Spectro Radiometer (MODIS), the Multi-angle Imaging Spectro Radiometer (MISR) FPAR, the Carbon Cycle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) FPAR, the Joint Research Center (JRC) FPAR, the Geoland Version1 (GEOV01) FPAR, the Global Land Surface Satellite (GLASS) FPAR, and so on [9; 10; 11]. For example, [PERSON] et al. [12; 13; 14] formed a MODIS global Leaf Area Index (LAI)/FPAR product (MOD15) based on a three-dimensional radiated transfer model and the Look-Up Table (LUT) algorithm from MODIS data. [PERSON] et al. [15] generated a GEOV01 global FPAR product based on a neural network from spot/vegetation data. [PERSON] et al. [16] generated JRC-FPAR based on the continuous vegetation canopy model and the 6S model combined the Medium Resolution Imaging Spectrometer (MERIS) Global Vegetation Index (MGVI). [PERSON] and [PERSON] [17] generated a CYCLOPES-FPAR product based on the PROSAIL model and an artificial neural network algorithm. [PERSON] et al. [18] used numerical experiments (based on the SAIL model) to generate the full spatial resolution of the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) onboard Meteosat Second Generation (MSG) instrument every day and provide FPAR products synthesized in 10 days, covering four specific geographical areas (Europe, South America, and Africa, including North Africa and South Africa). [PERSON] et al. [19] formed the Global Inventory Modeling and Mapping Stadies (GIMS)'s global series of FPAR products based on the AVHRR sensor and the neural network of existing LAI/FPAR products. [PERSON] et al. [20] produced GLASS/FPAR products based on a general regression neural network (GRNN) algorithm from the MODIS/AVHRR surface reflectance data. At present, there are relatively few studies focused on the inversion of FPAR using Chinese autonomous satellites. [PERSON] et al. [21] used China Environmental and Disaster Monitoring and Forecasting Small Satellite Constellation (HJ-1) CCD data to invert the corn FPAR based on the empirical statistical model of Normalized Difference Vegetation Index (NDVI)-FPAR in Yucheng Shandong Province. [PERSON] et al. [22] conducted research based on HJ-1 CCD data to establish a statistical model to invert FPAR, and the results of FPAR agree well with those of MODIS FPAR. However, serialized FPAR products based on Chinese satellite data have not been developed for long time or large space scales. In addition, the Chinese Fengyun satellite, with a medium-resolution imaging spectrometer, has been providing data for five bands with a 250 m spatial resolution since 2008, thereby facilitating the research on the quantitative remote sensing of vegetation.