## 5 Discussion  
The initial objective of the study was to obtain an accurate FPAR using remote sensing data. We considered two strategies for obtaining FPAR parameters using remote sensing. One strategy was based on the empirical statistical model, which is simple in calculation and has few parameters, but remains highly dependent on geographical location, topography, and vegetation type. The other strategy was to use a physical model. This model is more feasible than the first model when considering the energy balance in radiation transmission, although it has more variables; thus, this latter model was adopted for this study. The inversion of FPAR using domestic satellites combined with physical models is rare, and no long-term or large-scale serial FPAR products have been formed based on Chinese satellite data. Unlike the HJ-1 data used by [PERSON], [PERSON], and [PERSON] et al., this study uses FY-3C MERSI data and experiments on five different vegetation types in different terrains in China that are more representative [21; 22; 45]. This research had certain reference values for the promotion and application of Chinese satellites and the generation of Chinese satellite FPAR products. Based on the FY-3C MERSI data, this study introduced the PROSAIL model and LUT algorithm and proposed an FPAR inversion strategy. The cross-validation confirmed that the proposed method had high accuracy, but there were still limitations in the study, which offer some directions for future research. In this study, the classification of vegetation types during the early stages of FPAR inversion was based on MODIS land cover products (MCD12Q1). The time resolution of MCD12Q1 was 1 year, and its spatial resolution was 500 m. There were classification errors for the seasonal variation of FPARs for different vegetation types; these errors produced mixed pixels in the inversion accuracy, which made the inversion results lack objectivity. Later studies could extract time-series land cover classification data from FY-3C MERSI data, so the classification data could also have seasonal variation characteristics. Due to a ubiquitous ill-posed problem, the inversion of quantitative remote sensing always uses a small number of parameters to infer complicated ground parameters, which are known to be much less than unknown. This study used prior knowledge to alleviate this ill-posed problem as much as possible, but our prior knowledge was limited and there were still many uncertainties in the inversion of FPAR. For model parameter settings of different vegetation types, relying on prior knowledge is feasible, but it is more precise to base such settings on actual measurements. In addition, it was reasonable to use NDVI to divide vegetation into different densities, but the threshold setting needs to be further explored according to different vegetation types. In the cross-validation of FY-FPAR and MODIS-FPAR, GEOV1-FPAR, and GLASS-FPAR, the spatial consistency, time consistency, and correlation analyses were applied to objectively evaluate the inversion accuracy of FY-FPAR and also the error analysis (Section 4.3). However, using only the data of 2014, which were limited in time and frequency to account for seasonal variations, may have caused limitations in quantitative analysis. Subsequent studies can consider adding FY-3C MERSI data for many years to compare the seasonal variation between FY-FPAR and other FPAR products. Thus, a comparison of monthly FPARs, using the multi-year average of the month, will reduce the noise of the time-series curve. In addition, for human and material conditions, it is possible to more accurately evaluate the inversion accuracy by considering a comparison of the measured data of the FPAR. Lastly, the inversion of FPAR in this paper was based on the inversion of FPAR for five types of vegetation: grassland, cropland, shrub, broadleaf forest, and needleleaf forest. To form FPAR products, different vegetation types needed to be spliced and fused.