## 2 Materials and Methods
### Sentinel-2 Data  
Sentinel-2 data were used in this study. These data provide high spatial resolution surface reflectance products in 13 bands with a revisit period of approximately 5 days (twin satellites) and a spatial resolution ranging from 10 m to 60 m. They have high application value in agricultural monitoring. Sentinel-2 data at the L2A level, obtained from the Copernicus Data Open Access Hub ([[https://scichub.copernicus.eu/dhus/#/home](https://scichub.copernicus.eu/dhus/#/home)]([https://scichub.copernicus.eu/dhus/#/home](https://scichub.copernicus.eu/dhus/#/home)) (accessed on 15 January 2020)), were used in this study. Vegetation indices were calculated using SNAP 9.0.0 software, and the Biophysical Processor tool was used to calculate _f_PAR in the field area and to analyze the relationship between satellite winter wheat vegetation indices and _f_PAR. This tool relies mainly on radiative transfer models to generate a comprehensive database of vegetation characteristics and reflectance and then trains a neural network to estimate canopy characteristics from reflectance, providing canopy data with a maximum spatial resolution of 10 m, such as LAI, _f_PAR and fraction vegetation coverage (FVC).  
In this study, Sentinel-2 L2A data were downloaded from early March to late May in SZ. A total of 90 winter wheat plots were obtained by combining high-resolution Google Earth images. Among them, 60 plots were used as training samples and 30 plots as validation samples. These training samples are randomly and uniformly distributed within the selected spatial region of the experimental area. Based on a 100-tree random forest classifier, winter wheat mapping was performed with an overall classification accuracy of 96%. The obtained spatial distribution data of winter wheat were used to mask vegetation indices and _f_PAR to verify the correlation between satellite vegetation indices and _f_PAR during key phenological periods.  
In order to verify the applicability of the selected vegetation indices in other regions of the North China Plain, the same processing method was used to select winter wheat areas in ZK and HB, and the _f_PAR values based on the physical model were used to verify the applicability of the _f_PAR values obtained based on vegetation indices in different regions.  
Due to the different bandwidths between the high-spectral data measured by the ASD spectrometer and the Sentinel-2 data, in order to simultaneously compare the applicability of different vegetation indices at different scales, it is necessary to use the satellite's spectral response function to convert ground spectral data into satellite band reflectance. The method [23] was referenced to establish a one-to-one correspondence between ground reflectance and satellite reflectance. Sentinel 2 data were acquired simultaneously from two satellites, A and B, but there is a certain difference in the central wavelengths of the two satellites ([[https://sentiwiki.copernicus.eu/web/s2-mission#Acquisition-Resolutions](https://sentiwiki.copernicus.eu/web/s2-mission#Acquisition-Resolutions)]([https://sentiwiki.copernicus.eu/web/s2-mission#Acquisition-Resolutions](https://sentiwiki.copernicus.eu/web/s2-mission#Acquisition-Resolutions)) (accessed on 15 January 2020)). In order to facilitate the simulation of ASD data as the reflection of Sentinel 2 data, we have compromised the central wavelengths of the two satellites. For example, the central wavelength of the blue band for Sentinel 2a was 492.7 nm, while was 492.3 for Sentinel 2b. Therefore, we chose 492.5 nm as the center wavelength for the simulation and performed similar processing on other bands. The correspondence between the ASD spectrometer and the satellite is shown in Appendix B.