## 3 Results
### Model Development and Validation
#### 3.1.4 Comparison of CASIF and Tower GPP  
To compare TSIF with CASIF, we first generated the mosaics of three MODIS tiles (H09V05, H09V05, and H09V05) from May 2018 to December 2021 and extracted the subset of the mosaics covering California (columns from 1 to 2800, rows from 1 to 3500). We then aggregated TROPOSIF corresponding to the subset and trained the models with the same configuration mentioned above using the MODIS subset data and TROPOSIF. Finally, we generated TSIF.  
Figure 9: Spatial and temporal patterns of TSIF and TROPOSIF from January to December in 2020. White means no data due to pixel removal of water, barren and built-up, or cloud contamination (**a**–**x**).  
TSIF can capture similar spatial patterns as CASIF. Figure 11 shows an example for April and June. TSIF is in good spatial agreement with CASIF, but with a smaller magnitude; we used daily _SIF_ instead of instantaneous SIF. We also did not calculate the temporal correlation of each pixel between TSIF and CASIF because the daily correction factor used in TROPOSIF SIF_Corr_743 may introduce nonlinearities compared to CASIF. However, at six AmeriFlux eddy covariance sites, TSIF showed a strong temporal correspondence with CASIF and GPP (measured from 12:00 to 14:00 corresponding to the overpass time of TROPOMI) across four different ecosystems (corn, woody savanna, grassland, and wetland) (Figure 12). We found that TSIF can capture similar features of photosynthetic activity of different ecosystems as CASIF does; for example, both TSIF and CASIF can clearly capture the onset of photosynthesis as well as the seasonal cycle of GPP in a cornfield (US-Bi2) (Figure 12a).  
In order to further explore the SIF-GPP relationship, we also drew the scatterplots of TSIF and GPP as shown in Figure 13. The tower GPP was aggregated temporally to 16-day to match TSIF's temporal resolution. The results show that TSIF is very strongly correlated with GPP with R\({}^{2}\) > 0.81 except for one site (US-TW4, R\({}^{2}\) = 0.58). The slopes range from 11.56 to 22.18.  
Figure 10: The spatial details of the downscaled TSIF; (**a**,**d**,**g**) are 3 \(\times\) 3 grids from TROPOSIF; (**b**,**e**,**h**) are 60 \(\times\) 60 grids of TSIF corresponding to (**a**,**d**,**g**); and (**c**,**f**,**i**) are 60 \(\times\) 60 grids from MCD12Q1 land cover (2019). TROPOSIF and TSIF data correspond to MODI Grid Tile H27V05 and the date of the data is 28 July 2019.  
Figure 11: (**a,b**) CASIF and (**c,d**) TSIF on 7 April 2019 and 10 June 2019. CASIF are temporally aggregated to 16-day. The data gaps result from missing data in MODIS or TROPOSIF.  
Figure 12: Time series of CASIF, TSIF, and GPP at six eddy covariance sites from AmeriFlux. Black points are the GPP measurements between 12:00 and 14:00 corresponding to the overpass time of TROPOMI; gray points represent all GPP measurements from six eddy covariance sites; green points represent CASIF; red points represent TSIF. The left axis is for GPP, the right axis is for SIF. The _x_-axis shows the number of days since 1 May 2018 (**a–f**).  
In [PERSON]'s work [24], one of the most significant features is the apparent double peak in the seasonal cycle of SIF. The time series of TSIF in California (Figure 14), shows the same double peak feature in the seasonal cycle of croplands (CRO), evergreen needleleaf forests (ENF), and the statewide TSIF mean. Moreover, the results also suggest that the SIF-GPP relationship at coarse temporal resolution (16-day) is stronger than that at fine temporal resolution (daily) when compared to [PERSON]'s finding [24].