## 4 Discussion  
SIF is tightly linked with vegetation photosynthesis at various temporal scales. Theoretically, SIF has the potential to be an effective predictor of crop yields, because it is associated with electron transport in light reaction of photosynthesis, and therefore, closely related to GPP of plants [48; 49; 64]. However, the spatial resolution of most available satellite SIF is relatively low (40 km \(\times\) 80 km for GOME-2 SIF and 30 km \(\times\) 60 for SCIAMACHY SIF), and it is difficult to use such low-resolution satellite SIF to estimate crop yields at small scales, such as the county-level. The spatial resolution of  
Figure 7: Feature importance (FI) scores of predictors in the ’EVI + SIF + Climate’ model for corn (**left**) and soybean (**right**). Values of the x-axis are feature importance scores, and a variable with a larger FI score carries more importance in predicting the crop yield (P = precipitation; T = temperature; VPD = vapor pressure deficit). OCO-2 SIF is much higher than those of GOME-2 and SCIAMACHY. However, the swath width of OCO-2 is only 10.3 km. There are gaps around 100 km between two adjacent swaths [92]. The spatial and temporal discontinuities of OCO-2 SIF hinder the application of these data in estimating crop yields at regional scales. In this study, three years of OCO-2 SIF data in different months were used to fit the phonology curves of corn and soybean for individual counties. With the fitted curves, monthly SIF means and maxima were estimated for corn and soybean in different counties. These monthly SIF values in the peak growing season were significantly positively correlated with three-year mean county-level corn and soybean yields (Figure 3), confirming that SIF is an effective indicator of crop yields owing to its direct linkage with photosynthesis. However, compared to EVI, our processed SIF had relatively weaker correlations with corn and soybean yields (Figure 3) and poor ability to capture the spatial variations of yields (Table 1). This is due to the fact that the county-level EVI used here was calculated from spatially and temporally continuous direct observations by the satellite, while the SIF values used were estimated from the phenology curves which were fitted from limited direct observations in three years. The numbers of SIF observations were uneven for a county, and locations of footprints might differ in different years. The discrepancy between estimated and real county-level mean SIF would definitely weaken the effectiveness of OCO-2 SIF data in predicting crop yields. Therefore, the development of spatially and temporally continuous high-resolution SIF datasets is required for improving estimates of crop yields. In the study area, climate-based DNN models overall performed better than remote sensing-based DNN models in capturing spatial variations of county-level corn and soybean yields, possibly due to the strong controls of VPD and precipitation on yields (Figure 3). In addition, the departures of yields estimated by climate-based DNN models from census data showed less spatial variations relative to those form SIF and EVI-based DNN models (Figure 6). These results indicated that climate information played a more important role in controlling the spatial variations of corn and soybean yields in this region. EVI was an effective indicator of crop biomass, which is tightly linked with final yields. Therefore, the performance of EVI-based DNN models was close to that of climate-based DNN models (Table 2 and Figure 4). However, similar to SIF-based models, EVI-based models performed poorer than climate-based models in counties with low area fractions of corn and soybean (Figure 6 and Figure S2), indicating that there were impacts of land cover heterogeneity on crop estimates by remote sensing data. The combination of different sources of data might improve crop yield estimation (Table 1).