## 5 Conclusions  
In this study, we proposed a method to use the OCO-2 SIF product to predict yields of corn and soybean in the U.S. Corn Belt. The ability of OCO-2 SIF products to estimate county-level crop yields in the study area was compared with those of MODIS EVI and conventional climate data. The following conclusions could be drawn from this study. (1) Monthly mean and maximum SIF during the peak growing season showed similar significant correlations with corn and soybean yields to MODIS EVI. The DNNs with SIF as predictors were able to capture spatial variations of corn and soybean yields but performed much poorer than those based on MODIS EVI and climate variables. The performance of SIF and MODIS EVI-based DNNs varied with the areal dominance of crops. In areas with low areal fraction of crops, the SIF and EVI-based DNNs tended to underestimate corn and soybean yields noticeably. The performance of DNNs with climate variables as predictors exhibited less spatial variability. (2) The combination of remote sensing and climate data improved the estimation of crop yields. Our processed county-level SIF data could provide useful supplementary information to MODIS EVI and climatic variables in estimating crop yields. DNNs of EVI and climatic variables in conjunction with SIF obviously outperformed DNNs with only EVI or climatic variables as predictors. The integration of MODIS EVI and climatic variables was able to improve the estimating accuracy of crop yields significantly. Further addition of SIF into models with MODIS EVI and climatic variables only marginally enhanced crop yields prediction. (3) Feature importance analysis indicated that MODIS EVI and climate predictors (e.g., VPD and temperature) during the peak growing season (from June to August) played critical roles in predicting yields of corn and soybean in the Midwestern 12 states in the U.S. for the DNNs with all SIF, EVI, and climatic variables as predictors. By contrast, the importance of SIF was much smaller than MODIS EVI and climatic variables in those DNNs. **Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2072-4292/12/7/1111/s1](http://www.mdpi.com/2072-4292/12/7/1111/s1)]([http://www.mdpi.com/2072-4292/12/7/1111/s1](http://www.mdpi.com/2072-4292/12/7/1111/s1)), **Figure S1**: Corn and soybean harvested area fraction and production fraction of the 12 Midwest states in the U.S. from 2015 to 2017. The data were compiled from USDA NASS crop data layer (CDL) and crop yield datasets. **Figure S2**: Corn and soybean growing area fraction in each county. The fractions were three-year-mean values (from 2015 to 2017) calculated using USDA NASS CDL. APE (absolute percentage error) in the barplots were calculated by averaging county-level absolute PE values of 'SIF', 'EVI', and 'Climate' models. In the groups with area fraction equal to 5%-15%, APE values of both 'SIF' and 'EVI' models were significantly lower than the 'Climate' models for corn and soybean. **Figure S3**: DNN structure employed in this study. The neural network consisted of one input layer, three hidden layers, and one output layer. (n = the number of input variables; m = the number of records). **Figure S4**: Feature importance (FI) scores of predictors of the 'SIF + EVI' model for corn (left panels) and soybean (right panels). Values of the x-axis are feature importance scores, and the variable with larger FI score carries more importance in predicting the crop yield. **Figure S5**: Feature importance (FI) scores of predictors of the 'EVI + Climate' model for corn (left panels) and soybean (right panels). Values of the x-axis are feature importance scores, and the variable with larger FI score carries more importance in predicting the crop yield. (P = precipitation; T = temperature; VPD = vapor pressure deficit). **Figure S6**: Feature importance (FI) scores of predictors of the 'SIF + Climate' model for corn (left panels) and soybean (right panels).