## 4 Results
### Evaluation of the Improvement in Yield Prediction Accuracy  
Given the availability of cloud-free Landsat and MODIS data across the wheatbelt (2000-2018), in concert with the previously determined threshold (Figure 6) and finding (Figure 5), we can demonstrate which imagery (i.e., either MODIS, Landsat, and \(L_{LM}\)) is best suitable for 25-m pixel-level crop yield prediction (Figure 7). Figure 7 shows the selection when using multiple satellite products for crop yield estimates across the wheatbelt over the past two decades. For the wheatbelt north of 35\({}^{\circ}\) south (S), there is a higher probability of obtaining complete cloud-free Landsat observations over the growing season in the east-west overlapping areas of adjacent Landsat Path-Rows, whilst \(L_{LM}\) is optimal elsewhere north of 35\({}^{\circ}\) S. South of 35\({}^{\circ}\) S, MODIS is optimal, with \(L_{LM}\) being optimal in the east-west overlapping areas of adjacent Landsat Path-Rows (in Figure 7). Figure 6: The statistical analysis of missing data in Landsat for 25-m pixel-level yield prediction, by evaluating (**a**) \(R^{2}\) and (**b**) _RMSE_ against the fraction of Landsat missing data during the growing season. L–M blended data were used to fill the gaps (\(L_{LM}\)) in the incomplete Landsat series. Figure 7: Multi-sensor optimal data selection across the wheatbelt (2000–2018) for 25-m pixel-level crop yield prediction, using the probability of MODIS, Landsat, and \(L_{LM}\) images. Blue denotes regions where incomplete Landsat series have a fraction of missing data exceeding 42%, thus indicating where MODIS should be used for yield estimates because it provides more frequent observations than Landsat. Green areas show where adjacent Landsat orbits overlap and, thus, where a complete once every 16-day Landsat series over the whole growing season is available. Areas colored red are those where the fraction of Landsat missing data is below the 0.42 threshold identified previously in Figure 6 when L–M blended data improve the yield prediction accuracy. For 25-m pixel-level yield prediction, blended data should be preferred on average for 33% of the Australian wheatbelt area (Figure 8a). MODIS and Landsat data remain the preferred data sources in 50% and 17% of cases, respectively. Figure 8b shows that the area percentage is positively correlated with annual precipitation for MODIS and negatively correlated for Landsat and the L-M blended data. MODIS has a larger scatter when annual precipitation is greater than 400 mm/year. Of the 53-million-ha Australia wheatbelt, complete Landsat time series were suitable for 28.6% of that area, MODIS for 31.5%, and \(L_{\text{LM}}\) for 39.9% during the 2015 growing season (Figure 8a). In this season, there are 104 fields available for assessing the accuracy improvement in yield prediction accuracy for nationwide crop yield prediction (Table S1, Supplementary Materials). Within these observed yield data, 69 fields are located where L-M blended images can improve the prediction accuracy according to the previously defined (see Figure 6) 42% Landsat missing data threshold. Figure 9 shows the predicted yields against the observed values for 63 fields in Western Australia (i.e., WA), and six fields in eastern Australia (i.e., Queensland (QLD), New south Wales (NSW), Victoria (VIC), and South Australia (SA)). Figure 8: Yearly analysis of multi-sensor data selection for 25-m pixel-level crop yield prediction (2000–2018) by evaluating (**a**) the area percentage and (**b**) its correlation with the annual precipitation (mm/year). The white dashed line shows that the area percentage is 50. \(\mu\): mean of population values; \(\sigma\): standard deviation; \(r\): correlation coefficient. The symbols in (**b**) are labeled with the last two digits of the year.