## 5 Discussion  
Globally, wheat-growing regions are distributed within areas that experience a relatively high frequency of clouds [59]. Persistent cloud cover limits the use of HSR sensors over large spatial extents due to the low frequency of the observations, whereas the other sensors that have HTF are constrained by the LSR. Although blending improves the monitoring of rapidly changing processes such as crop growth, it may introduce unforeseen spatial and temporal variances in the blended images when images are often not observed concurrently [10], and implementing the current suite of algorithms across large areas is currently computationally expensive. Therefore, it is important to systematically quantify and evaluate the utility of blending imagery across time and space before embarking on the development of nationwide blending capabilities. This study developed a parsimonious approach to identify when and where blending is beneficial for crop yield prediction at the 25-m pixel resolution. When incomplete Landsat series falls below 42% of the possible imagery in the growing season, blending is recommended because it improves the accuracy of the yield estimates. When incomplete Landsat series exceeds 42% of data, the use of HTF LSR data (e.g., MODIS) for the 25-m pixel-level yield prediction is recommended, reducing the computational need for blending. LTF HSR data show benefits, for example, in providing detailed information on plant photosynthesis across space; thus, they should be solely used for crop yield prediction when enough cloud-free images are available throughout the growing season. Annual precipitation, which is strongly associated with cloudiness [60; 61] and, thus, partly governs the amount of missing Landsat data, indicates which data sources should be preferably used (Figure 8). For instance, MODIS should be used for the prediction for greater than 50% of the wheatbelt during wet years (e.g., 2003, 2010, 2011, and 2016). During dry years (e.g., 2006 and 2018), the proportion of the wheatbelt where Landsat is suitable for yield prediction increases by \(>\)10% compared to the average proportion (17%). For crop yield prediction, blended data can be optimally applied across approximately 33% of the wheatbelt during dry to normal years. Figure 9: Scattergram for C-Crop-predicted yield against observed values for 2015 at the field level for (**a**) Western Australia (\(n=63\)) and (**b**) eastern Australia (\(n=6\)) wheatbelt using MODIS and L–M blended data for gap-filling Landsat (\(L_{LM}\)) when the fraction of incomplete Landsat series is below 42% for the growing season. The \(RMSE\) statistics have units of t/ha. Crop production fluctuates from year to year due to the rapidly changing patterns of precipitation and temperature [42; 62; 63]. Over the last 50 years, the amount of precipitation received by the Australian wheatbelt declined [64]. Based on model projections, it was also suggested that the changing climatic conditions will affect both the frequency and intensity of the extreme events such as floods, heatwaves, and droughs, which will impact agricultural production [65; 66]. Within the context of the present study, an increase in the number of clear sky days can be beneficial for the use of RS in crop yield forecasting. Moreover, a decline in the number of rain days in recent decades within the wheatbelt area can also be noted (see Figure S4, Supplementary Materials). Here, this decline in the number of rain days is assumed to correlate with the increase in the number of days with clear skies. However, a decrease in the number of rain days does not necessarily imply an absence of clouds, as there could be non-precipitating clouds. However, studies such as [PERSON] et al. [67], using observations and Coupled Model Intercomparison Project Phase 5 (CMIP5) model outputs, showed that, on average, there is a decline in cloud amount across the region from 60\({}^{\circ}\) S-60\({}^{\circ}\) north (N).