## 6 Conclusions  
Sparse time series of satellite remote sensing, due to LTF and/or cloud contamination, represent one of the main barriers limiting accurate crop yield estimation at regional to national scales. Blending of HSR but LTF images with LSR but HTF images was proposed to increase the temporal resolution and maintain spatial details. In this study, the benefits of blending were tested for crop yield prediction across the Australian wheatbelt. We found that, when time series are gap-free, yield prediction from Landsat is the most accurate on both field and pixel scales. When Landsat time series contain <42% missing values during the growing season, blending is recommended for nationwide crop yield prediction at the 25-m pixel resolution. When Landsat time series contain >42% missing observations, MODIS outperforms blending. Across Australia, these recommendations would improve yield estimates by 0.15 t/ha for Western Australia and 0.28 t/ha for eastern Australia on average. By identifying where and when to blend, this work paves the way to more accurate monitoring of biophysical processes and yields, while keeping computational costs low. **Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2072-4292/12/10/1653/s1](http://www.mdpi.com/2072-4292/12/10/1653/s1)]([http://www.mdpi.com/2072-4292/12/10/1653/s1](http://www.mdpi.com/2072-4292/12/10/1653/s1)), Table S1: Summary of observed data (the number of field-years) used herein; Figure S1. Long-term (1901-2018) average monthly accumulated precipitation (mm/month) (left) and the long-term average rain days (right) across the wheatbelt for (a, b) Queensland, (c, d) New South Wales, (e, f) Victoria, (g, h) South Australia, (i, j) Western Australia, and (k, l) Tasmania. The precipitation data are sourced from [PERSON] et al. [37], and the wheat belt mask was resampled from native 2-km\({}^{2}\) horizontal resolution onto the precipitation data grid with 5-km\({}^{2}\) horizontal resolution using nearest neighbor [40]. The vertical black lines (a-f) represent one standard deviation of the monthly wheatbelt specific precipitation for each state. The horizontal line (g-l) represents the median of the data, the box spans from 25 th to 75 th quartiles of the data, and the rain day threshold is 0 mm/day. It should be noted that Northern Territory is not included in the analysis due to its small cropping area; Figure S2. An example of MODIS and Landsat blended NDVI using ESTARFM. The \(x\)-axis and \(y\)-axis are time (\(t\), DOY) and spatial resolution (m), respectively. Both MODIS and Landsat have valid observations at \(t_{1}\) and \(t_{2}\). The image at the center of the top row is a valid observation of MODIS at \(t_{s}\), and that at the center of the bottom row is Landsat-like blended data at \(t_{s}\) created using the 16-day MODIS composite images on the DOYs 257, 273, and 289, as well as the Landsat images acquired on the DOYs 257 and 289; Figure S3: The implementation flowchart of validation and identification of threshold for when to blend. The shaded areas indicate the original data sources; Figure S4: Long-term (1900-2018) average monthly accumulated precipitation (mm/month) across (a) eastern Australian, (b) Western Australia, and (c) the wheatbelt. The precipitation data are sourced from [PERSON] et al. [37]. **Author Contributions:** Conceptualization, Y.C., T.R.M., and R.J.D. ; methodology, Y.C. ; software, N.O. and N.G. ; validation, Y.C. ; formalism analysis, Y.C. ; investigation, Y.C. and F.W. ; resources, L.L. and N.G. ; writing--original draft preparation, Y.C. ; writing--review and editing, T.R.M., R.J.D., F.W., N.G., and R.L. ; visualization, Y.C. and N.G. ; supervision, T.R.M. and R.J.D. ; project administration, R.L. ; funding acquisition, R.L. All authors read and agreed to the published version of the manuscript. **Funding:** The authors acknowledged the support of the Digis escape Future Science Platform, funded by the CSIRO.