## 1 Introduction  
The world's human population is projected to increase by more than 35% by 2050 [1]. To contribute to improved global food security, the next generation of crop models and agricultural decision support tools needs to efficiently and consistently operate across various scales [2]. Accurate nationwide crop yield forecasts may ensure food security to the citizens. More accurate crop yield prediction at the subfield scale can provide farmers with more detailed information for guiding, within the growing season, in-field variable rate applications of fertilizer, herbicides, and pesticides. An efficient approach to monitor crop growth uses satellite observations providing repeated synoptic regional assessments ([PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2020). High-temporal-frequency (HTF) observations are required to accurately track crop development ([PERSON] et al., 2020) and predict yield ([PERSON] et al., 2020), and high-spatial-resolution (HSR) observations are necessary to capture within-field heterogeneity ([PERSON] et al., 2020). There is a trade-off between temporal frequency and spatial resolution ([PERSON] et al., 2020; [PERSON] et al., 2020; [PERSON] et al., 2020) as no single sensor can regularly image vast areas of the Earth used for nation-wide dryland cropping at a high spatial resolution. Commercial options of products combine HTF and HSR images achieved by increasing the number of satellites in orbit, such as PlanetScope; however, such commercial options are not affordable for national-scale monitoring, especially in a country as large as Australia (7.7 million km\({}^{2}\)), with the southern Australian cereal-based agricultural system notionally covering 530,000 km\({}^{2}\)([PERSON] et al., 2020). The Moderate Resolution Imaging Spectroradiometer (MODIS) provides complete coverage of the globe every day at a 250-m spatial resolution from red and near-infrared bands. This resolution constrains the capacity of describing cropping systems, crop growth, and field heterogeneities, especially when fields are small-to-moderate sized and landscapes are fragmented ([PERSON] et al., 2020; [PERSON] et al., 2020). Sensors with higher spatial resolution, such as Sentinel-2 or Landsat, are more suitable for these smaller fields/management units, but their lower temporal frequencies limit their ability to capture rapidly evolving crop processes, especially when factoring in the potential clouds ([PERSON] et al., 2020). [PERSON] et al. (2020) used valid Landsat observations (cloud cover \(<\)10%) during the growing season to generate optical-based vegetation indices (Vis) and then fitted a multilinear regression between these VIs and a large number of Agricultural Production Systems Simulator (APSIM) simulations for yield prediction. In a country like Australia, most of the arable non-irrigated land grows winter wheat, barley, oats, and canola, and their growth depends on the in-season rainfall; therefore, totally cloud-free time-series observations for their entire growing season are infrequent. To overcome these challenges, various methods for spatial filtering ([PERSON] et al., 2020), temporal gap-filling ([PERSON] et al., 2020; [PERSON] et al., 2020; [PERSON] et al., 2020), and data fusion ([PERSON] et al., 2020; [PERSON] et al., 2020; [PERSON] et al., 2020) were devised with a varying degree of success. As spatial filtering and temporal gap-filling disregard the spatial and temporal correlation of a pixel, they are highly sensitive to the choice of size/length of the spatial/temporal window ([PERSON] et al., 2020; [PERSON] et al., 2020). Data fusion techniques, on which this article focuses, were shown to improve the temporal resolution of fine-spatial-resolution data by blending observations from sensors with different spatial and temporal characteristics. Prominent examples are the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM; [PERSON] et al. (2020)) and the Enhanced STARFM (ESTARFM; [PERSON] et al. (2020)).