[PERSON]  
1 Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada; [PERSON]ESS] (J.S. ); [PERSON]ESS] (B.Q. ); [EMAIL_ADDRESS] (T.H. ); [PERSON]SS] (Y.Z. ); [EMAIL_ADDRESS] (T.D. ); [EMAIL_ADDRESS] (Q.J. ); [EMAIL_ADDRESS] (T.M.) [PERSON]ESS] (T.M.) [PERSON]  
1 Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada; [PERSON]ESS] (J.S. ); [PERSON]ESS] (B.Q. ); [EMAIL_ADDRESS] (T.H. ); [PERSON]SS] (Y.Z. ); [EMAIL_ADDRESS] (T.D. ); [EMAIL_ADDRESS] (Q.J. ); [EMAIL_ADDRESS] (T.M.) [PERSON]ESS] (T.M.) [PERSON]  
1 Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada; [PERSON]ESS] (J.S. ); [PERSON]ESS] (B.Q. ); [EMAIL_ADDRESS] (T.H. ); [PERSON]SS] (Y.Z. ); [EMAIL_ADDRESS] (T.D. ); [EMAIL_ADDRESS] (Q.J. ); [EMAIL_ADDRESS] (T.M.) [PERSON]ESS] (T.M.) [PERSON]  
1 Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada; [PERSON]ESS] (J.S. ); [PERSON]ESS] (B.Q. ); [EMAIL_ADDRESS] (T.H. ); [PERSON]SS] (Y.Z. ); [EMAIL_ADDRESS] (T.D. ); [EMAIL_ADDRESS] (Q.J. ); [EMAIL_ADDRESS] (T.M.) [PERSON]ESS] (T.M.) [PERSON]  
1 Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada; [PERSON]ESS] (J.S. ); [PERSON]ESS] (B.Q. ); [EMAIL_ADDRESS] (T.H. ); [PERSON]@cancanopy photosynthesis [9]. In particular, the fractional absorbed photosynthetically active radiation (fAPAR) is highly correlated with many spectral vegetation indices [10; 11]. Crop biomass accumulation is proportional to the cumulative absorbed photosynthetically active radiation (APAR) regulated by environmental stresses; therefore, fAPAR can be used to model crop yields with radiation use efficiency (RUE) models [12; 13]. Assimilation of time-series remote sensing products into process-based crop growth models ([14; 15; 16] or simple RUE models [17]) has become a research trend. Although it has been found that data assimilation is effective in determining uncertain parameters in the soil-crop-atmosphere system to improve crop growth modeling and yield estimation, it requires a large number of parameters to be localized and measured; hence, it is suitable for field or plot-level experiments but may not be suitable for regional-scale applications. Direct use of vegetation indices for yield modeling is also possible because they are found to be strongly correlated with crop yields during the grain filling stage [18]. In practice, regression models for crop yield estimation are often established using vegetation indices and reported or measured crop yields. Although empirical models may be region- or year-specific, they can be effective for regional-scale crop yield estimation across multiple years [1; 19; 20; 21; 22]. The normalized difference vegetation index (NDVI) derived from the advanced very high resolution radiometer (AVHRR) data has been extensively used because of its long history and global coverage [7; 23; 24; 25; 26]. Later on, data from the moderate resolution imaging spectroradiometer (MODIS) has been used more often because of better spatial, spectral, and radiometric resolutions [1; 21]. A recent research trend is to merge these coarse resolution satellite data with high-spatial low-temporal resolution satellite data (e.g., Landsat data) to generate high resolution time-series data for field level studies [17; 27; 28; 29]. Data volume and computation-resource requirement are high if this method is applied at the regional scale and over a long time period, although mature cloud computing techniques might provide a solution. Newly launched satellite constellations such as Sentinel-2, RapidEye, or PlanetScope will provide global coverage high temporal and high spatial resolution data. This will resolve problems encountered in studies on crop monitoring and yield estimation at the field or plot levels in the future; however, they do not provide a solution for retrospective studies.