## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Blending Algorithm**} & \multirow{2}{*}{**Remote Sensing (RS)**} & \multirow{2}{*}{**Crop Type**} & \multicolumn{3}{c}{**RS Variables (e.g., Vegetation**} \\  & & & & **Index (VI)/Study** & **RS-Bases Model to** & **Key Results and Accuracy** \\  & & & & & **Period/Region(Study Area & Estimate Crop Yields \\  & & & & **(mm)** & & \\ \hline \multirow{4}{*}{[28]} & Spatial and Temporal & MODIS and Hauring & \multirow{4}{*}{Winter wheat} & Normalized difference & \multirow{4}{*}{The estimated winter wheat biomass correlated well with observed biomass (\(R^{2}=0.88\) and \(MAE\) = \(17.2\) kg/h) using the blended data} \\  & Adaptive Vegetation & Satellite & & vegetation index (VI)/Study & & \\  & Index Fusion Model & Charge-Coupled & & Shandong, China/NR & & \\  & (STAVW) & Device (H-CCD) & & & (\(A\)) The accumulated NYU derived from the blended data gave a higher prediction accuracy & \\  & & & & (MR/D2008-2009(A) & (\(R^{2}=0.67\) and \(RMSE=0.36\) (fm) for wheat yield \\  & & & & & & \\ \multirow{4}{*}{[29]} & \multirow{4}{*}{STARFM} & Observation of Earth & \multirow{4}{*}{Winter wheat} & Rago county, Jangsu, and (B) & \multirow{4}{*}{Empirical model} & \multirow{4}{*}{(B) The accumulated NYU derived from the blended data} \\  & & & & & China(A) 0.36 km\({}^{2}\) and (B) & & \\  & & & & 0.30 km\({}^{2}\) & & \\ \cline{3-5}  & & & & Evaporation & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & Evaporation & & \\ \cline{3-5}  & & & & (ET)/2013/Central Valley, & & \\ \cline{3-5}  & & & & & (nm/day). The spatial pattern of cumulative ET & \\ \cline{3-5}  & & & & & (0.21 km\({}^{2}\)) & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \hline \multirow{4}{*}{[27]} & STARFM & MODIS and Landsat & \multirow{4}{*}{Winter wheat} & \multirow{4}{*}{Winter wheat} & \multirow{4}{*}{Winter wheat} & \multirow{4}{*}{Winter wheat} & \multirow{4}{*}{(GELA) 2013/Southwestern} & \multirow{4}{*}{(Semi-empirical model} & \multirow{4}{*}{(GELA) 193/The combined GLAL and GLAT} \\  & & & & & & 193 the combination of GLAT and GLAT \\ \cline{3-5}  & & & & & & 16 to further improvements (\(R^{2}=0.76\) and \(RMSE=1.76\) (fm)). & \\ \hline \multirow{4}{*}{[30]} & STARFM & MODIS and Landsat & \multirow{4}{*}{Com and soybean} & \multirow{4}{*}{NDV/2012-2014 Central Iowa, the US/200 km\({}^{2}\)} & \multirow{4}{*}{Epirical model} & \multirow{4}{*}{A linear correlation (\(R^{2}=0.83\)) between remotely sensed group-up dates and the emergence dates reported by NASA.} \\  & & & & & & & \\ \cline{3-5}  & & & & ET/2010-2014/Mead, NE, the US(A) 0.49 km\({}^{2}\), (B) 0.52 km\({}^{2}\), & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of studies that used data fusion approaches to estimate crop yields and other variables highly related to crop yield (e.g., growing season evapotranspiration and crop phenology). Records are sorted chronologically then alphabetically. When more than one site is reported in a single paper, these are identified with an (A) and (B) in the relevant columns as required. \(R^{2}\): the coefficient of determination; \(RMSE\): root-mean-square error; \(MAE\): mean absolute error; \(MAE\): relative \(MAE\); \(NR\): not reported. In Australia, dyland agriculture is practiced across the region known colloquially as the “whewheallet”.