## 2 Enhancing Food Production through RS Applications in Agriculture
### Modelling and Forecasting Future Crop Yields
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Location and Extent** & **Crop Type** & **RS Data/Resolution** & **Method** & **Model Performance** & **Reference** \\ \hline \multirow{2}{*}{17} & Heliomagian province in northeast & Corn & H-1 satellites/30 m & Nonlinear regression & R\({}^{2}\) = 0.92 & [131] \\  & China, Hongqing farm & & & & & \\ \cline{1-1}  & Australia, districts and countries & Canola & MODIS/250 m & C-Crop model & R\({}^{2}\) = 0.81 & [132] \\ \hline \multirow{2}{*}{19} & The U.S., national and county levels & Maize & MODIS/250 m & Linear trend model & RMSE = 4.37 busibles per acre & [133] \\ \cline{1-1}  & Germany, Osnorbcka University of & & UAV-Based & Partial least-squares regression, multiple & R\({}^{2}\) = 0.79 & [134] \\ \cline{1-1}  & Applied Sciences in Belm & & Hyperspectral Imagery & linear regression & R\({}^{2}\) = 0.79 & [135] \\ \cline{1-1} \cline{2-6}
21 & North China Plain & Wheat & MODIS/250 m & MCWLA-vheat model & R\({}^{2}\) = 0.42 & [135] \\ \cline{1-1} \cline{2-6}
22 & madison county, Molly Caren Farm & Corn & Aerial imagery and & Random forest (RF); neural network (NN); & R\({}^{2}\) = 0.53 & [136] \\ \cline{1-1} \cline{2-6}  & U.S., central Iowa & Corn & MODIS/250 m; & & & \\ \cline{1-1} \cline{2-6}  & U.S., west Tennessee & Cotton lint & Landsat 8 & Artificial neural network & R\({}^{2}\) = 0.86 & [138] \\ \cline{1-1} \cline{2-6}
25 & China, Quating Town, Halian city & Wheat & H-CCD/30 m & Wheat Grow model & RMSE = 0.92, 1.12 g m\({}^{-2}\) & [139] \\ \cline{1-1} \cline{2-6}
26 & Australia, Acadis Hills, & & & & \\ \cline{1-1}  & Northern Territory & Mango & World View-3/0.31 m & Artificial neural network & R\({}^{2}\) = 0.60 & [140] \\ \cline{1-1} \cline{2-6}
27 & Hungary, country & Wheat, & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1} \cline{2-6}
28 & Brazil, Itirapina–SioPaulo & Sugarcane & UAV & Multiple linear regression & R\({}^{2}\) = 0.69 & [142] \\ \cline{1-1} \cline{2-6}
29 & Australia, northern & & & & \\ \cline{1-1}  & grain-growing region & Wheat & Landsat 5 and 8/30 m & Linear mixed-effects model & RMSE = 0.79 Mg/ha & [143] \\ \cline{1-1} \cline{2-6}
30 & Lao, Rice Research Center & Rice & MS-720 spectroradiometer & partial least-squares regression & R\({}^{2}\) = 0.873 & [144] \\ \cline{1-1} \cline{2-6}
31 & Bangladesh, Munshigan District & Potato & Landsat 7 and 8/30 m & Regression analysis & R\({}^{2}\) = 0.81 & [145] \\ \cline{1-1} \cline{2-6}
32 & China, Rugo city, Jiangsu province & Rice & UAVs & Multiple linear regression function & R\({}^{2}\) = 0.75 & [146] \\ \cline{1-1} \cline{2-6}
33 & USA, not Noir vineyards & & & & \\ \cline{1-1}  & in California & Grape & Landsat 7 and 8/30 m & Linear function & R\({}^{2}\) = 0.8 & [147] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Location and Extent** & **Crop Type** & **RS Data/Resolution** & **Method** & **Model Performance** & **Reference** \\ \hline
34 & USA(Illinois) and China & & & & & \\  & (Heilongjiang Province) & & & & & \\ \cline{2-6}
35 & China, Yangling District & Wheat & & (HJ-1A/B/30 m, & & \\  & RADARSAT-2/8 m & & & & \\ \cline{2-6}
36 & Brazil, Sio Paulo State & Sugarance & MOODS/250 m & Neural network wrapper & R\({}^{2}\) = 0.61 & [149] \\ \cline{2-6}
37 & U.S., Missouri Mississippi & & & & \\ \cline{2-6}
38 & Franc, near Toulouse, regional scale & Maize & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \cline{2-6}
39 & India, Arrah district & Wheat & SkySat imagery/2 m & Linear regression model & R\({}^{2}\) = 0.62 & [151] \\ \cline{2-6}
40 & Spain