## 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 c} \hline  & **Location and Extent** & **Crop Type** & **RS Data/Resolution** & **Method** & **Model Performance** & **Reference** \\ \hline
1 & Pakistan, Faisalabad district & Maize & Landsat 8/30 m & Least absolute shrinkage and selection (LASSO) regression model & R\({}^{2}\) = 0.95 & [115] \\ \hline
2 & U.S., Midwest & Maize and soybean & MODIS/250 m & Random forest and other models (e.g., LASSO, ridge regression (RIDICE)) & R\({}^{2}\) = 0.78 & [116] \\ \hline
3 & Brazil, Rio Grande do Sul (RS) state & Soybean & MODIS/250 m & Multivariate OLS linear regression, random forest and LSTM neural networks & RMSE = 0.4 Mg h\({}^{-1}\) & [117] \\ \hline
4 & Canada, Prince Edward Island and Brunswick provinces & Potato & NDVI was measured using the Field/Southcast NDVI &
\begin{tabular}{c} Support vector regression (SVR), linear regression (LR), elastic net (ENN), k-nearest \\ neighbor (k-NN) \\ \end{tabular} & RMSE = 4.62 t/ha & [118] \\ \hline
5 & China, North China Plain & Wheat & MODIS/250 m & Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) & R\({}^{2}\) = 0.75 & [119] \\ \hline
6 & Iran, Boshnyeh city & barley & Sentinel-2/10 m & Gaussian process regression algorithm, decision tree, K-nearest neighbour regression & R\({}^{2}\) = 0.84 & [120] \\ \hline \multirow{2}{*}{7} & Middle Amur Region, Khabarovski & Soybean & MODIS/250 m & Linear regression model & RMSE = 0.13 t/ha & [121] \\  & Municipal District & & & & & \\ \hline
8 & Senegal, wetland of & & Sentinel-2/10 m & Linear regression model & RRMSE = 28\% & [122] \\  & Central Senegal & & & & & \\ \hline \multirow{2}{*}{9} & Wisconsin, Arlington Agricultural & \multirow{2}{*}{Allalfa} & UAV-Based Hyperspectral & \multirow{2}{*}{Ensemble modelling} & R\({}^{2}\) = 0.874 & [123] \\  & Research Station & & Imagery/few cm & & & \\ \hline \multirow{2}{*}{10} & South-East of Queensland in & \multirow{2}{*}{Sugarane} & Integrating Landsat-8 and & \multirow{2}{*}{Linear regression model} & R\({}^{2}\) = 0.87 (RMSE = 11.33 tha\({}^{-1}\)) & [124] \\  & Australia, Bundberg & & Sentinel-2 & & & \\ \hline
11 & South Wales, More Plains Shine & Wheat & Sentinel-2/10 m & Multivariate linear regression & R\({}^{2}\) = 0.93 (RMSE = 0.64 t/ha) & [125] \\ \hline \multirow{2}{*}{12} & China, County Level (e.g., Hebei, & Winter wheat & AVHRR/0.85 arc degrees & Long short-term memory (LSTM) & R\({}^{2}\) = 0.77 (RMSE = 721 kg/ha) & [126] \\  & Iran, Shandberg & & & & & \\ \hline
13 & Uruguay, Soriano site (field scale) & Winter wheat & Landsat-7, Landsat-8/30 m & Simple regression method & RMSE = 966 kg h\({}^{-1}\) & [127] \\ \hline \multirow{2}{*}{14} & Florida, the University of Florida in & \multirow{2}{*}{Strawberry} & \multirow{2}{*}{UAV} & Region-based convolutional neural network & \multirow{2}{*}{84.10\%} & [128] \\  & Cita site & & & & & \\ \hline
15 & US, Midwestern & Maize & Landsat 5, 7, and 8/30 m & Simple Algorithm For Yield estimates (SAFV) & R\({}^{2}\) = 0.62 & [129] \\ \hline \multirow{2}{*}{16} & China, Central China & \multirow{2}{*}{Olsseed rape} & \multirow{2}{*}{UAV} & Partial least squares regression, support vector machine regression, artificial neural network & R\({}^{2}\) = 0.7 & [130] \\ \cline{1-1}  & Agricultural University & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of relevant papers using RS data to predict yield for different crop types.