## 3 Results
### LAI Estimation Models Used for Forest, Crop and Grassland Measures  
Figure 7 shows the number of forests, crop and grassland LAI studies that used empirical, physical, hybrid and other models. The figure also shows the accuracy range of the methods used in the abovementioned models. To measure model performance, R\({}^{2}\) and RMSE were used. Of the 168 papers that were reviewed, 19 papers applied multiple models with a single data source. In addition, the review showed that few studies used image texture to estimate LAI and, therefore, this requires further investigation.  
Figure 6: Application of different sensor systems for forest, crop and grassland.  
This review showed that empirical models were the most often used models for estimating the LAI of forest environment from 2010 to 2020. In terms of physical and hybrid models, forests had the lowest number of studies compared to crops and grasslands. The differences may be attributed to the typically complex forest structures. The number of crop studies that used physical and hybrid models, particularly PROSIAL, was higher compared to forests and grasslands. In terms of model types, vegetation index-based models and reflectance models were the most often applied model types for forests, crops and grasslands. Table 2 illustrates the models and sensors used for forest, crop and grassland studies and the level of accuracy achieved by each model and Table 3 shows the number of studies that applied each type of model. The number of applied models was higher than the number of studies reviewed because 19 papers used more than one model.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Forest**} & \multicolumn{2}{c}{**Crop**} & \multicolumn{4}{c}{**Grassland**} \\ \hline
**Model** & **Sensors** & **N** & **R** & **RMSE** & **N** & **R** & **RMSE** & **N** & **R** & **RMSE** & **Method** \\ \hline  & Vls based & 27 & 0.14-0.97 & 0.05-2.41 & 22 & 0.59–0.82 & 0.52–1.53 & 25 & 0.58–0.83 & 0.20–1.71 & Wide range of Vls \\ \hline Empirical Models & \begin{tabular}{c} Reflectance \\ based \\ \end{tabular} & 15 & 0.58–0.97 & 0.10–1.08 & 9 & 0.45–0.95 & 0.03–1.82 & 10 & 0.36–0.95 & 0.02–1.83 & Regressions \\ \hline  & Derived metrics & 19 & 0.57–0.98 & 0.02–1.47 & 3 & 0.35–0.95 & 0.01–1.95 & 2 & 0.45–0.98 & 0.16-0.45 & Regressions \\ \hline  & \begin{tabular}{c} Machine \\ Learning \\ \end{tabular} & 8 & 0.84–0.95 & 0.43–1.95 & 13 & 0.58–0.97 & 0.34-1.94 & 9 & 0.44–0.97 & 0.14–1.66 & ML Algorithms \\ \hline  & PROSAIL & 2 & 0.81–0.95 & 0.41–0.48 & 8 & 0.83–0.96 & 0.13–1.45 & 4 & 0.38–0.98 & 0.13–1.48 & \\ \hline  & \begin{tabular}{c} DART \\ \end{tabular} & 2 & 0.75–0.87 & 0.46–0.52 & 0 & & & 0 & & & Look up \\ \hline  & \begin{tabular}{c} PROSPECT + \\ DART \\ \end{tabular} & 1 & 0.77 & & 0 & & & 0 & & & & \\ \hline  & \begin{tabular}{c} 4-Scale \\ bidirectional \\ distribution \\ (BRD) \\ \end{tabular} & 2 & 0.80–0.86 & 0.78–1.4 & 1 & 0.85 & 1.3 & 0 & & & & Iterative \\ \hline  & \
\begin{tabular}{c} Other models \\ \end{tabular} & 5 & 0.71–0.95 & 0.71–0.96 & 3 & 0.83–0.99 & 0.31–0.92 & 2 & 0.87–0.95 & 0.83–0.95 &
\begin{tabular}{c} LUTs, dynamic \\ model, etc. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Models and sensors used for forest, crop and grassland studies and the level of accuracy achieved by each model.  
Figure 7: Distribution of different model types for forest, crop and grassland LAI estimations.