# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 4 Applications of Ensemble Learning Algorithms  
The co-occurrence network (Figure 2) combined with our reading of the ensemble literature shows that typical applications of ensemble learning algorithms were mainly concerned with the following issues: yield prediction, forest structure and biomass estimation, mapping of natural hazards (e.g., land susceptibility to natural disasters), and spatial downscaling of land surface temperature and rainfall (Table 2). RF was the most frequently used ensemble algorithm (30 times) in the 43 applications listed in Table 2, followed by stacking (13 times), while boosting algorithms were less frequently used (Table 3). Only seven studies used XGBoost; GBRT and AdaBoost were used four times. MLP and kNN often served as reference models to evaluate the performance of an ensemble algorithm. Figure 2: Co-occurrence network of keywords in ensemble literature. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Application** & **Main Input Datasets or Causative Factors** & **Algorithms Used** & **Algorithm with the Best Accuracy** \\ \hline
[61] & Oil palm yield prediction & Landsat time series imagery & RF, AdaBoost & RF \\ \hline
[62] & Flood susceptibility mapping & Slope, elevation, plan curvature, topographic wetness index (TWI), topographic position index, convergence index, stream power index (SPI), distance to stream, drainage density, rainfall, lithology, soil type, land use (LULC), and normalized difference vegetation index distance (NDVI) & J48 ensemble model, MultiBoosting J48, AdaBoost J48, random subspace J48 \\ \hline
[63] & Drought risk assessment & Elevation, slope, distance from the stream, drainage density, temperature, humidity, precipitation, evaporation, soil moisture, soil depth, soil texture, NDVI, LULC, geomorphology, groundwater level, deep tone, agriculture-dependent population, and population density & SVM, RF, SVR, and their ensemble with bagging, boosting and Stacking & SVR-stacking \\ \hline
[64] & Downscaling climate variables & Sea surface temperature, air temperature, geopotential height, and sea level pressure & GBRT, SVR & GBRT \\ \hline
[65] & Flash flood susceptibility prediction & Altitude, slope, aspect, plan curvature, profile curvature, distance from river, distance from road, land use, lithology, soil depth, rainfall, SPI, and TWI & BRT, RF, PRF, RRF, ERT & ERT \\ \hline
[66] & Winter wheat yield prediction & MODIS EVI, climate data, and the subsequasonal-to-seasonal (S2S) atmospheric prediction data & MLR, XGBoost, RF, SVR & XGBoost \\ \hline
[67] & Estimation of canopy height and growing stock volume & Airborne laser scanner (ALS), phased array type L-band synthetic aperture radar (SAR), and Landsat data & RF & — \\ \hline
[68] & Flood susceptibility mapping & Slope, elevation, plan curvature, NDVI, SPI, TWI, lithology, land use, rainfall, stream density, and distance to river & LMT, logistic regression, RF, Bayesian logistic regression, RF, Bagging-LMT \\ \hline
[25] & Forest biomass estimation & Advanced land observing satellite 2 L band, and Sentinel-1C band SAR, shuttle radar topography mission (SRTM) digital elevation model (DEM) data, and Sentinel-2 data & RF, RF Kriging & RF Kriging \\ \hline
[46] & Spatial interpolation of daily maximum air temperature & LST, NDVI, elevation, slope, aspect, solar radiation, global man-made impervious surface, human built-up and settlement extent, latitude, and longitude & Cokriging, MLR, SVR, RF, stacking, simple average ensemble & Stacking \\ \hline
[69] & Individual tree dendrometry & Field data, unmanned aerial vehicle(UAV)-Lidar data & SVR, MLP, RF, XGBoost & SVR \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of reviewed applications that used ensemble learning algorithms.