# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 4 Applications of Ensemble Learning Algorithms
### Spatial Downscaling  
Remote sensing data are often spatially downscaled to obtain fine resolution (FR) data from coarse resolution (CR) remote sensing data. The finer resolution data provide more spatial details and thus bridge the gap between what CR data provide and what regional applications require. Statistical downscaling methods have frequently been used in several domains to obtain FR parameters since they require less computation and running time, and are more accurate than other downscaling methods such as dynamic downscaling [104].  
The statistical downscaling procedure for retrieving FR data from CR data is as follows: (1) develop models relating CR parameters and predictor variables or ancillary variables at a coarse resolution; (2) apply the CR models to the FR data, assuming that the relationships between target parameters and predictor variables remain unchanged at different spatial scales; and (3) obtain the target FR parameters from the models and FR predictor variables at a fine resolution. A variety of machine learning algorithms, especially the increasingly used ensemble learning algorithms, have been used as the relationships between target parameters and predictor variables are often nonlinear and complex.  
RF has been widely used to upscale large-scale precipitation, land surface temperature (LST), and soil moisture remote sensing data. For example, [PERSON] et al. [91] established nonparametric relationships between precipitation and six indicators (EVI, altitude, slope, aspect, latitude, and longitude) using RF and spatially downscaled annual precipitation for 2001-2012 from 0.25\({}^{\circ}\) pixels to 1 km pixels over the Tibetan Plateau. [PERSON] et al. [97] used RF to downscale the Tropical Rainfall Measuring Mission (TRMM) monthly precipitation product from 25 km resolution to produce monthly precipitation data for China at 1 km resolution. [PERSON] and [PERSON] [80] developed a model relating LST to digital elevation data, land cover type, and surface reflectance in the red and near-infrared bands using RF; they downscaled the spatial resolution of LST from 1 km to 250 m, with the RMSEs from 1.41 to 1.92 K. When compared to the widely-adopted TsHARP sharpening method, downscaling accuracy using RF improved up to 19%. [PERSON] et al. [83] created an RF-based regression tree to downscale the daily SMAP soil moisture product at 9 km resolution and created a 1 km soil moisture product using Sentinel-1 data, MODIS NDVI, land cover, and auxiliary topography and soil properties. [PERSON] et al. [94] combined wavelet analysis with machine learning algorithms to create a wavelet support vector machine (WSVM) and a wavelet random forest (WRF) algorithm to downscale North American multimodel ensemble (NMME) precipitation forecasts. Their results showed improvement over quantile mapping, with an average decrease in RMSE of 18-40 mm (21-33%).  
Several ensemble boosting algorithms have also been used for spatial downscaling. [PERSON] et al. [92] created high resolution soil moisture maps covering the entire Tibetan Plateau using GBRT with SMAP soil moisture data and related variables derived from MODIS and DEM. Using GBRT, [PERSON] et al. [64] yielded a significant improvement in downscaling global climate model predictions, compared to SVR that was previously found as the most suitable for downscaling climate in Iran. [PERSON] et al. [95] developed a multifactor geographically weighted machine learning algorithm using Sentinel-2A data that combined the results from three base learners (XGBoost, MARS, and Bayesian ridge regression) and downscaled LST at 30 m resolution to 10 m resolution.