# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 1 Introduction  
Remote sensing is widely used in applications such as military reconnaissance, analysis of natural hazards, detection of land use and land cover change, measurement of sea ice distribution, precision agriculture, and estimation and mapping of carbon stocks [1; 2]. The primary properties of the target detected by the sensor in these applications are in the form of spatial, spectral, temporal, and polarization signatures [3]. Many models of physical processes have been developed that quantify the data relationships between sensor and target. These models include radiative transfer models, radiosity models, ray-tracing models, dynamic global vegetation models, and land surface microwave emission models, among others [4; 5; 6; 7; 8; 9]. However, it is generally difficult to obtain the parametric input required for a physical process model, especially at a large scale. Some studies have coupled a physical process model with a statistical method or used a statistical method with a machine learning algorithm to extract crucial predictions for a specific application. Since the remote sensing signals often provide a nonlinear representation of the target, machine learning and deep learning algorithms have been widely used in remote sensing applications, because they are not based on some underlying assumption regarding the distribution of the data and they have a potent capacity to capture nonlinear correlations [10; 11]. Support vector machines (SVM) and random forests (RF) are two well-known nonparametric machine learning algorithms that are used in many studies [12; 13; 14].  
Some studies have claimed that no single algorithm could outperform every other machine learning algorithm under all situations (sometimes called the no free lunch theorem) [15]. The ensemble learning technique has been developed in response to this claim [16; 17]. Ensemble learning techniques create multiple hypotheses and combine them to solve a given problem, in contrast to conventional machine learning techniques that aim to learn a single hypothesis from training data. [18; 19]. By combining multiple learnersand taking full advantage of these learners, ensemble algorithms have produced improved results and reduced the overfitting problem, and they possess the flexibility to deal with different tasks [17; 20; 21]. Bagging, boosting, and stacking are three well established ensemble techniques, although there exist some variants and other ensemble algorithms that have been used in practical applications [17]. Current implementations of these algorithms in R and Python are shown in Table 1.  
We present a general overview of the bagging, boosting, and stacking ensemble techniques used in remote sensing. We first review the principles underlying the three types of ensemble algorithms in Section 2 and describe our literature search for publications about bagging, boosting, and stacking ensembles in Section 3. In Section 4, we focus on several fields of study that widely use ensemble techniques for objectives such as crop yield prediction, estimation of forest structure parameters, mapping natural hazards, and spatial downscaling, as well as other applications. Finally, we examine the issues related to ensemble algorithms and future directions.