## 3 Innovation
### Innovative Mapping and Forecasting Methods
#### 3.3.3 Characteristics of Machine Learning Methods
Overall, tree-based algorithms are appealing due to their interpretability and comprehensibility. These algorithms permit examination of the tree structure and the variables utilized at each decision point, providing an importance (or relative influence) value for each explanatory variable. As a result, they can yield new insights, such as novel functional relationships between response and explanatory variables. [PERSON] et al. [103] have utilized such variable importance measures for feature selection. The ability to analyze the model structure is critical for resolving model issues to achieve robust crop parameter retrieval from satellite images [104]. Conversely, ML algorithms based on kernel methods are complex, computationally intensive, and often considered \"black box\" models. The training process necessitates the parameterization of multiple interrelated hyperparameters, and there is no straightforward method to compute a variable's importance or to explore the model's internal workings. Nonetheless, these ML methods have garnered considerable attention in recent studies utilizing simulated and real data from various hyperspectral and multispectral sensors (see Table 1). SVR are likely the most widely applied kernel-based algorithms in remote sensing applications, supporting diverse kernels, among which Gaussian Radial Basis Functions (RBF), Sigmoid, Polynomial, and Linear kernels are particularly popular [105]. Moreover, the GPR demonstrates high accuracy and an ability to estimate uncertainty in the response variable, thus assessing the reliability of soil salinity [106]. Similar to tree-based algorithms, GPR can elucidate the environmental variables associated with soil salinity. GPR also accommodates various kernels, including anisotropic squared exponential and scaled Gaussian kernels. The algorithm requires fewer hyperparameters, specifically the scaling factor, length-scale of the explanatory variables, and the standard deviation of the estimated noise. In contrast, ANN and deep learning algorithms are among the most widely utilized approaches in deep learning. As the name suggests, these algorithms draw inspiration from neuroscience. They consist of a hierarchical, interconnected structure of weighted artificial neurons. Key hyperparameters include the number of hidden layers, weights, the number of nodes per layer, learning rates, the characteristics of nonlinearity, and regularization parameters. ANN structures are typically optimized using learning algorithms such as the computationally intensive Levenberg-Marquardt or more efficient methods like gradient backpropagation. Recent advancements, including recurrent neural networks (RNN) [107], convolutional neural networks (CNN) [85], and long short-term memory networks (LSTM) [108], offer promising prospects for enhancing the retrieval accuracy of crop parameters. The general advantages of this series of algorithms include robust nonlinear modeling capabilities, substantial data processing capacity, and strong generalization ability. However, different ML methods present distinct limitations, as illustrated in Table 2. These limitations pertain to their capacity to manage diverse datasets, computational complexity and efficiency, portability (i.e., applicability to new regions and time periods), as well as transparency and interpretability, which allow for examination of the internal mechanisms of the algorithms to understand their functionality across various scenarios.