## 3 Innovation
### Innovative Mapping and Forecasting Methods
#### 3.3.3 Characteristics of Machine Learning Methods  
An ML regression algorithm, also referred to as a nonlinear, nonparametric regression algorithm, establishes a nonlinear functional relationship between soil salinity and spectral reflectance data. This method does not assume normality, unlike linear algorithms such as ordinary least squares regression (OLS) and PLSR. ML techniques utilize all available environmental variables--whether derived from remote sensing or non-remote sensing sources--to relate soil salinity without imposing assumptions. Generally, ML approaches are categorized into three types: tree-based methods (or tree ensembles), kernel-based methods, and deep learning (or neural networks), based on their architectural design. Recent studies have widely employed ML methods to estimate soil salinity in farmland, bare land, and Salt Lake District (see Table 1). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
\begin{table}
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**Algorithm Type** & **Source** & **ML** & **Soil Depth (cm)** & **Sensor** & **Explanatory Variables** & **Experimental Site** \\ \hline  & [96] & SVR & 30 & Sentinel-1 & Spectral bands & Ben Tre Province, Mekong Delta, Vietnam \\ \cline{2-6}  & GPR & & & & & \\ \cline{2-6}  & [90] & ANN & 20 & SR 3500 spectrometer & Vegetation index, Salinity & Pingluo County, Ningxia \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \cline{2-6}  & SVR & 20 & Sentinel-2 & Vegetation index, Salinity & Lake Urumia, northwest Iran \\ \hline  & [39] & ANN & 20 & Sentinel-2 & Vegetation index, Salinity & Inner Mongolia, China \\ \cline{2-6}  & ANN & 20 & Sentinel-2 & Integer and Spectral bands & Inner Mongolia, China \\ \hline  & [99] & BPNN & 20 & HJ 1; Landsat \& QLI & Vegetation index and Spectral bands & Ebenur Lake, Xinjang Province, China \\ \cline{2-6}  & [59] & BPNN & 20 & Hyperspectral UAV & Vegetation index, Salinity & Heaton irrigation District, Wuyuan County, Baynaozer City, Inner Mongolia, China \\ \cline{2-6}  & [44] & BPNN & 20 & Hyperspectral UAV & Vegetation index, Salinity & Vegetation index and Spectral bands \\ \cline{2-6}  & [87] & ELM & 20 & Hyperspectral UAV & Vegetation index, Salinity & Theurai River basin, Jiuguan City, Gansu Province, China \\ \cline{2-6}  & [87] & DNN & unspecified & Landsat \& QLI & Vegetation index, salinity & Mekong Delta, Vietnam \\ \hline  & [96] & RBF & 30 & Sentinel-1 & Spectral bands & Ben Tre Province, Mekong Delta, Vietnam \\ \cline{2-6}  & MLP & & & & & \\ \cline{2-6}  & [94] & ELM & 20 & VNIR-SWIR spectra & FOD & Theurai River basin, Jiuguan City, Gansu Province, China \\ \hline  & \multicolumn{1}{c}{} & & & & & \\ \cline{2-6}  & \multicolumn{1}{c}{} & & & & & \\ \cline{2-6}  & \multicolumn{1}{c}{} & & & & & \\ \end{tabular}
\end{table} 
Table 1. Cont. Tree-based ML methods are typically simpler, more intuitive, and more popular than kernel-based methods and deep learning algorithms. This class of algorithms originated from Classification and Regression Trees (CART); however, CART has become less common in recent years due to the emergence of more robust and updated variants such as RF and Gradient Boosting Regression Trees (GBRT), also known as Gradient Boosting Machines (GBM) [100]. Recently, [PERSON] and [PERSON] [101] introduced XGBoost, an advanced, scalable, and sparse-aware enhancement of GBM. The XGBoost algorithm aims to improve GBM implementation by: (1) efficiently handling missing values, (2) rapidly constructing trees and building large models through parallel computation, and (3) applying highly regularized formalizations and gradients to prevent overfitting. Consequently, XGBoost generally outperforms other algorithms [102]. Nevertheless, RF remain prevalent in the literature due to their strong performance and relatively few required hyperparameters, demonstrating robustness and high accuracy in predicting various soil salinities.