## 3 Innovation
### Utilization of Satellite Sensors and Machine Learning Methods  
Remote sensing data and iterative machine algorithms continue to enhance the predictive accuracy of salinity models. Among the satellite sensors referenced in published papers (104) are Landsat, Sentinel-1/2, MODIS, GF-1/2/5, HJ-I, and PlanetScope (Figure 2). Additionally, multispectral (UAV-M) and hyperspectral (UAV-H) sensors are being investigated to facilitate near-surface flights. Statistics indicated that Landsat was the most frequently utilized sensor in these studies, accounting for 37.10% of usage, followed by Sentinel-2 and their combined use at 6.50%. This trend may be attributed to the fact that the data are freely available and accessible. As the demand for scene diversification--such as near-surface and agricultural settings--increases, the need for high spatial resolution data becomes more pronounced, alongside considerations of cost. In the context of spaceborne remote sensing data, there exists a relationship among the temporal resolution, spectral resolution, and spatial resolution of satellite imagery [109]. The Landsat series of medium-resolution multispectral remote sensing satellites is among the most widely utilized data sources currently available. It has achieved a relative balance between spatial and spectral resolutions; however, its revisit period typically spans ten days, which may hinder the accurate depiction of the dynamic processes involved in soil salinization occurrence and development [110]. Although the spatial resolution of this sensor is relatively consistent, variations exist in its spectral band configurations (such as center wavelength and spectral range), necessitating calibration when employing long time series data for interdecadal soil salinization monitoring [111]. Furthermore, the European Space Agency successfully launched the Sentinel-2 series of multispectral satellites in 2015 and 2017, creating satellite constellations that fulfill the requirements for revisit frequency and coverage, thereby providing reliable data sources for remote sensing monitoring of soil salinization [11,112]. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Advantage** & **Disadvantage** \\ \hline \multirow{4}{*}{Tree-based} & RF reduces the risk of overfitting and is simple & RF requires a large training set for better \\  & and & performance \\  & efficient for a single estimator & RF suffer from high dimensionality and \\  & XGBoets high efficiency and accuracy & collinearity \\  & GBRT has the ability to generalize & \\ \hline \multirow{4}{*}{Kernel-based} & GPR provides uncertainty estimation, is & \\  & suitable for & \\  & small sample data sets, and deals with & \\  & nonlinear problems & SVM is computationally expensive when \\  & GPR has good interpolation capabilities & training data is large \\  & SVR has higher accuracy and lower & \\  & computation cost & \\ \hline \multirow{4}{*}{Deep learning-based} & BPNN has a wide range of application and & Computationally expensive during training \\  & strong fitting ability & Opaque “Blackbox” models \\  & DNN are capable of learning complex & ANN is unpredictable when presented with \\  & representations & unseen spectra \\  & of data & ANN is complex \\  & ELM training is fast & ANN is sensitive to dimensionality \\  & CNN is good at extracting spatial features & ANN requires sufficiently large training sets \\  & CNN is good at complex image data & ANN, CNN and DNN require large amounts \\  & ANN is computationally efficient in & of data and resources to train and are sensitive \\  & application mode & \\  & LSTM is suitable for processing time series data & ELM is not strong enough in learning \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Advantages and disadvantages of various machine learning algorithms documented in the literature for regression and classification. In recent years, high-resolution optical satellite remote sensing data, such as the Worldview series and PlanetScope have gradually been utilized in soil salinization monitoring [58; 113].