Remote sensing (RS) technology can rapidly obtain spatial distribution information on soil salinization. However, (1) the scale effects resulting from the mismatch between ground-based “point” salinity data and remote sensing pixel-based “spatial” data often limit the accuracy of remote sensing monitoring of soil salinity, and (2) the same salinity RS monitoring model usually provides inconsistent or sometimes conflicting explanations for different data. Therefore, based on Landsat 8 imagery and synchronously collected ground-sampling data of two typical study regions (denoted as N and S, respectively) of the Yichang Irrigation Area in the Hetao Irrigation District for May 2013, this study used geostatistical methods to obtain “relative truth values” of salinity corresponding to the Landsat 8 pixel scale. Additionally, based on Landsat 8 multispectral data, 14 salinity indices were constructed. Subsequently, the Correlation-based Feature Selection (CFS) method was used to select sensitive features, and a strategy similar to the concept of ensemble learning (EL) was adopted to integrate the single-feature-sensitive Bayesian classification (BC) model in order to construct an RS monitoring model for soil salinization (Nonsaline, Slightly saline, Moderately saline, Strongly saline, and Solonchak). The research results indicated that (1) soil salinity exhibits moderate to strong variability within a 30 m scale, and the spatial heterogeneity of soil salinity needs to be considered when developing remote sensing models; (2) the theoretical models of salinity variance functions in the N and S regions conform to the exponential model and the spherical model, with $R^2$ values of 0.817 and 0.967, respectively, indicating a good fit for the variance characteristics of salinity and suitability for Kriging interpolation; and (3) compared to a single-feature BC model, the soil salinization identification model constructed using the concept of EL demonstrated better potential for robustness and effectiveness.

The timely and accurate monitoring of regional soil salinity is crucial for the sustainable development of land and the stability of the ecological environment in arid and semi-arid regions. However, due to the spatiotemporal heterogeneity of soil properties and environmental conditions, improving the accuracy of soil salinization monitoring remains challenging. This study aimed to explore whether partitioned modeling based on salinization degrees during both the bare soil and vegetation cover periods can enhance the accuracy of regional soil salinity prediction. Specifically, this study integrated in situ hyperspectral data and satellite multispectral data using spectral response functions. Subsequently, machine learning methods such as random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR) were employed, in combination with sensitive spectral indices, to develop a multi-source remote sensing soil salinity estimation model optimized for different salinization degrees (mild or lower salinization vs. moderate or higher salinization). The performance of this partitioned modeling approach was then compared with an overall modeling approach that does not distinguish between salinization degrees to determine the optimal modeling strategy. The results highlight the effectiveness of considering regional soil salinization degrees in enhancing the sensitivity of spectral indices to soil salinity and improving modeling accuracy. Classifying salinization degrees helps identify spectral variable combinations that are more sensitive to the construction of soil salinity content (SSC) models, positively impacting soil salinity estimation. The partitioned modeling strategy outperformed the overall modeling strategy in both accuracy and stability, with R² values reaching 0.84 and 0.80 and corresponding RMSE values of 0.1646% and 0.1710% during the bare soil and vegetation cover periods, respectively. This study proposes an optimized modeling strategy based on regional salinization degrees, providing scientific evidence and technical support for the precise assessment and effective management of soil salinization.

The accurate identification of the water layer condition of paddy fields is a prerequisite for precise water management of paddy fields, which is important for the water-saving irrigation of rice. Until now, the study of unmanned aerial vehicle (UAV) remote sensing data to monitor the moisture condition of field crops has mostly focused on dry crops, and research on the water status of paddy fields has been relatively limited. In this study, visible and thermal infrared images of paddy fields at key growth stages were acquired using a UAV remote sensing platform, and three model input variables were constructed by extracting the color features and temperature features of each field, while K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and logistic regression (LR) analysis methods were applied to establish a model for identifying the water layer presence in paddy fields. The results showed that KNN, SVM, and RF performed well in recognizing the presence of water layers in paddy fields; KNN had the best recognition accuracy (89.29%) via algorithm comparison and parameter preference. In terms of model input variables, using multisource remote sensing data led to better results than using thermal or visible images alone, and thermal data was more effective than visible data for identifying the water layer status of rice fields. This study provides a new paradigm for monitoring the water status of rice fields, which will be key to the precision irrigation of paddy fields in large regions in the future.