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Soil salinization seriously affects the sustainable development of agricultural production; thus, the timely, efficient, and accurate estimation of soil salt content (SSC) has important research significance. In this study, the feasibility of soil salt content retrieval using machine learning models was explored based on a UAV (unmanned aerial vehicle) multi-spectral remote sensing platform. First, two variable screening methods (Pearson correlation analysis and Grey relational analysis) are used to screen the characteristic importance of 20 commonly used spectral indices. Then, the sensitive spectral variables were divided into a vegetation index group, a salt index group, and a combination variable group, which represent the model. To estimate SSC information for soil depths of 0–20 cm and 20–40 cm, three machine learning regression models were constructed: Support Vector Machine (SVM), Random Forest (RF), and Backpropagation Neural Network (BPNN). Finally, the salt distribution map for a 0–20 cm soil depth was drawn based on the best estimation model. The results of experiments show that GRA is better than PCA in improving the accuracy of the estimation model, and the combination variable group containing soil moisture information performs best. The three machine learning models have achieved good prediction effects to some extent. The accuracy and stability of the model are considered comprehensively, the prediction effect of 0–20 cm is higher than that of 20–40 cm, and the validation set coefficient of determination (R2), Root-Mean-Square-Error (RMSE), and Mean Absolute Error (MAE) of the best inversion model are 0.775, 0.055, and 0.038, and the soil salt spatial map based on the optimal estimation model can reflect the salinization distribution in the study area. Therefore, this study shows that a UAV multi-spectral remote sensing platform combined with machine learning models can better monitor farmland soil salt content.

Soil salinization is a severe soil degradation issue in arid and semiarid regions. The distribution of soil salinization can prove useful in mitigating soil degradation. Remote sensing monitoring technology is available for obtaining the distribution of soil salinization rapidly and nondestructively. In this study, experimental data were collected from seven study areas of the Hetao Irrigation District from July to August in 2021 and 2022. The soil salt content (SSC) was considered at various soil depths, and the crop type and time series were considered as environmental factors. We analyzed the effects of various environmental factors on the sensitivity response of unmanned aerial vehicle (UAV)-derived spectral index variables to the SSC and assessed the accuracy of SSC estimations. The five indices with the highest correlation with the SSC under various environmental factors were the input parameters used in modeling based on three machine learning algorithms. The best model was subsequently used to derive prediction distribution maps of the SSC. The results revealed that the crop type and time series did not affect the relationship strength between the SSC and spectral indices, and that the classification of the crop type and time series can considerably enhance the accuracy of SSC estimation. The mask treatment of the soil pixels can improve the correlation between some spectral indices and the SSC. The accuracies of the ANN and RFR models were higher than SVR accuracy (optimal R2 = 0.52–0.79), and the generalization ability of ANN was superior to that of RFR. In this study, considering environmental factors, a UAV remote sensing estimation and mapping method was proposed. The results of this study provide a reference for the high-precision prediction of soil salinization during the vegetation cover period.

Background Spring irrigation with freshwater is widely used to reduce soil salinity and increase the soil water content in arid areas. However, this approach requires a huge amount of freshwater, which is problematic given limited freshwater resources. Utilizing brackish water for spring irrigation in combination with magnetized water technology may be a promising alternative strategy. Results The objective of this study was to evaluate the effects of four spring irrigation methods (freshwater spring irrigation (FS), magnetized freshwater spring irrigation (MFS), brackish water spring irrigation (BS), and magnetized brackish water spring irrigation (MBS)) on soil water and salt distribution, emergence, growth, and photosynthetic characteristics of cotton seedlings. The results showed that for both freshwater and brackish water, magnetized water irrigation can increase the soil water content for improved desalination effect of irrigation water. Additionally, spring irrigation with magnetized water promoted cotton emergence and seedling growth. Compared with FS treatment, cotton finial emergence rate, emergence index, vigor index, plant height, stem diameter, and leaf area index of MFS treatment increased by 6.25, 7.19, 12.98, 15.60, 8.91, and 20.57%, respectively. Compared with BS treatment, cotton finial emergence rate, emergence index, vigor index, plant height, stem diameter, and leaf area index of MBS treatment increased by 27.78, 39.83, 74.79, 26.40, 14.01, and 57.22%, respectively. Interestingly, we found that spring irrigation with magnetized water can increase the chlorophyll content and net photosynthetic rate of cotton seedlings. The rectangular hyperbolic model (RHM), non-rectangular hyperbolic model (NRHM), exponential model (EM), and modified rectangular hyperbolic model (MRHM) were used to fit and compare the cotton light response curve, and MRHM was determined to be the optimal model to fit the data. This model was used to calculate the photosynthetic parameters of cotton. Compared with FS treatment, the net photosynthetic rate ( P nmax ), dark respiration rate ( R d ), light compensation point ( I c ), light saturation point ( I sat ), and the range of available light intensity (Δ I ) of MFS were increased by 5.18, 3.41, 3.18, 2.29 and 2.19%, respectively. Compared with BS treatment, the P nmax , R d , I c , I sat and Δ I of MBS were increased by 26.44, 29.48, 30.05, 5.13, and 2.27%, respectively. Conclusion The results show that spring irrigation with magnetized brackish water may be a feasible method to reduce soil salt and increase soil water content when freshwater resources are insufficient.

Research on the variation in soil water, heat, and salt in unsaturated zones during the freeze–thaw process has great significance in efficiently utilizing water resources and preventing soil salinization. The freeze–thaw field experiment was carried out with the lysimeter as the test equipment to analyze characteristics of the soil freeze–thaw process, profile water content, main ion content, and salt content of three textured soils with the groundwater table depth of 0.5 m. The results showed that the soil temperature gradient and freezing depth were greater as the average soil particle size increased. The increment of water content at the depth of 0 to 30 cm in sandy loam and loamy sand decreased by 40.20~93.10% and 28.14~65.52% compared with that in sandy soil, and the average increment of salt content at the depth of 0 to 30 cm decreased as the average soil particle size increased during the freeze–thaw period. The average content of Ca2+, Na+, Cl−, and SO42− in loamy sand and sandy soil decreased by 4.37~45.50% and 22.60~70.42% compared with that in sandy loam at the end of the freeze–thaw period, and the correlation between soil salt content and water content decreased with the increase in the average soil particle size. The research results can provide a theoretical basis for soil salinization prevention and crop production in shallow groundwater areas.

Soil salinity is a serious problem facing many countries globally, especially those with semi-arid and arid climates. Soil salinity can have negative influences on soil microbial activity as well as many chemical and physical soil processes, all of which are crucial for soil health, fertility, and productivity. Soil salinity can negatively affect physiological, biochemical, and genetic attributes of cultivated plants as well. Plants have a wide variety of responses to salinity stress and are classified as sensitive (e.g., carrot and strawberry), moderately sensitive (grapevine), moderately tolerant (wheat) and tolerant (barley and date palm) to soil salinity depending on the salt content required to cause crop production problems. Salinity mitigation represents a critical global agricultural issue. This review highlights the properties and classification of salt-affected soils, plant damage from osmotic stress due to soil salinity, possible approaches for soil salinity mitigation (i.e., applied nutrients, microbial inoculations, organic amendments, physio-chemical approaches, biological approaches, and nano-management), and research gaps that are important for the future of food security. The strong relationship between soil salinity and different soil subdisciplines (mainly, soil biogeochemistry, soil microbiology, soil fertility and plant nutrition) are also discussed.

Soil salinization, a significant global challenge, threatens sustainable development. This study explores the potential of magnetized ionized water irrigation and Bacillus subtilis application to mitigate this issue. The former method is hypothesized to enhance soil salt leaching, while the latter is expected to improve soil nutrient availability, thereby increasing microbial diversity. To address the unclear impact of these interventions on soil quality and cotton productivity, this study employs four different experimental methods: magnetized ionized water irrigation (M), application of 45 kg ha−1 B. subtilis (B), a combination of 45 kg ha−1 B. subtilis with magnetized ionized water irrigation (MB), and a control treatment with no intervention (CK). This study aims to clarify the effects of these treatments on soil bulk density (BD), field capacity (FC), salinity and alkalinity, nutrient content, microbial activity, and cotton crop yield and quality. Additionally, it aims to evaluate the efficacy of these methods in improving saline soil conditions by developing a soil quality index. The results showed that using magnetized ionized water for irrigation and applying B. subtilis, either alone or together, can effectively lower soil pH and salt levels, enhance microbial diversity and abundance, and improve the yield and quality of cotton. Notably, B. subtilis application significantly decreased BD and enhanced FC and nutrient content (p < 0.05). A correlation was found where soil nutrient content decreased as pH and salt content increased. Furthermore, a strong correlation was observed between the major soil bacteria and fungi with BD, FC, and salt content. Comparatively, M, B, and MB significantly boosted (p < 0.01) the soil quality index by 0.21, 0.52, and 0.69 units, respectively, and increased (p < 0.05) cotton yield by 5.7%, 14.8%, and 20.1% compared to CK. Therefore, this research offers eco-friendly and efficient methods to enhance cotton production capacity in saline soil.

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.