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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.

【Objective】Cotton is a major economic crop in saline-alkali regions of southern Xinjiang, where soil salinity and water availability affect crop growth and yield. Optimizing irrigation is essential for improving water use efficiency (WUE) and maintaining stable production of cotton in these regions. This paper experimentally studies suitable drip-irrigation strategies for cotton cultivated in saline-alkali soil with plastic film mulch.【Method】A field experiment was conducted from April to August 2018 to evaluate the influence of plant row spacing and irrigation methods on cotton growth. There were four irrigation-amount treatments by irrigating 45, 37.5, 30 mm and 22.5 mm of water in each irrigation; the irrigation interval was 10 and 7 days. In the experiment, we measured soil water and salt content in the root zone, growth and WUE of the cotton. 【Result】During the first 70 days after sowing, water content in the 0-40 cm soil layer decreased with decreasing irrigation amount. At the seedling stage, soil moisture difference between wide and narrow plant row spacing was negligible, while by the bud stage, water content difference in soil below 40 cm between different row spacings became increasingly pronounced. Soil salt content in the top 0-40 cm soil layer showed minimal variation among treatments during the seedling stage, but significant differences emerged after flowering began. Salt content in the 60-100 cm soil layer steadily declined in the first 80 days post-sowing and then stabilized thereafter. Irrigation 45 mm every 10 days during the bud stage followed by 30 mm every 7 days during the flowering stage achieved the highest WUE of 1.42 kg/m3, with a seed cotton yield of (6 916.2 ± 338.6) kg/hm2. Irrigating 45 mm every 10 days during the bud stage and 37.5 mm every 7 days during the flowering stage yielded the highest seed cotton production of (7 703.9 ± 641.9) kg/hm2, with a WUE of 1.37 kg/m3. 【Conclusion】For cotton cultivated with drip irrigation under plastic film mulch in saline-alkali soils, the optimal irrigation strategy is irrigating 45 mm every 10 days during the bud stage followed by 37.5 mm every 7 days during the flowering stage. This can effectively balance soil moisture and salinity dynamics, while maximizing water use efficiency and seed cotton yield.

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.

Dramatic changes in temperature and rainfall with global warming can significantly alter the moisture status of topsoil, thereby inducing soil structure degradation. However, few studies have reported the variation in permeability of saline soils during drying, which contributes to further refining the mechanism of wetting‒drying effect on soil properties. In this study, the permeability and microstructure of sodic-saline loessal soil with different initial water contents (IWCs) and salt contents (ISCs) obtained from pre-saturation and subsequent drying were explored using constant head permeability tests and SEM observations. The results show that the permeability coefficient decreases exponentially with time. The maximum permeability coefficient ( K max ) of the samples decreases with decreasing IWC and ISC, while the relatively stable permeability coefficient ( K rs ) is less affected. The microscopic results show that during the seepage process, the porosity and pore diameter of samples with low IWC gradually decrease, accompanied by a weakening of pore directionality and an increase in fractal dimension. In contrast, samples with high IWC show an initial increase followed by a decrease in porosity, pore diameter and pore directionality, alongside a gradual decrease in fractal dimension. The drying process promotes the formation of inter-aggregate pores and weakens aggregate stability, leading to significant microstructural disturbances in low IWC samples upon rewetting. The increase in salt content enhances particle cementation but also creates additional channels for rapid permeability. These findings carry practical implications for the prevention and control of soil erosion and engineering geohazards in saline soil regions under the impact of climate change.

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.

Groundwater mineralization is one of the main factors affecting the transport of soil water and salt in saline-sodic areas. To investigate the effects of groundwater with different levels of salinity on evaporation and distributions of soil water and salt in Songnen Plain, Northeast China, five levels of groundwater sodium adsorption ration of water (SARw) and total salt content (TSC mmol/L) were conducted in an oil column lysimeters. The five treated groundwater labeled as ST0: 0, ST0: 10, ST5: 40, ST10: 70 and ST20: 100, were prepared with NaCl and CaCl 2 in proportion, respectively. The results showed the groundwater evaporation (GWE) and soil evaporation (SE) increased firstly and then decreased with the increase of groundwater salinity. The values of GWE and SE in ST10: 70 treatment were the highest, which were 2.09 and 1.84 times the values in the ST0: 0 treatment with the lowest GWE and SE. There was a positive linear correlation between GWE and the Ca 2+ content in groundwater, with R 2 = 0.998. The soil water content (SWC) of ST0: 0 treatment was significantly ( P < 0.05) less than those of other treatments during the test. The SWC of the ST0: 0 and ST0: 10 treatments increased with the increase of soil depth, while the other treatments showed the opposite trend. Statistical analysis indicated the SWC in the 0–60 cm soil layer was positively correlated with the groundwater TSC and its ion contents during the test. Salt accumulation occurred in the topsoil and the salt accumulation in the 0–20 cm soil layer was significantly ( P < 0.05) greater than that in the subsoil. This study revealed the effects of the salinity level of groundwater, especially the Ca 2+ content and TSC of groundwater, on the GWE and distributions of soil water and salt, which provided important support for the prevention and reclamation of soil salinization and sodificaton in shallow groundwater regions.

Soil salinization is an ecological challenge across the world. Particularly in arid and semi-arid regions where evaporation is rapid and rainfall is scarce, both primary soil salinization and secondary salinization due to human activity pose serious concerns. Soil is subject to various human disturbances in Xinjiang in this area. Samples with a depth of 0–10 cm from 90 soils were taken from three areas: a slightly disturbed area (Area A), a moderately disturbed area (Area B), and a severely disturbed area (Area C). In this study, we first calculated the hyperspectral reflectance of five spectra (R, R, 1/R, lgR, 1/lgR, or original, root mean square, reciprocal, logarithm, and reciprocal logarithm, respectively) using different fractional-order differential (FOD) models, then extracted the bands that passed the 0.01 significance level between spectra and total salt content, and finally proposed a partial least squares regression (PLSR) model based on the FOD of the significance level band (SLB). This proposed model (FOD-SLB-PLSR) is compared with the other three PLSR models to predict with precision the total salt content. The other three models are All-PLSR, FOD-All-PLSR, and IOD-SLB-PLSR, which respectively represent PLSR models based on all bands, all fractional-order differential bands, and significance level bands of the integral differential. The simulations show that: (1) The optimal model for predicting total salt content in Area A was the FOD-SLB-PLSR based on a 1.6 order 1/lgR, which provided good predictability of total salt content with a RPD (ratio of the performance to deviation) between 1.8 and 2.0. The optimal model for predicting total salt content in Area B was a FOD-SLB-PLSR based on a 1.7 order 1/R, which showed good predictability for total salt content with RPDs between 2.0 and 2.5. The optimal model for predicting total salt content in Area C was a FOD-SLB-PLSR based on a 1.8 order lgR, which also showed good predictability for total salt content with RPDs between 2.0 and 2.5. (2) Soils subject to various disturbance levels had optimal FOD-SLB-PLSR models located in the higher fractional order between 1.6 and 1.8. This indicates that higher-order FODs have a stronger ability to extract feature data from complex information. (3) The optimal FOD-SLB-PLSR model for each area was superior to the corresponding All-PSLR, FOD-All-PLSR, and IOD-SLB-PLSR models in predicting total salt content. The RPD value for the optimal FOD-SLB-PLSR model in each area compared to the best integral differential model showed an improvement of 9%, 45%, and 22% for Areas A, B, and C, respectively. It further showed that the fractional-order differential model provides superior prediction over the integral differential. (4) The RPD values that provided an optimal FOD-SLB-PLSR model for each area were: Area A (1.9061) < Area B (2.0761) < Area C (2.2892). This indicates that the prediction effect of data processed by fractional-order differential increases with human disturbance increases and results in a higher-precision model.

Soil salt content (SSC) is normally featured with obvious spatiotemporal variations in arid and semi-arid regions. Space factors such as elevation, temperature, and spatial locations are usually used as input variables for a model to estimate the SSC. However, whether temporal patterns of salt-affected soils (identified as temporal spectral patterns) can indicate the SSC level and be applied as a covariate in a model to estimate the SSC remains unclear. Hence, temporal changes in soil spectral patterns need to be characterized and explored as to their use as an input variable to improve SSC estimates. In this study, a total of 54 field samples and a time-series of Sentinel-2 multispectral images taken at monthly intervals (from October 2017 to April 2018) were collected in the Yinbei area of western China. Then, two-date satellite images were used to quantify significant spectral changes over time using spectral change vector analysis, and four two-date-based index methods were used to characterize soil spectral changes. Lastly, the optimal two-date-based spectral indices and multispectral bands were used as input variables to build the estimation models using a random forest algorithm. Results showed that the two-date-based spectral index could be applied as an input variable to improve the accuracy of SSC estimation at a regional scale. Temporal changes in salt-induced spectral patterns can be indicated by the band difference in the wavelength range from 400 nm to 900 nm. Three two-date-based indices designated as D28a (i.e., the band difference between band 2 from an image acquired in April 2018 and band 8a from an image acquired in December 2017), D22, and D28 were the optimal parameters for characterizing salt-induced spectral changes, which were dominated by the total brightness, chloride, and sulfate accumulation of the soils. The model did not yield satisfactory estimation results (RPD = 1.49) when multispectral bands were used as the input variables. Multispectral bands coupled with two two-date-based indices (D22 and D28a) used as the input variables produced the best estimation result (R2 = 0.92, RPD = 3.27). Incorporating multispectral bands and two-date-based indices into the random forest model provides a remotely-sensed strategy that effectively supports the monitoring of soil salt content.

The distribution of vegetation in coastal wetlands is significantly influenced by soil properties. However, the mechanisms of how soil characteristics impact the physiological processes of Tamarix chinensis forests remain underexplored. This study examined changes in the soil physicochemical properties and structural attributes of natural T. chinensis forests in the Yellow River Delta with increasing distance from the shoreline. T. chinensis trees were classified into healthy, intermediate, and dying categories based on growth potential, and dynamic changes in salt ions and non-structural carbohydrates (NSCs) were investigated. Results indicated that increasing distance from the shoreline corresponded to decreased soil salinity and pH, and increased soil moisture. T. chinensis mortality rate decreased, while tree height and ground diameter increased with distance. Soil salt content was positively correlated with T. chinensis mortality, but negatively correlated with tree height and ground diameter. Trees with lower growth potential had higher Na+ but lower K+ and K+/Na+ ratio. Soil salt content was positively correlated with root and stem Na+, while soil moisture was positively correlated with leaf NSCs. These findings suggest that soil salt content and moisture significantly influence T. chinensis ion absorption and NSC accumulation, with sodium toxicity being a key factor in the spatial distribution of T. chinensis forests.

Groundwater plays a significant role in influencing the growth and distribution of Robinia pseudoacacia L. plantations, with the largest planting area in the Yellow River Delta, by affecting the soil water–salt environment. This study aimed to clarify the mechanism of groundwater’s influence on the growth of R. pseudoacacia under different levels of groundwater mineralization (GWM) and groundwater depth (GWD). We simulated GWM of 0, 2 and 4 g L−1, and GWD of 0.8, 1.3 and 1.8 m. As GWM increased, soil relative water content (SRWC) and soil salt (dissolved salt) content (SSC) increased; sapling biomass (SB), stem mass (SM), leaf mass (LM), photosynthesis characteristics (maximum net photosynthetic rate (Pn), stomatal conductance (gs), intercellular CO2 concentration (Ci), transpiration rate (E) and water use efficiency (WUE)) decreased; root mass (RM), root mass ratio (RMR) and root–shoot ratio (RSR) first increased then decreased; stem mass ratio (SMR) first decreased then increased; and leaf mass ratio (LMR) increased. As GWD increased, SRWC decreased, but SSC first increased then decreased; SB, RM, RMR, RSR, and photosynthesis characteristics increased; SM and LM first increased then decreased; and SMR and LMR decreased. SRWC and SSC were negatively correlated with SB and photosynthesis characteristics. SRWC was negatively correlated with RMR and RSR, whereas it was positively correlated with LMR. SSC was negatively correlated with SMR, whereas it was positively correlated with LMR. The first principal component, including SB, RM, and photosynthesis characteristics, was related to sapling growth. The second principal component, including RMR, SMR, and RSR, was mainly related to biomass allocation. In conclusion, GWM and GWD affected the soil water and salt content, which were key factors influencing the photosynthesis and growth of R. pseudoacacia. Adjustments in biomass allocation and photosynthesis were the main adaptive strategies of R. pseudoacacia to salt, drought, and flooding stress.