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【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 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.

Soil salinization is the most prevalent form of land degradation in arid, semi-arid, and coastal regions of China, posing significant challenges to local crop yield, economic development, and environmental sustainability. However, limited research exists on estimating soil salinity at different depths under vegetation cover. This study employed field-controlled soil experiments to collect multi-source remote sensing data on soil salt content (SSC) at varying depths beneath barley growth. Three types of feature variables were derived from the images and filtered using the boosting decision tree (BDT) method. In addition, four machine learning algorithms coupled with seven variable combination groups were applied to establish comprehensively soil salinity estimation models. The performances of estimation model for different crop coverage ratios and soil depth were then evaluated. The results showed that the gaussian process regression (GPR) model, based on the whole variable group for depths of 0 ~ 10 cm and 30 ~ 40 cm, outperformed other models, achieving validation R 2 values of 0.774 and 0.705, with RMSE values are 0.185% and 0.31%, respectively. For depths of 10 ~ 20 cm and 20 ~ 30 cm, the random forest (RF) models, incorporating spectral index and texture data, demonstrated superior accuracy with R 2 values of 0.666 and 0.714. The study confirms that SSC can be quantitatively estimated at various depths using the machine learning model based on multi-source remote sensing, providing a valuable approach for monitoring soil salinization.

Soil salinization severely restricts the development of global industry and agriculture and affects human beings. In the arid area of Northwest China, oasis saline-alkali land threatens the development of agriculture and food security. This paper develops and optimizes an inversion monitoring model for monitoring the soil salt content using unmanned aerial vehicle (UAV) multispectral remote sensing data. Using the multispectral remote sensing data in three research areas, the soil salt inversion models based on the support vector machine regression (SVR), random forest (RF), backpropagation neural network (BPNN), and extreme learning machine (ELM) were constructed. The results show that the four constructed models based on the spectral index can achieve good inversion accuracy, and the red edge band can effectively improve the soil salt inversion accuracy in saline-alkali land with vegetation cover. Based on the obtained results, for bare land, the best model for soil salt inversion is the ELM model, which reaches the determination coefficient (Rv2) of 0.707, the root mean square error RMSEv of 0.290, and the performance deviation ratio (RPD) of 1.852 on the test dataset. However, for agricultural land with vegetation cover, the best model for soil salinity inversion using the vegetation index is the BPNN model, which achieves Rv2 of 0.836, RMSEv of 0.027, and RPD of 2.100 on the test dataset. This study provides technical support for rapid monitoring and inversion of soil salinization and salinization control in irrigation areas.

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.

【Objective】 Soil moisture sensors have been increasingly used in different fields to measure soil water content at high temporal resolution, but its reliability depends on many factors. In this paper, we investigate the effect of soil salinity on their accuracy. 【Method】 The laboratory experiment was conducted at the Irrigation Test Station of the First Division of Xinjiang Alaer Corps. FDR and TDR sensors were used in the experiment. We compared five salinity treatments: 2, 3, 5, 7, 9 mS/cm; the soil moisture was adjusted to the field capacity followed by natural evaporation. The absolute error (AE), relative error (RE) and standard deviation (SD) were used to evaluate the accuracy and consistency of the sensors. 【Result】 Soil moisture and salinity both affected accuracy and consistency of the sensors. Without calibration, the accuracy of the CSF11 and ML2x sensors was high and measurements were consistent, while the EC-5 and TDR305H sensors were less accurate and consistent due to the combined influence of soil salinity and moisture. Calibration significantly improved the accuracy and consistence of all four sensors. 【Conclusion】 Considering accuracy and consistency, the expensive ML2x sensors worked best when salt content does not exceed 9 mS/cm. The less expensive CSF11 sensors also worked well when soil salt is low. The EC-5 sensors can measure soil moisture reasonably well for soil with moderate and high salinity. When the soil moisture is less than 20 cm3/cm3, TDR305H sensors is accurate and reliable.

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 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.

【Objective】The Kubuqi Desert in Inner Mongolia is an ecologically fragile region; understanding the spatiotemporal variation of topsoil salt content (SSC) and its controlling factors is crucial for sustainable land management in the region. This paper develops a method to map SSC variation over time and identify the key meteorological drivers influencing SSC distribution. 【Method】 Based on measured soil data, an SSC inversion model was developed using ReliefF feature selection and machine learning algorithms. The model was then applied to generate surface SSC maps for the northern margin of the Kubuqi Desert from 2000 to 2024. Trend analysis, correlation analysis, and geographic detector methods were used to examine the spatiotemporal variation in SSC and its relationships with meteorological variables. 【Result】 ① The PSO-SVM algorithm outperformed PLSR and RF in inversely calculating SSC, with R2 = 0.63, RMSE = 0.009 and MAE = 0.007. ② From 2000 to 2004, the annual SSC at the northern edge of the desert showed a fluctuating but increasing trend. ③ Spatially, the SSC was significantly heterogeneous, with its value increasing from the southwest to the north. At the pixel scale, SSC was strongly correlated with temperature and precipitation, but weakly with vapor pressure and mixing ratio. ④ Temperature, precipitation, vapor pressure deficit, and wind speed had high explanatory power for the SSC changes, representing the primary meteorological drivers of spatiotemporal variation of SSC. 【Conclusion】SSC in the northern margin of the Kubuqi Desert shows pronounced spatiotemporal variation. Temperature and precipitation are the dominant factors shaping this variation. These results can help improve management of salt-affected soil in the desert.