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Soil salinization is a significant global threat to sustainable agricultural development, with soil salinity serving as a crucial indicator for evaluating soil salinization. Remote sensing technology enables large-scale inversion of soil salinity, facilitating the monitoring and assessment of soil salinization levels, thus supporting the prevention and management of soil salinization. This study employs multi-source remote sensing data, selecting 8 radar polarization combinations, 10 spectral indices, and 3 topographic factors to form a feature variable dataset. By applying a normalized weighted variable optimization method, highly important feature variables are identified. AdaBoost, LightGBM, and CatBoost machine learning methods are then used to develop soil salinity inversion models and evaluate their performance. The results indicate the following: (1) There is generally a strong correlation between radar polarization combinations and vegetation indices, and a very high correlation between various vegetation indices and the salinity index S3. (2) The top five feature variables, in order of importance, are Aspect, VH2, Normalized Difference Moisture Index (NDMI), VH, and Vegetation Moisture Index (VMI). (3) The method of normalized weighted importance scoring effectively screens important variables, reducing the number of input feature variables while enhancing the model's inversion accuracy. (4) Among the three machine learning models, CatBoost performs best overall in soil salt content (SSC) prediction. Combined with the top five feature variables, CatBoost achieves the highest prediction accuracy (R2 = 0.831, RMSE = 2.653, MAE = 1.034) in the prediction phase. This study provides insights for the further development and application of methods for collaborative inversion of soil salinity using multi-source remote sensing data. [ABSTRACT FROM AUTHOR]

The validation of remotely sensed evapotranspiration (ET) products is important for the development of ET estimation models and the accuracy of the scientific application of the products. In this study, different ET products such as HiTLL, MOD16A2, ETMonitor, and SoGAE were compared using multi-source remote sensing data and ground-based data to evaluate their applicability in the Heihe River Basin (HRB) during 2010–2019. The results of the comparison with the site observations show that ETMonitor provides a more stable and reliable estimation of ET than the other three products. The ET exhibited significant variations over the decade, characterized by a general increase in rates across the HRB. These changes were markedly influenced by variations in land use and topographical features. Specifically, the analysis showed that farmland and forested areas had higher ET rates due to greater vegetation cover and moisture availability, while grasslands and water bodies demonstrated lower ET rates, reflecting their respective land cover characteristics. This study further explored the influence of various factors on ET, including land use changes, NDVI, temperature, and precipitation. It was found that changes in land use, such as increases in agricultural areas or reforestation efforts, directly influenced ET rates. Moreover, meteorological conditions such as temperature and precipitation patterns also played crucial roles, with warmer temperatures and higher precipitation correlating with increased ET. This study highlights the significant impact of land use and climatic factors on spatiotemporal variations in ET within the HRB, underscoring its importance for optimizing water resource management and land use planning in arid regions. [ABSTRACT FROM AUTHOR]

Accurate estimation of surface evapotranspiration (ET) in the Heihe River Basin using remote sensing data is crucial for understanding water dynamics in arid regions. In this paper, by coupling physical constraints and machine learning for hybrid modeling, we develop a hybrid model based on surface conductance optimization. A hybrid modeling algorithm, two physical process-based ET algorithms (Penman–Monteith-based and Priestley–Taylor-based ET algorithms), and three pure machine learning algorithms (Random Forest, Extreme Gradient Boosting, and K Nearest Neighbors) are comparatively analyzed for estimating the ET. The results showed that, in general, the machine learning model optimized by parameters was able to better predict the surface conductance of the hybrid model. Driver analyses showed that radiation, normalized difference vegetation index (NDVI), and air temperature had high correlations with ET. The hybrid model had a better prediction performance for ET than the other five models, and it improved the R2 of the two physical process-based algorithms to 0.9, reduced the root mean square error (RMSE) to 0.5 mm/day, reduced the BIAS to 0.2 mm/day, and improved the Kling–Gupta efficiency (KGE) to 0.9. The hybrid model outperformed the others across different time scales, displaying lower BIAS, RMSE, and higher KGE. Spatially, its ET patterns aligned with regional vegetation changes, with superior accuracy in annual ET estimation compared to the other models. Comparison with other ET products shows that the estimation results based on the hybrid model have better performance. This approach not only improves the accuracy of ET estimation but also improves the understanding of the physical mechanism of ET estimation by pure machine learning models. This study can provide important support for understanding ET and hydrological processes under different climatic and biotic vegetation in other arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

As the demand for food continues to rise, soil salinization and sodification pose an increasingly pressing challenge. Currently, there is a knowledge gap regarding how to effectively improve saline‐sodic soils to support sustainable agricultural production, especially the lack of systematic analysis on the effects of different amendments at a global scale. To address this gap, this study aims to explore the feasibility of using exogenous amendments to ameliorate saline‐sodic soils, conducting a global‐scale meta‐analysis based on 685 data pairs from 70 published studies. Our results showed that applying amendments to saline‐sodic soils significantly reduced electrical conductivity of saturated paste extract (ECe) by 33.0% and exchangeable sodium percentage (ESP) by 44.6%, while simultaneously increasing crop yield by 50.7%. Heterogeneity analysis further unveiled significant variations (p < 0.05) in the overall effect size, driven by factors such as initial soil properties (salinity and ESP levels), amendment type, climate and practical management conditions (application dose and experimental duration). The categorical variable analysis showed that, compared to soils with other salinization levels, the application of amendments in severe salinization soils was most effective in reducing soil ECe and enhancing crop yield. Considering the goals of mitigating soil salinity, sodicity, and increasing crop yield, the study suggests the application of mixed‐type amendments in practical settings. It is noteworthy that while extremely high doses (greater than 40 t ha−1) effectively increased crop yield, they also posed a risk of salt accumulation. In conclusion, this research offers critical insights for sustainable agriculture, guiding future work on soil health and food security in the context of global environmental challenges. [ABSTRACT FROM AUTHOR]

Accurate and reliable precipitation data are important for analyzing regional precipitation distribution, water resource management, and ecological environment construction. Due to the scarcity of meteorological stations in the Turpan–Hami region, precipitation observation conditions are limited, and it is difficult to obtain precipitation data. Firstly, the applicability of TRMM 3B43v7, GPM_3IMERGM 06, and CMORPH CDR satellite precipitation data for the Turpan–Hami Region was evaluated, and the products with better applicability were selected. Next, the Extreme Gradient Boosting Algorithm (XGBoost) and the Shapley Additive Explanations for Machine Learning (SHAP) model were combined to carry out a feature importance analysis on the climate factors affecting precipitation (mean temperature, actual evapotranspiration, wind speed, cloud cover), from which climate factors with a greater influence on precipitation were selected. Combined with climate factors, normalized difference vegetation index (NDVI), slope, aspect, and elevation as explanatory variables, a Multi-Scale Geographically Weighted Regression (MGWR) model was constructed to obtain the monthly precipitation data of 1 km spatial resolution in the Turpan–Hami area from 2001 to 2020. Finally, the spatiotemporal distribution characteristics and changing trend of precipitation in the Turpan–Hami region from 2001 to 2020 were analyzed. The results show that (1) GPM_3IMERGM 06 satellite precipitation data exhibits good applicability in the Turpan–Hami region. (2) The precision verification of the downscaling results from a monthly scale and an annual scale shows that the accuracy and spatial resolution of the data are improved after downscaling. (3) From 2001 to 2020, the precipitation in the Turpan–Hami region showed an insignificantly increasing trend. [ABSTRACT FROM AUTHOR]

Soil moisture content is an important measure of soil health, and high-precision soil moisture trend analysis is essential for understanding regional ecological quality in the context of climate change, flood monitoring, and water cycle processes. However, in the arid regions of Central Asia, where data are severely lacking, obtaining high-spatial-resolution, continuous soil moisture data is difficult due to the scarcity of stations. Moreover, because soil moisture is easily affected by evaporation time, surface morphology, and anthropogenic factors, mature theoretical models or empirical or semiempirical models to measure soil moisture are also lacking. To investigate the distribution and trend of soil moisture in the Ebinur Lake water, in this study, microwave remote sensing and visible remote sensing data were selected as inputs, and the Global Land Data Assimilation System (GLDAS-2.2) data products were downscaled using the GTWR model, which increased the spatial scale from 27,830 m × 27,830 m to 30 m × 30 m. The phenomena involved in the soil moisture change cycle, spatial distribution, temporal variation, and internal randomness distance were analyzed in the study area through wavelet analysis, Theil–Sen trend analysis, the Mann–Kendall (MK) test, and a variogram. This study obtained high-resolution continuous soil moisture data in the arid and data-scarce region in Central Asia, thus broadening the field of multisource remote sensing analysis and providing a theoretical basis for the construction of precision agriculture in northwest China. [ABSTRACT FROM AUTHOR]

Many different machine learning approaches have been applied for various purposes. However, there has been limited guidance regarding which, if any, machine learning models and covariate sets might be optimal for predicting soil salinity across different oases in the Xinjiang Uyghur Autonomous Region (XJUAR) of China. This study aimed to compare five machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), Multiple Adaptive Regression Splines (MARS), Classification and Regression Trees (CART), Random Forest tree ensembles (RF), and Stochastic Gradient Treeboost (SGT), to predict soil salinity in three geographically distinct areas (the Qitai, Kuqa, and Yutian oases). A total of 21 data sets from three oases were used to evaluate the performance of the algorithm and to screen the optimal variables. The results show the following indices are considered to be important indicators for quantitative assessment of soil salinity: EEVI, CSRI, EVI2, GDVI, SAIO, and SIT. Comparison results show that SGT is the most suitable algorithm for predicting soil salinity in arid areas. This study provides a comprehensive comparison of machine learning techniques for soil salinity prediction and may assist in the modeling and variable selection of digital soil mapping in the XJUAR of China.

With the development of human society, the balance between the minimum ecological instream flow requirement (MEIFR), which is an essential part of the ecological water demand in arid areas, and anthropogenic water depletion has received increasing attention. However, due to the lack of hydrological station data and river information on arid basins, previous researchers usually considered only the individual ecological water demand of rivers, lakes, or oases. To address this issue, a new method that combines river hydraulic parameters and the wet circumference obtained by an unmanned aerial vehicle (UAV) and remote sensing hydrological station (RSHS) technologies was applied to obtain the MEIFR and, then, systematically and quantitatively explore the balance from the perspective of the entire basin of Aiding Lake from 1990 to 2022, which is the lowest point of Chinese terrestrial territory. The results showed the following: (1) since 1990, the discharge of the seven rivers in the study area increased by 1–6%, and the MEIFR of these rivers increased by 15–100%; both quantities decreased by 3–5% from the upper to the lower reaches of the basin; (2) the surface area and water level of Aiding Lake decreased by 5% and 14%, respectively, but the MEIFR first decreased by 25% from 1990 to 2013 and, then, increased by 66.7% from 2013 to 2022; and (3) from 2011 to 2022, the MEIFR and anthropogenic water depletion exhibited a balance. Against the background of climate change, this research revealed that the MEIFR of the rivers in the Aiding Lake Basin have shown an upward trend over the past 30 years and quantitatively determined the above balance relationship and the period of its occurrence. This study supplied a method that could provide guidance for water resource management by decision-makers at a global level, thus helping achieve the sustainable development goals (SDGs). [ABSTRACT FROM AUTHOR]

There are many kinds of switches lying midway in receiving-departure tracks at railway stations. For some special switches lying midway in receiving-departure tracks, the existing technical conditions for switches lying midway in receiving-departure tracks cannot meet the actual application requirements. This paper introduces the application scenarios for different types of switches lying midway receiving-departure tracks in actual projects, and analyzes what special treatments computer based interlocking needs to provide for each application scenario on the basis of the standard protection circuit for switches lying midway in receiving-departure tracks, to ensure the safety of train operation while meeting the needs of transportation operations at stations. [ABSTRACT FROM AUTHOR]

Ecosystem water use efficiency (WUE) and ecosystem photosynthetic efficiency (EPE) are key indicators in studies of the carbon–water cycle in terrestrial ecosystems. Analyses of WUE and EPE can enhance our understanding of the relationship between ecosystem light use efficiency and WUE. Although several studies of individual indexes (i.e., either WUE or EPE) have been conducted, analyses of variation in both WUE and EPE, as well as their relationships, have rarely been conducted. Here, we analyzed spatial and temporal variation in WUE and EPE in Central Asia. Specifically, time trend analysis was conducted to characterize temporal and spatial changes in WUE and EPE in Central Asia from 2001 to 2020 at different altitudes and latitudes. Pearson correlation analysis was used to characterize the effects of precipitation and temperature on WUE and EPE. WUE decreased and EPE increased in Central Asia over the 20-year study period; this might have been due to interannual variations in precipitation and temperature. WUE was highest in August, and EPE was highest in June and July. Substantial spatial heterogeneity in WUE and EPE was observed; WUE was highly variable in Central Asia as well as in western and southern Central Asia. Major changes in EPE were observed in northern, eastern, and southern Central Asia. We also found that both WUE and EPE decreased with the increase in altitude. WUE was positively correlated with temperature and negatively correlated with precipitation, whereas EPE was positively correlated with both temperature and precipitation. The increase in photosynthetic efficiency might be one of the main factors contributing to increases in ecosystem productivity in arid environments. The temporal and spatial variation in WUE and EPE observed in our study will aid ecosystem research, providing a reliable theoretical basis for ecosystem research in areas with scarce large-scale data, integrated water resources management, and ecosystem restoration efforts. Our findings also enhance our understanding of the terrestrial carbon–water cycle and have implications for predicting ecosystem responses to climate change. The results of this study provide insights that will aid studies of the terrestrial carbon–water cycle under the background of climate change. It is of great significance to further study the carbon water cycle in the future. [ABSTRACT FROM AUTHOR]