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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]

Preventing soil salinization is key to the healthy development of agroecosystems in arid regions. Notably, long‐term anthropogenic irrigation accelerates secondary salinity accumulation in arid areas; however, the regional‐scale spatial patterns of soil salinity pre‐ and post‐irrigation season remain unclear. Accordingly, field observations over two periods (n = 182 samples), along with environmental covariates and the randomForest algorithm, were used to estimate the horizontal and vertical spatial distributions (12 soil depths) of soil salinity in the Weiku Oasis (0–60 cm) at a 30 m spatial resolution. The results showed that the prediction accuracy was greater for the non‐irrigated season than for the irrigated season. Moreover, soil salinity was low in the interior of the oasis, high in the desert oasis interlacing zone, and significantly decreased with increasing soil depth. Variable contributions indicated that the effects of SoilGrids variables on soil salinity increased with greater soil depth. During the irrigation season, an inflection point for salt aggregation was observed at ~55 cm for farmlands, grasslands, and shrubs, with the greatest variation seen in farmlands. The findings here provide insights into the improvement of irrigation techniques in arid zone agroecosystems. [ABSTRACT FROM AUTHOR]

Various climatic factors and their interactions affect China's snow depth alterations. However, a measure of the relative importance of climatic factors regarding the variance in snow depth is lacking. Here, we use a heretofore largest dataset, quality‐controlled daily observations from 1,665 meteorological stations, and statistical analyses to identify proxy drivers and their contributions regarding regional variability and trends in China's snow depth and snow depth‐climate relationships from 1951 to 2019. Results show that seasonal snow depth alterations are substantial in China and caused by various climatic drivers. In winter, observed positive trends in the northwest and northeast of China mainly respond to augmentation in solid precipitation and snow accumulation in fall. For the Tibetan Plateau, the negative snow depth anomaly seems to respond to the rising air temperature below the freezing point along with some secular changes in solid precipitation and shallower fall snowpacks. All these trends in winter snow depth over the northwest, northeast, and Tibetan Plateau of China account approximately for 30% of the total interannual variance in spring snow depth in these regions. In the milder central north and south of China, snow accumulation is not evident. Besides solid precipitation and rising air temperature below the freezing point, atmospheric humidity is also notable for its effect on the interannual variance in winter snow depth in these regions. In spring and fall, however, the negative effect of warming climate on snow depth becomes remarkably more important. It seems that about 13–33% (12–50%) of the total interannual variance in the widespread decrease (insignificant perturbation), except for some weak increases in the northeast region (decreases in the Tibetan Plateau), of China's spring (fall) snow depth are caused by the rising air temperature below the freezing point. [ABSTRACT FROM AUTHOR]

The microwave dielectric constant is a key bridge in establishing the relationship between microwave remote sensing and soil salinity (electrical conductivity, EC). However, the response between microwave dielectric spectrum type, frequency, and soil salinity is still unclear. The purpose of this study is to reveal the dielectric spectrum and frequency range closely related to soil salinity. In this study, 129 surface soil samples were collected, and soil EC and microwave dielectric spectrum (frequency range of 0.3–20 GHz) were measured. Four variable selection methods were used to select important spectral variables, and the relationship between these spectral variables and soil salt was constructed using a random forest model. The results showed that the combination of ε′ (real part) and ε′′ (imaginary part) revealed the most implicit information related to soil salinity. Considering the important spectral variables, the model accuracy was generally improved to the full‐dielectric spectrum model (the ratios of the performance to the interquartile distance (RPIQ) improved by 0.11–0.40); furthermore, the variable iterative space shrinkage (VISSA) method could most effectively select important spectral variables (RPIQ = 2.32). The selected spectral‐RF models could provide important frequencies for estimating soil salinity; moreover, the low‐frequency range (0.3–4 GHz) and new important frequency ranges (e.g., 7 and 15 GHz) able to respond to soil salinity were identified. This study provides a new strategy for monitoring the soil salinization risk using multiple frequencies microwave remote sensing techniques. [ABSTRACT FROM AUTHOR]

Knowledge of aerosol dynamics is essential to combating atmospheric aerosol pollution, and there is a growing interest in aerosol changes and their drivers. However, the effects of interactions between natural and anthropogenic drivers of aerosol are not well understood. Here, we analyse changes in aerosol optical depth (AOD) in Xinjiang, China using the multiangle implementation of atmospheric correction aerosol products over 2001–2019 and investigate the driving factors of aerosol dynamics using a random forest (RF) and a geographical detector. The results show the dominant AOD changes in the quasi‐period of 3.21 months, 7.86 months, 1.19 years, for seasonal, half‐year, and interannual variations of aerosols. The increasing and then decreasing nonlinear trends were observed in the interannual AOD variation during 19 years period. The importance ranking results of the two models indicated that meteorological factors dominated the spatial distribution of AOD in Xinjiang (72.73% for RF and 65.78% for the geographic detector), and that interactions between factors enhanced the explanatory power in AOD changes. In addition, the influence of anthropogenic factors on AOD was increasing in North Xinjiang, and the influence of precipitation and population on AOD was increasing in East Xinjiang. However, the influence of drivers on AOD in South Xinjiang was basically constant over time, showing the spatially heterogeneous relationship between AOD and drivers. This study emphasized the spatial heterogeneity and driving factors of small‐scale aerosols in arid regions and so can guide targeted air pollution prevention and control in local areas. [ABSTRACT FROM AUTHOR]

The key to saline land management and soil salinity control is to clarify the dynamics of soil salinity and the characteristics of environmental factors. However, due to the high heterogeneity of soil salinity, the influence of environmental factors on soil salinity variation at specific scales and locations is still unclear. The purpose of this study is to reveal the variability characteristics and controlling factors of soil salinity in the Wei‐Ku Oasis during different seasons. One hundred and eighty‐two topsoil samples (0–20 cm) were acquired during the irrigation and non‐irrigation seasons (IRRS and NIRRS) in the study area. This study predicted soil surface salinity at 90 m spatial resolution from randomForest (RF) models and measured data. Based on the mapping, a bi‐dimensional empirical modal decomposition (BEMD) algorithm was applied to extract the variation of the soil salinity at different scales and locations in the IRRS and NIRRS. Subsequently, correlation analysis, semi‐variance functions, and redundancy analysis were applied to dissect the correlation and main control factors between soil salinity and the environment at three different scales. The results indicated that soil salinity was greater in the NIRRS at the Weiku Oasis. Soil salinity was decomposed into five intrinsic modal functions with spatial variability scales of 6.20–50.61 km and 7.93–50.18 km during the IRRS and NIRRS, respectively. The relationships between environmental factors and soil salinity were scale‐ and location‐specific, suggesting a complex interrelationship between topography, temperature, vegetation cover, and soil salinity. The normalized difference vegetation index was found to be the main factor affecting small‐scale seasonal variation in soil salinity, whereas the large‐scale spatial distribution of soil salinity was most affected by evapotranspiration and temperature. These findings highlight the effectiveness of BEMD in revealing the scale and location control of soil salinity. [ABSTRACT FROM AUTHOR]

Soil is the largest carbon reservoir in terrestrial ecosystems, and thus minor changes in it can dramatically affect atmospheric CO2 concentrations. In the northwestern arid zone of China, the prediction of soil organic carbon (SOC) changes is often limited by the scarcity of soil samples and the scale and depth of research, which limit the understanding of carbon cycling processes in arid zone terrestrial ecosystems. Therefore, this study produced digital soil maps of SOC stocks (SOCS) for two periods (1980‐1990 and 2010‐2020) at a 90‐m resolution based on historical soil profile data and a random forest model. The results showed that the prediction accuracy for SOCS in the topsoil (0–30 cm) was superior to that of the subsoil (30–100 cm). Among them, the mean annual evapotranspiration, normalized difference vegetation index during the growing season, multi‐year mean temperature, and clay content were the main environmental factors affecting the spatial distribution of SOCS. In the past 30 years, the SOCS of the northwestern arid zone has decreased by 585.50 Tg, with a mean decline of 19.52 Tg C yr−1. The changes in SOCS caused by land‐use conversion and reductions in SOCS were further shown to be attributable to grassland desertification and agricultural reclamation. These findings are valuable for exploring the carbon cycle in terrestrial ecosystems in the context of global climate change and for achieving China's goal of carbon neutrality. [ABSTRACT FROM AUTHOR]

Abstract: Soil salinization leads to a significant degradation for oasis land. Variations in soil salinity are controlled by geologic, geomorphic, climatic, and hydrologic factors that are scale dependent. Many factors characterize soil salinity and its variations. Empirical mode decomposition (EMD) and correlation analysis are usually applied to examine the variations of soil salinity at different scales. However, few researches have been conducted on the modeling of these variations among different scales that requires further development and refinement. This paper investigates the potential of assessing scale‐specific variations in soil salinity via EMD using 2 modeling approaches (random forest and linear models). The remotely sensed environmental factors including land surface temperature, evapotranspiration, Tropical Rainfall Measuring Mission precipitation, and digital elevation model products were used as inputs for the models. The Junggar Basin in Xinjiang Province of China was selected as study area because the oasis was quite typical in the whole country and even in mid‐Asia. Soil salinity data and environmental factors were first decomposed using the EMD algorithm. Then, the decomposed components of the remotely sensed environmental factors were evaluated, and the most important components of each factor were selected to model the salinity variations that were represented by the decomposed components of soil salinity. The salinity variations estimated from the environmental factors were favorably consistent with the decomposed components of the soil salinity data, with coefficients of determination (R2) ranging from 0.24–0.52 and 0.73–0.98 for the linear and random forest models, respectively. In addition, land surface temperature and salinity were coupled well at the 326‐ and 334‐km scale. Our results showed that reasonably accurate results can be obtained using the proposed approach. [ABSTRACT FROM AUTHOR]

Desertification is among the most severe global environmental and the socio-economic problems in the world. This paper is a first attempt to link scientific research to national policymaking on desertification in China. We aim to trace scientific research findings in the national policies and strategies of desertification prevention in China. One example is the large-scale plantation programme in the dust source region threatening Beijing and Tianjin since 1998. It has been suggested that the recent increased forest cover due to plantations in North China has helped reduce dust storm emissions and contributed to mitigating dust storm weather in Beijing and Tianjin. Reforestation/afforestation policy remains in the Chinese national environmental strategies for the new national forestation programme (2011-2020). Overgrazing during recent decades has been blamed for land degradation and desertification in Northwest China by many scholars. Small field experiments prove that vegetation in desertified/degraded land could recover if isolated from human activities. Since 1998, natural recovery has become one powerful national force to prevent land desertification and recover natural vegetation. One example is selected at Cele County, Xinjiang Uygur Autonomous Region to showcase how vegetation could thrive at large scale in natural arid and semi-arid climate if isolated from human intervention. The plantation project has pushed the stakeholders to better understand their negative impact on the environment, especially the overgrazing behaviour. After that, the household income and living level have also been significantly enhanced; however, it is not clear whether the project induced labour migration. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]