Search Results (232)

Accurate high-resolution mapping of soil organic carbon (SOC) is essential for agricultural management and carbon pool assessment in arid lakeside oases, a fragile aquatic-terrestrial transition ecosystem. However, targeted high-precision SOC mapping for typical lakeside oases remains insufficient: existing models have poor adaptability to the highly fragmented oasis landscapes, and fine-resolution SOC spatial products for the representative Bosten Lake oasis are lacking. To address this inadequacy, we integrated Sentinel-2 imagery with topographic, bioclimatic, and spectral environmental covariates and developed four machine learning models (Random Forest, XGBoost, SVR with RBF kernel, Cubist) for SOC prediction, based on 153 topsoil samples (0–20 cm) collected via stratified random sampling in the study area. Model performance was validated through 5-fold cross-validation, the optimal model was selected for 10 m resolution SOC mapping, and dominant driving factors were identified via SHAP analysis. The results showed that SOC content in the study area ranged from 2.37 to 20.63 g·kg⁻¹ (mean = 10.59 g·kg⁻¹), with moderate spatial variability (CV = 34.86%). The Cubist model achieved the highest mapping accuracy (R² = 0.8166, RMSE = 1.5812 g·kg⁻¹, MAE = 0.9247 g·kg⁻¹). The generated high-resolution SOC map clearly revealed a spatial pattern of high values in the eastern well-irrigated cropland and low values in bare and salinized areas at the oasis edge. The Bare Soil Index (BSI), surface roughness, and Normalized Difference Red Edge Index 1 (NDRE1) were the dominant factors controlling SOC spatial distribution. This study mitigates the inadequacy of high-precision SOC mapping in typical arid lakeside oases, and the proposed framework is readily applicable to other fragmented arid landscapes worldwide and provides reliable spatial data and a scalable technical framework for precision agriculture and sustainable land management in similar fragile ecosystems. Full article

Understanding vegetation sustainability is crucial for ensuring ecological security in dryland interior river systems. Focusing on the Manas River Basin in Xinjiang, our research extracted Landsat time-series data from 2000 to 2024 via Google Earth Engine, employing statistical approaches alongside Geodetector modeling to quantitatively evaluate the spatiotemporal dynamics of vegetation sustainability and its influencing factors. Our findings reveal that the basin’s Normalized Difference Vegetation Index (NDVI) displayed a significant upward trajectory (Sen’s slope = 0.010/yr, R² = 0.95, p < 0.01), with distinct temporal phases: the period 2000–2013 was characterized by rapid oasis expansion driven by cultivated land, while the period 2014–2024 was characterized by systematic vegetation improvement with a stabilizing land use pattern. Spatially, areas exhibiting extremely significant improvement accounted for 56.24% of the total basin area (concentrated mainly in artificial oases and the mid-mountain zone), and non-significant degradation accounted for only 1.89%. Land use type and soil texture were identified as the dominant spatial differentiation factors, followed by annual precipitation, with all pairwise factor interactions exhibiting enhancement effects. By identifying the optimal thresholds for vegetation growth (annual average temperature of 0.82–3.96 °C, elevation of 1826–2598 m, and loamy sand), this study defines the boundaries for sustainable vegetation development. These findings deliver a theoretical foundation for zonation management and habitat rehabilitation planning, supplying decision-making support for safeguarding regional ecological security and fostering sustainable development of oasis systems in arid Central Asia. Full article

Understanding trade-offs and synergies among ecosystem services (ESs) along environmental gradients is crucial for sustainable oasis management. This study investigated four key ESs—carbon storage (CS), habitat quality (HQ), water yield (WY), and soil conservation (SC)—in three typical oases along water–heat gradients in arid northwestern China. The InVEST model was used to quantify ESs in 1990, 2005, and 2022, and Pearson correlation, geographically weighted regression, K-means clustering, and random forest models were applied to analyze service relationships, ecosystem service bundles (ESBs), and driving factors. The results showed that CS and HQ maintained strong synergies, while the WY–SC relationship shifted from weak trade-offs under drier conditions to stronger synergies under more favorable water–heat conditions. Geographically weighted regression revealed spatial heterogeneity and directional asymmetry in ES relationships. Four ESB types were identified: ecologically fragile zones, ecological transition or buffer zones, agricultural production zones, and core ecological source zones. Driving-factor analysis indicated that vegetation-related services were mainly associated with land-cover structure and vegetation growth, whereas hydrological and erosion-related services were more closely linked to precipitation, potential evapotranspiration, temperature, and topography. These findings support differentiated oasis management through ecological restoration, development regulation, water-saving agriculture, and strict ecological protection. Full article

Accurate soil salinization monitoring in arid oases is crucial for agricultural sustainability and ecological security. However, existing deep learning-based approaches often suffer from insufficient use of multi-scale information and inadequate modeling of feature interactions, limiting their accuracy for retrieving complex salinity patterns. To address these limitations, we propose a multi-scale attention-based optimized hybrid deep learning model that integrates multi-scale 1D convolutional neural networks (1D-CNN), bidirectional gated recurrent units (Bi-GRU), and Transformer mechanisms (termed SMS–1D-CNN–Bi-GRU–Transformer). In this study, “scale” refers to the receptive-field scale formed by different 1D convolutional kernel sizes. The model employs a multi-scale feature extraction module to capture remote sensing signals across different scales, a multi-scale attention mechanism to adaptively weight the most informative features, and a Bi-GRU–Transformer module to explore complex sequential and global feature relationships. The proposed framework is applied to an oasis irrigation zone in Weili County, Xinjiang, using hyperspectral data from the ZY-1E satellite, topographic indices, and spectral-derived variables. The proposed method outperforms conventional 1D-CNN, GRU–Transformer, and other benchmark models on the test set—showing improvements of 2.8% in the coefficient of determination (0.952) and 18.9% in the root mean square error (0.867 g·kg⁻¹), demonstrating practical utility for precision land management and salinity monitoring in vulnerable irrigated ecosystems. Full article

Desert reclamation into oases promotes soil organic carbon (SOC) accumulation across soil profiles, with nitrogen (N) fertilization being a key driver. However, the possible role of soil microorganisms in coupled C–N processes remains poorly understood in desert regions. We conducted a soil incubation experiment to evaluate the effects of N addition to varied soil layers on soil properties, CO₂ efflux, and microbial communities. The fertilized treatments (N, NP, and NPK) were compared with the unfertilized control (CK). All treatments were derived from the original desert soil. After incubation, SOC content decreased by 8–28% below the topsoil (20–100 cm) in the CK treatment, while it increased by 6–32% throughout the soil profile (0–100 cm) in all fertilizer treatments. Compared to the CK, all fertilizer treatments reduced daily and cumulative CO₂ emissions throughout the soil profile, with NP and NPK treatments showing greater reductions (3–19%). Fertilizer addition consistently enriched the phylum Firmicutes—notably the genera Virgibacillus and Bacillus—while lowering the relative abundance of other major phyla. After incubation, all treatments reduced microbial diversity and richness, with the most pronounced declines observed under fertilization. These community shifts were closely linked to changes in SOC and total N below the topsoil. These findings demonstrate that N-based fertilization promotes SOC accumulation in desert regions through microbial community restructuring. This study highlights the important role of exogenous nutrients, particularly N, in regulating C–N cycling and organic C sequestration in deep soil during desert oasis transformation. Full article

Today, the reuse of treated wastewater is considered an important and strategic key driver of integrated and sustainable water and soil management in extremely arid desert regions, where significant constraints due to water scarcity, soil salinization, and the fragility of agricultural ecosystems within palm oases place a strain on all sustainable development policies. Through this study, we conducted a comprehensive evaluation of the performance of the treatment, as well as the constraints related to salinity and the implications for land management of the activated sludge wastewater treatment plant located in the Timimoun desert oasis in southern Algeria. Through monthly monitoring over a 12-month period, we conducted an analysis of physicochemical, nutritional, and microbiological parameters, as well as a seasonal analysis, in addition to calculating irrigation suitability indicators using first-order kinetic modeling of COD degradation. The results showed high reduction rates for COD (90%), BOD₅ (90.5%), and TSS (93.8%), confirming the resilience and effectiveness of biological treatment under very difficult and hostile climatic conditions. Furthermore, the ultraviolet disinfection process ensures microbiological quality that enables the reuse of treated water for agriculture. Despite this, the treated wastewater exhibited moderate salinity and sodicity levels, reflected by EC values ranging from 2.4 to 2.8 dS/m and an SAR value of 6.2, which remain important limiting factors for the long-term sustainability of wastewater reuse. Therefore, this study provides valuable scientific data for developing sound and sustainable water and land management policies in the harsh climate of Saharan oases. Full article

Multisource remote sensing data is utilised for the purpose of monitoring annual and interannual changes associated with climate change in the water bodies of the Chotts of Merouane and Melrhir, which are located in the Zone of Chotts in North Africa. These endorheic depressions are distinguished by recurrent flooding events of varying magnitude and frequency, which are contingent on fluctuations in climate parameters. It has been determined that certain cities located within the surrounding watersheds, such as Biskra, are subject to the intermittent threat of severe flooding. This has been shown to result in land degradation and soil salinisation during the drying-up process. A detailed examination of chronological data from the 1960s onwards reveals a decline in the frequency of flooding in Chott Melrhir in recent years. It is noteworthy that the region has not experienced any substantial flooding since 2020. This phenomenon is concomitant with the marked decline in precipitation levels observed in the region. Since 1980, there have been at least ten significant floods, resulting in varying degrees of damage and disruption. In contrast, Chott Merouane exhibits a more consistent hydrological pattern, with water flowing almost year-round due to wastewater and the drainage of the palm groves by the Oued Righ. Until the 1970s, the occurrence of flooding in the region was exclusively attributable to the direct overflow of the Biskra River and its tributaries. However, from the 1980s onwards, a new type of flooding emerged, linked to insufficient infiltration and drainage capacity in the soil and sewage systems during rainfall that was sometimes considered normal. The hydrological regime in the area has severe ramifications for the water supply and the state of the oases, which are vulnerable to salinisation. Full article

Monitoring cropland dynamics in arid regions is critical for balancing food security with water scarcity constraints. However, distinguishing fragmented agricultural oases from spectrally similar desert vegetation remains a persistent challenge due to spectral confusion and landscape heterogeneity. To address these challenges, this study developed the STGP-OCE feature cube on the Google Earth Engine platform (GEE) by integrating the Oasis Cooling Effect (OCE) into the commonly used STGP (Spectral, Textural, Geomorphic, and Phenological) feature space, coupled with the XGBoost ensemble model. Through ablation experiments and feature importance analysis, we quantified the feature construction mechanism for arid regions. Oasis Cooling Intensity emerged as the most influential variable (Gain score: 0.315), demonstrating that the thermal signature of continuous anthropogenic irrigation serves as a robust thermodynamic proxy to resolve the spectral ambiguity between crops and drought-tolerant desert vegetation. By hierarchically coupling this thermal indicator with textural features to suppress fragmentation noise, topographic constraints to filter non-arable terrain, and phenological trajectories, the STGP-OCE feature cube achieved an Overall Accuracy of 95.12% and a Precision of 94.95%, significantly outperforming models built on lower-dimensional cubes as well as existing global land cover products. We generated a 10 m annual cropland dataset for Xinjiang, China, revealing a substantial 32.9% expansion (19,360 km²) from 2015 to 2024, mainly occurring in vulnerable oasis–desert transition zones and coinciding with reported reclamation activities. These highlight the continuous agricultural encroachment into desert margins, while the proposed STGP-OCE cube provides a reliable methodology for high-precision cropland monitoring in arid regions. Full article

The Aksu River Basin oasis, a typical arid ecological environment, faces considerable ecological and public health risks from fluoride accumulation in soil and groundwater. However, systematic investigations integrating soil–groundwater co-enrichment mechanisms with multi-pathway health risk assessments under environmentally relevant conditions remain scarce. We examined spatial fluoride distribution in the soil–groundwater system, associated health risks, and key driving mechanisms. Based on 2009 soil and 264 groundwater samples, we applied radial basis function (RBF) interpolation, Getis-Ord G_i* hotspot analysis, the geo-accumulation index (I_geo), the ecological risk index (ER), and the U.S. EPA health risk assessment model to evaluate pollution levels, ecological risks, and health impacts on adults and children. Spearman’s correlation analysis revealed relationships with 12 environmental factors, including topography, climate, soil properties, and vegetation. Key results are as follows: (1) High-fluoride soils (>700 mg·kg⁻¹) clustered in the eastern basin, while groundwater fluoride increased along a west–east gradient, with RBF interpolation yielding the highest accuracy; (2) soil fluoride was generally “unpolluted–moderate risk” (mean I_geo = −0.14, ER = 1.40), whereas groundwater posed the primary health risk, with a mean hazard quotient of 1.83 for children via drinking water, indicating non-carcinogenic risk; (3) soil enrichment was driven by evaporation–concentration–alkaline activation, while groundwater enrichment followed a convergence–concentration–evaporation mechanism, being negatively correlated with elevation. Groundwater fluoride presents a clear health risk, particularly to children, arising from high geological background levels and intense evaporation. Managing fluoride pollution and safeguarding drinking water quality in arid oasis regions is consequential. These findings provide a scientific basis for sustainable groundwater management and public health protection in arid oases. Full article

Soil salinization is one of the primary forms of land degradation in arid and semi-arid regions, severely constraining agricultural production in Xinjiang’s oases. Unmanned aerial vehicle (UAV) imagery provides an effective means for precise monitoring of soil salinization, with image spatial resolution being a key factor affecting assessment accuracy. However, traditional single-scale remote sensing monitoring methods rely solely on spectral and textural features at the leaf scale (0.1 m resolution captures leaf-scale characteristics), neglecting the contribution of multi-scale features (single-row canopy scale and single-membrane-covered area scale (6-row crop canopy)) to soil salinity. For instance, 0.5–1 m reflects single-row canopy scale, while 2 m reflects single-membrane-covered area scale. Therefore, this study developed a multi-scale UAV imagery and machine learning framework to enhance soil electrical conductivity prediction accuracy. This study focuses on oasis cotton fields in Shaya County, Xinjiang. Based on UAV multispectral imagery, we resampled data to generate eight datasets at different spatial resolutions: 0.1, 0.5, 1, 1.5, 2, 2.5, 5, and 10 m. For each resolution, we calculated 21 spectral indices and 48 texture features to construct a feature set. At both single and multispatial scales, spectral indices, texture features, and their spectral-texture fusion features were constructed. Combining these with Backpropagation Neural Network (BPNN), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGBoost) models, a soil EC estimation framework was developed. The impact of three feature combination schemes on cotton field soil conductivity estimation using single-scale UAV imagery was compared. The accuracy of soil EC estimation for cotton fields was compared between multi-spatial scale and single-scale UAV image features. The optimal combination strategy for a multi-spatial scale and multiple features was determined. Results indicate that combining spectral and texture features yields the highest estimation accuracy for cotton field soil electrical conductivity in single-scale analysis. Multi-spatial scale image features outperform single-scale image features in estimating cotton field soil electrical conductivity accuracy. By comparing different feature combinations, when integrating 0.5 m spatial-scale spectra (S1, EVI, DVI, NDVI, Int1, SI) with 0.1 m texture features (RE1_ent, R_cor, RE1_cor, G_hom, B_mea, R_con, NIR_con), the XGBoost model achieved the optimal prediction accuracy ($R^2$ = 0.693, RMSE = 0.515 dS/m), outperforming the methods using multiple features at a single scale. This study developed a novel multi-scale image feature fusion technique to construct a machine learning model. This method describes the image characteristics of soil electrical conductivity at different geographical scales, providing a reference approach for the rapid and accurate prediction of soil electrical conductivity in arid regions. Full article

Rapid urbanization and agricultural expansion in arid regions have profoundly altered carbon cycles and landscape stability. Focusing on the Hexi Corridor, China, this study integrates multi-source geospatial data (1990–2020) to analyze the spatiotemporal evolution and driving factors of land-use carbon emissions (LUCE) and landscape ecological risks (LER). By integrating carbon accounting, LER assessment, bivariate spatial autocorrelation, and the Optimal Parameter Geographic Detector (OPGD), we quantify the intricate relationship between carbon dynamics and landscape integrity. Results indicate a transformative pattern of anthropogenic expansion and natural contraction, with a 2315.49 km² net loss of unused land. Net carbon emissions surged 4.6-fold, while forest and grassland sinks exhibited a significant “lock-in effect” due to fragile ecological foundations. Simultaneously, LER followed an “inverted U-shaped” trajectory; the refined 5 × 5 km grid scale revealed a significant drop in high-risk areas from 44.65% to 10.96% following ecological restoration. Spatial analysis reveals a significant “spatial mismatch” between LUCE and LER, with oases manifesting “high carbon–low risk” clustering. Driver detection confirms a driving asymmetry. LUCE is dominated by anthropogenic factors (nighttime light, q > 0.90), whereas LER is profoundly constrained by natural backgrounds. Future governance must shift toward a collaborative system centered on source-based emission control and precise regional management to synergize low-carbon transition with landscape security. Full article

Soil salinization in arid oases constrains soil functioning and crop production, making spatially explicit monitoring important for land management. Multispectral optical remote sensing enables large-area salinity assessment, but in oasis environments such as the Keriya Oasis, its performance can be limited by spectral confusion between salt crusts and bright bare soils, sparse vegetation cover, and strong surface heterogeneity. Synthetic aperture radar (SAR), by contrast, provides all-weather imaging capability and sensitivity to surface scattering and dielectric-related conditions, but its salinity interpretation is often affected by surface complexity and environmental coupling. To address these, a spectral index–polarimetric scattering integration framework that combines RADARSAT-2 and Landsat-8 OLI features within a simple two-dimensional (2D) feature space was developed. Two groups of models were constructed from variables selected through a data-driven screening process: (1) polarimetric feature space models based on combinations such as VanZyl volume scattering with Pauli odd-bounce or Touzi alpha scattering; and (2) multi-source feature space models that integrate the optimal polarimetric component with key spectral indicators such as SI4 and MSAVI. Among all tested models, VanZyl_vol-SI4 achieved the best performance (fitting: R² = 0.749, RMSE = 5.798 dS m⁻¹, MAE = 4.086 dS m⁻¹; validation: R² = 0.716, RMSE = 5.566 dS m⁻¹, MAE = 4.528 dS m⁻¹). The results indicate that integrating PolSAR scattering information with optical indices can improve salinity mapping relative to single-source feature spaces in the Keriya Oasis. The proposed 2D framework provides a concise way to compare different feature combinations and supports regional identification of salt-affected soils. Full article

Schistosomiasis is a major neglected tropical disease in sub-Saharan Africa. This study compared the epidemiology of urinary schistosomiasis among children living in two distinct ecosystems in Mauritania: the Senegal River Basin (Trarza region) and the arid oasis areas (Adrar and Tagant regions). A cross-sectional study was conducted between February 2023 and February 2024 involving 856 children across 14 sites. Urine samples were collected from school-aged children and subjected to macroscopic and microscopic examinations. A questionnaire was administered to each child to determine sociodemographic factors. Environmental and geographical factors were documented in the localities. The prevalence rate of urinary schistosomiasis among children in the Senegal River Valley was 32.4%. In the oases zone, prevalences were 6.43% and 3.35% in Tagant and Adrar, respectively. Macroscopic hematuria was 29.1%, 6.04%, and 4.18% in Trarza, Adrar, and Tagant, respectively. The intensities of infection were 48.9, 6.43, and 40 eggs/10 mL in the Trarza, Adrar, and Tagant regions, respectively. Based on sex, prevalence was higher among boys in the Trarza and Tagant regions, while in Adrar, it was higher among girls. Prevalence among children using polluted water sources with dense vegetation in the department of Tékane, in the Trarza region, was significantly higher (35.7%) than among those using cleaner water sources (21%). Urinary schistosomiasis remains highly prevalent among children living along the Senegal River, while considerably lower transmission was observed in oasis settings. These findings highlight the strong influence of environmental and water-related factors on transmission dynamics and underscore the need for targeted, context-specific control strategies. Full article

The Saoura Valley (southwestern Algeria) hosts14 oases that primarily depend on groundwater in an endorheic basin. The hydrogeological system is bisected by the Saoura Wadi into two distinct compartments: an active, interconnected eastern compartment (Mio–Plio–Quaternary alluvial aquifer and terraces of the Great Western Erg) and a passive, fossil western compartment (Guir Hamada and Cambro–Ordovician aquifers). In September 2024, 51 groundwater samples were collected from nine oases. Temperature ranged from 16.2 to 31.4 °C and pH ranged from 7.1 to 7.85. Total dissolved solids (TDS) varied widely (179–4480 mg/L; median of 454 mg/L), with electrical conductivity between 280 and 7000 µS/cm. Three main hydrochemical facies were identified: Ca–Mg–SO₄–Cl (30%), Na–Cl–SO₄ (55%), and hypersaline types in the terminal inferoflux zone. Nitrate concentrations exceeded the WHO guideline (50 mg/L) in 22% of samples, attributed to localized agricultural and domestic inputs. Geochemical evolution is controlled by evaporite dissolution (gypsum, halite), cation exchange, and evaporative concentration, with a downstream salinity gradient from freshwaters near the Great Western Erg toward hypersaline inferoflux. Comparison with historical data (1941, 1963, and earlier studies) indicates a trend of increasing salinization since the 1990s, associated with intensive borehole pumping and irrigation return flow. These findings suggest risks to the long-term sustainability of the Saoura oases. Full article

Drought is a highly destructive natural disaster that inflicts severe economic losses. Its formation mechanisms are complex, yet existing studies have often focused on single driving factors, leaving the synergistic effects of multiple factors insufficiently explored. Based on multi-source data from Xinjiang spanning 1981–2020, this study systematically examined the combined impacts of atmospheric circulation, underlying surface conditions, and human activities on drought, using the multi-temporal-scale Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSI), along with partial correlation analysis, spatial autocorrelation, and principal component analysis. The results show that Xinjiang experienced a pronounced drying trend over the past 40 years, with the seasonal SPEI and SSI both exhibiting significant declines. Drought intensity was higher in northern Xinjiang than in the south. Correlations between drought indices and circulation indices, such as Atlantic Multidecadal Oscillation (AMO), were relatively weak, indicating a limited regulatory influence of large-scale circulation on regional drought under the dual constraints of topography and an inland setting. Among underlying surface factors, slope significantly influenced drought spatial patterns. Mountainous areas and basin interiors showed positive spatial correlations, characterized respectively by high–high clustering (high slope and high drought index) and low–low clustering (low slope and low drought index). In contrast, basin margins exhibited low–high clustering (low slope surrounded by high drought index), reflecting negative spatial correlation. Aspect showed no significant effect. Vegetation cover displayed clear seasonal coupling with drought, with strong negative correlations in spring due to intensified water stress. Human activities also played a prominent role. Since the mid-1990s, the expansion of built-up land and increased agricultural water use have shifted drought–land use relationships toward low–high clustering (low drought index surrounded by high land-use intensity) in southern Xinjiang oases, and toward low–low clustering (low drought index and low land-use intensity) in eastern Xinjiang. Meanwhile, ecological restoration projects promoted a transition from low–high to high–high clustering (high drought index and high land-use intensity) in some areas, alleviating local drying trends. Principal component analysis further revealed a shift in the dominant driver: land-use change was the primary factor before 2005, whereas vegetation cover became the key driver thereafter. By clarifying the mechanisms underlying multi-factor interactions in drought in Xinjiang, this study provides scientific support for integrated water resource management, ecological conservation, and climate adaptation strategies in arid regions. Full article