Search Results (8)

Accurate estimation of soil organic carbon (SOC) content is crucial for assessing terrestrial ecosystem carbon stocks. Although traditional methods offer relatively high estimation accuracy, they are limited by poor timeliness and high costs. Combining measured data, remote sensing technology, and machine learning (ML) algorithms enables rapid, efficient, and accurate large-scale prediction. However, single ML models often face issues like high feature variable redundancy and weak generalization ability. Integrated models can effectively overcome these problems. This study focuses on the Weigan–Kuqa River oasis (Wei-Ku Oasis), a typical arid oasis in northwest China. It integrates Sentinel-2A multispectral imagery, a digital elevation model, ERA5 meteorological reanalysis data, soil attribute, and land use (LU) data to estimate SOC. The Boruta algorithm, Lasso regression, and its combination methods were used to screen feature variables, constructing a multidimensional feature space. Ensemble models like Random Forest (RF), Gradient Boosting Machine (GBM), and the Stacking model are built. Results show that the Stacking model, constructed by combining the screened variable sets, exhibited optimal prediction accuracy (test set R² = 0.61, RMSE = 2.17 g∙kg⁻¹, RPD = 1.61), which reduced the prediction error by 9% compared to single model prediction. Difference Vegetation Index (DVI), Bare Soil Evapotranspiration (BSE), and type of land use (TLU) have a substantial multidimensional synergistic influence on the spatial differentiation pattern of the SOC. The implementation of TLU has been demonstrated to exert a substantial influence on the model’s estimation performance, as evidenced by an augmentation of 24% in the R² of the test set. The integration of Boruta–Lasso combination screening and Stacking has been shown to facilitate the construction of a high-precision SOC content estimation model. This model has the capacity to provide technical support for precision fertilization in oasis regions in arid zones and the management of regional carbon sinks. Full article

Using the Weigan River oasis as the research area and based on Landsat and measured groundwater depth data, the temperature vegetation drought index (TVDI) was calculated, and the groundwater depth that was measured in the field was used to establish a groundwater level prediction model (R² = 0.644). Groundwater distribution in the Weigan River oasis was monitored from 2007 to 2020, and the model was verified using data from September 2013 and June 2015. The results indicate the following: (1) From 2000 to 2015, the groundwater depth of the Weigan River oasis was increased in a fluctuating manner and increased from 2.80 m to 5.79 m, and these values fluctuated sharply with a range of change of 106.79%. (2) The correlation coefficient R² between the measured and predicted water levels in the two periods is 0.67 and 0.61, and the verification effect is good. (3) In the period from 2007 to 2020, the groundwater depth in the irrigated area exhibited a declining trend, where it decreased from the northwest and northeast to the southwest and southeast. (4) In irrigated areas, the GDP, population, and grain yield exerted a greater impact on groundwater depth. However, precipitation and evaporation were not significantly correlated with groundwater depth. Full article

Soil moisture (SM) is a critical parameter in Earth’s water cycle, significantly impacting hydrological, agricultural, and meteorological research fields. The challenge of estimating surface soil moisture from synthetic aperture radar (SAR) data is compounded by the influence of vegetation coverage. This study focuses on the Weigan River and Kuche River Delta Oasis in Xinjiang, employing high-resolution Sentinel-1 and Sentinel-2 images in conjunction with a modified Water Cloud Model (WCM) and the grayscale co-occurrence matrix (GLCM) for feature parameter extraction. A soil moisture inversion method based on stacked ensemble learning is proposed, which integrates random forest, CatBoost, and LightGBM. The findings underscore the feasibility of using multi-source remote sensing data for oasis moisture inversion in arid regions. However, soil moisture content estimates tend to be overestimated above 10% and underestimated below 5%. The CatBoost model achieved the highest accuracy (R² = 0.827, RMSE = 0.014 g/g) using the top 16 feature parameter groups. Additionally, the R² values for Stacking1 and Stacking2 models saw increases of 0.008 and 0.016, respectively. Thus, integrating multi-source remote sensing data with Stacking models offers valuable support and reference for large-scale estimation of surface soil moisture content in arid oasis areas. Full article

The oasis serves as the primary supply of cultivable land, along with the hub for human production and habitation in Xinjiang. Accordingly, predicting the land use of these areas based on various goals is an effective instrument for encouraging the sensible distribution of resource space. The study investigated the creation of a land use-allocation optimization model based on the various objectives of ecological protection, food security, and urban growth using the Weigan–Kuche River oasis as an example. The GMOP-PLUS model’s restriction conversion area was adjusted to include the findings of the land suitability evaluation. Additionally, it optimized and simulated the spatial arrangement and quantitative structure of land usage in the Weigan–Kuche River oasis in 2035. The results indicate the following: (1) the model’s overall accuracy is 89.36%, and its Kappa coefficient is 0.872, more than 0.8. Thus, the model can be considered for adoption in the future when predicting changes in land use in the districts and counties of the Weigan–Kuche River oasis; (2) based on the results of the land suitability evaluation, the percentage of areas that are most suited for agricultural development, urban development, and ecological protection is 39.32%, 24.21%, and 14.06%, respectively; and (3) the three scenarios satisfy the various demands for growth within the oasis, and the land use structure of the oasis varies considerably in response to the various development objectives, with the construction and cultivated land undergoing the most substantial modifications. The multi-scenario simulation of land usage in the oasis can provide essential support and a range of perspectives for future land spatial planning and socioeconomic development decision-making in the Weigan–Kuche River oasis. This is essential for both the efficient use of land resources and sustainable development. Full article

Groundwater is essential to residents, ecology, agriculture, and industry. The depletion of groundwater impacted by climatic variability and intense human activities could threaten water, food, and socioeconomic security in arid regions. A thorough understanding of groundwater level dynamics and its response to land-cover change is necessary for groundwater management and ecosystem improvement, which are poorly understood in arid desert regions due to a scarcity of field monitoring data. In our study, spatiotemporal characteristics of groundwater level impacted by land-cover change and its relationship with vegetation were examined using 3-years in-situ monitoring data of 30 wells in the desert regions of Tarim Basin during 2019–2021. The results showed that the depth to groundwater level (DGL) exhibited obvious spatial and seasonal variations, and the fluctuation of DGL differed significantly among the wells. The cultivated land area increased by 1174.6, 638.0, and 732.2 km² during 2000–2020 in the plains of Yarkand, Weigan-Kuqa, and Dina Rivers, respectively, mainly transferring from bare land and grassland. Annual average Normalized Difference Vegetation Index (NDVI) values increased with time during the period in the plains. DGL generally exhibited a weakly increasing trend from 2019 to 2021, mainly due to human activities. Land-cover change significantly affected the groundwater level dynamic. Generally, the groundwater system was in negative equilibrium near the oasis due to agricultural irrigation, was basically in dynamic equilibrium in the desert region, and was in positive equilibrium near the Tarim River Mainstream due to irrigation return water and streamflow. NDVI of natural desert vegetation was negatively correlated with DGL in the desert regions (R² = 0.78, p < 0.05). Large-scale land reclamation and groundwater overexploitation associated with water-saving irrigation agriculture development have caused groundwater level decline in arid oasis-desert regions. Hence, controlling groundwater extraction intensity, strengthening groundwater monitoring, and promoting water-saving technology would be viable methods to sustainably manage groundwater and maintain the ecological environment in arid areas. Full article

Soil salinization is a serious resource and ecological problem globally. The Weigan River–Kuqa River Delta Oasis is a key region in the arid and semi-arid regions of China with prominent soil salinization. The saline soils in the oasis are widely distributed over a large area, causing great harm to agricultural development and the environment. Remote sensing monitoring can provide a reference method for the management of regional salinization. We extracted the spectral indices and performed a correlation analysis using soil measurement data and Sentinel-2 remote sensing data. Then, two-dimensional feature space inversion models for soil salinity were constructed based on the preferred spectral indices, namely, the canopy response salinity index (CRSI), composite spectral response index (COSRI), normalized difference water index (NDWI), and green atmospherically resistant vegetation index (GARI). The soil salinity in a typical saline zone in the Weigan River–Kuqa River Delta Oasis was monitored and analyzed. We found that the inversion of the CRSI-COSRI model was optimal (R² of 0.669), followed by the CRSI-NDWI (0.656) and CRSI-GARI (0.604) models. Therefore, a model based on the CRSI-COSRI feature space can effectively extract the soil salinization information for the study area. This is of great significance to understanding the salinization situation in the Weigan River–Kuqa River Delta Oasis, enriching salinization remote sensing monitoring methods, and solving the soil salinization problem in China. Full article

The analysis of water-soluble salts in the soil is an important basic work for the development and research of the salinity monitoring and salinization control of saline soils, aiming at the complexity of the development of soil salinization in oases in arid and semi-arid areas. Based on the regionalization theory, GPS positioning technology was adopted in this paper to conduct fixed point sampling of the Weigan River Basin oasis from April to May 2020. Soil sampling levels were between 0 m and 0.25 m, between 1 m and 2 m, and between 2 m and 3 m, respectively, and soil physical and chemical properties were analyzed and tested from May to June 2022. The spatial variability of soil salinity at different depths in the Weigan River Basin oasis was quantitatively studied by GIS and geostatistics, and the results show that: (1) the soil salinity of each layer in the Weigan River Basin oasis was generally high, with an average value of 1.27%, 0.87%, and 0.79%, respectively, and a variation coefficient between 1.023 and 1.265, showing strong variability. (2) A reasonable number of soil sampling should be based on the 95% confidence level and 20% relative error. Therefore, the number of soil samples in the corresponding layers should not be less than 89, 70, and 91. (3) Optimal fitting models of soil salinity at the layers between 0 m and 0.25 m and between 1 m and 2 m were both spherical models, while the optimal fitting model of soil salinity at the layer between 2 m and 3 m was an exponential model. Soil salinity at different depths is affected by random factors and structural factors. Soil salinity at the layer between 0 m and 0.25 m showed moderate spatial correlation, while soil salinity at the layers between 1 m and 2 m and between 2 m and 3 m showed strong spatial correlation. (4) Three layers of soil salinity had obvious anisotropy, and the maximum variation direction was northwest–southeast. The nested model can overcome the influence of directionality on the fitting of soil salinity variation function in the layer between 0 m and 0.25 m and improve the spatial interpolation accuracy. (5) In terms of spatial pattern, the area of high soil salinity was located along the Tarim River and at the edge of oasis (i.e., desert–oasis ecotone). On the whole, the salinity shows a gradual increasing trend from the inner part of the oasis (i.e., cultivation area) to the edge of the oasis, and from the northwest to the southeast. (6) The spatial distribution pattern of soil salinity in the study area was mainly affected by the differences in topography, groundwater level, and water quality, while agricultural activities intensified the formation of the distribution pattern, reflecting the complexity of the development of soil physical and chemical properties in arid and semi-arid areas. This study provided a reference for the improvement, management, and rational utilization of saline soil in the Weigan River Basin oasis. Full article

Studying the relationship between agricultural irrigation water requirements (IWR) and water supply is significant for optimizing the sustainable management of water resources in Tarim River Basin (TRB). However, the related studies have not quantified the total IWR and the imbalance of irrigation water supply and requirements in the TRB. The study analyzed the spatial-temporal variations of IWR by a modified Penman–Monteith (PM) method during 1990–2015. Five major crops—rice, wheat, maize, cotton, and fruit trees—are chosen for calculating the IWR. It was found that the IWR increased significantly, from 193.14 × 10⁸ m³ in 1990 to 471.89 × 10⁸ m³ in 2015, for a total increase of 278.74 × 10⁸ m³. For the first period (1990–2002), the total IWR remained stable at 200 × 10⁸ m³ but started to increase from 2003 onwards. Significantly more irrigation water was consumed in the oasis regions of the Tienshan Mountains (southern slope) and the Yarkand River (plains). Furthermore, there was an intensified conflict between IWR and water supply in the major sub-basins. The ratios of IWR to river discharge (IWR/Q) for the Weigan-Kuqa River Basin (WKRB), Aksu River Basin (ARB), Kaxgar River Basin (KGRB), and Yarkand River Basin (YRB) were 0.93, 0.68, 1.05, and 0.79, respectively. The IWR/Q experienced serious annual imbalances, as high flows occurred in July and August, whereas critical high IWR occurred in May and June. Seasonal water shortages further aggravate the water stress in the arid region. Full article