To obtain timely, accurate, and reliable information on wheat yield dynamics. The UAV DJI Wizard 4-multispectral version was utilized to acquire multispectral images of winter wheat during the tasseling, grouting, and ripening periods, and to manually acquire ground yield data. Sixteen vegetation indices were screened by correlation analysis, and eight textural features were extracted from five single bands in three fertility periods. Subsequently, models for estimating winter wheat yield were developed utilizing multiple linear regression (MLR), partial least squares (PLS), BP neural network (BPNN), and random forest regression (RF), respectively. (1) The results indicated a consistent correlation between the two variable types and yield across various fertility periods. This correlation consistently followed a sequence: heading period > filling period > mature stage. (2) The model’s accuracy improves significantly when incorporating both texture features and vegetation indices for estimation, surpassing the accuracy achieved through the estimation of a single variable type. (3) Among the various models considered, the partial least squares (PLS) model integrating texture features and vegetation indices exhibited the highest accuracy in estimating winter wheat yield. It achieved a coefficient of determination (R²) of 0.852, a root mean square error (RMSE) of 74.469 kg·hm⁻², and a normalized root mean square error (NRMSE) of 7.41%. This study validates the significance of utilizing image texture features along with vegetation indices to enhance the accuracy of models estimating winter wheat yield. It demonstrates that UAV multispectral images can effectively establish a yield estimation model. Combining vegetation indices and texture features results in a more accurate and predictive model compared to using a single index.

The purposes of this study are to reveal the spatial pattern and dynamic changes of NDVI in the northern slope of the Tianshan Mountains for an extended period and to explore whether the spatial and temporal evolution of NDVI in different spatial scales is consistent so as to provide a reasonable theoretical basis for the selection of appropriate remote sensing spatial resolution in the study area. The GIMMS NDVI remote sensing data set was used to resample the NDVI data with three spatial resolutions of 0.5 km × 0.5 km, 1 km × 1 km, and 8 km × 8 km. The Mann-Kendall method was used to analyze the spatial-temporal evolution characteristics of vegetation NDVI on the NTSM from 1981 to 2015. The results showed that the interannual variation trend and spatial distribution of vegetation NDVI were consistent at different spatial scales. The change of NDVI displayed an increasing trend with changes concentrated in the middle of the NTSM. Five distinct trends were observed: no significant change (35% of the area), significant positive change (26%), significant single peak change (15%), a significant U-shaped change relationship (12%), and significant negative change (11%). Remote sensing NDVI data with a spatial resolution of 8 km could be used to analyze the long-term interannual variation trend of vegetation NDVI on the NTSM.

Soil salinization has a significant impact on agricultural production and ecology. There is an urgent demand to establish an effective method that monitors the spatial and temporal distribution of soil salinity. In this study, a multi-indicator soil salinity monitoring model was proposed for monitoring soil salinity in Bachu County, Kashgar Region, Xinjiang, from 2002 to 2022. The model was established by combining multiple predictors (spectral, salinity, and composite indices and topographic factors) and the accuracy of the four models (Random Forest [RF], Partial Least Squares [PLS], Classification Regression Tree [CART], and Support Vector Machine [SVM]) was compared. The results reveal the high accuracy of the optimized prediction model, and the order of the accuracy is observed as RF > PLS > CART > SVM. The most accurate model, RF, exhibited an R² of 0.723, a root mean square error (RMSE) of 2.604 g·kg⁻¹, and a mean absolute error (MAE) of 1.95 g·kg⁻¹ at a 0–20 cm depth. At a 20–40 cm depth, RF had an R² value of 0.64, an RMSE of 3.62 g·kg⁻¹, and an MAE of 2.728 g·kg⁻¹. Spatial changes in soil salinity were observed throughout the study period, particularly increased salinization from 2002 to 2012 in the agricultural and mountainous areas within the central and western regions of the country. However, salinization declined from 2012 to 2022, with a decreasing trend in salinity observed in the top 0–20 cm of soil, followed by an increasing trend in salinity at a 20–40 cm depth. The proposed method can effectively extract large-scale soil salinity and provide a practical basis for simplifying the remote sensing monitoring and management of soil salinity. This study also provides constructive suggestions for the protection of agricultural areas and farmlands.

Soil salinization significantly jeopardizes agricultural productivity and ecological stability in southern Xinjiang’s oasis regions, highlighting the urgent need to examine its spatial–temporal trends and driving mechanisms for improved resource management. Utilizing soil salinity measurements collected in 2010 and 2023, the current research applied multiple environmental variables processed via the Google Earth Engine (GEE) platform to evaluate the predictive capability of four machine learning algorithms—random forest (RF), Gradient Boosting Decision Tree (GBDT), Classification and Regression Tree (CART), and Support Vector Machine (SVM)—for accurate large-scale salinity mapping. Subsequently, a piecewise structural equation model (piecewiseSEM) was employed to quantitatively analyze the driving factors of soil salinization. Correlation analysis revealed seven critical variables—Red, NDSI, kNDVI, SDI, ET, elevation, and SM—as the most influential among the 41 environmental factors assessed for their impact on soil salinity. The performance evaluation ranked the models as follows: RF > GBDT > SVM > CART, with RF achieving the highest predictive accuracy (R² = 0.756, RMSE = 2.265 g·kg⁻¹, MAE = 1.468 g·kg⁻¹). Between 2010 and 2023, soil salinization severity in the region exhibited a slight overall decrease; however, the extent of this reduction was relatively modest. The proportion of moderately and severely salinized areas declined, accompanied by reduced spatial variability, whereas the extent of mildly salinized soils increased markedly. These findings imply that soil salinity primarily experiences internal redistribution within the surface layers, with limited downward leaching. Evapotranspiration (ET) and soil moisture (SM) were identified as the dominant drivers affecting salinity dynamics during both periods, with the influence of SM becoming more pronounced over time. This trend highlights that in conditions of limited natural variability, human-induced irrigation practices have emerged as the primary regulator of soil salinity levels. The findings of this study provide novel methodologies and data support for the monitoring and prevention of soil salinization in arid regions.

This study aims to assess the spatiotemporal changes in ecological environment quality (EEQ) in arid regions, using Xinjiang as a case study, from 2000 to 2023, with an improved remote sensing ecological index (IRSEI). Due to the complex ecology of arid regions, the traditional remote sensing ecological index (RSEI) has limitations in capturing ecological dynamics. To address this, we propose an enhanced IRSEI model that replaces normalization with standardization, improving robustness against outliers. Additionally, the kernel normalized difference vegetation index (kNDVI) and normalized difference salinity index (NDSI) are integrated to assess saline areas more effectively. The methodology includes time series analysis, spatial distribution analysis, and statistical evaluations using the difference method, coefficient of variation, and the Hurst index. Results show that the IRSEI more accurately reflects ecological dynamics than the RSEI. Temporal analysis reveals stable overall EEQ, with some areas improving. Spatially, the environment is generally better in the north and in mountainous regions than in the south and plains. Statistical evaluations suggest a positive trend in ecological changes, with improved areas surpassing degraded ones. This study contributes to the monitoring, protection, and management of arid region ecosystems, emphasizing the need for high-resolution data and further analysis.

Soil salinization in arid regions has drawn considerable attention due to its constraints on agricultural productivity and ecological security. Climate and soil texture, as key drivers at the macroscale, still lack systematic quantitative assessments regarding their mechanisms in shaping the long-term dynamics of salinity, and comparative evaluations of their relative contributions remain insufficient. Therefore, there is an urgent need to explore the spatiotemporal variations in soil salinization in arid regions and its responses to climate and soil texture. This study was based on salinity sampling sites collected in southern Xinjiang in 2023. A Random Forest (RF)-based inversion model was constructed using spectral indices derived from Landsat-9 and Sentinel-2 as environmental predictors. The predictive performance of models using all variables was compared with those using RF-based feature selection. The optimal model was then applied to retrieve soil salinity concentrations for 2008, 2013, 2018, and 2023 at four equidistant time points, enabling the spatiotemporal evolution of soil salinization across the study area to be assessed. Finally, a Boosted Regression Tree (BRT) model was employed to quantify the driving contributions of climate and soil texture. Results showed that the feature-selected Landsat-9 model performed best, with an R² of 0.747, significantly outperforming the Sentinel-2 model. The mean soil salinity concentration declined rapidly from 2008 to 2013, followed by a relatively slower but sustained decrease thereafter. The proportion of non-salinized land increased from 3.08% to 30.81%. The Sen’s slope−Mann−Kendall test indicated that 78.6% of salinity levels exhibited a significant downward trend, while 18.8% showed a slight increase. The relative contribution analysis indicated that climatic factors consistently exerted a stronger influence on the evolution of soil salinization than soil texture. Specifically, the contribution of climatic variables increased from 65.2% in 2008 to 66.8% in 2023, whereas that of soil texture decreased slightly from 34.8% to 33.2%. Among the climatic variables, the effect of potential evapotranspiration gradually weakened, while the impacts of temperature and precipitation continued to intensify. In contrast, soil texture variables played a comparatively minor yet stable role throughout the study period. These findings provide an effective framework for long-term monitoring of soil salinization and offer critical insights for adaptive management in arid regions under climate change.