This study analysed the spatial pattern change of land use from 2005 to 2015 in Xinjiang and proposed a Markov–Future Land Use Simulation composite model to predict land-use change by 2025 under natural growth and ecological protection scenarios. The Criteria Importance Though Intercrieria Correlation method was proposed to construct the landscape ecological risk index based on land-use data. By calculating the risk index centroid and standard deviation ellipses, the spatio-temporal patterns and changing characteristics of landscape ecological risk in different scenarios in 2005, 2015, and 2025 were evaluated, and the driving forces of their evolution were also analysed. The results indicated that: (1) The characteristics of land use pattern change under the natural growth scenario from 2015 to 2025 are similar to those from 2005 to 2015. Under an ecological protection scenario, restricting the expansion of construction land, forest land, and grassland, which are the central ecological lands, can be effectively protected. Compared to the natural growth scenario, these areas increased by 507 and 724 km², respectively. (2) From 2005 to 2015, landscape ecological risk in Xinjiang generally showed a decreasing trend. Compared to the natural growth scenario in 2025, ecological protection can effectively reduce the ecological risk of the landscape. The highest, higher, and medium ecological risks were reduced by 0.39%, 3.11%, and 4.43%, respectively, while the areas with the lowest and lower ecological risks increased by 0.16% and 7.77%, respectively. (3) Under the natural growth scenario from 2005 to 2025, the centroid of landscape ecological risk in Xinjiang generally shifted to the southwest. To some extent, the ecological protection scenario can make the distribution of the highest, the higher, and the medium ecological risk areas more dispersed and the distribution of the lower ecological risk areas more concentrated. (4) Temperature is positively correlated with landscape ecological risk, while rainfall, Digital Elevation Model, slope, and aspect are negatively correlated with landscape ecological risk. There is also a correlation between soil types and landscape ecological risk. This research can reference the theory and method of landscape ecological risk and provide decision support for the sustainable development of the ecological environment of Xinjiang.

Accurate monitoring of soil moisture in arid agricultural regions is essential for improving crop production and the efficient management of water resources. This study focuses on Shihezi City in Xinjiang, China. We propose a novel method for soil moisture retrieval by integrating Sentinel-1 and Sentinel-2 remote sensing data. Dual-polarization parameters (VV + VH and VV × VH) were constructed and tested. Pearson correlation analysis showed that these polarization combinations carried the most useful information for soil moisture estimation. We then applied Shapley Additive exPlanations (SHAP) for feature selection, and a Stacking model was used to perform soil moisture inversion based on the selected features. SHAP values derived from the coupled support vector regression (SVR) and random forest regression (RFR) models were used to select an additional six key features for model construction. Building on this framework, a comparative analysis was conducted to evaluate the predictive performance of multivariate linear regression (MLR), RFR, SVR, and a Stacking model that integrates these three models. The results demonstrate that the Stacking model outperformed other approaches in soil moisture retrieval, achieving a higher R² of 0.70 compared to 0.52, 0.61, and 0.62 for MLR, RFR, and SVR, respectively. This process concluded with the use of the Stacking model to generate a county-level farmland soil moisture distribution map, which provides an objective and practical approach to guide agricultural management and the optimized allocation of water resources in arid regions.

Timely and precise yield estimation is of great significance to agricultural management and macro-policy formulation. In order to improve the accuracy and applicability of cotton yield estimation model, this paper proposes a new method called SENP (Seedling Emergence and Number of Peaches) based on Amazon Web Services (AWS). Firstly, using the high-resolution visible light data obtained by the Unmanned Aerial Vehicle (UAV), the spatial position of each cotton seedling in the region was extracted by U-Net model of deep learning. Secondly, Sentinel-2 data were used in analyzing the correlation between the multi-temporal Normalized Difference Vegetation Index (NDVI) and the actual yield, so as to determine the weighting factor of NDVI in each period in the model. Subsequently, to determine the number of bolls, the growth state of cotton was graded. Finally, combined with cotton boll weight, boll opening rate and other information, the cotton yield in the experimental area was estimated by SENP model, and the precision was verified according to the measured data of yield. The experimental results reveal that the U-Net model can effectively extract the information of cotton seedlings from the background with high accuracy. And the precision rate, recall rate and F1 value reached 93.88%, 97.87% and 95.83% respectively. NDVI based on time series can accurately reflect the growth state of cotton, so as to obtain the predicted boll number of every cotton, which greatly improves the accuracy and universality of the yield estimation model. The determination coefficient (R2) of the yield estimation model reached 0.92, indicating that using SENP model for cotton yield estimation is an effective method. This study also proved that the potential and advantage of combining the AWS platform with SENP, due to its powerful cloud computing capacity, especially for deep learning, time-series crop monitoring and large scale yield estimation. This research can provide the reference information for cotton yield estimation and cloud computing platform application.