The ecological environment of arid and semi-arid regions (ASARs) faces significant challenges, highlighting the need for a robust indicator system to assess ecological environmental quality (EEQ) and sustainability. This study investigates Central Asia (CA) using the Google Earth Engine (GEE) to develop a new remote sensing-based ecological index (ASAEI), assessing EEQ from 2000 to 2022 using the CatBoost–SHAP model. The results reveal a distinct spatial pattern in the ASAEI: the southwestern and southeastern regions face more severe ecological challenges, while the northern and central-southern areas exhibit better ecological conditions. The ASAEI exhibits a strong spatial autocorrelation, with high-value clusters in the northern and central-southern regions, where vegetation is dense, and low-value clusters in the southwestern and southeastern desert and Gobi regions. Over time, we observed that ecological degradation shifts from west to east. Overall, ecological restoration in CA exceeds the extent of degradation. Notably, Kazakhstan is primarily experiencing degradation, while other subregions predominantly show signs of restoration. Our analysis indicates that climate conditions and land use types are the primary factors influencing changes in the ASAEI. Furthermore, we project that 54.5% of the CA region will exhibit an improved EEQ, highlighting the need for restoration efforts in the western areas. The ASAEI offers a novel perspective and methodology for assessing EEQ in ASARs, with significant scientific implications.

Soil moisture is a key factor in the exchange of energy and matter between the soil and atmosphere, playing a vital role in the hydrological cycle and agricultural management. Traditional monitoring methods are limited in achieving large-scale, real-time observations, while deep learning offers new avenues to model the complex nonlinear relationships between spectral features and soil moisture content. This study focuses on the Wei-Ku Oasis in Xinjiang, using multi-source remote sensing data (Landsat series and Sentinel-1) and in situ multi-layer soil moisture measurements. The BOSS feature selection algorithm was applied to construct 46 feature parameters, including vegetation indices, soil indices, and microwave indices, and to identify optimal variable sets for each depth. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and their hybrid model (CNN-LSTM) were used to build soil moisture inversion models at various depths. Their performances were systematically compared on both training and testing sets, and the optimal model was used for spatiotemporal mapping. The results show that the CNN-LSTM-based multi-depth soil moisture inversion model achieved superior performance, with the 0–10 cm model showing the highest accuracy and a testing R² of 0.64, outperforming individual models. The testing R² values for the soil moisture inversion models at depths of 10–20 cm, 20–40 cm, and 40–60 cm were 0.59, 0.54, and 0.59, respectively. According to the mapping results, soil moisture in the 0–60 cm profile of the Wei-Ku Oasis exhibited a vertical gradient, increasing with depth. Spatially, soil moisture was higher in the central oasis and lower toward the periphery, forming a “center-high, edge-low” pattern. This study provides a high-accuracy method for multi-layer soil moisture remote sensing in arid regions, offering valuable data support for oasis water resource management and precision irrigation planning.

Dryland degradation increasingly compromises ecosystem stability, food production, and regional development. Clarifying its long-term evolutionary patterns and regional variations is therefore essential for formulating targeted management strategies. Leveraging GEE, we assembled multiple remote-sensing products together with land-cover information to establish a 1-km-resolution assessment framework describing vegetation conditions, drought pressure, potential soil salinity, and the ecological status associated with different land-cover types. An entropy-based weighting scheme was subsequently employed to derive the land degradation risk index (LDRI). This index enabled an assessment of changes in land degradation risk across arid Northwest China over the period 2001–2024, while also allowing the relative contributions of the principal driving factors to be evaluated. The analysis indicated that degradation risk generally weakened throughout the region, and shifts among risk categories occurred predominantly through stepwise movement between neighboring levels. Areas shifting toward lower-risk classes accounted for 75.36% of the total area experiencing risk-class changes. Nevertheless, High risk areas continued to be concentrated in the central-western desert belt, while the overall spatial pattern showed limited variation. Driver analysis indicated that vegetation productivity and hydrothermal conditions had relatively high explanatory power, while interactions among factors generally exhibited enhanced effects. Further stratified analysis showed that land degradation risk in vegetation zoning was mainly controlled by moisture conditions and vegetation productivity, whereas hydrothermal conditions exerted a stronger influence in non-vegetation zoning. The proposed LDRI provides a distinct analytical framework for the long-term monitoring and spatially differentiated management of land degradation risk across arid Northwest China, with broader implications for the scientific assessment and management of dryland degradation.