Monitoring ecosystem dynamics in arid regions requires robust indicators that can capture spatial heterogeneity and diverse ecological drivers. In this study, we introduce and evaluate two novel ecological indices: the Arid-region Remote Sensing Ecological Index (ARSEI), specifically designed for desert environments, and the Composite Remote Sensing Ecological Index (CoRSEI), which integrates both desert and non-desert systems. These indices are compared with the traditional Remote Sensing Ecological Index (RSEI) in the Tarim River Basin from 2000 to 2023. Principal component analysis (PCA) revealed that RSEI maintained the highest structural compactness (average PCA1 = 87.49%). In contrast, ARSEI (average PCA1 = 78.62%) enhanced sensitivity to albedo and vegetation (NDVI) in arid environments. Spearman correlation analysis further demonstrated that ARSEI was more strongly correlated with NDVI (ρ = 0.49) and precipitation (ρ = 0.62) than RSEI, confirming its improved responsiveness under water-limited conditions. CoRSEI exhibited higher internal consistency and spatial adaptability (mean values ranging from 0.45 to 0.56), with slight ecological improvements observed between 2000 and 2023. Ecological drivers varied across habitat types. In desert areas, evapotranspiration, precipitation, and soil moisture were the main determinants of ecological status, showing high coupling and synchrony. In non-desert regions, soil moisture and precipitation remained dominant, but vegetation indices and disturbance factors (e.g., fire density) exerted stronger long-term influences. Partial dependence analyses further confirmed nonlinear, region-specific responses, such as the threshold effects of precipitation on vegetation growth. Overall, our findings highlight the importance of differentiated ecological modeling. ARSEI enhances sensitivity in desert ecosystems, whereas CoRSEI captures landscape-scale variability across desert and non-desert regions. Both indices contribute to more accurate long-term ecological assessments in hyper-arid environments.

Background: Sophora alopecuroides L. (S. alopecuroides L.) is a perennial herb widely distributed throughout Xinjiang, China. It is characterized by its rapid diffusion ability. Methods: To reveal the ecological mechanism of the rapid spread of S. alopecuroides, and to elucidate the ecological stoichiometric characteristics of C, N, and P (and the influencing factors) in the leaves of S. alopecuroides, leaves were sampled from four habitats—forest, roadside, farmland, and desert—across the Yili River Valley. The variation rules of the ecological stoichiometric characteristics of C, N, and P in the leaves of S. alopecuroides were analyzed. Correlations between the ecological stoichiometric characteristics of leaves and environmental factors were examined using redundancy analysis (RDA). Results: (1) The C, N, and P contents of S. alopecuroides leaves were 391.30–533.10 g/kg, 8.90–43.14 g/kg, and 0.71–2.04 g/kg, respectively, and the C/N, C/P, and N/P ratios were 10.34–4.94, 209.05–698.73, and 10.78–31.43 respectively. (2) The C content and C/P ratio of S. alopecuroides leaves were the highest in the desert habitat, leaf N content and N/P ratio were the highest in the forest habitat, leaf P content was the highest in the farmland habitat, and the leaf C/N ratio was the highest in the roadside habitat. (3) RDA showed that available potassium (AK) and pH were the main factors affecting the ecological stoichiometric characteristics of S. alopecuroides leaves in Yili Valley (p ≤ 0.05), and these factors were positively correlated with C, N, P, and N/P, and negatively correlated with C/P and C/N. AK was the dominant factor that affected the P content of S. alopecuroides leaves, and appropriate reduction of K fertilizer would be conducive to restraining the spread of S. alopecuroides. Soil C, N, P, and K content, soil organic matter (OM), nitrate nitrogen (NO₃⁻-N), ammonium nitrogen (NH₄⁺-N), and AK had no significant effect on the ecological stoichiometric characteristics of leaves (p > 0.05).

Grasslands play a vital role in the global ecosystem. Efficient and reproducible methods for estimating the grassland aboveground biomass (AGB) are crucial for understanding grassland growth, promoting sustainable development, and assessing the carbon cycle. Currently, the available methods are limited by their computational inefficiency, model transfer, and sampling scale. Therefore, in this study, the estimation of grassland AGB over a large area was achieved by coupling the PROSAIL model with the support vector machine regression (SVR) method. The ill-posed inverse problem of the PROSAIL model was mitigated through kernel-based regularization using the SVR model. The Zoigê Plateau was used as the case study area, and the results demonstrated that the estimated biomass accurately reproduced the reference AGB map generated by zooming in on on-site measurements (R² = 0.64, RMSE = 43.52 g/m², RRMSE = 15.13%). The estimated AGB map also maintained a high fitting accuracy with field sampling data (R² = 0.69, RMSE = 44.07 g/m², RRMSE = 14.21%). Further, the generated time-series profiles of grass AGB for 2022 were consistent with the trends in local grass growth dynamics. The proposed method combines the advantages of the PROSAIL model and the regression algorithm, reduces the dependence on field sampling data, improves the universality and repeatability of grassland AGB estimation, and provides an efficient approach for grassland ecosystem construction and planning.

Net primary productivity (NPP) is a key metric for evaluating ecosystem carbon sink capacity and defining vegetation. Despite extensive research on vegetation NPP, much relies on coarse spatial resolution data, which often overlooks regional spatial heterogeneity, causing inaccuracies in NPP estimates. Therefore, this study employed the improved CASA model, based on GF-SG and kNDVI methods, to estimate vegetation NPP at a 30 m spatial resolution on the Zoigê Plateau from 2001 to 2020. The effects of anthropogenic and climatic factors on NPP were quantified through residual and partial correlation analyses. These results indicated the following: (1) NDVI derived from the GF-SG fusion method aligns closely with Landsat NDVI (R² ≈ 0.9). When contrasted with using NDVI alone, incorporating kNDVI into the CASA model enhances NPP assessment accuracy. (2) Vegetation NPP on the Zoigê Plateau has fluctuated upward by 2.09 gC·m⁻²·a⁻¹ over the last two decades, with higher values centrally and lower at the edges. (3) Monthly partial correlation analysis indicates almost no temporal effects in NPP response to temperature (97.42%) but significant cumulative effects in response to precipitation (80.3%), with longer accumulation periods in the south. Annual analysis reveals that NPP correlates more strongly with temperature than precipitation. (4) NPP changes are jointly influenced by climate change (48.46%) and human activities (51.54%), with the latter being the dominant factor. This study deepens the understanding of NPP dynamics in the Zoigê Plateau and offers insights for estimating NPP at high spatial-temporal resolutions.