Soil organic carbon (SOC) as an indicator of soil quality, plays a dual role in stabilizing oasis ecosystems and regulating carbon sequestration in arid, lakeside environments. However, the accurate estimation of SOC using visible-near-infrared (VNIR) spectral data is limited by spectral redundancy and high dimensionality. This research enhances SOC estimation accuracy by combining wavelet analysis and machine learning in the lakeside oasis of Bosten Lake in Xinjiang. SOC content was measured for each sample (82 samples from the 0–20 cm depth), and their corresponding VNIR spectral data were obtained. The hyperspectral reflectance data were processed using continuous wavelet transform (CWT) and discrete wavelet transform (DWT). The successive projections algorithm (SPA), Boruta, and competitive adaptive reweighted sampling (CARS) algorithm identified relevant spectral bands to develop SOC estimation models based on partial least squares regression (PLSR), backpropagation neural networks (BPNN), and random forest (RF) algorithms. The results revealed that CWT offered superior noise reduction performance, particularly at low decomposition scales (1–5), achieving a 19.21% improvement in noise suppression compared to DWT. The optimal CWT-based model showed 23.20% improvement in residual prediction deviation (RPD) compared to the DWT-based counterpart. Feature selection algorithms significantly improved estimation accuracy, with enhancements of up to 49.04% in the determination coefficient (R 2) and 58.23% in RPD. Among the algorithms, CARS provided the highest improvement, followed by SPA and Boruta. Thus, the combination of CWT-1-CARS and the RF algorithm showed the strongest nonlinear modeling performance. This configuration achieved calibration metrics of R 2 = 0.79, root mean square error (RMSE) = 2.57, and RPD = 2.23 to outperform the original spectral models, with improvements of 63.3% over PLSR (RPD = 1.84) and BPNN (RPD = 1.91). The spatial interpolation analysis showed 91.3% consistency with field-measured SOC values, validating the model’s practical reliability. The most sensitive spectral response bands for SOC were primarily located in the visible range (401–504 nm) and the near-infrared range (1,638–2,369 nm). This study establishes a robust technical foundation for accurate estimation of SOC, for precise ecological monitoring, and sustainable management of arid, lakeside oases.

Changes in key ecosystem service parameters (Water balance residual (eWBR), Carbon storage (CS) and carbon sequestration (C.Seq)),and their response to land cover conversions and climate variability as an index of ecosystem restoration and degradation, in arid and semi-arid endorheic inland basins, vis-à-vis the Sustainable Development Goals (SDGs) remained underexplored. Thus, this study used the multi-layer perceptron model to simulate and predict land cover changes (LCC), the CASA-GRAMI, the InVEST, and Hargreaves-Sumani models estimated the ecosystem services. Whereas, the Ordinary Least Square Regression predicted changes in ecosystem services from anthropo-climatic factors while the Theil-Sen slopes, pixel correlations, and the advanced geostatistical methods examined the trends and responses of ecosystem services to land conversions and climate extremes. The results revealed degraded baseline condition in C.Seq and CS coefficients for LCB and ASB (1.858 and -0.025 and -0.002). In LCB, temperature and NDVI predicted a decreased eWBR, while, precipitation and LCC predicted a decreased CS. Also, the depletion of shrublands occasioned by its conversion to cropland degraded CS and C.Seq, opposing the SDGS. Furthermore, increased precipitation restored CS and C.Seq and vice versa. Contrastingly, in ASB, the temperature and precipitation predicted an increase eWBR, while the temperature predicted a decrease in CS. Furthermore, the conversion of bareland and grassland to cropland restored CS and C.Seq, as well as, reduced precipitation restored CS due to snowmelt and temperature increase. Temperature increases in LCB degrades CS and eWBR, while in ASB, it restores CS and carbon sink. The findings underscore the importance of adaptive and sustainable land and water management, climate strategies, and continuous monitoring of land cover changes to enhance ecosystem services and health to meet the SDGs through Inter-regional cooperation and knowledge sharing.