To address the challenges of large prediction errors and limited reliability in conventional modeling approaches, this study proposes a hybrid framework that integrates optimization and deep learning techniques. The method employs an Improved Whale Optimization Algorithm (IWOA) to fine-tune parameters for GNSS data fitting, ensuring accurate signal feature extraction. These parameters are then fed into a Long Short-Term Memory (LSTM) network to model spatiotemporal dependencies through deep temporal pattern learning. Finally, a Gradient Boosted Decision Trees (GBDT) module is used to correct residual errors, particularly for predictions with large deviations, thereby improving overall accuracy and robustness. Unlike conventional models, this hybrid framework effectively mitigates large errors and improves reliability by leveraging a multi-stage approach. Experimental results confirm that the IWOA-LSTM-GBDT framework significantly outperforms traditional models. On the long-term prediction task at station JC03, it achieves a 37.9% reduction in Root Mean Square Error (RMSE), a 32.4% decrease in Mean Absolute Error (MAE), and a 4.6% increase in the coefficient of determination (R²) compared to the baseline LSTM. Compared to the IWOA-LSTM variant without residual correction, the complete framework further reduces RMSE by 7.2%, MAE by 2.3%, and increases R² by 0.5%. However, the framework may require significant computational resources, and its performance may be sensitive to the quality of input data, particularly for stations with limited measurements.

Satellite-observed greening in arid regions is often interpreted as ecological restoration success, yet this assessment may conflate natural recovery with agricultural expansion. We developed an Arid Remote Sensing Ecological Index (ARSEI) incorporating a Comprehensive Salinity Index (CSI) to address systematic biases in the traditional RSEI when applied to irrigated drylands. ARSEI scores were validated against MODIS Net Primary Production (NPP) ($R^2>0.75$ at the regional scale), confirming its reliability in capturing ecosystem productivity, while CSI effectively maps the upper-bound of surface salinization potential dictated by intrinsic soil properties. Applied to China’s Mu Us Sandy Land (2000–2024), the ARSEI reveals that 2327 km² of sandy land—54% of current cropland—was converted to agriculture, creating “assessment-induced false greening” signals. While the traditional RSEI increased monotonically (+135%), the ARSEI shows a nuanced pattern with plateau (2010–2015) and decline (2015–2020) phases, reflecting salinization risks masked by high crop NDVI. Optimal Parameters-Based Geographical Detector analysis demonstrates that Land Cover × Precipitation interactions (q = 0.28) drive spatial heterogeneity through irrigation-mediated water redistribution. The ARSEI provides a dialectical evaluation framework: acknowledging agricultural greening’s economic benefits while monitoring subsurface degradation risks. This study offers a critical methodological advance for sustainable land assessment in global drylands undergoing agricultural intensification.