A Causal Remote Sensing Framework to Disentangle Climate and Anthropogenic Drivers of Grassland Recovery on the Qinghai–Tibet Plateau

Disentangling the impacts of ecological restoration from climate change is an ongoing challenge in remote sensing since the traditional correlative approaches often cannot elucidate causal mechanisms. To overcome this, we introduce a Causal Remote Sensing Framework that uses multi-source satellite data (2000–2020), machine learning (XGBoost, SHAP) and causal inference (T-Learner) to build pixel-level counterfactuals. Using this framework, we assessed the Return Grazing to Grassland Program (RGGP) on the Qinghai–Tibet Plateau. Our results demonstrate that a warming and wetting climate improved Water yield (WY) while at the same time decreasing sand fixation (SF) in 83.6% of the region. Notably, the restoration project became the main factor that slowed this decline. After controlling for observational selection bias, the program had a net positive effect of (+6.02 t hm−2), reducing degradation in 64.6% of treated areas. This framework provides a practical way for the remote sensing community to go beyond change monitoring to allow the diagnosis of the causal mechanisms in complex human-environment systems.