Improved Land Surface Phenology Detection in China’s Drylands and Associated Spatiotemporal Trends

Vegetation phenology is a sensitive indicator of climate change in China’s drylands (aridity index, AI < 0.65). However, accurate phenological monitoring remains challenging due to low signal-to-noise ratios, persistent soil background interference, and the scarcity of ground phenological sites. Existing global phenology products also perform poorly in hyper-arid and arid regions. This study developed an optimal phenology detection framework for China’s drylands by systematically evaluating various vegetation indices, noise-reduction techniques, fitting functions, and dynamic thresholds against ground observations, generating a dataset at 500-m resolution spanning 2001–2024. Specifically, we determined vegetation index thresholds to distinguish vegetated from non-vegetated pixels based on 453 field survey sites. Our results indicate that the Normalized Difference Phenology Index (NDPI) coupled with a 10% threshold and polynomial fitting provided the highest accuracy for Start of Season (SOS) (RMSE = 12.02 days). For End of Season (EOS), EVI2 combined with a 70% threshold and self-weighted double-logistic fitting yielded superior performance (RMSE = 19.89 days). Compared to the MODIS global phenology product (MCD12Q2), our dataset demonstrates significantly higher accuracy (higher R and lower RMSE) and broader spatial coverage, particularly in hyper-arid and arid regions. Spatiotemporal analysis reveals that SOS was earlier while EOS was later in more arid areas, potentially reflecting the opportunistic life strategies of ephemeral plants. Notably, a trend of delayed SOS was observed in these regions, which we potentially linked to the shifts in precipitation regimes under global change. This optimized framework and the resulting Chinese dryland phenology dataset provide a robust foundation for assessing ecosystem resilience and carbon cycle dynamics in water-limited environments.