A Long-Term, High-Resolution SIF Reconstruction for Poyang Lake

This dataset provides a long-term, high-resolution, and gap-filled daily record of wetland photosynthetic activity and its controlling environmental variables for Poyang Lake, China’s largest seasonal floodplain wetland. Using MODIS surface reflectance products, we first generated continuous daily 250 m water/land classifications through the Spatio-Temporal Neighborhood Similarity Gap-Filling (STNS-GF) method, and reconstructed separate NDVI time series for water and land pixels using the Harmonic Analysis of Time Series (HANTS) algorithm. Daily water surface coverage (WSC) at 500 m resolution was subsequently derived from these classifications. An XGBoost machine learning model was trained to reconstruct SIF at 500 m spatial resolution from 2000 to 2023, integrating OCO-2/OCO-3 SIF retrievals with daily WSC, NDVI, and ERA5-Land meteorological variables. Cross-validation demonstrated robust performance (validation RMSE = 0.137 mW·m⁻²·nm⁻¹·sr⁻¹; MAE = 0.104 mW·m⁻²·nm⁻¹·sr⁻¹). The dataset comprises three core variables: Water Surface Coverage (WSC): daily fractional water coverage at 500 m. NDVI: daily normalized difference vegetation index for water and land pixels, gap-filled and smoothed. SIF: daily solar-induced chlorophyll fluorescence at 500 m, representing terrestrial and aquatic photosynthetic activity. These data support research on wetland carbon cycling, eco-hydrological modeling, drought impact assessment, and validation of satellite-based photosynthesis products, and provide a critical basis for adaptive management under increasing climate extremes. Temporal Coverage: 2000–02–24 to 2023–12–31 Spatial Resolution: 500 m