China Natural Forest 500-m 8-day Gap-filled Gross Primary Productivity Dataset, 2005–2020

CN-NF-MOD17GPP-Fill v1.0 is a quality-controlled and gap-filled gross primary productivity (GPP) dataset developed for China’s natural forests. The dataset is derived from the MODIS MOD17A2HGF Version 6.1 product, using both the Gpp_500m science data layer and the associated Psn_QC_500m quality-control layer. It provides a temporally complete 500-m, 8-day GPP time series for the constant natural forest extent of China from 2005 to 2020. The original MOD17A2HGF product provides 46 8-day composites per year, yielding 736 temporal layers over the 16-year study period. Raw integer GPP values were converted to physical units using the product scale factor, screened using bitwise interpretation of the Psn_QC layer, and spatially constrained to the constant natural forest mask. Continuous GPP values and QC layers were processed separately to preserve their respective data characteristics. Missing values caused by quality-control masking, cloud contamination, or data gaps were reconstructed using a hierarchical pixel-level gap-filling algorithm. Valid observations were retained unchanged. One-step gaps were filled by linear interpolation, two- to three-step gaps were reconstructed using Savitzky–Golay smoothing, and longer gaps were filled using a pixel-level climatological estimate. When pixel-level observations were insufficient, the algorithm progressively fell back to neighbouring DOY windows, the pixel-wise temporal median, and finally the forest-wide DOY climatology. Each output value is accompanied by a provenance-oriented quality code that identifies whether the value is observed, smoothed, climatology-filled, or interpolated. This design allows users to filter or weight observations according to their reconstruction reliability. The dataset is designed to support studies of forest productivity dynamics, phenological transitions, drought responses, ecosystem resilience, and cross-product robustness analyses in China’s natural forests. CN-NF-MOD17GPP-Fill v1.0 should be described as a quality-controlled and gap-filled derivative dataset, not as an independent ground-truth validation product.