A 1 km Nationwide Long-Term NDVI Dataset for China based on AVHRR NDVI (1982–2015)

The Normalized Difference Vegetation Index (NDVI) serves as a crucial indicator for assessing extended vegetation trends. Existing 1 km MODIS NDVI products start from 2000, leaving a gap in high‑resolution observations for 1982–2000 and limiting systematic studies of vegetation change across China. To fill this spatio-temporal void, we built a 1 km nationwide NDVI time series dataset covering 1982–2015 by downscaling the 5 km AVHRR NDVI with a High‑Performance Super‑Resolution (HPSR) method. The proposed network architecture is composed of three key submodules that work sequentially and synergistically: (i) A multi-scale feature extraction network first extracts texture information across different receptive fields through parallel convolutions at varying scales, achieving multi-scale fusion via feature splicing. (ii) The resulting fused features are then passed to an attention refinement network that utilizes the Convolutional Block Attention Module (CBAM), incorporating channel and spatial attention mechanisms, to adaptively weight the fused features, thereby emphasizing significant vegetation regions while reducing irrelevant noise. (iii) Finally, the refined features are fed into a super-resolution network that utilizes up-sampling modules and residual blocks to reconstruct the feature maps to a high resolution. The model spatially downscales AVHRR NDVI data from an approximately 5 km resolution to 1 km, utilizing high-quality 1 km MODIS NDVI as a reference dataset, and integrating DEM terrain factors and the China Land Cover Database (CLCD) as ancillary data. The experimental findings indicate that, in comparison to the original AVHRR NDVI, HPSR reduces mean absolute error (MAE) by 30.21 %, root mean square error (RMSE) by 17.97 %, increases peak signal to noise (PSNR) ratio by 12.87 %, and improves structural similarity (SSIM) index by 3.375 %. We produced a 1 km nationwide NDVI dataset for China (1982–2015), which resolves the traditional trade‑off between spatial resolution and temporal coverage and furnishes a consistent, high‑quality baseline for long‑term vegetation monitoring and ecological conservation across the country.