A daily gap-free normalized difference vegetation index dataset from 1981 to 2023 in China.

ABSTRACT: Long-term, daily, and gap-free Normalized Difference Vegetation Index (NDVI) is of great significance for a better Earth system observation. However, gaps and contamination are quite severe in current daily NDVI datasets. This study developed a daily 0.05° gap-free NDVI dataset from 1981-2023 in China by combining valid data identification and spatiotemporal sequence gap-filling techniques based on the National Oceanic and Atmospheric Administration daily NDVI dataset. The generated NDVI in more than 99.91% of the study area showed an absolute percent bias (|PB|) smaller than 1% compared with the original valid data, with an overall R² and root mean square error (RMSE) of 0.79 and 0.05, respectively. PB and RMSE between our dataset and the MODIS daily gap-filled NDVI dataset (MC

SUBMITTER: Li H 

PROVIDER: S-EPMC11111700 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature