A 1-km June-September monthly aboveground net primary productivity dataset for northern China's grasslands from 2002 to 2020
Description
This dataset contains 1-km June-September monthly aboveground net primary productivity (ANPP) raster maps for stable grasslands in northern China from 2002 to 2020. The dataset includes target-month ANPP layers for June, July, August, and September, with files organized by year and named as ANPP-YYYY-MM.tif. The June 2002 layer is not included because the corresponding phenology-related inputs were incomplete at the beginning of the phenology record used in this study. The ANPP rasters are provided in GeoTIFF format with units of g m^-2 yr^-1. The dataset also includes a stable grassland mask, a grassland occurrence frequency raster, multi-year mean ANPP, linear trend slope, file-level inventory, metadata, missing-file information, and a script for generating the stable grassland mask. The monthly layers are target-month ANPP maps generated from month-specific remote-sensing inputs and lagged climate sequences. They should not be interpreted as cumulative monthly productivity or monthly biomass production. Pixels outside the stable grassland extent are assigned as NoData.
Files
Steps to reproduce
The monthly ANPP layers were generated following the workflow described in the associated manuscript. Field ANPP observations were combined with multi-source remote-sensing, climate, phenology, and terrain variables. Stable grassland pixels were identified from annual CLCD grassland maps from 2002 to 2020 using a 0.80 stability threshold, which was rounded up to 16 of the 19 years. Month-specific remote-sensing inputs and lagged climate sequences were then used to produce target-month ANPP maps for June, July, August, and September. The included script can be used to reproduce the stable grassland frequency raster and binary stable grassland mask from the annual CLCD inputs. The final GeoTIFF files were organized by year and named as ANPP-YYYY-MM.tif.
Institutions
- Hainan UniversityHainan, Haikou