Light use efficiency (LUE) based bimonthly gross primary productivity (GPP) for global grasslands at 30 m spatial resolution (2000-2022).

The article describes production of a high spatial resolution (30 m) bimonthly light use efficiency (LUE) based gross primary productivity (GPP) data set representing grasslands for the period 2000 to 2022. The data set is based on using reconstructed global complete consistent bimonthly Landsat archive (400TB of data), combined with 1 km MOD11A1 temperature data and 1° CERES Photosynthetically Active Radiation (PAR). First, the LUE model was implemented by taking the biome-specific productivity factor (maximum LUE parameter) as a global constant, producing a global bimonthly (uncalibrated) productivity data for the complete land mask. Second, the GPP 30 m bimonthly maps were derived for the global grassland annual predictions and calibrating the values based on the maximum LUE factor of 0.86 gCmdMJ. The results of validation of the produced GPP estimates based on 527 eddy covariance flux towers show an R-square between 0.48-0.71 and root mean square error (RMSE) below 2.3 gCmd for all land cover classes. Using a total of 92 flux towers located in grasslands, the validation of the GPP product calibrated for the grassland biome revealed an R-square between 0.51-0.70 and an RMSE smaller than ~2 gCmd. The final time-series of maps (uncalibrated and grassland GPP) are available as bimonthly (daily estimates in units of gCmd) and annual (daily average accumulated by 365 days in units of gCmyr) in Cloud-Optimized GeoTIFF (23TB in size) as open data (CC-BY license). The recommended uses of data include: trend analysis ., to determine where are the largest losses in GPP and which could be an indicator of potential land degradation, crop yield mapping and for modeling GHG fluxes at finer spatial resolution. Produced maps are available SpatioTemporal Asset Catalog (http://stac.openlandmap.org) and Google Earth Engine.