Global Grassland Productivity Over Two Decades: 30m Bimonthly and Annual Gross Primary Productivity through Light Use Efficiency Modeling

This study presents a high-resolution mapping framework for estimating GPP in grasslands over the period 2000-2022 at a spatial resolution of 30 meters. The GPP values are derived utilizing a Light Use Efficiency (LUE) model using 30-m Landsat reconstructed images coupled with 1-km MOD11A1 temperature data and 1-degree CERES Photosynthetically Active Radiation (PAR). To implement the LUE model, we used the biome-specific productivity factor (maximum LUE parameter) as a global constant. This resulted in a productivity map that did not require specific land cover maps as inputs, allowing data users to calibrate GPP values accordingly to specific biomes/regions of interest. We then derived GPP maps for global grassland ecosystems based on maps produced by the Global Pasture Watch research consortium and calibrated the GPP values using the maximum LUE factor of 0.86 gCm⁻²d⁻¹ MJ^-1. Nearly 500 eddy covariance flux towers were used for validating the GPP estimates, resulting in R² between 0.48-0.71 and RMSE below 2.3 gCm⁻²d⁻¹ considering all land cover classes. The final time-series of maps (uncalibrated and grassland GPP) will be available as bimonthly and annual periods in Cloud-Optimized GeoTIFF (23 TB in size) as open data (CC-BY license). Users will be able to access the maps using the SpatioTemporal Asset Catalog (http://stac.openlandmap.org) and Google Earth Engine upon publication. In the meantime, beta versions of the product can be accessed through the Global Pasture Watch Early Access data program (https://survey.alchemer.com/s3/7859804/Pasture-Early-Adopters). This dataset is the first global GPP time-series map with a spatial resolution of 30 m covering a 23 year period to our knowledge.