Light Use Efficiency (LUE) based bimonthly Gross Primary Productivity (GPP) for global grasslands at 30 m spatial resolution (2000-2022)
- 1. OpenGeoHub Foundation
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2.
World Resources Institute
- 3. Remote Sensing and GIS Laboratory (LAPIG/UFG)
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4.
Euro-Mediterranean Center for Climate Change
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5.
Center for International Forestry Research
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6.
World Agroforestry Centre
- 7. Alliance of Bioversity International
Description
The paper 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 1o 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 gCm-2d-1MJ-1. The results of validation of the produced GPP estimates based on more than 500 eddy covariance flux towers show an R2 between 0.48-0.71 and RMSE bellow ~2.3 gCm-2d-1 for all land cover classes; for grasslands the RMSE was 1.53 gCm-2d-1. The final time-series of maps (uncalibrated and grassland GPP) are available as bimonthly (daily estimates in units of gCm-2d-1) and annual (daily average accumulated by 365 days in units of gCm-2yr-1 ) in Cloud-Optimized GeoTIFF (~23TB in size) as open data (CC-BY license). The recommended uses of data include: trend analysis e.g. 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 will be made available via 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), which provides data in exchange for feedback.
Technical info (English)
Visit the use-case page on the OEMC website to learn more: https://earthmonitor.org/land-degradation-neutrality-tool/