Global pastures and grasslands productivity time series mapped at 30-m spatial resolution using Light Use Efficiency Model

Pastures and grasslands are the largest land cover of Earth's surface, comprising fundamental landscapes for water and nutrient cycling, food production, biodiversity conservation and land management in the planet. Monitoring the conditions and productivity aspects of these lands can lead to major contributions for land degradation mitigating in line with sustainable development goals defined by the United Nations (UN) 2030 agenda. Nevertheless, an operational approach able to monitor productivity of pastures and grasslands at global scale and high spatial resolution (e.g. 30-m) is a challenging research problem. Aiming to contribute with this topic, the current work present a methodology to derive 30-m bi-monthly time-series of Gross Primary Productivity (GPP) for pastures and grasslands of the world based on GLAD Landsat ARD (collection-2) and a customized Light Use Efficiency Model (LUE). The Landsat imagery were aggregated by every two months and gapfilled by an temporal interpolation based on Fast Fourier Transform (FFT). The complete, consistent and gapfilled Landsat time-series was used to estimate the Fraction of Photosynthetically Active Radiation (FPAR) and Land Surface Water Index (LSWI), which combined with 1-km MODIS temperature (MOD11A2) and 1° CERES Photosynthetically Active Radiation (SYN1deg v4.1 - PAR) images resulted in 30-m global GPP time-series product from 2000 onwards. Our preliminary validation approach, based on FLUXNET2015 data, indicated a R2 of 0.67 and RMSE of 2.06 for in-situ stations located Europe. We are working to release the first version of the product as open data (CC-BY license) in the context of the Global Pasture Watch project and the World Resources Institute's Land & Carbon Lab, establishing partnerships with local organization and research institute to collect feedback and additional validation data to improve further versions of the product.