Using the pyVPRM framework to estimate biospheric carbon fluxes from city to global scales

The Vegetation Photosynthesis and Respiration Model (VPRM) is a data-driven light-use-efficiency model for estimating biospheric carbon dioxide fluxes based on satellite-derived vegetation indices, such as the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI), which provide high spatial resolution information on land surface conditions. High temporal resolution is achieved through meteorological driving data, including 2 m air temperature and surface shortwave radiation. The model parameters are calibrated for each vegetation type using regional eddy-covariance flux measurements from previous years. VPRM is a well-established approach that has been widely applied to quantify gross primary productivity and ecosystem respiration and to interpret atmospheric CO₂ concentration measurements in terms of biogenic and anthropogenic flux contributions. In many cases VPRM fluxes are also used as priors for atmospheric inversions.

pyVPRM is an open and modular Python-based framework that facilitates the application of VPRM across a wide range of spatial scales, from urban domains to continental and global analyses. Its flexible design allows users to combine different satellite products (e.g. MODIS, VIIRS, Sentinel-2), land-cover classifications (e.g. ESA WorldCover, Copernicus Dynamic Land Cover, MapBiomas), and meteorological data sources (e.g. local observations or reanalysis products such as ERA5).

In this poster, we present recent developments in the pyVPRM framework, demonstrate typical application workflows, and discuss best practices for model configuration and evaluation. A central aim of this contribution is to engage with the user community, gather feedback on current capabilities and limitations, and discuss future directions for collaborative model development and applications.