A Vegetation Photosynthesis and Respiration Model (VPRM) for the post-MODIS era

The data-driven VPRM model is a simple light-use-efficiency model, driven by satellite-derived indices of Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) to extract information at high spatial resolution. High temporal resolution is provided through meteorological driving data, namely 2-m temperature and shortwave radiation at the surface. Four parameters per vegetation type are fit using flux tower measurements from the region for previous years. This well-established model has been widely used to model carbon exchange fluxes (gross primary productivity and respiration) between the land biosphere and the atmosphere. A common application is as a background (prior) model for estimating carbon fluxes through inversion techniques at regional scales, given the high temporal and spatial resolution of the fluxes compared to complex process models.

Historically, VPRM relied on data from the 500-m-resolution MODIS satellite and a static 1-km land cover classification map. As the MODIS mission is coming to an end, a new solution was needed going forward. This presentation introduces an updated VPRM framework - pyVPRM - capable of handling satellite data from MODIS, VIIRS, and Sentinel-2, as well as high-resolution land cover products, e.g. ESA WorldCover, Copernicus Global Land Service or MapBiomas Land Cover Data. The high spatial resolution of the Sentinel-2 reflectances and updated land cover maps allows vegetated area within cities and crop fields to be resolved. In addition, the framework provides an interface to generate VPRM inputs for use in online mesoscale models, such as the greenhouse gas module of the Weather Research and Forecasting Model (part of the WRF-Chem distribution). In our presentation we provide an overview of the new model, present fit parameters using data from eddy-covariance towers and show model flux results for nearly a quarter century of data across the Amazon basin at different spatial resolutions.