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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. Two 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, the VPRM preprocessor relied on data from the 500-m-resolution MODIS satellite and a static 1-km land cover classification map. As MODIS approaches discontinuation, this presentation introduces an updated VPRM software framework - pyVPRM - capable of handling satellite data from MODIS, VIIRS, and Sentinel-2, as well as high-resolution land cover products from ESA WorldCover and the Copernicus Global Land Service. The extremely high spatial resolution of the Sentinel-2 reflectances and updated land cover maps now allow vegetated area within cities to be resolved. In addition, the framework naturally 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 model, present fit parameters for all cases using data from European eddy-covariance towers, and present exemplary applications ranging from city to continental scales.
Glauch et al. (Mon,) studied this question.