Especially the influence of vegetation canopy properties and their spatio-temporal dynamics on feedbacks between the land surface and the atmosphere (i.e., temperature, precipitation, humidity, atmospheric boundary layer properties) are not conclusively understood. One major reason for this is that high-resolution observational data products (c.f. on vegetation canopy moisture) are not available yet at high spatial resolution (decameter scale) and on multi-year time scales: neither from remote sensing nor from models. In addition, land surface heterogeneities (e.g. variety in vegetation cover) can have significant impacts on feedback processes between canopy and proximate atmosphere, but their representation in models is not sufficient at high spatial resolutions. To bridge these gaps, remote-sensing based products are being developed to account for some of the multiple varying vegetation conditions.
In that sense, the idea of Project 2 is to monitor a range of vegetation canopy properties, encompassing water content pools (e.g., soil and vegetation moisture) and fluxes (e.g., evapotranspiration) at both field (link to in situ data) and regional (link to earth system model data) scales. The approach takes advantage of the potential synergies among optical, passive and active microwave sensors, which offer complementary information to transform remote sensing signals (e.g., microwave attenuation) into biophysical variables (e.g., gravimetric vegetation moisture, evapotranspiration, or vegetation structure and density). These unique and unprecedented datasets of satellite-based multi-sensor remote sensing will be fed into land-atmosphere (L-A) models to determine and analyze boundary layer properties and L-A feedbacks. All these land surface variables can synergistically contribute to understanding the link between soil, vegetation and the atmospheric boundary layer processes and to initialize L-A models.
P2 directly focuses on the high-resolution (decameter-scale) determination of states and spatio-temporal dynamics of the moisture, temperature and topography of the vegetation canopy to track moisture and temperature distributions for assessing transpiration and the shape as well as 3D-dynamics of the atmospheric roughness sublayer. This is achieved by blending of multi-sensor remote sensing observations (e.g. Copernicus Sentinel satellites & space-borne LiDARs). Spatio-temporally dynamic information of these vegetation variables will be prepared for integration into the fleet of land-atmosphere models of LAFI for assessing boundary layer properties and understanding of L-A feedbacks.
