Temporal interpolation of land surface fluxes derived from remotesensing – results with an Unmanned Aerial System

Abstract. Remote sensing imagery can provide snapshots of rapidly changing land surface variables, e.g. evapotranspiration (ET), land surface temperature (Ts), net radiation (Rn), soil moisture (SM) and gross primary productivity (GPP), for the time of sensor overpass. However, discontinuous data acquisitions limit the applicability of remote sensing for water resources and ecosystem management. Methods to interpolate between remote sensing snapshot data and to upscale them from instantaneous to daily time scale are needed. We developed a dynamic Soil Vegetation Atmosphere Transfer model to interpolate land surface state variables that change rapidly between remote sensing observations. The Soil-Vegetation, Energy, water and CO2 traNsfer model (SVEN), which combines the snapshot version of the remote sensing Priestley Taylor Jet Propulsion Laboratory ET model and light use efficiency GPP models, incorporates now a dynamic component for the ground heat flux based on the force-restore method and a water balance bucket model to estimate SM and canopy wetness at half-hourly time step. A case study was conducted to demonstrate the method using optical and thermal data from an Unmanned Aerial System in a willow plantation flux site (Risoe, Denmark). Based on model parameter calibration with the snapshots of land surface variables at the time of flight, SVEN interpolated the snapshot Ts, Rn, SM, ET and GPP to continuous records for the growing season of 2016 with forcing from continuous climatic data and NDVI. Validation with eddy covariance and other in-situ observations indicates that SVEN can well estimate daily land surface fluxes between remote sensing acquisitions with root mean square deviations of the simulated daily Ts, Rn, SM, LE and GPP equal to 2.35 °C, 14.49 W m−2, 1.98 % m3 m−3, 16.62 W m−2 and 3.01 g C m−2 d−1, respectively. This study demonstrates that, in this deciduous tree planation, temporally sparse optical and thermal remote sensing observations can be used as ground truth to calibrate soil and vegetation parameters of a simple land surface modelling scheme to estimate low persistence or rapidly changing land surface variables with the use of few forcing variables. This approach can also be applied with remotely sensed data from other platforms to fill temporal gaps, e.g. cloud induced data gaps in satellite observation.