Abstract
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 (θ), 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 an instantaneous to a daily timescale 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” (SVEN) model, which combines the snapshot version of the remote sensing Priestley–Taylor Jet Propulsion Laboratory ET model and light use efficiency GPP models, now incorporates a dynamic component for the ground heat flux based on the “force-restore” method and a water balance “bucket” model to estimate θ and canopy wetness at a half-hourly time step. A case study was conducted to demonstrate the method using optical and thermal data from an unmanned aerial system at 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 UAS-based snapshots to continuous records of Ts, Rn, θ, ET, and GPP for the 2016 growing season with forcing from continuous climatic data and the normalized difference vegetation index (NDVI). Validation with eddy covariance and other in situ observations indicates that SVEN can estimate daily land surface fluxes between remote sensing acquisitions with normalized root mean square deviations of the simulated daily Ts, Rn, θ, LE, and GPP of 11.77 %, 6.65 %, 19.53 %, 14.77 %, and 12.97 % respectively. In this deciduous tree plantation, this study demonstrates that temporally sparse optical and thermal remote sensing observations can be used 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 observations.
Highlights
Continuous estimates of the coupled exchanges of energy, water, and CO2 between the land surface and the atmosphere are essential to understand ecohydrological processes (Jung et al, 2011), to improve agricultural water management (Fisher et al, 2017), and to inform policy decisions for societal applications (Denis et al, 2017)
By considering root mean square deviation (RMSD) values of Ts that are less than 2 ◦C and RMSD values of θ that are as small as possible, we selected the point close to the red arrow in Fig. 4, which corresponds to the RMSD values of θ and Ts that are equal to 2.99 % m3 m−3 and 1.92 ◦C respectively
Of Csat, b, Cveg, and SWSmax at this Pareto-front point are equal to 6.94 × 10−6 K m2 J−1, 5.20, 2.18 × 10−6 K m2 J−1, and 5.54 × 10−1 m respectively
Summary
Continuous estimates of the coupled exchanges of energy, water, and CO2 between the land surface and the atmosphere are essential to understand ecohydrological processes (Jung et al, 2011), to improve agricultural water management (Fisher et al, 2017), and to inform policy decisions for societal applications (Denis et al, 2017). Optical and thermal remote sensing can provide snapshots of these fluxes, such as soil moisture (θ ; Carlson et al, 1995; Sandholt et al, 2002), evapotranspiration (ET; Fisher et al, 2008; Mu et al, 2011), or gross primary productivity (GPP; Running et al, 2004), using land surface reflectance or temperature Both optical and thermal satellite observations present gaps during cloudy periods, and these gaps may coincide with a time when such information is needed (Westermann et al, 2011), for instance, the prevalence of cloudy weather during the crop growing season in monsoonal regimes (García et al, 2013) and high-latitude re-. Methods are needed to temporally interpolate and upscale the instantaneous records into continuous daily, monthly, or annual estimates (Alfieri et al, 2017; Huang et al, 2016)
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