PROgRESSIon – Investigating the Prototyping of Operational Estimation of Energy Fluxes and Soil Moisture Content Using a Variant of the “Triangle” Inversion Methodology

Accurately estimating the spatio-temporal distribution of energy, mass and momentum at the surface-atmosphere interface can help develop a better n understanding of the complex interactions of the Earth system. By linking deter-ministic land surface process model, such as SimSphere, to the spatialised infor-mation provided by Earth Observation (EO) data, a more powerful synergistic av-enue can be developed to take advantage of the temporal and spatial benefits of both modelling and EO-based approaches. The “triangle” method, utilises the tri-angular distribution of land surface temperature (LST) and vegetation index (VI) formed by a satellite-derived scatter plot, linked with SimSphere under a full range of vegetation cover and soil moisture availability, to derive spatial estimates of energy fluxes and soil moisture content (SMC). To this end, the objective of this study was to implement the “triangle” method using AATSR satellite data products to derive and subsequently validate spatially explicit maps of land surface heat fluxes and SM for different ecosystems in Europe.

The “triangle”-derived estimations of soil moisture exhibited a minor overestima-tion of the in-situ observations, and an error distribution of 0.097 vol vol-1, an in-crease on the required operational accuracy of 0.040 vol vol-1. Agreement results were also poor (R = 0.435). However, in overall, these results were comparable to previous validation studies of the “triangle” method implementation. Results for the LE and H fluxes were within accuracy range of 50 Wm-2, with RMSD values of 41.15 Wm-2 and 44.37 Wm-2 respectively. Furthermore, there was a good agreement between the “triangle”-derived and in-situ observed instantaneous LE and H fluxes, exhibited by high R values (0.88 and 0.69 respectively). Our study is one of few studies validating the “triangle” method over different ecosystems in Europe. It is a significant step forward in supporting the operational development of this method using remote sensing data in deriving key land surface parameters at a global scale.