Today, there is an urgent need for a better understanding of natural processes in the Earth System and of land surface interactions (LSIs).This need hasbecome even more important due to climate change and the requirement to meet the demands for adequatefood and watersupplies for the planet. Thus, developing methodologies to understand LSIs at different spatial scales and their response to climate change is essential to assessing the dynamics and the space-time distribution of key biophysical parameters and to ensure the long-term security of Earth’s environment and human population. In this context, being able to accurately estimate parameters such as latent heal flux (LE) which is proportional to evapotranspiration (ET) and sensible (H) heat fluxes as well as surface soil moisture (SSM) is of paramount importance, given their crucial role in many physical processes of the Earth system and the need for accurate estimates of their values in numerous scientific disciplines.
In the last decade or so, Earth Observation (EO) technology has undoubtedly played a key role in this direction, particularly as regards the retrievals of LE & H fluxes and SSM, providing products of LEH fluxes and SSM that satisfy many practical applications and research requirements. Yet, at present EO-based prediction of such parameters is performed by means of quite diverse ways. Additionally, the vast majority of the techniques use primarily, if not solely, EO data from spaceborne sensors. The use (or possible integration) of airborne EO data such as those from unmanned aerial vehicles (UAVs) has not been adequately exploreddespite the advantages (e.g. on demand acquisition, high spatial resolution) and their potential for integration in so-called "smart applications" suitable for mobile devices (smart phones). Advances in UAV technology --both hardware and software-- over the last 5 years or so have revolutionised the way EO data is integrated in science and practical applications. Furthermore, global EO-based operational mapping of LE/H fluxes and/or SSM, particularly at high spatio-temporal resolution, is completely lacking or underdeveloped. The launch of new satellites (e.g. ESA’s SMOS & Sentinels, NASA’s SMAP and Landsat 8), demonstrates the real need and the true potential of spaceborne sensors to provide spatially and temporally consistent and reliable information at previously unattained spatio-temporal resolutions. The synergy of EO with land biosphere models (LBMs), specifically Soil Vegetation Atmosphere Transfer (SVAT) models, has been identified as a promising avenue towards addressing this challenge. To this end, during the last decade research teams around the world have focused on a specific group of synergistic techniques that have their physical basis in the contextual interpretation of an EO-derived scatterplot of Surface Temperature (Ts) and a Vegetation Index (VI). A specific sub-group of those approaches which links the Ts/VI space with a land biosphere model (LBM), namely "SimSphere" (essentially a SVAT model) via the so-called “triangle” method has been the focus of various research groups globally due to its potential. Variants of this technique have also been investigated to develop global operational retrievals of SSM and/or in downscaling such products. Research findings from numerous studies conducted recently have made evident the need to urgently investigate several potential advances related to both SimSphere and the "triangle" in order to address existing knowledge gaps. Such advances could provide the opportunity to further enhance the applicability of these methods towards the development of a fully inclusive modelling scheme for studying LSI's at multiple observational scales. On this premise, ENviSION -EO’s overall aim is to explore both the SimSphere's and "triangle's" ability to pioneer the development of robust, state of the art, viable methodologies to observe, overa range of scales, a multitude of LSIs by globally exploiting contemporary EO data.As such, the present project aimed to address the following 4 objectives:(1) Facilitate the implementation of cutting-edge modelling interventions to the SimSphere LSM in a multifaceted way towards developing a land surface modelling tool for advancing our understanding and modelling of LSIs; (2) Appraise the “triangle's” potential for providing estimates of key parameters characterising LSIs using Sentinels-3 and UAV data respectively.; (3) Extend the “triangle” allowing the retrieval of new, previously unexplored biophysical parameters characterising LSIs and investigate the presence of new physical properties encapsulated in the satellite-derived Ts/VI feature space, and (4) Investigate the spatio-temporal downscaling of the SSM SMOS operational product at resolutions suitable for watershed to regional studies via the synergies with Sentinel-3 unfolding the ‘triangle’ potential for this purpose.
