Epsilon Open Archive

Abstract

The past decades have seen rapid advancements in space-based monitoring of essentialwater cycle variables, providing products related to precipitation, evapotranspiration, and soilmoisture, often at tens of kilometer scales. Whilst these data effectively characterize water cyclevariability at regional to global scales, they are less suitable for sustainable management of localwater resources, which needs detailed information to represent the spatial heterogeneity of soil andvegetation. The following questions are critical to effectively exploit information from remotelysensed and in situ Earth observations (EOs): How to downscale the global water cycle products to thelocal scale using multiple sources and scales of EO data? How to explore and apply the downscaledinformation at the management level for a better understanding of soil-water-vegetation-energyprocesses? How can such fine-scale information be used to improve the management of soiland water resources? An integrative information flow (i.e., iAqueduct theoretical framework) isdeveloped to close the gaps between satellite water cycle products and local information necessary forsustainable management of water resources. The integrated iAqueduct framework aims to addressthe abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations,analytical- and physical-based models, as well as big-data analytics with machine learning algorithms.This paper provides a general overview of the iAqueduct theoretical framework and introduces somepreliminary results.