Abstract
The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the 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 some preliminary results.
Highlights
Because soil moisture is determined by several physical features characterized by strong spatial gradients and temporal variability, these dynamics and features have to be taken into consideration in order to reach a reliable estimate of soil moisture using Earth observations (EOs) (Figure 2a)
The use of combined technologies may help in describing the spatial patterns of land surface features closely related to soil moisture, providing measurements over a range of scales moving from centimeters up to several meters, and enabling links to EO data from tens of meters to kilometers (Figures 2b and 3)
The soil spectral measurement performed in the field will be compared with the one acquired in the laboratory and from the remote sensing sensors
Summary
Water resources in many regions, including Europe, are under increasing pressure, due to population growth, economic development, and climate change [1,2]. Various satellite missions monitor the global water cycle, especially for the variables related to precipitation, evapotranspiration, and soil moisture but often at (tens of) kilometer scales [5]. Whilst these data are highly effective to characterize water cycle variation at the regional to global scale, they are less suitable for the management of water resources at field and catchment scales [10,11,12]. Kalman filter (EnKF) to reduce bias [54] and predict RZSM over broad spatial extents
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