The capabilities of virtual gauging stations in satellite monitoring of water bodies

Remote sensing technologies provide effective tools for monitoring and assessing the state of inland water bodies, enabling extraction of various hydrological parameters from satellite observation. Central Asian and some African countries are currently implementing practical programs aimed at mitigating water scarcity and improving the management of transboundary water resources. Rivers and their tributaries flowing across national boundaries require continuous monitoring to support early warning of droughts and floods at the basin scale.

Conventional ground-based hydrological stations are traditionally used to measure water level, estimate daily river discharge, and support hydrological forecasting. However, limitations related to accessibility, data-sharing restrictions, and the high cost of installation and maintenance often constrain their spatial coverage and long-term operation.  Virtual gauging station (VGS) represents a complementary remote-sensing approach, providing time series derived from the long-term satellite image archives. A VGS is defined as a free-shaped polygon on the map used to analyze data within the borders of this polygon and collect observations based on the requirements. Currently, VGS applications primarily rely on optical satellite imagery from Sentinel-2, Landsat-4, -5, -7, -8, -9 missions to estimate water surface area (WSA) using spectral water index (MNDWI, AWEI or AWEI_sh). Variations in WSA serves as a proxy for surface water availability and river dynamics. 

In addition, VGS can be used to enrich satellite altimetry-based water level (H) time series. For this purpose, the VGS polygon is calibrated using reference altimetric observations obtained from open-access data source (e.g. SDSS, DAHITI, Hydroweb). Calibration involves estimating the parameters of a regression model describing the functional relationship between water level and water surface area.  The resulting values can finally be integrated into hydrological models to support short-term river discharge forecasting. Thus, VGS provides continuous hydrological information independent of ground-based measurements, while optional validation against in-situ observations allows for the assessment of the model uncertainty.  Based on the experimental analysis, optimal placement of VGS polygons is recommended dynamically active river sections that account for annual riverbed displacement, as well as in river reaches located near satellite altimeter ground tracks to improve calibration accuracy.

The experiments demonstrated that correlation between ground truth and forecasted water level values is upper 0,85 and mean absolute error is lower than 0,3 m. The following result has been obtained using linear regression which shows that application of more complex forecasting models could significantly improve the results.