Print Email Facebook Twitter Using ERS spaceborne microwave soil moisture observations to predict groundwater head in space and time Title Using ERS spaceborne microwave soil moisture observations to predict groundwater head in space and time Author Sutanudjaja, E.H. De Jong, S.M. Van Geer, F.C. Bierkens, M.F.P. Publication year 2013 Abstract The study presented in this paper is to investigate the possibility of using spaceborne remote sensing data for groundwater head prediction. Remotely-sensed soil moisture time series of SWI (Soil Water Index) derived from ERS (European Remote Sensing) scatterometers are used to predict groundwater head dynamics in the Rhine-Meuse basin, where over four thousand observed groundwater head time series are available. Our study consists of three evolving research steps. First, the correlation between observed time series of groundwater head and SWI is investigated. Second, SWI time series are used as input to a transfer-function noise (TFN) model for temporal prediction (forecasts) of groundwater heads. Third, TFN models with spatially interpolated parameters are used with SWI time series for spatio-temporal prediction of groundwater heads. Here, HAND (Height Above Nearest Drainage) as derived from a digital elevation model is used as auxiliary information. Results show that the correlation between SWI and groundwater head time series is apparent, particularly in areas with shallow groundwater, and that correlation increases when a time-lag is taken into account. Temporal predictions with TFN models reproduce observed groundwater head time series well at locations with shallow groundwater, but results are poor for locations with deep groundwater. The spatio-temporal prediction method is not able to estimate the absolute value of groundwater heads. However, head variation in terms of timing and amplitude is predicted reasonably well, in particular in areas with shallow groundwater. This suggests that, once a groundwater model is suitably calibrated, remotely sensed soil moisture data could be used to improve groundwater prediction in an operational data-assimilation framework. © 2013 Elsevier Inc. Subject Earth & EnvironmentGM - GeomodellingEELS - Earth, Environmental and Life SciencesGeological Survey NetherlandsGeosciencesEnergy / Geological Survey NetherlandsKalman filterRhine-Meuse basinSoil Water Index (SWI)Transfer function-noise (TFN) modelAuxiliary informationDigital elevation modelEuropean remote sensingRemotely sensed soil moistureRhine-Meuse basinSoil water indicesSpaceborne remote sensingSpatio-temporal predictionForecastingKalman filtersMeteorological instrumentsRemote sensingSoil moistureSpace opticsTime seriesGroundwater To reference this document use: http://resolver.tudelft.nl/uuid:d19a29fc-c60f-43ce-9e88-35c0ccba039c DOI https://doi.org/10.1016/j.rse.2013.07.022 TNO identifier 478865 ISSN 0034-4257 Source Remote Sensing of Environment, 138, 172-188 Document type article Files To receive the publication files, please send an e-mail request to TNO Library.