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 & Environment
GM - Geomodelling
EELS - Earth, Environmental and Life Sciences
Geological Survey Netherlands
Geosciences
Energy / Geological Survey Netherlands
Kalman filter
Rhine-Meuse basin
Soil Water Index (SWI)
Transfer function-noise (TFN) model
Auxiliary information
Digital elevation model
European remote sensing
Remotely sensed soil moisture
Rhine-Meuse basin
Soil water indices
Spaceborne remote sensing
Spatio-temporal prediction
Forecasting
Kalman filters
Meteorological instruments
Remote sensing
Soil moisture
Space optics
Time series
Groundwater
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