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Special Issue "Remote Sensing and Modelling of Water Storage Dynamics from Bedrock to Atmosphere"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 8175

Special Issue Editors

Geodesy and Earth Observation Group, Department of Planning, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark
Interests: satellite gravity; satellite altimetry; satellite remote-sensing data assimilation; calibration; inversion
Special Issues, Collections and Topics in MDPI journals
Forschungszentrum Jülich, Institute of Bio- and Geosciences: Agrosphere (IBG-3), Wilhelm-Johnen-Straße, 52428 Jülich, Germany
Interests: remote sensing for hydrology; passive microwave remote sensing for surface soil moisture estimation; hydrological modeling; data assimilation; radiometer-radar data fusion; Unmanned Aerial Systems (UAS)
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German Aerospace Center, Microwaves and Radar Institute, Münchener Strasse 20, 82234 Wessling, Germany
Interests: multisensor data integration; data-to-model assimilation; surface and subsurface hydrology; active and passive remote sensing; radiometer; SAR; LiDAR; radiative transfer; polarimetry; parameter extraction; Earth system; soil; root zone; vegetation; plant ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Earth observation (EO) satellite missions provide invaluable global-to-regional estimates of hydrologic variables from atmosphere to lithosphere. From these EO missions, the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) record data that are inverted to so-called terrestrial water storage (TWS), i.e., a virtual summation of surface and subsurface water storage. The separation of TWS into its individual water-storage compartments requires careful postprocessing that challenges the use of these data for hydrological applications. Here, specific analysis of auxiliary mission data would improve the knowledge of spatial and temporal dynamics of TWS and its subdomains. Satellite altimetry missions provide measurements of water-level changes in seas, lakes, and rivers. Various satellite missions, including SMOS, SMAP, ASCAT, AMSR2, and Sentinel-1, provide multiyear to multidecadal soil-moisture estimates at various soil depths. Moreover, seasonal changes in vegetation water content related to microwaves (e.g., SMOS vegetation optical depth) or optical observations (e.g., MODIS normalized difference water index) may also influence TWS dynamics. However, a reliable and physically sound relationship between measured signals, and their respective spatial and temporal dynamics with water storage is considerably complex and demands research in this scientific field.

Despite the increasing interest in using remote-sensing techniques for subsurface hydrology, its benefits need to be realized for monitoring vegetation, surface, and root-zone soil water storage, as well as groundwater storage from space. Therefore, the goal of this Special Issue is to demonstrate the contribution of satellite observations, and physical, conceptual, and/or statistical modeling techniques for estimating these water-storage components and their changes from  local (catchment) to global (hydrological-cycle) scales. Contributions introducing the latest developments in terms of new sensors and satellite missions that will be available in the near future, as well as those addressing the integration of remote-sensing products with surface and groundwater process models, including techniques for data assimilation, are particularly invited. Contributions dealing with all components of water storage between atmosphere and bedrock are welcome, and may include in situ measurements and/or nonremotely sensed datasets for parameter estimation at various spatial and temporal scales. Examples of potential focus areas are:

  • Advances in combining remote-sensing techniques with hydrological (process) models to provide spatially distributed, high-resolution water-storage variations.
  • Multisensor approaches for the estimation of water-storage status and variations to indicate individual contributions, when applicable, of soil water, lakes, vegetation, and groundwater contributions to TWS signals.
  • Space- and air-borne or ground-based experiments to study water-storage estimation techniques of compartments between Earth surface and bedrock.
  • Case studies on a global or local scale for dedicated water-storage assessment with intercomparisons of in situ observations, remote sensing products, and modeling results.
  • Data assimilation, machine learning, and statistical or physical approaches to improve TWS estimation.

Prof. Dr. Ehsan Forootan
Dr. Carsten Montzka
Dr. Thomas Jagdhuber
Prof. Dr. Vagner Ferreira
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • water storage
  • water content
  • hydrology
  • hydrological modeling
  • remote sensing
  • microwaves
  • radiometer
  • altimeter
  • SAR
  • multisensor
  • data fusion
  • assimilation
  • lakes
  • rivers
  • soil moisture
  • root-zone moisture
  • groundwater

Published Papers (2 papers)

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24 pages, 10058 KiB  
Long-Term Lake Area Change and Its Relationship with Climate in the Endorheic Basins of the Tibetan Plateau
Remote Sens. 2021, 13(24), 5125; - 17 Dec 2021
Cited by 10 | Viewed by 2117
Lakes are sensitive indicators of climate change in the Tibetan Plateau (TP), which have shown high temporal and spatial variability in recent decades. The driving forces for the change are still not entirely clear. This study examined the area change of the lakes [...] Read more.
Lakes are sensitive indicators of climate change in the Tibetan Plateau (TP), which have shown high temporal and spatial variability in recent decades. The driving forces for the change are still not entirely clear. This study examined the area change of the lakes greater than 1 km2 in the endorheic basins of the Tibetan Plateau (EBTP) using Landsat images from 1990 to 2019, and analysed the relationships between lake area and local and large-scale climate variables at different geographic scales. The results show that lake area in the EBTP has increased significantly from 1990 to 2019 at a rate of 432.52 km2·year−1. In the past 30 years, lake area changes in the EBTP have mainly been affected by local climate variables such as precipitation and temperature. At a large scale, Indian Summer Monsoon (ISM) has correlations with lake area in western sub-regions in the Inner Basin (IB). While Atlantic Multidecadal Oscillation (AMO) has a significant connection with lake area, the North Atlantic Oscillation (NAO) does not. We also found that abnormal drought (rainfall) brought by the El Niño/La Niña events are significantly correlated with the lake area change in most sub-regions in the IB. Full article
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27 pages, 9344 KiB  
Improving the Resolution of GRACE Data for Spatio-Temporal Groundwater Storage Assessment
Remote Sens. 2021, 13(17), 3513; - 04 Sep 2021
Cited by 41 | Viewed by 4836
Groundwater has a significant contribution to water storage and is considered to be one of the sources for agricultural irrigation; industrial; and domestic water use. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a unique opportunity to evaluate terrestrial water storage (TWS) [...] Read more.
Groundwater has a significant contribution to water storage and is considered to be one of the sources for agricultural irrigation; industrial; and domestic water use. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a unique opportunity to evaluate terrestrial water storage (TWS) and groundwater storage (GWS) at a large spatial scale. However; the coarse resolution of GRACE limits its ability to investigate the water storage change at a small scale. It is; therefore; needed to improve the resolution of GRACE data at a spatial scale applicable for regional-level studies. In this study; a machine-learning-based downscaling random forest model (RFM) and artificial neural network (ANN) model were developed to downscale GRACE data (TWS and GWS) from 1° to a higher resolution (0.25°). The spatial maps of downscaled TWS and GWS were generated over the Indus basin irrigation system (IBIS). Variations in TWS of GRACE in combination with geospatial variables; including digital elevation model (DEM), slope; aspect; and hydrological variables; including soil moisture; evapotranspiration; rainfall; surface runoff; canopy water; and temperature; were used. The geospatial and hydrological variables could potentially contribute to; or correlate with; GRACE TWS. The RFM outperformed the ANN model and results show Pearson correlation coefficient (R) (0.97), root mean square error (RMSE) (11.83 mm), mean absolute error (MAE) (7.71 mm), and Nash–Sutcliffe efficiency (NSE) (0.94) while comparing with the training dataset from 2003 to 2016. These results indicate the suitability of RFM to downscale GRACE data at a regional scale. The downscaled GWS data were analyzed; and we observed that the region has lost GWS of about −9.54 ± 1.27 km3 at the rate of −0.68 ± 0.09 km3/year from 2003 to 2016. The validation results showed that R between downscaled GWS and observational wells GWS are 0.67 and 0.77 at seasonal and annual scales with a confidence level of 95%, respectively. It can; therefore; be concluded that the RFM has the potential to downscale GRACE data at a spatial scale suitable to predict GWS at regional scales. Full article
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