Special Issue "Remote Sensing for Water Storage and Soil Moisture Estimates"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 1316

Special Issue Editors

1. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
2. Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Interests: GRACE; terrestrial water storage; data assimilation; land surface model; satellite remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals
USDA Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA
Interests: data assimilation; remote sensing; soil moisture; land-atmosphere coupling; land surface modeling; hydrological modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The accuracy of terrestrial water storage measurement (comprising, e.g., soil moisture, groundwater, surface water, and canopy interception) is crucial for a sufficient understanding of the terrestrial water cycle and land–atmosphere interaction. Remotely sensed terrestrial water storage (from, e.g., GRACE) and surface soil moisture (from, e.g., ASCAT, SMOS) observations with varied spatial and temporal characteristics have been successfully exploited to improve our ability to assess water resource availability and the climate/anthropogenic influence. The present challenge is the coarse spatiotemporal resolution and uncertainty of the observations. Innovative development, together with new datasets (from, e.g., GRACE-FO, Swarm, SMAP, Sentinel-1), may maximize the observations’ spatial-temporal detail and accuracy. With these ideas in mind, we would like to invite international research communities involved in remotely sensed terrestrial water storage and soil moisture observations to submit their recent developments for publication. The topics of this Special Issue include, but are not limited to, the following:

- Review of remote sensing techniques for terrestrial water storage and soil moisture;

- Applications in water resource assessment, climate variability, and natural hazards;

- Spatiotemporal resolution enhancement and the development or result of the downscaling approach;

- Accuracy assessment, validating remote sensing data against ground measurement or model outputs, including intercomparison between observations;

- Development of data processing techniques, e.g., filtering, retrieval algorithm;

- Univariate or multivariate data assimilation or data fusion of remotely sensed terrestrial water storage or soil moisture observations;

- Applying machine learning techniques for time series reconstruction or spatial resolution enhancement.

Dr. Natthachet Tangdamrongsub
Dr. Jianzhi Dong
Guest Editors

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  • terrestrial water storage
  • soil moisture
  • satellite remote sensing
  • water resource and climate variation
  • spatiotemporal resolution enhancement
  • data fusion and data assimilation
  • comparison and accuracy assessment

Published Papers (1 paper)

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Assessing Performances of Multivariate Data Assimilation Algorithms with SMOS, SMAP, and GRACE Observations for Improved Soil Moisture and Groundwater Analyses
Water 2022, 14(4), 621; https://doi.org/10.3390/w14040621 - 17 Feb 2022
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Multivariate data assimilation (DA) of satellite soil moisture (SM) and terrestrial water storage (TWS) observations has recently been used to improve SM and groundwater storage (GWS) simulations. Previous studies employed the ensemble Kalman approach in multivariate DA schemes, which assumes that model and [...] Read more.
Multivariate data assimilation (DA) of satellite soil moisture (SM) and terrestrial water storage (TWS) observations has recently been used to improve SM and groundwater storage (GWS) simulations. Previous studies employed the ensemble Kalman approach in multivariate DA schemes, which assumes that model and observation errors have a Gaussian distribution. Despite the success of the Kalman approaches, SM and GWS estimates can be suboptimal when the Gaussian assumption is violated. Other DA approaches, such as particle smoother (PS), ensemble Gaussian particle smoother (EnGPS), and evolutionary smoother (EvS), do not rely on the Gaussian assumption and may be better suited to non-Gaussian error systems. The objective of this paper is to evaluate the performance of these four DA approaches (EnKS, PS, EnGPS, and EvS) in multivariate DA systems by assimilating satellite data from the Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery And Climate Experiment (GRACE) missions into the Community Atmosphere and Biosphere Land Exchange (CABLE) land surface model. The analyses are carried out in Australia’s Goulburn River catchment, where in situ SM and groundwater data are available to comprehensively validate the DA performance. Results show that all four DA approaches have outstanding performances and improve correlation coefficients of SM and GWS estimates by ~20% and 100%, respectively. The EvS outperforms the others, but its benefit is relatively marginal compared to Gaussian approaches (e.g., EnKS). This is due to the fact that SM and TWS error distributions in this study are close to Gaussian: a suitable condition for, e.g., EnKS, EnGPS. The robust performance of EvS appears to be the optimal approach for jointly assimilating multi-source hydrological observations to improve regional hydrological analyses. Full article
(This article belongs to the Special Issue Remote Sensing for Water Storage and Soil Moisture Estimates)
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