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Special Issue "Microwave Remote Sensing for Hydrology"

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: 31 December 2019

Special Issue Editors

Guest Editor
Dr. Joaquín Muñoz Sabater

European Centre for Medium Range Weather Forecasts (ECMWF), Shinfield Road, RG2 9AX, Reading, UK
Website | E-Mail
Interests: data assimilation for land surface processes; microwave remote sensing; generation of satellite-based Climate Data Records; land surface reanalysis; climate change and socio-economic impacts; weather forecasting
Guest Editor
Dr. Luca Brocca

Research Institute for Geo-Hydrological Protection, National Research Council, Via della Madonna Alta 126, I-06128 Perugia, Italy
Website | E-Mail
Interests: use of remote sensing observations for hydrological applications; use of soil moisture observations for landslide prediction, erosion, numerical weather prediction; hydrologic and hydraulic modelling; real-time flood forecasting; flooding risk analysis; flood frequency assessment (under climate change)
Guest Editor
Dr. Maria Piles

Image Processing Lab, Universitat de València, Parc Científic, Catedrático José Beltrán, 2, 46980 Paterna (València), Spain
Website | E-Mail
Interests: Earth observation; microwave remote sensing; estimation of soil moisture and vegetation biogeophysical parameters; development of multi-sensor techniques for enhanced retrievals with focus on agriculture, forestry, wildfire prediction, extreme detection and climate studies

Special Issue Information

Dear Colleagues,

The current understanding of the Earth’s global hydrological cycle has benefited from advances in active and passive spaceborne microwave sensors and techniques. The most recent availability of microwave data from the Copernicus Sentinel-1 mission adds to other innovative data, such as those from the Advanced SCATterometer (ASCAT), the Soil Moisture and Ocean Salinity (SMOS), the Advanced Microwave Scanning Radiometer (AMSR-2), or the Soil Moisture Active Passive (SMAP) missions. They provide a unique wealth of multi-sensor multi-frequency microwave data suited for hydrological studies. For instance, they are able to provide timely information of key input parameters for hydrological simulations such as soil moisture and the snow state.

Data assimilation techniques have been able to integrate the above key microwave data and point-based land surface observations into hydrological models in a physically consistent manner, either by simply adjusting the simulated hydrological variable by a numerical model, or by calibrating model parameters governing the water distribution and exchange among different soil layers, which are difficult to define at large spatial scales.

In this Special Issue, we welcome original research and case studies focusing on recent advances in microwave remote sensing for hydrologic research and applications. Contributions may include but are not limited to:

  • Data assimilation techniques for hydrological studies using data from microwave sensors;
  • The synergetic use of active and passive microwave data to improve the characterization of the water state of the soil;
  • Case studies showing the potential benefit brought by microwave data into hydrological research;
  • The development of coupling schemes aiming at merging remote sensing data and land surface models for hydrologic forecasting;
  • Innovative studies using the potential of Copernicus missions to enhance hydrological applications;

Dr. Joaquín Muñoz Sabater
Dr. Luca Brocca
Dr. Maria Piles
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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 papers will be 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 1800 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.

Keywords

  • microwave sensors
  • hydrological forecasting
  • hydrological applications
  • data assimilation
  • soil moisture
  • snow state
  • Sentinel
  • SMOS
  • SMAP
  • AMSR-2
  • ASCAT
  • multi-sensor synergy

Published Papers (3 papers)

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Research

Open AccessArticle Satellite Soil Moisture for Agricultural Drought Monitoring: Assessment of SMAP-Derived Soil Water Deficit Index in Xiang River Basin, China
Remote Sens. 2019, 11(3), 362; https://doi.org/10.3390/rs11030362
Received: 29 December 2018 / Revised: 6 February 2019 / Accepted: 9 February 2019 / Published: 11 February 2019
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Abstract
Agricultural drought can have long-lasting and harmful impacts on both the ecosystem and economy. Therefore, it is important to monitor and predict agricultural drought accurately. Soil moisture is the key variable to define the agricultural drought index. However, in situ soil moisture observations [...] Read more.
Agricultural drought can have long-lasting and harmful impacts on both the ecosystem and economy. Therefore, it is important to monitor and predict agricultural drought accurately. Soil moisture is the key variable to define the agricultural drought index. However, in situ soil moisture observations are inaccessible in many areas of the world. Remote sensing techniques enrich the surface soil moisture observations at different tempo-spatial resolutions. In this study, the Level 2 L-band radiometer soil moisture dataset was used to estimate the Soil Water Deficit Index (SWDI). The Soil Moisture Active Passive (SMAP) dataset was evaluated with the soil moisture dataset obtained from the China Land Soil Moisture Data Assimilation System (CLSMDAS). The SMAP-derived SWDI (SMAP_SWDI) was compared with the atmospheric water deficit (AWD) calculated with precipitation and evapotranspiration from meteorological stations. Drought monitoring and comparison were accomplished at a weekly scale for the growing season (April to November) from 2015 to 2017. The results were as follows: (1) in terms of Pearson correlation coefficients (R-value) between SMAP and CLSMDAS, around 70% performed well and only 10% performed poorly at the grid scale, and the R-value was 0.62 for the whole basin; (2) severe droughts mainly occurred from mid-June to the end of September from 2015 to 2017; (3) severe droughts were detected in the southern and northeastern Xiang River Basin in mid-May of 2015, and in the northern basin in early August of 2016 and end of November 2017; (4) the values of percentage of drought weeks gradually decreased from 2015 to 2017, and increased from the northeast to the southwest of the basin in 2015 and 2016; and (5) the average value of R and probability of detection between SMAP_SWDI and AWD were 0.6 and 0.79, respectively. These results show SMAP has acceptable accuracy and good performance for drought monitoring in the Xiang River Basin. Full article
(This article belongs to the Special Issue Microwave Remote Sensing for Hydrology)
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Graphical abstract

Open AccessArticle An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images
Remote Sens. 2019, 11(3), 349; https://doi.org/10.3390/rs11030349
Received: 21 January 2019 / Accepted: 1 February 2019 / Published: 10 February 2019
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Abstract
As an essential ecological parameter, soil moisture is important for understanding the water exchange between the land surface and the atmosphere, especially in the Loess Plateau (China). Although Synthetic Aperture Radar (SAR) images can be used for soil moisture retrieval, it is still [...] Read more.
As an essential ecological parameter, soil moisture is important for understanding the water exchange between the land surface and the atmosphere, especially in the Loess Plateau (China). Although Synthetic Aperture Radar (SAR) images can be used for soil moisture retrieval, it is still a challenge to mitigate the impacts of complex terrain over hilly areas. Therefore, the objective of this paper is to propose an improved approach for soil moisture estimation in gully fields based on the joint use of the Advanced Integral Equation Model (AIEM) and the Incidence Angle Correction Model (IACM) from Sentinel-1A observations. AIEM is utilized to build a simulation database of microwave backscattering coefficients from various radar parameters and surface parameters, which is the data basis for the retrieval modeling. IACM is proposed to correct the deviation between the local incidence angle at the scatterer and the radar viewing angle. The study area is located in the Loess Plateau of China, where the main land cover is mostly bare land and the terrain is complex. The Sentinel-1A SAR data in C-band with dual polarization acquired on October 19th, 2017 was adopted to extract the VV&VH polarimetric backscattering coefficients. The in situ measurements of soil moisture were collected on the same day of the SAR acquisition, for evaluating the accuracy of the SAR-derived soil moisture. The results showed that, firstly, the estimated soil moisture with volumetric content between 0% and 20% was in the majority. Subsequently, both the RMSE of estimation values (0.963%) and the standard deviation of absolute errors (0.957%) demonstrated a good accuracy of the improved approach. Moreover, the evaluation of IACM confirmed that the improved approach coupling IACM and AIEM was more efficient than employing AIEM solely. In conclusion, the proposed approach has a strong ability to estimate the soil moisture in the gully fields of the Loess Plateau from Sentinel-1A data. Full article
(This article belongs to the Special Issue Microwave Remote Sensing for Hydrology)
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Open AccessArticle Evaluation of Sub-Kilometric Numerical Simulations of C-Band Radar Backscatter over the French Alps against Sentinel-1 Observations
Remote Sens. 2019, 11(1), 8; https://doi.org/10.3390/rs11010008
Received: 29 October 2018 / Revised: 17 December 2018 / Accepted: 18 December 2018 / Published: 20 December 2018
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Abstract
This study compares numerical simulations and observations of C-band radar backscatter in a wide region (2300 km2) in the Northern French Alps. Numerical simulations were performed using a model chain composed of the SAFRAN meteorological reanalysis, the Crocus snowpack model and [...] Read more.
This study compares numerical simulations and observations of C-band radar backscatter in a wide region (2300 km 2 ) in the Northern French Alps. Numerical simulations were performed using a model chain composed of the SAFRAN meteorological reanalysis, the Crocus snowpack model and the radiative transfer model Microwave Emission Model for Layered Snowpacks (MEMLS3&a), operating at a spatial resolution of 250-m. The simulations, without any bias correction, were evaluated against 141 Sentinel-1 synthetic aperture radar observation scenes with a resolution of 20 m over three snow seasons from October 2014 to June 2017. Results show that there is good agreement between observations and simulations under snow-free or dry snow conditions, consistent with the fact that dry snow is almost transparent at C-band. Under wet snow conditions, although the changes in time and space are well correlated, there is a significant deviation, up to 5 dB, between observations and simulations. The reasons for these discrepancies were explored, including a sensitivity analysis on the impact of the liquid water percolation scheme in Crocus. This study demonstrates the feasibility of performing end-to-end simulations of radar backscatter over extended geographical region. This makes it possible to envision data assimilation of radar data into snowpack models in the future, pending that deviations are mitigated, either through bias corrections or improved physical modeling of both snow properties and corresponding radar backscatter. Full article
(This article belongs to the Special Issue Microwave Remote Sensing for Hydrology)
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Graphical abstract

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