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Remote Sensing
Special Issues
Retrieval, Validation and Application of Satellite Soil Moisture Data
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Retrieval, Validation and Application of Satellite Soil Moisture Data

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (20 November 2017) | Viewed by 89869

Special Issue Editors

Prof. Dr. Wolfgang Wagner

grade E-Mail Website
Guest Editor
Research Group Remote Sensing, Department of Geodesy and Geoinformation (GEO), Vienna University of Technology (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
Interests: remote sensing; geophysical parameter retrieval; airborne laser scanning; full-waveform lidar; radar remote sensing; soil moisture
Special Issues, Collections and Topics in MDPI journals
Dr. Claudia Notarnicola

E-Mail Website
Guest Editor
EURAC Research – Institute for Earth Observation, Viale Druso 1, 39100 Bolzano, Italy
Interests: retrieval of bio-physical parameters from optical and radar data; multi-sensor data fusion; integrated approach for environmental monitoring in mountain areas
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. John J. Qu

E-Mail Website
Guest Editor
Professor and Director, GENRI & ESTC, Department of Geography and GeoInformation Science (GGS), Global Environment and Natural Resources Institute (GENRI), College of Science, George Mason University, Fairfax, VA 22030, USA
Interests: remote sensing; earth system and climate science; soil moisture and drought monitoring; water-energy-food nexus; environment and fire science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dongryeol Ryu

E-Mail Website
Guest Editor
Department of Infrastructure Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC 3010, Austrilia
Interests: microwave remote sensing of soil moisture; hydrological applications of remote sensing; hydrological data assimilation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, we have seen a proliferation in the provision and use of satellite soil moisture data derived from active and passive microwave sensors (SMAP, SMOS, ASCAT, AMRS-2, Sentinel-1, etc.) and optical/thermal imagers. However, there are still many open scientific question related to the way of how soil moisture data are being retrieved, validated and applied. Additionally, there is a high need to develop novel approaches for improving the spatio-temporal sampling of the data and their accuracy. Therefore, the purpose of this Special Issue is to discuss and reconcile recent methodological advances in the development, validation and application of global satellite soil moisture data. This Special Issue is related to the 4th Satellite Soil Moisture Validation and Application Workshop, which will place at the Vienna University of Technology (TU Wien) from 19–20 September 2017 (http://smw.geo.tuwien.ac.at/). Topics to be discussed at the workshop are:

  • What is the quality of the current satellite products and what can we expect in the near future?
  • What is information content at Level 1 and how to exploit the availability of multiple satellites?
  • Who is using satellite soil moisture data and for what purpose?
  • What are the best practices in validating soil moisture products?
  • What are the main limitations of satellite soil moisture data from a user’s perspective?
  • What is the future of satellite-based soil moisture remote sensing?

We invite in particular authors attending the workshop to submit their papers to this Special Issue of Remote Sensing.

Authors are required to check and follow the specific Instructions to Authors, https://www.mdpi.com/journal/remotesensing/instructions.

Univ.-Prof. Wolfgang Wagner
Dr. Claudia Notarnicola
Univ.-Prof. John J. Qu
Univ.-Prof. Dongryeol Ryu
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 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.

Published Papers (13 papers)

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Research

16 pages, 3208 KiB  
Article
Estimating Vegetation Water Content and Soil Surface Roughness Using Physical Models of L-Band Radar Scattering for Soil Moisture Retrieval
Remote Sens. 2018, 10(4), 556; https://doi.org/10.3390/rs10040556 - 4 Apr 2018
18 pages, 4302 KiB  
Article
The Spatiotemporal Response of Soil Moisture to Precipitation and Temperature Changes in an Arid Region, China
Remote Sens. 2018, 10(3), 468; https://doi.org/10.3390/rs10030468 - 16 Mar 2018
25 pages, 5363 KiB  
Article
A New Soil Moisture Downscaling Approach for SMAP, SMOS, and ASCAT by Predicting Sub-Grid Variability
Remote Sens. 2018, 10(3), 427; https://doi.org/10.3390/rs10030427 - 9 Mar 2018
19 pages, 5803 KiB  
Article
Surface Freshwater Limitation Explains Worst Rice Production Anomaly in India in 2002
Remote Sens. 2018, 10(2), 244; https://doi.org/10.3390/rs10020244 - 6 Feb 2018
16 pages, 2903 KiB  
Article
Downscaling of Surface Soil Moisture Retrieval by Combining MODIS/Landsat and In Situ Measurements
Remote Sens. 2018, 10(2), 210; https://doi.org/10.3390/rs10020210 - 1 Feb 2018
23 pages, 3381 KiB  
Article
The Effect of Three Different Data Fusion Approaches on the Quality of Soil Moisture Retrievals from Multiple Passive Microwave Sensors
Remote Sens. 2018, 10(1), 107; https://doi.org/10.3390/rs10010107 - 13 Jan 2018
13120 KiB  
Article
Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China
Remote Sens. 2018, 10(1), 31; https://doi.org/10.3390/rs10010031 - 25 Dec 2017
12474 KiB  
Article
Estimation of Soil Moisture Index Using Multi-Temporal Sentinel-1 Images over Poyang Lake Ungauged Zone
Remote Sens. 2018, 10(1), 12; https://doi.org/10.3390/rs10010012 - 22 Dec 2017
10582 KiB  
Article
Data Assimilation to Extract Soil Moisture Information from SMAP Observations
Remote Sens. 2017, 9(11), 1179; https://doi.org/10.3390/rs9111179 - 17 Nov 2017
6762 KiB  
Article
Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China
Remote Sens. 2017, 9(11), 1111; https://doi.org/10.3390/rs9111111 - 2 Nov 2017
4862 KiB  
Article
Soil Moisture Data Assimilation in a Hydrological Model: A Case Study in Belgium Using Large-Scale Satellite Data
Remote Sens. 2017, 9(8), 820; https://doi.org/10.3390/rs9080820 - 10 Aug 2017
7640 KiB  
Article
First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau
Remote Sens. 2017, 9(7), 714; https://doi.org/10.3390/rs9070714 - 12 Jul 2017
13813 KiB  
Article
Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China
Remote Sens. 2017, 9(6), 574; https://doi.org/10.3390/rs9060574 - 8 Jun 2017
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