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Remote Sensing
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GNSS-Reflectometry and Remote Sensing of Soil Moisture
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GNSS-Reflectometry and Remote Sensing of Soil Moisture

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (5 January 2023) | Viewed by 9289

Special Issue Editors

Prof. Dr. Iñigo Molina

E-Mail Website
Guest Editor
Department of Topographic and Cartographic Engineering, Universidad Politécnica de Madrid, Madrid, Spain
Interests: soil moisture content (SMC); global navigation satellite systems reflectometry (GNSS-R); active-passive sensors; earth-science applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shuanggen Jin

E-Mail Website
Guest Editor
Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
Interests: GNSS meteorology; remote sensing; space/planetary exploration
Special Issues, Collections and Topics in MDPI journals
Dr. Andrés Calabia

E-Mail Website
Guest Editor
School of Land Surveying, Geodesy and Mapping Engineering, Universidad Politécnica de Madrid, 28031 Madrid, Spain
Interests: monitoring earth’s environments through space geodesy and remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

In many different scientific fields, the great significance of soil moisture content (SMC) is pointed out as an environmental factor for land surface dynamics monitoring, as regards such areas as evapotranspiration, droughts, floods, etc., while it simultaneously regulates energy and water exchange between the land and the atmosphere and other hydrological processes. SMC also enables the controlling of water runoff and surface erosion. Moreover, since SMC is coupled with other environmental variables, such as land surface temperature, land cover or rain precipitation, SMC is commonly used as the input parameter for many climate models. In agriculture, SMC is a crucial indicator of plant growth and crop yield.

In the last few decades, near-Earth satellites have provided an unprecedented opportunity to sense SMC from space using a wide diversity of techniques and sensors. Microwave instruments, passive (SMOS, AMSR2, etc.) or active (Sentinel-1, ALOS-2, TerraSar-X), have already shown their ability to retrieve surface SMC information. Analogously, some studies on SMC retrieval are also reported with passive optical devices (L8-OLI, Sentinel-2). For the last several years, an emerging and challenging technology based on the opportunity signal, GNSS Reflectometry (GNSS-R), has been exploited for SMC sensing. In fact, several space-borne GNSS constellations have been used in conjunction with GNSS-R missions such as theTechDemoSat-1 and the Cyclone GNSS (CYGNSS). Moreover, this technology can also be operated from RPAS vehicles or static configurations.

This Special Issue aims to present the most recent advances, algorithms and methodologies of GNSS-Reflectometry and Remote Sensing for Soil Moisture Content retrieval. The topics considered in this Special Issue are as follows:

Physics, methods and algorithms for SMC estimation from GNSS-R, as well as from other active and passive remote sensors.

The synergistic use of active and passive sensors for SMC estimation;

Vegetation and bare-soil roughness assessment and compensation for SMC retrieval;

Validation strategies of SMC products;

Applications of SMC in different Earth science topics include: climate change; environmental monitoring; land surface dynamics; agriculture; forest biomass; drought/flood, etc.

Dr. Iñigo Molina
Prof. Dr. Shuanggen Jin
Dr. Andrés Calabia
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.

Keywords

  • GNSS-R
  • Active/Passive remote sensing
  • Soil Moisture Content estimation
  • Validation strategies
  • Water cycle
  • Climate change

Published Papers (4 papers)

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Research

22 pages, 4396 KiB  
Article
Soil Moisture Estimation Based on Polarimetric Decomposition and Quantile Regression Forests
by Li Zhang, Xiaolei Lv and Rui Wang
Remote Sens. 2022, 14(17), 4183; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174183 - 25 Aug 2022
Cited by 1 | Viewed by 1401
Abstract
The measurement of surface soil moisture (SSM) assists in making agricultural decisions, such as precision irrigation and flooding or drought predictions. The critical challenge for SSM estimation in vegetation-covered areas is the coupling between vegetation and surface scattering. This study proposed an SSM [...] Read more.
The measurement of surface soil moisture (SSM) assists in making agricultural decisions, such as precision irrigation and flooding or drought predictions. The critical challenge for SSM estimation in vegetation-covered areas is the coupling between vegetation and surface scattering. This study proposed an SSM estimation method based on polarimetric decomposition and quantile regression forests (QRF) to overcome this problem. Model-based polarimetric decomposition separates volume scattering, double-bounce scattering, and surface scattering, while eigenvalue-based polarimetric decomposition provides additional parameters to describe the scattering mechanism. The combined use of these parameters explains the polarimetric SAR scattering information from multiple perspectives, such as vegetation, surface roughness, and SSM. As different crops differ in morphology and structure, it is essential to investigate the potential of varying polarimetric parameters to estimate SSM in areas covered by different crops. QRF, a regression method applicable to high-dimensional predictor variables, is used to estimate SSM from these parameters. In addition to the SSM estimates, QRF can also provide the predicted uncertainty intervals and quantify the importance of the different parameters in the SSM estimates. The performance of QRF in SSM estimation was tested using data from the soil moisture active passive validation experiment 2012 (SMAPVEX12) and compared with copula quantile regression (CQR). The SSM estimated by the proposed method was consistent with the in situ SSM, with the root-mean-square-error ranging from 0.037 cm3/cm3 to 0.079 cm3/cm3 and correlation coefficients ranging from 0.745 to 0.905. Meanwhile, the method proposed in this study can provide both the uncertainty of SSM estimation and the importance of different polarimetric parameters. Full article
(This article belongs to the Special Issue GNSS-Reflectometry and Remote Sensing of Soil Moisture)
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22 pages, 14072 KiB  
Article
Calibration and Validation of CYGNSS Reflectivity through Wetlands’ and Deserts’ Dielectric Permittivity
by Iñigo Molina, Andrés Calabia, Shuanggen Jin, Komi Edokossi and Xuerui Wu
Remote Sens. 2022, 14(14), 3262; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143262 - 06 Jul 2022
Cited by 4 | Viewed by 1746
Abstract
The reflection of Global Navigation Satellite Systems (GNSS) signals, namely GNSS-Reflectometry (GNSS-R), has recently proven to be able to monitor land surface properties in the microwave spectrum, at a global scale, and with very low revisiting time. Moreover, this new technique has numerous [...] Read more.
The reflection of Global Navigation Satellite Systems (GNSS) signals, namely GNSS-Reflectometry (GNSS-R), has recently proven to be able to monitor land surface properties in the microwave spectrum, at a global scale, and with very low revisiting time. Moreover, this new technique has numerous additional advantages, including low cost, low power consumption, lightweight and small payloads, and near real-time massive data availability, as compared to conventional monostatic microwave remote sensing. However, the GNSS-R surface reflectivity values estimated through the bistatic radar equation, and the Fresnel coefficients have shown a lack of coincidence with real surface reflectivity data, mostly due to calibration issues. Previous studies have attempted to avoid this matter with direct regression methods between uncalibrated GNSS-R reflectivity data and external soil moisture content (SMC) products. However, calibration of GNSS-R reflectivity used in traditional inversion models is still a challenge, such as those to estimate SMC, freeze/thaw, or biomass. In this paper, a successful procedure for GNSS-R reflectivity calibration is established using data from the CYGNSS (Cyclone GNSS) constellation. The scale and bias parameters are estimated from the theoretical dielectric properties of water and dry sand, which are well-known and empirically validated values. We employ four calibration areas that provide maximum range limits of reflectivity, such as deserts and wetlands. The CYGNSS scale factor and the bias parameter resulted in a = 3.77 and b = 0.018, respectively. The derived scale and bias parameters are applied to the CYGNSS dataset, and the retrieved SMC values through the Fresnel reflection coefficients are in excellent agreement with the Soil Moisture Active Passive (SMAP) SMC product. Then, the SMAP SMC is used as a reference true value, and provides a standard linear regression with an R-square coefficient of 0.803, a root mean square error (RMSE) of 0.084, and a Pearson’s correlation coefficient of 0.896. Full article
(This article belongs to the Special Issue GNSS-Reflectometry and Remote Sensing of Soil Moisture)
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15 pages, 6141 KiB  
Article
Improving CyGNSS-Based Land Remote Sensing: Track-Wise Data Calibration Schemes
by Qingyun Yan, Ting Hu, Shuanggen Jin, Weimin Huang, Yan Jia, Tiexi Chen and Jian Wang
Remote Sens. 2021, 13(14), 2844; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142844 - 20 Jul 2021
Cited by 2 | Viewed by 2398
Abstract
Cyclone Global Navigation Satellite System (CyGNSS) data have been used for generating several intermediate products, such as surface reflectivity (Γ), to facilitate a wide variety of land remote sensing applications. The accuracy of Γ relies on precise knowledge of the effective [...] Read more.
Cyclone Global Navigation Satellite System (CyGNSS) data have been used for generating several intermediate products, such as surface reflectivity (Γ), to facilitate a wide variety of land remote sensing applications. The accuracy of Γ relies on precise knowledge of the effective instantaneous radiative power (EIRP) of the transmitted GNSS signals in the direction of the specular reflection point, the precise knowledge of zenith antenna patterns which in turn affects estimates of EIRP, the good knowledge of receive antenna patterns etc. However, obtaining accurate estimates on these parameters completely is still a challenge. To solve this problem, in this paper, an effective method is proposed for calibrating the CyGNSS Γ product in a track-wise manner. Here, two different criteria for selecting data to calibrate and three reference options as targets of the calibrating data are examined. Accordingly, six calibration schemes corresponding to six different combinations are implemented and the resulting Γ products are assessed by (1) visual inspection and (2) evaluation of their associated soil moisture retrieval results. Both visual inspection and retrieval validation demonstrate the effectiveness of the proposed schemes, which are respectively demonstrated by the immediate removal/fix of track-wisely noisy data and obvious enhancement of retrieval accuracy with the calibrated Γ. Moreover, the schemes are tested using all the available CyGNSS level 1 version 3.0 data and the good results obtained from such a large volume of data further illustrate their robustness. This work provides an effective and robust way to calibrate the CyGNSS Γ result, which will further improve relevant remote sensing applications in the future. Full article
(This article belongs to the Special Issue GNSS-Reflectometry and Remote Sensing of Soil Moisture)
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19 pages, 9329 KiB  
Article
Time-Lagged Correlation between Soil Moisture and Intra-Annual Dynamics of Vegetation on the Mongolian Plateau
by Li Na, Risu Na, Yongbin Bao and Jiquan Zhang
Remote Sens. 2021, 13(8), 1527; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081527 - 15 Apr 2021
Cited by 14 | Viewed by 2297
Abstract
Soil moisture is a reliable water resource for plant growth in arid and semi-arid regions. Characterizing the interaction between soil moisture and vegetation is important for assessing the sustainability of terrestrial ecosystems. This study explores the spatiotemporal characteristics of four soil moisture layers [...] Read more.
Soil moisture is a reliable water resource for plant growth in arid and semi-arid regions. Characterizing the interaction between soil moisture and vegetation is important for assessing the sustainability of terrestrial ecosystems. This study explores the spatiotemporal characteristics of four soil moisture layers (layer 1: 0–7 cm, layer 2: 7–28 cm, layer 3: 28–100 cm, and layer 4: 100–289 cm) and the time-lagged correlation with the normalized difference vegetation index (NDVI) for different vegetation types on an intra-annual scale on the Mongolian Plateau (MP). The most significant results indicated that: (1) the four layers of soil moisture can be roughly divided into rapid change (layers 1 and 2), active (layer 3), and stable (layer 4) layers. The soil moisture content in the different vegetation regions was forest > grassland > desert vegetation. (2) The soil moisture in layer 1 showed the strongest positive correlation with NDVI in the whole area; meanwhile, the soil moisture of layers 2 and 3 showed the strongest negative correlation with the NDVI mainly in grassland and desert, and layer 4 showed the strongest negative correlation with the NDVI in the forest. (3) Mutual responses of NDVI and deep layer soil moisture required a longer time compared with the shallow layer. In the annual time scale, the NDVI was affected by the change in soil moisture in most of the study area, except for coniferous forest and desert vegetation regions. (4) Under the different stages of vegetation change, the soil moisture changes advance than NDVI about 3 months during the greening stage, while the NDVI changes advance than soil moisture by 0.5 months during the browning stage. Regardless of the stage, changes in soil moisture are initiated from the shallow layer and advance to the deep layer. The results of this study provide deep insight into the relationship between soil moisture and vegetation in arid and semi-arid regions. Full article
(This article belongs to the Special Issue GNSS-Reflectometry and Remote Sensing of Soil Moisture)
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