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Soil Moisture Retrieval using Radar Remote Sensing Sensors

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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 (31 December 2019) | Viewed by 69112

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Special Issue Editors

Dr. Mehrez Zribi

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Guest Editor

French National Centre for Scientific Research | CNRS, Centre d’études spatiales de la biosphère (CESBIO), Universite Paul Sabatier Toulouse III, Toulouse, France
Interests: airborne instrumentation for land surfaces; microwave remote sensing; GNSS-R; GNSS; land surfaces; spatial hydrology
Special Issues, Collections and Topics in MDPI journals

Dr. Nicolas Baghdadi

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Guest Editor

French National Institute for Agriculture, Food, and Environment (INRAE), Maison de la Télédétection—UMR TETIS, 500 rue JF Breton, CEDEX 05, 34093 Montpellier, France
Interests: environmental science; irrigation and water management; soil science; microwave remote sensing; lidar
Special Issues, Collections and Topics in MDPI journals

Dr. Clement Albergel

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Guest Editor

ESA - European Space Agency, Climate Office, Oxfordshire, UK
Interests: land surface modelling; climate change; hydrology; data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil moisture plays an essential role in the understanding of the continental water cycle. It is a key parameter in the separation of precipitation water between infiltration, runoff and evapotranspiration processes and in water management. In this context, active microwave remote sensing has shown a high potential to retrieve surface soil moisture through the use of SAR and other radar sensors (scatterometer, altimeter, GNSS-R, etc.). In the last few years, with the arrival of new sensors with important capacities in terms of spatial and temporal resolutions, it becomes possible to propose operational soil moisture products and to assimilate this parameter in water process modeling. This Special Issue has as principal objective to present the principal algorithms and methodologies around the use of active sensors (Sentinel1, Alos-2, TERRASAR-X, RADARSAT, ASCAT, CYGNSS, etc.) in the estimation and use of soil moisture. Different topics are considered:

Signal physics of radar measurement and backscattering modeling over soil surfaces
Inversion algorithms to estimate soil moisture
Roughness and vegetation effects of radar signal
Synergy between radar and other sensors for soil moisture retrieval
Assimilation of soil moisture products in process models

Dr. Mehrez Zribi
Dr. Nicolas Baghdadi
Dr. Clément Albergel
Guest Editors

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Keywords

Soil moisture
Radar
Scattering modeling
Assimilation
Agriculture applications
Hydrology applications

Published Papers (12 papers)

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Editorial

Jump to: Research, Review

3 pages, 183 KiB

Open AccessEditorial

Editorial for the Special Issue “Soil Moisture Retrieval using Radar Remote Sensing Sensors”

by Mehrez Zribi, Clément Albergel and Nicolas Baghdadi

Remote Sens. 2020, 12(7), 1100; https://doi.org/10.3390/rs12071100 - 30 Mar 2020

Cited by 4 | Viewed by 2849

Abstract

Soil moisture is a key parameter when it comes to understanding the processes related to the water cycle on continental surfaces (infiltration, evapotranspiration, runoff, etc [...] Full article

(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)

Research

Jump to: Editorial, Review

21 pages, 5802 KiB

Open AccessArticle

Soil Moisture Content from GNSS Reflectometry Using Dielectric Permittivity from Fresnel Reflection Coefficients

by Andres Calabia, Iñigo Molina and Shuanggen Jin

Remote Sens. 2020, 12(1), 122; https://doi.org/10.3390/rs12010122 - 01 Jan 2020

Cited by 55 | Viewed by 4827

Abstract

Global Navigation Satellite Systems-Reflectometry (GNSS-R) has shown unprecedented advantages to sense Soil Moisture Content (SMC) with high spatial and temporal coverage, low cost, and under all-weather conditions. However, implementing an appropriated physical basis to estimate SMC from GNSS-R is still a challenge, while previous solutions were only based on direct comparisons, statistical regressions, or time-series analyses between GNSS-R observables and external SMC products. In this paper, we attempt to retrieve SMC from GNSS-R by estimating the dielectric permittivity from Fresnel reflection coefficients. We employ Cyclone GNSS (CYGNSS) data and effectively account for the effects of bare soil roughness (BSR) and vegetation optical depth by employing ICESat-2 (Ice, Cloud, and land Elevation Satellites 2) and/or SMAP (Soil Moisture Active Passive) products. The tests carried out with ICESat-2 BSR data have shown the high sensitivity in SMC retrieval to high BSR values, due to the high sensitivity of ICESat-2 to land surface microrelief. Our GNSS-R SMC estimates are validated by SMAP SMC products and the results provide an R-square of 0.6, Root Mean Squared Error (RMSE) of 0.05, and a zero p-value, for the 4568 test points evaluated at the eastern region of China during April 2019. The achieved results demonstrate the optimal capability and potential of this new method for converting reflectivity measurements from GNSS-R into Land Surface SMC estimates. Full article

(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)

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20 pages, 6992 KiB

Open AccessArticle

Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data

by Jamal Ezzahar, Nadia Ouaadi, Mehrez Zribi, Jamal Elfarkh, Ghizlane Aouade, Said Khabba, Salah Er-Raki, Abdelghani Chehbouni and Lionel Jarlan

Remote Sens. 2020, 12(1), 72; https://doi.org/10.3390/rs12010072 - 24 Dec 2019

Cited by 69 | Viewed by 5931

Abstract

The main objective of this work was to retrieve surface soil moisture (SSM) by using scattering models and a support vector machine (SVM) technique driven by backscattering coefficients obtained from Sentinel-1 satellite images acquired over bare agricultural soil in the Tensfit basin of Morocco. Two backscattering models were selected in this study due to their wide use in inversion procedures: the theoretical integral equation model (IEM) and the semi-empirical model (Oh). To this end, the sensitivity of the SAR backscattering coefficients at

V V

(

σ_{v v}^{\circ}

) and

V H

(

σ_{v h}^{\circ}

) polarizations to in situ soil moisture data were analyzed first. As expected, the results showed that over bare soil the

σ_{v v}^{\circ}

was well correlated with SSM compared to the

σ_{v h}^{\circ}

, which showed more dispersion with correlation coefficients values (r) of about

0.84

and

0.61

for the

V V

and

V H

polarizations, respectively. Afterwards, these values of

σ_{v v}^{\circ}

were compared to those simulated by the backscatter models. It was found that IEM driven by the measured length correlation L slightly underestimated SAR backscatter coefficients compared to the Oh model with a bias of about

- 0.7

dB and

- 1.2

dB and a root mean square (RMSE) of about

1.1

dB and

1.5

dB for Oh and IEM models, respectively. However, the use of an optimal value of L significantly improved the bias of IEM, which became near to zero, and the RMSE decreased to

0.9

dB. Then, a classical inversion approach of

σ_{v v}^{\circ}

observations based on backscattering model is compared to a data driven retrieval technic (SVM). By comparing the retrieved soil moisture against ground truth measurements, it was found that results of SVM were very encouraging and were close to those obtained by IEM model. The bias and RMSE were about 0.28 vol.% and 2.77 vol.% and

- 0.13

vol.% and 2.71 vol.% for SVM and IEM, respectively. However, by taking into account the difficultly of obtaining roughness parameter at large scale, it was concluded that SVM is still a useful tool to retrieve soil moisture, and therefore, can be fairly used to generate maps at such scales. Full article

(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)

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21 pages, 6101 KiB

Open AccessEditor’s ChoiceArticle

Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France

by Daniel Chiyeka Shamambo, Bertrand Bonan, Jean-Christophe Calvet, Clément Albergel and Sebastian Hahn

Remote Sens. 2019, 11(23), 2842; https://doi.org/10.3390/rs11232842 - 29 Nov 2019

Cited by 21 | Viewed by 3954

Abstract

This paper investigates to what extent soil moisture and vegetation density information can be extracted from the Advanced Scatterometer (ASCAT) satellite-derived radar backscatter (σ°) in a data assimilation context. The impact of independent estimates of the surface soil moisture (SSM) and leaf area index (LAI) of diverse vegetation types on ASCAT σ° observations is simulated over southwestern France using the water cloud model (WCM). The LAI and SSM variables used by the WCM are derived from satellite observations and from the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model, respectively. They permit the calibration of the four parameters of the WCM describing static soil and vegetation characteristics. A seasonal analysis of the model scores shows that the WCM has shortcomings over karstic areas and wheat croplands. In the studied area, the Klaus windstorm in January 2009 damaged a large fraction of the Landes forest. The ability of the WCM to represent the impact of Klaus and to simulate ASCAT σ° observations in contrasting land-cover conditions is explored. The difference in σ° observations between the forest zone affected by the storm and the bordering agricultural areas presents a marked seasonality before the storm. The difference is small in the springtime (from March to May) and large in the autumn (September to November) and wintertime (December to February). After the storm, hardly any seasonality was observed over four years. This study shows that the WCM is able to simulate this extreme event. It is concluded that the WCM could be used as an observation operator for the assimilation of ASCAT σ° observations into the ISBA land surface model. Full article

(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)

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28 pages, 6253 KiB

Open AccessEditor’s ChoiceArticle

Impacts of Radiometric Uncertainty and Weather-Related Surface Conditions on Soil Moisture Retrievals with Sentinel-1

by Harm-Jan F. Benninga, Rogier van der Velde and Zhongbo Su

Remote Sens. 2019, 11(17), 2025; https://doi.org/10.3390/rs11172025 - 28 Aug 2019

Cited by 23 | Viewed by 3906

Abstract

The radiometric uncertainty of Synthetic Aperture Radar (SAR) observations and weather-related surface conditions caused by frozen conditions, snow and intercepted rain affect the backscatter (

σ^{0}

) observations and limit the accuracy of soil moisture retrievals. This study estimates Sentinel-1’s radiometric uncertainty, [...] Read more.

The radiometric uncertainty of Synthetic Aperture Radar (SAR) observations and weather-related surface conditions caused by frozen conditions, snow and intercepted rain affect the backscatter (

σ^{0}

) observations and limit the accuracy of soil moisture retrievals. This study estimates Sentinel-1’s radiometric uncertainty, identifies the effects of weather-related surface conditions on

σ^{0}

and investigates their impact on soil moisture retrievals for various conditions regarding soil moisture, surface roughness and incidence angle. Masking rules for the surface conditions that disturb

σ^{0}

were developed based on meteorological measurements and timeseries of Sentinel-1 observations collected over five forests, five meadows and five cultivated fields in the eastern part of the Netherlands. The Sentinel-1

σ^{0}

observations appear to be affected by frozen conditions below an air temperature of 1

^{\circ} C

, snow during Sentinel-1’s morning overpasses on meadows and cultivated fields and interception after more than

1.8

m

m

of rain in the 12 h preceding a Sentinel-1 overpass, whereas dew was not found to be of influence. After the application of these masking rules, the radiometric uncertainty was estimated by the standard deviation of the seasonal anomalies timeseries of the Sentinel-1 forest

σ^{0}

observations. By spatially averaging the

σ^{0}

observations, the Sentinel-1 radiometric uncertainty improves from

0.85

dB for a surface area of

0.25

h a

0.30

dB for 10

h a

for the VV polarization and from

0.89

dB to

0.36

dB for the VH polarization, following approximately an inverse square root dependency on the surface area over which the

σ^{0}

observations are averaged. Deviations in

σ^{0}

were combined with the

σ^{0}

sensitivity to soil moisture as simulated with the Integral Equation Method (IEM) surface scattering model, which demonstrated that both the disturbing effects by the weather-related surface conditions (if not masked) and radiometric uncertainty have a significant impact on the soil moisture retrievals from Sentinel-1. The soil moisture retrieval uncertainty due to radiometric uncertainty ranges from

0.01

m^{3}

m^{- 3}

up to

0.17

m^{3}

m^{- 3}

for wet soils and small surface areas. The impacts on soil moisture retrievals are found to be weakly dependent on the surface roughness and the incidence angle, and strongly dependent on the surface area (or the

σ^{0}

disturbance caused by a weather-related surface condition for a specific land cover type) and the soil moisture itself. Full article

(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)

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Graphical abstract

22 pages, 7441 KiB

Open AccessArticle

Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data

by Minfeng Xing, Binbin He, Xiliang Ni, Jinfei Wang, Gangqiang An, Jiali Shang and Xiaodong Huang

Remote Sens. 2019, 11(16), 1956; https://doi.org/10.3390/rs11161956 - 20 Aug 2019

Cited by 30 | Viewed by 4546

Abstract

Surface soil moisture (SSM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often obstructed by the vegetation effects on the backscattering during the growing season. This paper reports the retrieval of SSM from RADARSAT-2 SAR data that were acquired over wheat and soybean fields throughout the 2015 (April to October) growing season. The developed SSM retrieval algorithm includes a vegetation-effect correction. A method that can adequately represent the scattering behavior of vegetation-covered area was developed by defining the backscattering from vegetation and the underlying soil individually to remove the effect of vegetation on the total SAR backscattering. The Dubois model was employed to describe the backscattering from the underlying soil. A modified Water Cloud Model (MWCM) was used to remove the effect of backscattering that is caused by vegetation canopy. SSM was derived from an inversion scheme while using the dual co-polarizations (HH and VV) from the quad polarization RADARSAT-2 SAR data. Validation against ground measurements showed a high correlation between the measured and estimated SSM (R² = 0.71, RMSE = 4.43 vol.%, p < 0.01), which suggested an operational potential of RADARSAT-2 SAR data on SSM estimation over wheat and soybean fields during the growing season. Full article

(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)

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24 pages, 12146 KiB

Open AccessFeature PaperArticle

A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations

by Raphael Quast, Clément Albergel, Jean-Christophe Calvet and Wolfgang Wagner

Remote Sens. 2019, 11(3), 285; https://doi.org/10.3390/rs11030285 - 01 Feb 2019

Cited by 19 | Viewed by 5050

Abstract

We present the application of a generic, semi-empirical first-order radiative transfer modelling approach for the retrieval of soil- and vegetation related parameters from coarse-resolution space-borne scatterometer measurements (

σ_{0}

). It is shown that both angular- and temporal variabilities of ASCAT [...] Read more.

σ_{0}

). It is shown that both angular- and temporal variabilities of ASCAT

σ_{0}

measurements can be sufficiently represented by modelling the scattering characteristics of the soil-surface and the covering vegetation-layer via linear combinations of idealized distribution-functions. The temporal variations are modelled using only two dynamic variables, the vegetation optical depth (

τ

) and the nadir hemispherical reflectance (N) of the chosen soil-bidirectional reflectance distribution function (

B R D F

). The remaining spatial variabilities of the soil- and vegetation composition are accounted for via temporally constant parameters. The model was applied to series of 158 selected test-sites within France. Parameter estimates are obtained by using ASCAT

σ_{0}

measurements together with auxiliary Leaf Area Index (

L A I

) and soil-moisture (

S M

) datasets provided by the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land-surface model within the SURFEX modelling platform for a time-period from 2007–2009. The resulting parametrization was then used used to perform

S M

and

τ

retrievals both with and without the incorporation of auxiliary

L A I

and

S M

datasets for a subsequent time-period from 2010 to 2012. Full article

(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)

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22 pages, 5865 KiB

Open AccessArticle

Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data

by Safa Bousbih, Mehrez Zribi, Mohammad El Hajj, Nicolas Baghdadi, Zohra Lili-Chabaane, Qi Gao and Pascal Fanise

Remote Sens. 2018, 10(12), 1953; https://doi.org/10.3390/rs10121953 - 05 Dec 2018

Cited by 115 | Viewed by 8084

Abstract

This paper presents a technique for the mapping of soil moisture and irrigation, at the scale of agricultural fields, based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). The Kairouan plain, a semi-arid region in central Tunisia (North Africa), was selected as a test area for this study. Firstly, an algorithm for the direct inversion of the Water Cloud Model (WCM) was developed for the spatialization of the soil water content between 2015 and 2017. The soil moisture retrieved from these observations was first validated using ground measurements, recorded over 20 reference fields of cereal crops. A second method, based on the use of neural networks, was also used to confirm the initial validation. The results reported here show that the soil moisture products retrieved from remotely sensed data are accurate, with a Root Mean Square Error (RMSE) of less than 5% between the two moisture products. In addition, the analysis of soil moisture and Normalized Difference Vegetation Index (NDVI) products over cultivated fields, as a function of time, led to the classification of irrigated and rainfed areas on the Kairouan plain, and to the production of irrigation maps at the scale of individual fields. This classification is based on a decision tree approach, using a combination of various statistical indices of soil moisture and NDVI time series. The resulting irrigation maps were validated using reference fields within the study site. The best results were obtained with classifications based on soil moisture indices only, with an accuracy of 77%. Full article

(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)

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19 pages, 11927 KiB

Open AccessArticle

Sensitivity of TDS-1 GNSS-R Reflectivity to Soil Moisture: Global and Regional Differences and Impact of Different Spatial Scales

by Adriano Camps, Mercedes Vall·llossera, Hyuk Park, Gerard Portal and Luciana Rossato

Remote Sens. 2018, 10(11), 1856; https://doi.org/10.3390/rs10111856 - 21 Nov 2018

Cited by 69 | Viewed by 5681

Abstract

The potential of Global Navigation Satellite Systems-Reflectometry (GNSS-R) techniques to estimate land surface parameters such as soil moisture (SM) is experimentally studied using 2014–2017 global data from the UK TechDemoSat-1 (TDS-1) mission. The approach is based on the analysis of the sensitivity to SM of different observables extracted from the Delay Doppler Maps (DDM) computed by the Space GNSS Receiver–Remote Sensing Instrument (SGR-ReSI) instrument using the L1 (1575.42 MHz) left-hand circularly-polarized (LHCP) reflected signals emitted by the Global Positioning System (GPS) navigation satellites. The sensitivity of different GNSS-R observables to SM and its dependence on the incidence angle is analyzed. It is found that the sensitivity of the calibrated GNSS-R reflectivity to surface soil moisture is ~0.09 dB/% up to 30° incidence angle, and it decreases with increasing incidence angles, although differences are found depending on the spatial scale used for the ground-truth, and the region. The sensitivity to subsurface soil moisture has been also analyzed using a network of subsurface probes and hydrological models, apparently showing some dependence, but so far results are not conclusive. Full article

(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)

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23 pages, 3380 KiB

Open AccessArticle

Improving the Seasonal Representation of ASCAT Soil Moisture and Vegetation Dynamics in a Temperate Climate

by Isabella Pfeil, Mariette Vreugdenhil, Sebastian Hahn, Wolfgang Wagner, Peter Strauss and Günter Blöschl

Remote Sens. 2018, 10(11), 1788; https://doi.org/10.3390/rs10111788 - 11 Nov 2018

Cited by 18 | Viewed by 4530

Abstract

Previous validation studies have demonstrated the accuracy of the Metop-A ASCAT soil moisture (SM) product, although over- and underestimation during different seasons of the year suggest a need for improving the retrieval algorithm. In this study, we analyzed whether adapting the vegetation characterization [...] Read more.

τ

). SM and

τ

are retrieved from ASCAT using both a seasonal (mean climatological) and a dynamic vegetation characterization that allows for year-to-year changes. The retrieved SM and

τ

are compared with in situ and satellite SM, and with vegetation products (SMAP, AMSR2, and SPOT-VGT/PROBA-V). The study region is set in an agricultural area of Lower Austria that is characterized by heterogeneous land cover and topography, and features an experimental catchment equipped with a SM network (HOAL SoilNet). We found that a stronger vegetation correction within the SM retrieval improves the SM product considerably (increase of the Spearman correlation coefficient

r_{s}

by 0.15 on average, and

r_{s}

comparable to SMAP and AMSR2). The vegetation product derived with a dynamic vegetation characterization compares well to the reference datasets and reflects vegetation dynamics such as start and peak of season and harvest. Although some vegetation effects cannot be corrected by the adapted vegetation characterization, our results demonstrate the benefits of a parameterization optimized for regional conditions in this temperate climate zone. Full article

(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)

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18 pages, 11333 KiB

Open AccessFeature PaperArticle

Irrigation Mapping Using Sentinel-1 Time Series at Field Scale

by Qi Gao, Mehrez Zribi, Maria Jose Escorihuela, Nicolas Baghdadi and Pere Quintana Segui

Remote Sens. 2018, 10(9), 1495; https://doi.org/10.3390/rs10091495 - 18 Sep 2018

Cited by 116 | Viewed by 8836

Abstract

The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical–vertical) and VH (vertical–horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change. Full article

(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)

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Review

Jump to: Editorial, Research

26 pages, 2982 KiB

Open AccessReview

GNSS-Reflectometry and Remote Sensing of Soil Moisture: A Review of Measurement Techniques, Methods, and Applications

by Komi Edokossi, Andres Calabia, Shuanggen Jin and Iñigo Molina

Remote Sens. 2020, 12(4), 614; https://doi.org/10.3390/rs12040614 - 12 Feb 2020

Cited by 52 | Viewed by 8402

Abstract

The understanding of land surface-atmosphere energy exchange is extremely important for predicting climate change and weather impacts, particularly the influence of soil moisture content (SMC) on hydrometeorological and ecological processes, which are also linked to human activities. Unfortunately, traditional measurement methods are expensive and cumbersome over large areas, whereas measurements from satellite active and passive microwave sensors have shown advantages for SMC monitoring. Since the launch of the first passive microwave satellite in 1978, more and more progresses have been made in monitoring SMC from satellites, e.g., the Soil Moisture Active and Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions in the last decade. Recently, new methods using signals of opportunity have been emerging, highlighting the Global Navigation Satellite Systems-Reflectometry (GNSS-R), which has wide applications in Earth’s surface remote sensing due to its numerous advantages (e.g., revisiting time, global coverage, low cost, all-weather measurements, and near real-time) when compared to the conventional observations. In this paper, a detailed review on the current SMC measurement techniques, retrieval approaches, products, and applications is presented, particularly the new and promising GNSS-R technique. Recent advances, future prospects and challenges are given and discussed. Full article

(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)

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