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Special Issue "Recent Innovative Microwave Remote Sensing Instrumentation for Land Surface Applications"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 30 November 2017

Special Issue Editors

Guest Editor
Prof. Dr. Mehrez Zribi

CNRS Centre National de la Recherche Scientifique, Toulouse, France
Website | E-Mail
Interests: airborne instrumentation for land surfaces; microwave remote sensing; GNSS-R; GNSS; land surfaces; spatial hydrology
Guest Editor
Dr. Nicolas Baghdadi

Maison de la Télédétection, Irstea - UMR TETIS, 500 rue JF Breton, 34093 Montpellier Cedex 05, France
Website | E-Mail
Interests: SAR images applied to soil (surface roughness, soil moisture, texture); Lidar and Forest (canopy height and biomass); SAR images and biomass

Special Issue Information

Dear Colleagues,

In the last three decades, microwave remote sensing has shown a high potential in characterization of land surface parameters (soil moisture, vegetation biomass, water covers, etc.). In this context, a very rich activity has been developed to propose techniques (satellite, airborne, in situ) and methodologies to optimize contribution of microwave remote sensing, in terms of precision, spatial, and temporal resolutions.

This Special Issue is aimed to the submission of both review and original research articles related to recent innovative microwave remote sensing instrumentation for land surface applications (water resources, forest, agriculture, etc.). The Special Issue is open to contributions in instrumentation, methodologies for data processing, etc., in active microwaves (monostatic, bistatic measurements, interferometry, tomography, etc.), passive microwaves (antennas, interferometry, data processing (RFI, etc.)), GNSS, GNSS-R (instrumentation, data processing for in situ, airborne, satellite measurements, etc.).


Prof. Dr. Mehrez Zribi
Prof. Dr. Nicolas Baghdadi
Guest Editors

Manuscript Submission Information

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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. Sensors is an international peer-reviewed open access monthly 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
  • radiometer
  • interferometry
  • radar
  • SAR
  • altimetry
  • bistatic
  • GNSS
  • GNSS-R
  • data processing

Published Papers (10 papers)

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Research

Open AccessArticle Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution
Sensors 2017, 17(9), 1966; doi:10.3390/s17091966
Received: 10 July 2017 / Revised: 11 August 2017 / Accepted: 22 August 2017 / Published: 26 August 2017
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Abstract
The recent deployment of ESA’s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two
[...] Read more.
The recent deployment of ESA’s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015–November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded. Full article
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Open AccessArticle Optimizing Waveform Maximum Determination for Specular Point Tracking in Airborne GNSS-R
Sensors 2017, 17(8), 1880; doi:10.3390/s17081880
Received: 12 June 2017 / Revised: 13 July 2017 / Accepted: 14 August 2017 / Published: 16 August 2017
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Abstract
Airborne GNSS-R campaigns are crucial to the understanding of signal interactions with the Earth’s surface. As a consequence of the specific geometric configurations arising during measurements from aircraft, the reflected signals can be difficult to interpret under certain conditions like over strongly attenuating
[...] Read more.
Airborne GNSS-R campaigns are crucial to the understanding of signal interactions with the Earth’s surface. As a consequence of the specific geometric configurations arising during measurements from aircraft, the reflected signals can be difficult to interpret under certain conditions like over strongly attenuating media such as forests, or when the reflected signal is contaminated by the direct signal. For these reasons, there are many cases where the reflectivity is overestimated, or a portion of the dataset has to be flagged as unusable. In this study we present techniques that have been developed to optimize the processing of airborne GNSS-R data, with the goal of improving its accuracy and robustness under non-optimal conditions. This approach is based on the detailed analysis of data produced by the instrument GLORI, which was recorded during an airborne campaign in the south west of France in June 2015. Our technique relies on the improved determination of reflected waveform peaks in the delay dimension, which is related to the loci of the signals contributed by the zone surrounding the specular point. It is shown that when developing techniques for the correct localization of waveform maxima under conditions of surfaces of low reflectivity, and/or contamination from the direct signal, it is possible to correct and extract values corresponding to the real reflectivity of the zone in the neighborhood of the specular point. This algorithm was applied to a reanalysis of the complete campaign dataset, following which the accuracy and sensitivity improved, and the usability of the dataset was improved by 30%. Full article
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Open AccessArticle Establishment of a Site-Specific Tropospheric Model Based on Ground Meteorological Parameters over the China Region
Sensors 2017, 17(8), 1722; doi:10.3390/s17081722
Received: 11 May 2017 / Revised: 24 July 2017 / Accepted: 25 July 2017 / Published: 27 July 2017
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Abstract
China is a country of vast territory with complicated geographical environment and climate conditions. With the rapid progress of the Chinese BeiDou satellite navigation system (BDS); more accurate tropospheric models must be applied to improve the accuracy of navigation and positioning. Based on
[...] Read more.
China is a country of vast territory with complicated geographical environment and climate conditions. With the rapid progress of the Chinese BeiDou satellite navigation system (BDS); more accurate tropospheric models must be applied to improve the accuracy of navigation and positioning. Based on the formula of the Saastamoinen and Callahan models; this study develops two single-site tropospheric models (named SAAS_S and CH_S models) for the Chinese region using radiosonde data from 2005 to 2012. We assess the two single-site tropospheric models with radiosonde data for 2013 and zenith tropospheric delay (ZTD) data from four International GNSS Service (IGS) stations and compare them to the results of the Saastamoinen and Callahan models. The experimental results show that: the mean accuracy of the SAAS_S model (bias: 0.19 cm; RMS: 3.19 cm) at all radiosonde stations is superior to those of the Saastamoinen (bias: 0.62 cm; RMS: 3.62 cm) and CH_S (bias: −0.05 cm; RMS: 3.38 cm) models. In most Chinese regions; the RMS values of the SAAS_S and CH_S models are about 0.51~2.12 cm smaller than those of their corresponding source models. The SAAS_S model exhibits a clear improvement in the accuracy over the Saastamoinen model in low latitude regions. When the SAAS_S model is replaced by the SAAS model in the positioning of GNSS; the mean accuracy of vertical direction in the China region can be improved by 1.12~1.55 cm and the accuracy of vertical direction in low latitude areas can be improved by 1.33~7.63 cm. The residuals of the SAAS_S model are closer to a normal distribution compared to those of the Saastamoinen model. Single-site tropospheric models based on the short period of the most recent data (for example 2 years) can also achieve a satisfactory accuracy. The average performance of the SAAS_S model (bias: 0.83 cm; RMS: 3.24 cm) at four IGS stations is superior to that of the Saastamoinen (bias: −0.86 cm; RMS: 3.59 cm) and CH_S (bias: 0.45 cm; RMS: 3.38 cm) models. Full article
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Open AccessArticle Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1
Sensors 2017, 17(7), 1614; doi:10.3390/s17071614
Received: 21 June 2017 / Revised: 8 July 2017 / Accepted: 10 July 2017 / Published: 12 July 2017
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Abstract
Global Navigation Satellite System (GNSS) signals can be exploited to remotely sense atmosphere and land and ocean surface to retrieve a range of geophysical parameters. This paper proposes two new methods, termed as power-summation of differential Delay-Doppler Maps (PS-D) and pixel-number of differential
[...] Read more.
Global Navigation Satellite System (GNSS) signals can be exploited to remotely sense atmosphere and land and ocean surface to retrieve a range of geophysical parameters. This paper proposes two new methods, termed as power-summation of differential Delay-Doppler Maps (PS-D) and pixel-number of differential Delay-Doppler Maps (PN-D), to distinguish between sea ice and sea water using differential Delay-Doppler Maps (dDDMs). PS-D and PN-D make use of power-summation and pixel-number of dDDMs, respectively, to measure the degree of difference between two DDMs so as to determine the transition state (water-water, water-ice, ice-ice and ice-water) and hence ice and water are detected. Moreover, an adaptive incoherent averaging of DDMs is employed to improve the computational efficiency. A large number of DDMs recorded by UK TechDemoSat-1 (TDS-1) over the Arctic region are used to test the proposed sea ice detection methods. Through evaluating against ground-truth measurements from the Ocean Sea Ice SAF, the proposed PS-D and PN-D methods achieve a probability of detection of 99.72% and 99.69% respectively, while the probability of false detection is 0.28% and 0.31% respectively. Full article
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Open AccessArticle GNSS-R Altimetry Performance Analysis for the GEROS Experiment on Board the International Space Station
Sensors 2017, 17(7), 1583; doi:10.3390/s17071583
Received: 11 May 2017 / Revised: 2 July 2017 / Accepted: 3 July 2017 / Published: 6 July 2017
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Abstract
The GEROS-ISS (GNSS rEflectometry, Radio Occultation and Scatterometry onboard International Space Station) is an innovative experiment for climate research, proposed in 2011 within a call of the European Space Agency (ESA). This proposal was the only one selected for further studies by ESA
[...] Read more.
The GEROS-ISS (GNSS rEflectometry, Radio Occultation and Scatterometry onboard International Space Station) is an innovative experiment for climate research, proposed in 2011 within a call of the European Space Agency (ESA). This proposal was the only one selected for further studies by ESA out of ~25 ones that were submitted. In this work, the instrument performance for the near-nadir altimetry (GNSS-R) mode is assessed, including the effects of multi-path in the ISS structure, the electromagnetic-bias, and the orbital height decay. In the absence of ionospheric scintillations, the altimetry rms error is <50 cm for a swath <~250 km and for U10 <10 m/s. If the transmitted power is 3 dB higher (likely to happen at beginning of life of the GNSS spacecrafts), mission requirements (rms error is <50 cm) are met for all ISS heights and for U10 up to 15 m/s. However, around 1.5 GHz, the ionosphere can induce significant fading, from 2 to >20 dB at equatorial regions, mainly after sunset, which will seriously degrade the altimetry and the scatterometry performances of the instrument. Full article
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Open AccessArticle Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. A Hydrological Approach
Sensors 2017, 17(6), 1455; doi:10.3390/s17061455
Received: 12 April 2017 / Revised: 16 June 2017 / Accepted: 16 June 2017 / Published: 21 June 2017
Cited by 1 | PDF Full-text (4082 KB) | HTML Full-text | XML Full-text
Abstract
A methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. After pre-processing the remote sensing data, backscattering coefficient, Normalized Difference Vegetation Index (NDVI), thermal infrared temperature and incidence
[...] Read more.
A methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. After pre-processing the remote sensing data, backscattering coefficient, Normalized Difference Vegetation Index (NDVI), thermal infrared temperature and incidence angle parameters are assessed for their potential to infer ground measurements of SMC, collected at the top 5 cm. A non-linear approach using Artificial Neural Networks (ANNs) is tested. The methodology is applied in Western Crete, Greece, where a SMC gauge network was deployed during 2015. The performance of the proposed algorithm is evaluated using leave-one-out cross validation and sensitivity analysis. ANNs prove to be the most efficient in SMC estimation yielding R2 values between 0.7 and 0.9. The proposed methodology is used to support a hydrological simulation with the HEC-HMS model, applied at the Keramianos basin which is ungauged for SMC. Results and model sensitivity highlight the contribution of combining Sentinel-1 SAR and Landsat 8 images for improving SMC estimates and supporting hydrological studies. Full article
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Open AccessArticle A Forward GPS Multipath Simulator Based on the Vegetation Radiative Transfer Equation Model
Sensors 2017, 17(6), 1291; doi:10.3390/s17061291
Received: 1 April 2017 / Revised: 25 May 2017 / Accepted: 26 May 2017 / Published: 5 June 2017
PDF Full-text (1947 KB) | HTML Full-text | XML Full-text
Abstract
Global Navigation Satellite Systems (GNSS) have been widely used in navigation, positioning and timing. Nowadays, the multipath errors may be re-utilized for the remote sensing of geophysical parameters (soil moisture, vegetation and snow depth), i.e., GPS-Multipath Reflectometry (GPS-MR). However, bistatic scattering properties and
[...] Read more.
Global Navigation Satellite Systems (GNSS) have been widely used in navigation, positioning and timing. Nowadays, the multipath errors may be re-utilized for the remote sensing of geophysical parameters (soil moisture, vegetation and snow depth), i.e., GPS-Multipath Reflectometry (GPS-MR). However, bistatic scattering properties and the relation between GPS observables and geophysical parameters are not clear, e.g., vegetation. In this paper, a new element on bistatic scattering properties of vegetation is incorporated into the traditional GPS-MR model. This new element is the first-order radiative transfer equation model. The new forward GPS multipath simulator is able to explicitly link the vegetation parameters with GPS multipath observables (signal-to-noise-ratio (SNR), code pseudorange and carrier phase observables). The trunk layer and its corresponding scattering mechanisms are ignored since GPS-MR is not suitable for high forest monitoring due to the coherence of direct and reflected signals. Based on this new model, the developed simulator can present how the GPS signals (L1 and L2 carrier frequencies, C/A, P(Y) and L2C modulations) are transmitted (scattered and absorbed) through vegetation medium and received by GPS receivers. Simulation results show that the wheat will decrease the amplitudes of GPS multipath observables (SNR, phase and code), if we increase the vegetation moisture contents or the scatters sizes (stem or leaf). Although the Specular-Ground component dominates the total specular scattering, vegetation covered ground soil moisture has almost no effects on the final multipath signatures. Our simulated results are consistent with previous results for environmental parameter detections by GPS-MR. Full article
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Open AccessArticle Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
Sensors 2017, 17(6), 1210; doi:10.3390/s17061210
Received: 7 April 2017 / Revised: 21 May 2017 / Accepted: 21 May 2017 / Published: 25 May 2017
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Abstract
Winter wheat is the second largest food crop in China. It is important to obtain reliable winter wheat acreage to guarantee the food security for the most populous country in the world. This paper focuses on assessing the feasibility of in-season winter wheat
[...] Read more.
Winter wheat is the second largest food crop in China. It is important to obtain reliable winter wheat acreage to guarantee the food security for the most populous country in the world. This paper focuses on assessing the feasibility of in-season winter wheat mapping and investigating potential classification improvement by using SAR (Synthetic Aperture Radar) images, optical images, and the integration of both types of data in urban agricultural regions with complex planting structures in Southern China. Both SAR (Sentinel-1A) and optical (Landsat-8) data were acquired, and classification using different combinations of Sentinel-1A-derived information and optical images was performed using a support vector machine (SVM) and a random forest (RF) method. The interference coherence and texture images were obtained and used to assess the effect of adding them to the backscatter intensity images on the classification accuracy. The results showed that the use of four Sentinel-1A images acquired before the jointing period of winter wheat can provide satisfactory winter wheat classification accuracy, with an F1 measure of 87.89%. The combination of SAR and optical images for winter wheat mapping achieved the best F1 measure–up to 98.06%. The SVM was superior to RF in terms of the overall accuracy and the kappa coefficient, and was faster than RF, while the RF classifier was slightly better than SVM in terms of the F1 measure. In addition, the classification accuracy can be effectively improved by adding the texture and coherence images to the backscatter intensity data. Full article
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Open AccessFeature PaperArticle MERITXELL: The Multifrequency Experimental Radiometer with Interference Tracking for Experiments over Land and Littoral—Instrument Description, Calibration and Performance
Sensors 2017, 17(5), 1081; doi:10.3390/s17051081
Received: 28 February 2017 / Revised: 3 May 2017 / Accepted: 8 May 2017 / Published: 10 May 2017
PDF Full-text (6064 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
MERITXELL is a ground-based multisensor instrument that includes a multiband dual-polarization radiometer, a GNSS reflectometer, and several optical sensors. Its main goals are twofold: to test data fusion techniques, and to develop Radio-Frequency Interference (RFI) detection, localization and mitigation techniques. The former is
[...] Read more.
MERITXELL is a ground-based multisensor instrument that includes a multiband dual-polarization radiometer, a GNSS reflectometer, and several optical sensors. Its main goals are twofold: to test data fusion techniques, and to develop Radio-Frequency Interference (RFI) detection, localization and mitigation techniques. The former is necessary to retrieve complementary data useful to develop geophysical models with improved accuracy, whereas the latter aims at solving one of the most important problems of microwave radiometry. This paper describes the hardware design, the instrument control architecture, the calibration of the radiometer, and several captures of RFI signals taken with MERITXELL in urban environment. The multiband radiometer has a dual linear polarization total-power radiometer topology, and it covers the L-, S-, C-, X-, K-, Ka-, and W-band. Its back-end stage is based on a spectrum analyzer structure which allows to perform real-time signal processing, while the rest of the sensors are controlled by a host computer where the off-line processing takes place. The calibration of the radiometer is performed using the hot-cold load procedure, together with the tipping curves technique in the case of the five upper frequency bands. Finally, some captures of RFI signals are shown for most of the radiometric bands under analysis, which evidence the problem of RFI in microwave radiometry, and the limitations they impose in external calibration. Full article
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Open AccessArticle Terrestrial Water Storage in African Hydrological Regimes Derived from GRACE Mission Data: Intercomparison of Spherical Harmonics, Mass Concentration, and Scalar Slepian Methods
Sensors 2017, 17(3), 566; doi:10.3390/s17030566
Received: 18 December 2016 / Revised: 6 March 2017 / Accepted: 8 March 2017 / Published: 10 March 2017
PDF Full-text (3371 KB) | HTML Full-text | XML Full-text
Abstract
Spherical harmonics (SH) and mascon solutions are the two most common types of solutions for Gravity Recovery and Climate Experiment (GRACE) mass flux observations. However, SH signals are degraded by measurement and leakage errors. Mascon solutions (the Jet Propulsion Laboratory (JPL) release, herein)
[...] Read more.
Spherical harmonics (SH) and mascon solutions are the two most common types of solutions for Gravity Recovery and Climate Experiment (GRACE) mass flux observations. However, SH signals are degraded by measurement and leakage errors. Mascon solutions (the Jet Propulsion Laboratory (JPL) release, herein) exhibit weakened signals at submascon resolutions. Both solutions require a scale factor examined by the CLM4.0 model to obtain the actual water storage signal. The Slepian localization method can avoid the SH leakage errors when applied to the basin scale. In this study, we estimate SH errors and scale factors for African hydrological regimes. Then, terrestrial water storage (TWS) in Africa is determined based on Slepian localization and compared with JPL-mascon and SH solutions. The three TWS estimates show good agreement for the TWS of large-sized and humid regimes but present discrepancies for the TWS of medium and small-sized regimes. Slepian localization is an effective method for deriving the TWS of arid zones. The TWS behavior in African regimes and its spatiotemporal variations are then examined. The negative TWS trends in the lower Nile and Sahara at −1.08 and −6.92 Gt/year, respectively, are higher than those previously reported. Full article
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