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Recent Innovative Microwave Remote Sensing Instrumentation for Land Surface Applications

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

Deadline for manuscript submissions: closed (30 November 2017) | Viewed by 76141

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Dr. Mehrez Zribi

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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
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Dr. Nicolas Baghdadi

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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

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

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Keywords

microwave
radiometer
interferometry
radar
SAR
altimetry
bistatic
GNSS
GNSS-R
data processing

Published Papers (11 papers)

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Research

23 pages, 9989 KiB

Open AccessArticle

Mapping of Rice Varieties and Sowing Date Using X-Band SAR Data

by Hoa Phan, Thuy Le Toan, Alexandre Bouvet, Lam Dao Nguyen, Tien Pham Duy and Mehrez Zribi

Sensors 2018, 18(1), 316; https://doi.org/10.3390/s18010316 - 22 Jan 2018

Cited by 48 | Viewed by 8301

Abstract

Rice is a major staple food for nearly half of the world’s population and has a considerable contribution to the global agricultural economy. While spaceborne Synthetic Aperture Radar (SAR) data have proved to have great potential to provide rice cultivation area, few studies have been performed to provide practical information that meets the user requirements. In rice growing regions where the inter-field crop calendar is not uniform such as in the Mekong Delta in Vietnam, knowledge of the start of season on a field basis, along with the planted rice varieties, is very important for correct field management (timing of irrigation, fertilization, chemical treatment, harvest), and for market assessment of the rice production. The objective of this study is to develop methods using SAR data to retrieve in addition to the rice grown area, the sowing date, and the distinction between long and short cycle varieties. This study makes use of X-band SAR data from COSMO-SkyMed acquired from 19 August to 23 November 2013 covering the Chau Thanh and Thoai Son districts in An Giang province, Viet Nam, characterized by a complex cropping pattern. The SAR data have been analyzed as a function of rice parameters, and the temporal and polarization behaviors of the radar backscatter of different rice varieties have been interpreted physically. New backscatter indicators for the detection of rice paddy area, the estimation of the sowing date, and the mapping of the short cycle and long cycle rice varieties have been developed and assessed. Good accuracy has been found with 92% in rice grown area, 96% on rice long or short cycle, and a root mean square error of 4.3 days in sowing date. The results have been discussed regarding the generality of the methods with respect to the rice cultural practices and the SAR data characteristics. Full article

(This article belongs to the Special Issue Recent Innovative Microwave Remote Sensing Instrumentation for Land Surface Applications)

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8299 KiB

Open AccessArticle

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution

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

Sensors 2017, 17(9), 1966; https://doi.org/10.3390/s17091966 - 26 Aug 2017

Cited by 219 | Viewed by 15364

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 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

(This article belongs to the Special Issue Recent Innovative Microwave Remote Sensing Instrumentation for Land Surface Applications)

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13244 KiB

Open AccessArticle

Optimizing Waveform Maximum Determination for Specular Point Tracking in Airborne GNSS-R

by Erwan Motte and Mehrez Zribi

Sensors 2017, 17(8), 1880; https://doi.org/10.3390/s17081880 - 16 Aug 2017

Cited by 7 | Viewed by 3980

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 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

(This article belongs to the Special Issue Recent Innovative Microwave Remote Sensing Instrumentation for Land Surface Applications)

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2883 KiB

Open AccessArticle

Establishment of a Site-Specific Tropospheric Model Based on Ground Meteorological Parameters over the China Region

by Chongchong Zhou, Bibo Peng, Wei Li, Shiming Zhong, Jikun Ou, Runjing Chen and Xinglong Zhao

Sensors 2017, 17(8), 1722; https://doi.org/10.3390/s17081722 - 27 Jul 2017

Cited by 8 | Viewed by 3548

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 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

(This article belongs to the Special Issue Recent Innovative Microwave Remote Sensing Instrumentation for Land Surface Applications)

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11171 KiB

Open AccessArticle

Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1

by Yongchao Zhu, Kegen Yu, Jingui Zou and Jens Wickert

Sensors 2017, 17(7), 1614; https://doi.org/10.3390/s17071614 - 12 Jul 2017

Cited by 39 | Viewed by 5806

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 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

(This article belongs to the Special Issue Recent Innovative Microwave Remote Sensing Instrumentation for Land Surface Applications)

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5735 KiB

Open AccessArticle

GNSS-R Altimetry Performance Analysis for the GEROS Experiment on Board the International Space Station

by Adriano Camps, Hyuk Park, Ivan Sekulic and Juan Manuel Rius

Sensors 2017, 17(7), 1583; https://doi.org/10.3390/s17071583 - 06 Jul 2017

Cited by 17 | Viewed by 5310

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 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

(This article belongs to the Special Issue Recent Innovative Microwave Remote Sensing Instrumentation for Land Surface Applications)

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4082 KiB

Open AccessArticle

Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. A Hydrological Approach

by Dimitrios D. Alexakis, Filippos-Dimitrios K. Mexis, Anthi-Eirini K. Vozinaki, Ioannis N. Daliakopoulos and Ioannis K. Tsanis

Sensors 2017, 17(6), 1455; https://doi.org/10.3390/s17061455 - 21 Jun 2017

Cited by 104 | Viewed by 9707

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 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

(This article belongs to the Special Issue Recent Innovative Microwave Remote Sensing Instrumentation for Land Surface Applications)

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1947 KiB

Open AccessArticle

A Forward GPS Multipath Simulator Based on the Vegetation Radiative Transfer Equation Model

by Xuerui Wu, Shuanggen Jin and Junming Xia

Sensors 2017, 17(6), 1291; https://doi.org/10.3390/s17061291 - 05 Jun 2017

Cited by 5 | Viewed by 4230

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 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

(This article belongs to the Special Issue Recent Innovative Microwave Remote Sensing Instrumentation for Land Surface Applications)

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8137 KiB

Open AccessArticle

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

by Tao Zhou, Jianjun Pan, Peiyu Zhang, Shanbao Wei and Tao Han

Sensors 2017, 17(6), 1210; https://doi.org/10.3390/s17061210 - 25 May 2017

Cited by 87 | Viewed by 6471

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 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

(This article belongs to the Special Issue Recent Innovative Microwave Remote Sensing Instrumentation for Land Surface Applications)

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6064 KiB

Open AccessFeature PaperArticle

MERITXELL: The Multifrequency Experimental Radiometer with Interference Tracking for Experiments over Land and Littoral—Instrument Description, Calibration and Performance

by Jorge Querol, José Miguel Tarongí, Giuseppe Forte, José Javier Gómez and Adriano Camps

Sensors 2017, 17(5), 1081; https://doi.org/10.3390/s17051081 - 10 May 2017

Cited by 2 | Viewed by 6239

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 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

(This article belongs to the Special Issue Recent Innovative Microwave Remote Sensing Instrumentation for Land Surface Applications)

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3371 KiB

Open AccessArticle

Terrestrial Water Storage in African Hydrological Regimes Derived from GRACE Mission Data: Intercomparison of Spherical Harmonics, Mass Concentration, and Scalar Slepian Methods

by Ashraf Rateb, Chung-Yen Kuo, Moslem Imani, Kuo-Hsin Tseng, Wen-Hau Lan, Kuo-En Ching and Tzu-Pang Tseng

Sensors 2017, 17(3), 566; https://doi.org/10.3390/s17030566 - 10 Mar 2017

Cited by 15 | Viewed by 6225

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) 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

(This article belongs to the Special Issue Recent Innovative Microwave Remote Sensing Instrumentation for Land Surface Applications)

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