Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7326658
M. Huber, B. Wessel, A. Wendleder, J. Hoffmann, A. Roth
The digital elevation model (DEM) produced by TanDEM-X is an interferometric elevation model with global unprecedented quality, accuracy, and coverage. It represents an unedited surface model that means for example in case of water bodies it still contains noisy, random or void DEM values. For the most applications a DEM editing is crucial. After all, as every application has its own requirements in this paper we describe a framework especially developed for TanDEM-X DEM to automatically edit the DEM according to specific user requirements. Originally, the tools were developed for editing SRTM X-Band DEM. Currently this framework is extended within the RASOR project (Rapid Analysis and Spatialisation Of Risk) towards a specific editing to support multi-hazard risk assessment but also for general editing purposes.
{"title":"A framework for an automatical editing of TanDEM-X digital elevation models","authors":"M. Huber, B. Wessel, A. Wendleder, J. Hoffmann, A. Roth","doi":"10.1109/IGARSS.2015.7326658","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326658","url":null,"abstract":"The digital elevation model (DEM) produced by TanDEM-X is an interferometric elevation model with global unprecedented quality, accuracy, and coverage. It represents an unedited surface model that means for example in case of water bodies it still contains noisy, random or void DEM values. For the most applications a DEM editing is crucial. After all, as every application has its own requirements in this paper we describe a framework especially developed for TanDEM-X DEM to automatically edit the DEM according to specific user requirements. Originally, the tools were developed for editing SRTM X-Band DEM. Currently this framework is extended within the RASOR project (Rapid Analysis and Spatialisation Of Risk) towards a specific editing to support multi-hazard risk assessment but also for general editing purposes.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114300146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7326532
Xiao-po Zheng, H. Ren, Q. Qin, Lingjing Wu, Zhongling Gao, Yuejun Sun, Jianhua Wang, Xin Ye
Canopy water content (CWC) is one of the most important biochemical properties of plants, which can be estimated from remote sensing data conveniently by using vegetation water indices. This paper started from the analysis of some existing indices and then proposed two novel indices to estimate CWC. First, the area under part of near infrared and shortwave infrared reflectance curve were calculated. Then two indices, Area-based Normalized Index (ABNI) and Area-Based Ratio Index (ABRI) were developed by using ratio method and normalization method, respectively. From the validation results, the new indices were found to exponentially correlate with CWC more significantly than some classical indices, and the determination coefficient (R2) and root mean square error (RMSE) of the new method were 0.89 and 0.04, which indicated that the novel indices provided a promising way to monitor CWC.
{"title":"Retrieval of canopy water content using a new spectral area index method","authors":"Xiao-po Zheng, H. Ren, Q. Qin, Lingjing Wu, Zhongling Gao, Yuejun Sun, Jianhua Wang, Xin Ye","doi":"10.1109/IGARSS.2015.7326532","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326532","url":null,"abstract":"Canopy water content (CWC) is one of the most important biochemical properties of plants, which can be estimated from remote sensing data conveniently by using vegetation water indices. This paper started from the analysis of some existing indices and then proposed two novel indices to estimate CWC. First, the area under part of near infrared and shortwave infrared reflectance curve were calculated. Then two indices, Area-based Normalized Index (ABNI) and Area-Based Ratio Index (ABRI) were developed by using ratio method and normalization method, respectively. From the validation results, the new indices were found to exponentially correlate with CWC more significantly than some classical indices, and the determination coefficient (R2) and root mean square error (RMSE) of the new method were 0.89 and 0.04, which indicated that the novel indices provided a promising way to monitor CWC.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114589764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7326007
Simone Mancon, S. Tebaldini, A. M. Guarnieri, D. Giudici
In this paper, we propose a model-based procedure to estimate the accuracy of Sentinel-1 orbit products by the Multi-Squint (MS) phase. The technique exploits the results of single baseline MS analyses collected for each possible master and slave combination in a stack to estimate the absolute orbit error. Accordingly, as first step we state the geometric model of the InSAR phase and the MS phase model as derivative of the In-SAR phase geometric model with respect to the squint angle, then we describe the algorithm to estimate two components of baseline error using the theoretical model. In this paper we focus on the TOPSAR acquisition modes of Sentinel-1 assuming at the most a linear error in the known slave trajectory. In particular, we describe a dedicated methodology to measure baselines accuracy using bursts and swaths overlaps in data acquired by IW and EW acquisition modes. Finally, we suggest a technique to estimate, by a weigthed least-squase inversion, the absolute orbit error of each image in a stack. Experimental results of single and multi-baseline MS analysis obtained on Sentinel-1 data will be displayed.
{"title":"Orbit accuracy estimation by multi-squint phase: First Sentinel-1 results","authors":"Simone Mancon, S. Tebaldini, A. M. Guarnieri, D. Giudici","doi":"10.1109/IGARSS.2015.7326007","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326007","url":null,"abstract":"In this paper, we propose a model-based procedure to estimate the accuracy of Sentinel-1 orbit products by the Multi-Squint (MS) phase. The technique exploits the results of single baseline MS analyses collected for each possible master and slave combination in a stack to estimate the absolute orbit error. Accordingly, as first step we state the geometric model of the InSAR phase and the MS phase model as derivative of the In-SAR phase geometric model with respect to the squint angle, then we describe the algorithm to estimate two components of baseline error using the theoretical model. In this paper we focus on the TOPSAR acquisition modes of Sentinel-1 assuming at the most a linear error in the known slave trajectory. In particular, we describe a dedicated methodology to measure baselines accuracy using bursts and swaths overlaps in data acquired by IW and EW acquisition modes. Finally, we suggest a technique to estimate, by a weigthed least-squase inversion, the absolute orbit error of each image in a stack. Experimental results of single and multi-baseline MS analysis obtained on Sentinel-1 data will be displayed.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121908192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7326982
H. Carreno-Luengo, A. Amézaga, A. Bolet, David Vidal, Jaume Jane, J. F. Muñoz, R. Olivé, Adriano Camps
Scientific evaluation of the 3Cat-2 payload (PYCARO reflectometer) has been performed from the BEXUS 19 stratospheric balloon flight with an apogee of ~ 27,000 m over boreal forests and lakes. The payload was configured in closed-loop mode during this flight. Results show the first-ever multi-constellation Global Navigation Satellite Systems Reflectometry (GNSS-R) measurements at dual-band and dual-polarization.
{"title":"Multi-constellation, dual-polarization, and dual-frequency GNSS-R stratospheric balloon experiment over boreal forests","authors":"H. Carreno-Luengo, A. Amézaga, A. Bolet, David Vidal, Jaume Jane, J. F. Muñoz, R. Olivé, Adriano Camps","doi":"10.1109/IGARSS.2015.7326982","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326982","url":null,"abstract":"Scientific evaluation of the 3Cat-2 payload (PYCARO reflectometer) has been performed from the BEXUS 19 stratospheric balloon flight with an apogee of ~ 27,000 m over boreal forests and lakes. The payload was configured in closed-loop mode during this flight. Results show the first-ever multi-constellation Global Navigation Satellite Systems Reflectometry (GNSS-R) measurements at dual-band and dual-polarization.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122019232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7325918
Haitao Lv, Yong F. Wang, Yang Shen
An algorithm to remove thin clouds within the visible bands was developed based on the simplified radiative transfer equation and two assumptions. We evaluated the algorithm using a Landsat8 sub-image of 041/036 (path/row) acquired on 29 March 2014. Thin clouds disappeared visually. With a nearly cloud-free image acquired on 14 April 2014 as the “truth”, the spatial coefficients between the “truth” image and the image before and after the algorithm increased from 0.47 to 0.83 for Band1, 0.55 to 0.82 for Band2, 0.73 to 0.88 for Band3, and 0.82 to 0.88 for Band4. The increase of the spatial coefficients quantitatively indicated the validity of the algorithm.
{"title":"Removal of thin clouds in visible bands using spectrum characteristics of the visible bands","authors":"Haitao Lv, Yong F. Wang, Yang Shen","doi":"10.1109/IGARSS.2015.7325918","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7325918","url":null,"abstract":"An algorithm to remove thin clouds within the visible bands was developed based on the simplified radiative transfer equation and two assumptions. We evaluated the algorithm using a Landsat8 sub-image of 041/036 (path/row) acquired on 29 March 2014. Thin clouds disappeared visually. With a nearly cloud-free image acquired on 14 April 2014 as the “truth”, the spatial coefficients between the “truth” image and the image before and after the algorithm increased from 0.47 to 0.83 for Band1, 0.55 to 0.82 for Band2, 0.73 to 0.88 for Band3, and 0.82 to 0.88 for Band4. The increase of the spatial coefficients quantitatively indicated the validity of the algorithm.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122024133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7325800
Pei-Jyun Hsieh, Cheng-Hsaun Li, Bor-Chen Kuo
Many research shows that we will encounter the Highes phenomenon when dealing with the high-dimensional data classification problem. In addition, non-linear support vector machine (SVM) has been shown that it can conquer the problem efficiently. However, the SVM is a black-box model based on the whole features and does not provide the feature importance or “good” feature subset for classification and other applications. In 2012, an automatic kernel parameter selection (APS) based on kernel-based within- and between-class separability measures were proposed. Moreover, the application for determining the kernel parameters of the full bandwidth RBF (FRBF) kernel was proposed. In this study, the bandwidths of the FRBF kernel were considered as the weights of the features when the feature values are rescaled by computing the z-scores. Experimental results on the Indian Pine Site dataset showed that the SVM based on the proposed feature subset outperforms than the SVMs based on the RBF kernel and FRBF kernel.
{"title":"A nonlinear feature selection method based on kernel separability measure for hyperspectral image classification","authors":"Pei-Jyun Hsieh, Cheng-Hsaun Li, Bor-Chen Kuo","doi":"10.1109/IGARSS.2015.7325800","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7325800","url":null,"abstract":"Many research shows that we will encounter the Highes phenomenon when dealing with the high-dimensional data classification problem. In addition, non-linear support vector machine (SVM) has been shown that it can conquer the problem efficiently. However, the SVM is a black-box model based on the whole features and does not provide the feature importance or “good” feature subset for classification and other applications. In 2012, an automatic kernel parameter selection (APS) based on kernel-based within- and between-class separability measures were proposed. Moreover, the application for determining the kernel parameters of the full bandwidth RBF (FRBF) kernel was proposed. In this study, the bandwidths of the FRBF kernel were considered as the weights of the features when the feature values are rescaled by computing the z-scores. Experimental results on the Indian Pine Site dataset showed that the SVM based on the proposed feature subset outperforms than the SVMs based on the RBF kernel and FRBF kernel.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116811864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7326581
Zongliang Zhang, Ming Cheng, Xinqu Chen, Menglan Zhou, Yifei Chen, Jonathan Li, Hongshan Nie
Traditional road surveying methods rely largely on in-situ measurements, which are time consuming and labor intensive. Recent Mobile Laser Scanning (MLS) techniques enable collection of road data at a normal driving speed. However, extracting required information from collected MLS data remains a challenging task. This paper focuses on examining the current status of automated on-road object extraction techniques from 3D MLS points over the last five years. Several kinds of on-road objects are included in this paper: curbs and road surfaces, road markings, pavement cracks, as well as manhole and sewer well covers. We evaluate the extraction techniques according to their method design, degree of automation, precision, and computational efficiency. Given the large volume of MLS data, to date most MLS object extraction techniques are aiming to improve their precision and efficiency.
{"title":"Turning mobile laser scanning points into 2D/3D on-road object models: Current status","authors":"Zongliang Zhang, Ming Cheng, Xinqu Chen, Menglan Zhou, Yifei Chen, Jonathan Li, Hongshan Nie","doi":"10.1109/IGARSS.2015.7326581","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326581","url":null,"abstract":"Traditional road surveying methods rely largely on in-situ measurements, which are time consuming and labor intensive. Recent Mobile Laser Scanning (MLS) techniques enable collection of road data at a normal driving speed. However, extracting required information from collected MLS data remains a challenging task. This paper focuses on examining the current status of automated on-road object extraction techniques from 3D MLS points over the last five years. Several kinds of on-road objects are included in this paper: curbs and road surfaces, road markings, pavement cracks, as well as manhole and sewer well covers. We evaluate the extraction techniques according to their method design, degree of automation, precision, and computational efficiency. Given the large volume of MLS data, to date most MLS object extraction techniques are aiming to improve their precision and efficiency.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129573399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7325858
Chenzhou Liu, Jiancheng Shi
Using time series Aquarius middle beam scatterometer observations, the two vegetation parameters C and D in water cloud model were estimated. Vegetation backscatter was derived using two models: Oh model was used to describe the scattering from bare soil surface, while water cloud model was implemented to account for the effect of vegetation canopy. The vegetation parameters were estimated by minimizing the deviations between the Aquarius scatterometer observations and backscatter coefficients simulated by the water cloud model. The retrieved vegetation parameters are vegetation-specific, which are assumed constant for each vegetation types. By using the retrieved parameters to simulate the scatterometer observations, it was found that the error of the simulation (RMSE) was less than 3 dB in most areas. This research demonstrated that the water cloud model could be applied to global scatterometer observations if the vegetation parameters are appropriately set.
{"title":"Estimation of water cloud parameters using time series aquarius middle beam data","authors":"Chenzhou Liu, Jiancheng Shi","doi":"10.1109/IGARSS.2015.7325858","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7325858","url":null,"abstract":"Using time series Aquarius middle beam scatterometer observations, the two vegetation parameters C and D in water cloud model were estimated. Vegetation backscatter was derived using two models: Oh model was used to describe the scattering from bare soil surface, while water cloud model was implemented to account for the effect of vegetation canopy. The vegetation parameters were estimated by minimizing the deviations between the Aquarius scatterometer observations and backscatter coefficients simulated by the water cloud model. The retrieved vegetation parameters are vegetation-specific, which are assumed constant for each vegetation types. By using the retrieved parameters to simulate the scatterometer observations, it was found that the error of the simulation (RMSE) was less than 3 dB in most areas. This research demonstrated that the water cloud model could be applied to global scatterometer observations if the vegetation parameters are appropriately set.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129864195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7326430
S. Tebaldini, F. Rocca, A. Meta, A. Coccia
In this paper we discuss 3D tomographic techniques for processing airborne SAR data acquired from largely irregular trajectories. The discussion is based on the L-Band data-set acquired over the Mittelbergferner glacier in 2014 in the frame of the ESA campaign AlpTomoSAR. Signal focusing is based on Time Domain Back Projection (TDBP), concerning the generation of both 2D SLC data stacks and 3D Tomographic data cubes, as this approach allows to correctly cope with random trajectory deviations, as well as with range and azimuth shifts depending on focusing height. Data Phase Calibration is also considered, in order to recover phase screens due to an imperfect knowledge of flight trajectories.
{"title":"A processing driven approach to airborne multi-baseline SAR tomography","authors":"S. Tebaldini, F. Rocca, A. Meta, A. Coccia","doi":"10.1109/IGARSS.2015.7326430","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326430","url":null,"abstract":"In this paper we discuss 3D tomographic techniques for processing airborne SAR data acquired from largely irregular trajectories. The discussion is based on the L-Band data-set acquired over the Mittelbergferner glacier in 2014 in the frame of the ESA campaign AlpTomoSAR. Signal focusing is based on Time Domain Back Projection (TDBP), concerning the generation of both 2D SLC data stacks and 3D Tomographic data cubes, as this approach allows to correctly cope with random trajectory deviations, as well as with range and azimuth shifts depending on focusing height. Data Phase Calibration is also considered, in order to recover phase screens due to an imperfect knowledge of flight trajectories.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128405986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7326191
Xuefeng Peng, Xiuwan Chen, Han Xiao, W. Wan, Ting Yang, Zhenyu Yang
More and More efforts have been made concerning the GNSS Reflectometry (GNSS-R) technique since GPS signals being found to be sensitive to geophysical properties, i.e., ocean surface roughness and soil moisture. Compared to airborne observations, ground-based research could focus on the models using the reflected GNSS signal, regardless of the atmospheric attenuation and the reflection zone's movement. Two ground-based GNSS-R experiments were conducted recently in Beijing. This paper proposes a statistical model based on least squares histogram fitting to process the acquired data. Although either the model error or the mismatching of the measuring depth could lead to the discrepancy between the estimated and in situ soil moisture, this approach can isolate the estimated values from different parts of the mixed surface and estimate soil moisture of a homogeneous surface more reasonably than the simply averaging method.
{"title":"Estimating soil moisture content using GNSS-R technique based on statistics","authors":"Xuefeng Peng, Xiuwan Chen, Han Xiao, W. Wan, Ting Yang, Zhenyu Yang","doi":"10.1109/IGARSS.2015.7326191","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326191","url":null,"abstract":"More and More efforts have been made concerning the GNSS Reflectometry (GNSS-R) technique since GPS signals being found to be sensitive to geophysical properties, i.e., ocean surface roughness and soil moisture. Compared to airborne observations, ground-based research could focus on the models using the reflected GNSS signal, regardless of the atmospheric attenuation and the reflection zone's movement. Two ground-based GNSS-R experiments were conducted recently in Beijing. This paper proposes a statistical model based on least squares histogram fitting to process the acquired data. Although either the model error or the mismatching of the measuring depth could lead to the discrepancy between the estimated and in situ soil moisture, this approach can isolate the estimated values from different parts of the mixed surface and estimate soil moisture of a homogeneous surface more reasonably than the simply averaging method.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128694500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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