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Deployment and performance of the NASA D3R during the GPM OLYMPEx field campaign NASA D3R在GPM OLYMPEx野外战役中的部署和性能
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-11-03 DOI: 10.1109/IGARSS.2016.7729553
V. Chandrasekar, R. Beauchamp, Haonan Chen, M. Vega, M. Schwaller, D. Willie, Aaron Dabrowski, Mohit Kumar, W. Petersen, D. Wolff
{"title":"Deployment and performance of the NASA D3R during the GPM OLYMPEx field campaign","authors":"V. Chandrasekar, R. Beauchamp, Haonan Chen, M. Vega, M. Schwaller, D. Willie, Aaron Dabrowski, Mohit Kumar, W. Petersen, D. Wolff","doi":"10.1109/IGARSS.2016.7729553","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7729553","url":null,"abstract":"The NASA D3R was successfully deployed and operated throughout the NASA OLYMPEx field campaign. A differential phase based attenuation correction technique has been implemented for D3R observations. Hydrometeor classification has been demonstrated for five distinct classes using Ku-band observations of both convection and stratiform rain. The stratiform rain hydrometeor classification is compared against LDR observations and shows good agreement in identification of mixed-phase hydrometeors in the melting layer.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123385752","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}
引用次数: 7
Evaluation of assimilated SMOS Soil Moisture data for US cropland Soil Moisture monitoring 同化SMOS土壤水分数据对美国农田土壤水分监测的评价
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-11-03 DOI: 10.1109/IGARSS.2016.7730366
Zhengwei Yang, R. Shrestha, W. Crow, John T. Bolten, Iva Mladenova, Genong Yu, L. Di
{"title":"Evaluation of assimilated SMOS Soil Moisture data for US cropland Soil Moisture monitoring","authors":"Zhengwei Yang, R. Shrestha, W. Crow, John T. Bolten, Iva Mladenova, Genong Yu, L. Di","doi":"10.1109/IGARSS.2016.7730366","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7730366","url":null,"abstract":"Remotely sensed soil moisture data can provide timely, objective and quantitative crop soil moisture information with broad geospatial coverage and sufficiently high resolution observations collected throughout the growing season. This paper evaluates the feasibility of using the assimilated ESA Soil Moisture Ocean Salinity (SMOS) Mission L-band passive microwave data for operational US cropland soil surface moisture monitoring. The assimilated SMOS soil moisture data are first categorized to match with the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) survey-based weekly soil moisture observation data, which are ordinal. The categorized assimilated SMOS soil moisture data are compared with NASS's survey-based weekly soil moisture data for consistency and robustness using visual assessment and rank correlation. Preliminary results indicate that the assimilated SMOS soil moisture data highly co-vary with NASS field observations across a large geographic area. Therefore, SMOS data have great potential for US operational cropland soil moisture monitoring.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116113304","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}
引用次数: 7
Comparing supervised algorithms in Land Use and Land Cover classification of a Landsat time-series 比较Landsat时间序列土地利用和土地覆盖分类中的监督算法
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-11-01 DOI: 10.1109/IGARSS.2016.7730346
Thayse Nery, R. Sadler, Maria Solis-Aulestia, B. White, M. Polyakov, M. Chalak
{"title":"Comparing supervised algorithms in Land Use and Land Cover classification of a Landsat time-series","authors":"Thayse Nery, R. Sadler, Maria Solis-Aulestia, B. White, M. Polyakov, M. Chalak","doi":"10.1109/IGARSS.2016.7730346","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7730346","url":null,"abstract":"Machine learning algorithms (MLAs) are often applied to identify Land Use and Land Cover (LULC) changes, but typically to only a limited set of imagery. This leaves the consistency of MLAs performance through time poorly understood. The research objective was therefore to compare the performance of six MLAs across a time-series of Landsat imagery (1979, 1992, 2003, 2014), all processed in the same manner. Here Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Random Forests (RF), Learning Vector Quantization (LVQ), Recursive Partitioning, Regression Trees (RPART) and Stochastic Gradient Boosting (GBM) were evaluated. The results demonstrated that SVM achieved higher overall accuracies and kappa coefficients, and a slightly improved fit at individual class level, than the second best classifier RF. Both classifiers clearly outperformed the other algorithms. These results suggest that SVMs (or RFs) should be prioritised when classifying time-series imagery for LULC change detection.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132593507","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}
引用次数: 24
Synoptic capabilities of the GNSS-R interferometric technique with the SPIR instrument 使用SPIR仪器的GNSS-R干涉测量技术的天气能力
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-11-01 DOI: 10.1109/IGARSS.2016.7730462
F. Fabra, E. Cardellach, S. Ribo, Weiqiang Li, A. Rius, J. Praks, E. Rouhe, Jaakko Seppänen, M. Martín-Neira
{"title":"Synoptic capabilities of the GNSS-R interferometric technique with the SPIR instrument","authors":"F. Fabra, E. Cardellach, S. Ribo, Weiqiang Li, A. Rius, J. Praks, E. Rouhe, Jaakko Seppänen, M. Martín-Neira","doi":"10.1109/IGARSS.2016.7730462","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7730462","url":null,"abstract":"We present in this paper the work done to test the synoptic capabilities of the GNSS-R interferometric technique as a means towards sea surface altimetry estimation, which is an integrated part of the assessment of the GEROS-ISS mission. For this purpose, a new software receiver has been developed and tested: SPIR. With the lessons learned after a first flight campaign, some system modifications were performed and a second experimental campaign has been recently carried on. Initial results show interferometric waveforms that present the quality required for altimetric analysis.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125037065","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}
引用次数: 4
Microwave brightness temperature of snow: Observations and simulations 雪的微波亮度温度:观测与模拟
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-11-01 DOI: 10.1109/IGARSS.2016.7730844
M. Hallikainen, J. Lemmetyinen
{"title":"Microwave brightness temperature of snow: Observations and simulations","authors":"M. Hallikainen, J. Lemmetyinen","doi":"10.1109/IGARSS.2016.7730844","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7730844","url":null,"abstract":"The brightness temperature of snow-covered terrain was monitored from January through April 1985 using tower-based radiometers operating at 1, 16.5, and 37 GHz (vertical and horizontal polarization) in southern Finland. Ground truth data on snow, soil and weather were collected. Layered dielectric, extinction and wetness information on snow at the test site was obtained with free-space transmission systems operating at 12 and 35 GHz. In this paper we report the 37 and 16.5 GHz vertically polarized brightness temperatures (incidence angle 50 degrees off nadir) for melting and refreezing snow over a 26-hour period and compare experimental and theoretical results.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126819554","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}
引用次数: 1
Building blocks for semiempirical models for forest parameter extraction from interferometric X-band SAR images 干涉x波段SAR图像森林参数提取的半经验模型构建模块
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-11-01 DOI: 10.1109/IGARSS.2016.7729185
J. Praks, Aire Olesk, K. Voormansik, O. Antropov, K. Zalite, M. Noorma
{"title":"Building blocks for semiempirical models for forest parameter extraction from interferometric X-band SAR images","authors":"J. Praks, Aire Olesk, K. Voormansik, O. Antropov, K. Zalite, M. Noorma","doi":"10.1109/IGARSS.2016.7729185","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7729185","url":null,"abstract":"In this work we provide basic building blocks for semi-empirical models to be applied mainly for forest height extraction from X-band interferometric SAR images. The work uses Random Volume over Ground model as the main theoretical framework, and relies on the measurement data represented by over 3000 measurements points collected in Estonia in 2011 and 2012. Here we demonstrate that the best argument for empirical models which relate coherence and forest parameters is relative interferometric tree height (tree height divided by InSAR Height of ambiguity). Our results suggest that a very simple linear model with no additional a priori parameters can be used as a first approach for estimation of forest height. However, if more extensive dataset are available, a zero extinction model can provide improvement. Moreover, proposed semi-empirical models can also be used to predict forest properties related to forest extinction coefficient. All the derived model approximations are demonstrated by model simulations and verified with extensive dataset of forest measurements. Relation of semi-empirical parameters to physics based model parameters is discussed and the models accuracy is analyzed based on empirical dataset.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117179448","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}
引用次数: 3
Improving SMOS soil moisture algorithm performance in forested areas with multisensor SAR data 利用多传感器SAR数据改进森林地区SMOS土壤湿度算法的性能
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-11-01 DOI: 10.1109/IGARSS.2016.7729428
Jaakko Seppänen, J. Praks, O. Antropov
{"title":"Improving SMOS soil moisture algorithm performance in forested areas with multisensor SAR data","authors":"Jaakko Seppänen, J. Praks, O. Antropov","doi":"10.1109/IGARSS.2016.7729428","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7729428","url":null,"abstract":"In this paper, we propose a new approach for improving boreal forest soil moisture estimation using L-band microwave radiometer. The effect is achieved by introducing improved description of forest canopy contribution from multisensor SAR measurements. Spaceborne L-band radiometer is a valuable tool for providing soil moisture estimates globally. Unfortunately, complex vegetation layer, such as forest, can hamper the accuracy of soil moisture retrieval leading to rather poor results particularly over boreal forest areas. Currently, the L-band Microwave Emission of the Biosphere (L-MEB) model adopted in the Soil Moisture and Ocean Salinity (SMOS) Level 2 Soil Moisture algorithm, uses Leaf Area Index (LAI) in order to to account for forest canopy contribution to total emission. However, it can argued that LAI presents poorly the actual structure of the coniferous forest. The LAI is calibrated to represent only the leaves, but at L-band, the main contribution to emission and attenuation is due to branches, while trunks and leaves have smaller effects. Here, we tested several combinations of spaceborne SAR data as a substitute of LAI in temperature brightness models for soil moisture retrieval. Particularly when L-band ALOS PALSAR stripmap data were used, the agreement between modelled and measured TB has improved from 0.46 to 0.55 in the L-MEB model.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127701276","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}
引用次数: 0
Range-angle dependent detection for FDA-MIMO radar 基于距离角的FDA-MIMO雷达检测
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-10-01 DOI: 10.1109/RADAR.2016.8059400
Shengyuan Li, Linrang Zhang, Nan Liu, Shiyang Tang, Shanshan Zhao
{"title":"Range-angle dependent detection for FDA-MIMO radar","authors":"Shengyuan Li, Linrang Zhang, Nan Liu, Shiyang Tang, Shanshan Zhao","doi":"10.1109/RADAR.2016.8059400","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059400","url":null,"abstract":"In this paper, a target detector with frequency diverse array (FDA) multiple-input and multiple-output (MIMO) radar in a homogenous environment is proposed. Unlike the conventional MIMO radar, the system uses FDA transmitting array. The essence of the proposed technique is to formulate a range-angle dependent detector with FDA MIMO radar. The detector assumes the signals belonging to conic regions, and relies on secondary data. Then, the detector performance is evaluated via Monte Carlo simulations. The results show that our system has interesting detection capabilities with range-angle dependent property, and it could be easily adapted to real scenarios.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130516822","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}
引用次数: 0
Semantic segmentation of hyperspectral images with the fusion of LiDAR data 基于LiDAR数据融合的高光谱图像语义分割
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7729651
Hakan Aytaylan, S. E. Yüksel
{"title":"Semantic segmentation of hyperspectral images with the fusion of LiDAR data","authors":"Hakan Aytaylan, S. E. Yüksel","doi":"10.1109/IGARSS.2016.7729651","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7729651","url":null,"abstract":"Semantic segmentation is an emerging field in the computer vision community where one can segment and label an object all at once. In this paper, we propose a semantic segmentation algorithm that takes into account both the hyperspectral images and the LiDAR data. In our segmentation framework, we propose a new energy function that is composed of two terms: a unary energy term and a pairwise energy term. The unary energy term provides the segmentation maps for the hyperspectral data as well as for the LiDAR data which is explained with Fisher Vectors. The pairwise spatial term uses both the UTM coordinates as well as the LiDAR data. Finally, the system is solved with graph-cuts. We report the effect of the parameters in energy minimization and show that the best results are achieved with an SVM-MRF classifier among the several classifiers.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114967255","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}
引用次数: 5
Earth Observation data access interoperability implementation among space agencies 空间机构间地球观测数据访问互操作性实施
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7729938
S. Miura
{"title":"Earth Observation data access interoperability implementation among space agencies","authors":"S. Miura","doi":"10.1109/IGARSS.2016.7729938","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7729938","url":null,"abstract":"Heterogeneous Earth Observation (EO) satellite data is available from many agencies. In order to make best use of these data, interoperability for data access is very important. In this paper, the importance of data access interoperability and the work of CEOS (Committee on Earth Observation Satellites) WGISS (the Working Group on Information Systems and Services) to improve data access interoperability are described.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115403654","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}
引用次数: 1
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