Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358922
A. Verma, D. Haldar
The polarimetric signature (PS) at two different crop-specific frequencies using fully polarimetric Radarsat-2 (C-band) and ALOS2/PALSAR2 (L-band) SAR data was generated and evaluated for crop and other dominant feature characterization. PS is a 3D-representation of the polarimetric information in different polarization bases that provides a better illustration of the target which is limited in the case of conventional methods. Differential response was observed at C- and L-band, surface scattering was dominant at L-band (cross-pol response of ~ 0.11) owing to its high penetration capability whereas at C-band (cross-pol response of ~ 0.25) volume component was found to be prevalent due to its extended interaction with crop canopy components. Also, variation in PS among the crop-types was observed at the same frequency. As the increase in Pedestal height (PH) can be attributed to multiple and/or volume scattering, for cotton high PH was noticed at C-band (0.28) than at L-band (0.11). Similarly, Paddy resulted in a PH of 0.22 and 0.09 at C-band and L-band respectively. The polarization signature for various crops (as was observed to be different) can be very useful in crop discrimination, parameters retrieval, and crop condition monitoring.
{"title":"Extraction and evaluation of polarimetric signature of various crop types using C-band and L-band fully polarimetric SAR data","authors":"A. Verma, D. Haldar","doi":"10.1109/InGARSS48198.2020.9358922","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358922","url":null,"abstract":"The polarimetric signature (PS) at two different crop-specific frequencies using fully polarimetric Radarsat-2 (C-band) and ALOS2/PALSAR2 (L-band) SAR data was generated and evaluated for crop and other dominant feature characterization. PS is a 3D-representation of the polarimetric information in different polarization bases that provides a better illustration of the target which is limited in the case of conventional methods. Differential response was observed at C- and L-band, surface scattering was dominant at L-band (cross-pol response of ~ 0.11) owing to its high penetration capability whereas at C-band (cross-pol response of ~ 0.25) volume component was found to be prevalent due to its extended interaction with crop canopy components. Also, variation in PS among the crop-types was observed at the same frequency. As the increase in Pedestal height (PH) can be attributed to multiple and/or volume scattering, for cotton high PH was noticed at C-band (0.28) than at L-band (0.11). Similarly, Paddy resulted in a PH of 0.22 and 0.09 at C-band and L-band respectively. The polarization signature for various crops (as was observed to be different) can be very useful in crop discrimination, parameters retrieval, and crop condition monitoring.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"82 1","pages":"37-41"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89408737","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 : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358918
R. Das, S. Bandopadhyay, M. Das, M. Chowdhury
In contrast to existing research that used ground-based observations, in this research we used space-borne observations to study global air quality change during COVID-19 pandemic in 20 countries. It is observed that during lockdown, PM2.5 has reduced in the most of the countries by 56% in 2020 compared to the previous year, whereas, Ghana and Russia show an increasing pattern. It is observed that NO2 has dropped in most of the countries by 3% to 31%, whereas UK and South Africa exhibit an increasing trend. Although spatial variability, low spatial resolution, and mixed pixel impurity may obscure the observation, but the study suggests a space-borne approach can be useful for investigating change in air quality to provide a general insight during COVID-19 pandemic. Our space-borne observations show an improvement in air quality by considerable drop in contaminants in the air in most of the countries except Russia and Ghana during COVID lockdown.
{"title":"Global Air Quality Change Detection During Covid-19 Pandemic Using Space-Borne Remote Sensing and Global Atmospheric Reanalysis","authors":"R. Das, S. Bandopadhyay, M. Das, M. Chowdhury","doi":"10.1109/InGARSS48198.2020.9358918","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358918","url":null,"abstract":"In contrast to existing research that used ground-based observations, in this research we used space-borne observations to study global air quality change during COVID-19 pandemic in 20 countries. It is observed that during lockdown, PM2.5 has reduced in the most of the countries by 56% in 2020 compared to the previous year, whereas, Ghana and Russia show an increasing pattern. It is observed that NO2 has dropped in most of the countries by 3% to 31%, whereas UK and South Africa exhibit an increasing trend. Although spatial variability, low spatial resolution, and mixed pixel impurity may obscure the observation, but the study suggests a space-borne approach can be useful for investigating change in air quality to provide a general insight during COVID-19 pandemic. Our space-borne observations show an improvement in air quality by considerable drop in contaminants in the air in most of the countries except Russia and Ghana during COVID lockdown.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":" 45","pages":"158-161"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91410557","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 : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358969
Paul Caesar M. Flores, L. David, F. Siringan
The construction of the Jaro Floodway in 2012 resulted to a rapid progradation of the shoreline in 8 years. This study examined the long- and short-term changes in area covered by mangroves at the river mouth area of this flood canal by utilizing historical maps (1947 and 1988), and Landsat images. A total of 44 Landsat images were used that covered the time periods 1998, 2000-2001, 2004, 2006, 2008, 2010-2011, 2013-2014, 2016, and 2018. Five images were used for each time period and the mangrove cover for each image was determined by using a supervised classification scheme. The set of rasters for each time period was then averaged to generate the final classification map. From 1947 to 1988, mangrove cover increased from 7.01 to 43.83 ha as a result of channel avulsion of the Jaro River due to fishpond construction at the former river mouth. However, it started to decrease until 2008 (3.42 ha) due to widespread fishpond conversion. Then, it rapidly increased to 40.05 ha in 2018. This increase is primarily attributed to the rapid expansion of the intertidal zone in the discharge area of the Jaro Floodway which is due to high sedimentation and low accommodation space.
{"title":"Mangrove Forest Cover Change (1947-2018) at The River Mouth Section of The Jaro Floodway, Iloilo City, Philippines","authors":"Paul Caesar M. Flores, L. David, F. Siringan","doi":"10.1109/InGARSS48198.2020.9358969","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358969","url":null,"abstract":"The construction of the Jaro Floodway in 2012 resulted to a rapid progradation of the shoreline in 8 years. This study examined the long- and short-term changes in area covered by mangroves at the river mouth area of this flood canal by utilizing historical maps (1947 and 1988), and Landsat images. A total of 44 Landsat images were used that covered the time periods 1998, 2000-2001, 2004, 2006, 2008, 2010-2011, 2013-2014, 2016, and 2018. Five images were used for each time period and the mangrove cover for each image was determined by using a supervised classification scheme. The set of rasters for each time period was then averaged to generate the final classification map. From 1947 to 1988, mangrove cover increased from 7.01 to 43.83 ha as a result of channel avulsion of the Jaro River due to fishpond construction at the former river mouth. However, it started to decrease until 2008 (3.42 ha) due to widespread fishpond conversion. Then, it rapidly increased to 40.05 ha in 2018. This increase is primarily attributed to the rapid expansion of the intertidal zone in the discharge area of the Jaro Floodway which is due to high sedimentation and low accommodation space.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"98 1","pages":"246-249"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85766479","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 : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358979
V. Kalaranjini, S. Dinesh Kumar, S. Ramakrishnan, R. Kokila Priya
Uttarakand constitutes 5.43% of Indian Forest cover with extremely and highly fire prone forest areas. The objective of this study is to assess the recent occurrence of forest fires in Uttarakand and to map the burnt areas with Sentinel-1 Synthetic Aperture Radar (SAR) and validate it with the Sentinel-2 as CoVID-19 hindered the field assessment and ground truth validation. The data is processed in Sentinel Application Platform (SNAP) and mapped with ArcGIS. Cross-validated with optical indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index NDWI, Normalized Burn Ratio (NBR) and the firsthand information from Forest Survey of India (FSI) for an area of 10. 83sq.Km, the results are summarized.
{"title":"Burnt Area Detection Using Sar Data – A Case Study of May, 2020 Uttarakand Forest Fire","authors":"V. Kalaranjini, S. Dinesh Kumar, S. Ramakrishnan, R. Kokila Priya","doi":"10.1109/InGARSS48198.2020.9358979","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358979","url":null,"abstract":"Uttarakand constitutes 5.43% of Indian Forest cover with extremely and highly fire prone forest areas. The objective of this study is to assess the recent occurrence of forest fires in Uttarakand and to map the burnt areas with Sentinel-1 Synthetic Aperture Radar (SAR) and validate it with the Sentinel-2 as CoVID-19 hindered the field assessment and ground truth validation. The data is processed in Sentinel Application Platform (SNAP) and mapped with ArcGIS. Cross-validated with optical indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index NDWI, Normalized Burn Ratio (NBR) and the firsthand information from Forest Survey of India (FSI) for an area of 10. 83sq.Km, the results are summarized.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"1 1","pages":"241-245"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81159828","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 : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358943
V. Jain, S. Shitole, V. Turkar, A. Das
Speckle in SAR images makes it difficult to interpret the image thus reducing the effectiveness of image processing. In remote sensing, image scene classification is an elementary problem which aims to label an image automatically with a specific semantic category. The classification performance of SAR data with speckle is inadequate for many applications. Thus, speckle removal becomes an important pre-processing step for SAR data classification. This study investigates the impact and importance of speckle filtering for classification using ALOS-PALSAR-2 data on San Fran-cisco area. Wishart classifier is chosen for classification of filtered and unfiltered SAR data. The influence of DFT based speckle reduction framework is investigated in terms of classification accuracy.
{"title":"Impact of DFT Based Speckle Reduction Filter on Classification Accuracy of Synthetic Aperture Radar Images","authors":"V. Jain, S. Shitole, V. Turkar, A. Das","doi":"10.1109/InGARSS48198.2020.9358943","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358943","url":null,"abstract":"Speckle in SAR images makes it difficult to interpret the image thus reducing the effectiveness of image processing. In remote sensing, image scene classification is an elementary problem which aims to label an image automatically with a specific semantic category. The classification performance of SAR data with speckle is inadequate for many applications. Thus, speckle removal becomes an important pre-processing step for SAR data classification. This study investigates the impact and importance of speckle filtering for classification using ALOS-PALSAR-2 data on San Fran-cisco area. Wishart classifier is chosen for classification of filtered and unfiltered SAR data. The influence of DFT based speckle reduction framework is investigated in terms of classification accuracy.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"10 1","pages":"54-57"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85383753","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 : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358967
Manjit Hota, Sudarshan Rao B, U. Kumar
In this paper, detection, and segmentation of power line in Unmanned Aerial Vehicles (UAV) multi-spectral images using convolutional neural network is proposed. Initially, the multi-spectral images captured from UAV were calibrated and pre-processed, following which they were fed into deep CNN for semantic segmentation to perform a binary classification; each pixel was assigned either of the two classes - "power line" or "no power line". Semantic segmentation was performed with different networks such as U-Net, SegNet and PSPNet. Qualitative (visual inspection) and quantitative analysis of the results showed that U-Net outperformed other networks with an overall accuracy of around 99% with a competitive execution latency, making it useful for real time analysis of power lines from UAV data.
{"title":"Power Lines Detection and Segmentation In Multi-Spectral Uav Images Using Convolutional Neural Network","authors":"Manjit Hota, Sudarshan Rao B, U. Kumar","doi":"10.1109/InGARSS48198.2020.9358967","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358967","url":null,"abstract":"In this paper, detection, and segmentation of power line in Unmanned Aerial Vehicles (UAV) multi-spectral images using convolutional neural network is proposed. Initially, the multi-spectral images captured from UAV were calibrated and pre-processed, following which they were fed into deep CNN for semantic segmentation to perform a binary classification; each pixel was assigned either of the two classes - \"power line\" or \"no power line\". Semantic segmentation was performed with different networks such as U-Net, SegNet and PSPNet. Qualitative (visual inspection) and quantitative analysis of the results showed that U-Net outperformed other networks with an overall accuracy of around 99% with a competitive execution latency, making it useful for real time analysis of power lines from UAV data.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"41 1","pages":"154-157"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75829170","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 : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358971
Dharmendra Singh, C. Nanda
Air quality is an important parameter related to the human health. Aerosol Optical Depth (AOD) is an important variable that indicates column integrated particulate matter in the air and used for air quality assessment. Thus in the current study AOD derived from Moderate Resolution Imaging Spectroradiometer (MODIS) at a spatial resolution of 3 km have been used as an indicator of air quality. Results indicate that the AOD concentration has decreased by 35% during the lockdown period in the month of April 2020 as compared to the years 2016 to 2019 in the same month. This indicates that the air quality was improved during the lockdown amid COVID-19 over the Haryana state. The same was conformed from the reduction of Particulate Matter (PM2.5) concentration by 68% during the lockdown period as compared to the year 2019 for NCR region of Haryana.
{"title":"Aerosol Optical Depth (AOD) Variation Over Haryana Due to Lockdown Amid Covid-19 as an Indicator of Air Quality","authors":"Dharmendra Singh, C. Nanda","doi":"10.1109/InGARSS48198.2020.9358971","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358971","url":null,"abstract":"Air quality is an important parameter related to the human health. Aerosol Optical Depth (AOD) is an important variable that indicates column integrated particulate matter in the air and used for air quality assessment. Thus in the current study AOD derived from Moderate Resolution Imaging Spectroradiometer (MODIS) at a spatial resolution of 3 km have been used as an indicator of air quality. Results indicate that the AOD concentration has decreased by 35% during the lockdown period in the month of April 2020 as compared to the years 2016 to 2019 in the same month. This indicates that the air quality was improved during the lockdown amid COVID-19 over the Haryana state. The same was conformed from the reduction of Particulate Matter (PM2.5) concentration by 68% during the lockdown period as compared to the year 2019 for NCR region of Haryana.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"39 1","pages":"170-172"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77588071","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 : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358919
Nirag Doshi, Tejas Turakhia, A. Nair, M. Pandya, Rajesh C. Iyer
Air Surface Temperature (Tair) available from meteorological stations, provides only limited information about spatial patterns over wide areas. The use of remote sensing data can help overcome this problem, particularly in areas with low station density, having the potential to improve the estimation of Tair at both regional and global scales. A study has been carried out to understand the relationship between Land Surface Temperature (LST), available from INSAT 3D, and Tair, available from ground meteorological station. The result shows good correlation for winter season but it keeps reducing as we move towards monsoon probably due to increase in the extreme temperature and data unavailability. We also observed low root mean square error (RMSE) of ~1.5 °C for months of winter season while it increases to ~4.5 °C in June. We conclude that there is a good agreement between LST and air temperature, although the two temperatures have different physical meaning and responses to atmospheric conditions.
{"title":"Estimating Air Temperature using Land Surface Temperature products of INSAT-3D satellite","authors":"Nirag Doshi, Tejas Turakhia, A. Nair, M. Pandya, Rajesh C. Iyer","doi":"10.1109/InGARSS48198.2020.9358919","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358919","url":null,"abstract":"Air Surface Temperature (Tair) available from meteorological stations, provides only limited information about spatial patterns over wide areas. The use of remote sensing data can help overcome this problem, particularly in areas with low station density, having the potential to improve the estimation of Tair at both regional and global scales. A study has been carried out to understand the relationship between Land Surface Temperature (LST), available from INSAT 3D, and Tair, available from ground meteorological station. The result shows good correlation for winter season but it keeps reducing as we move towards monsoon probably due to increase in the extreme temperature and data unavailability. We also observed low root mean square error (RMSE) of ~1.5 °C for months of winter season while it increases to ~4.5 °C in June. We conclude that there is a good agreement between LST and air temperature, although the two temperatures have different physical meaning and responses to atmospheric conditions.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"74 1","pages":"177-180"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79270723","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 : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358974
Akhil Masurkar, R. Daruwala, V. Turkar
The most commonly present noise in Polarimetric Synthetic Aperture Radar (POLSAR) images is the Speckle Noise. This paper focuses on the removal of Speckle from SAR images using morphological operations like opening and closing which are based on the principles of erosion and dilation. A quantitative analysis of the image quality after processing with morphological operations is carried out using the most used, full reference and no reference quality metrics. The full reference quality metrics considered are Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The no reference quality metrics considered are Blind/Reference less Image Spatial Quality Evaluator (BRISQUE), Natural Image Quality Evaluator (NIQE), and Perception based Image Quality Evaluator (PIQE). The technique is focused around preserving point targets while removing noise. The results of proposed filters are compared with the existing filters. It is observed that the proposed technique can reduce the speckle significantly.
{"title":"A Novel Method to Remove Speckle from Polsar Images using Morphological Operations","authors":"Akhil Masurkar, R. Daruwala, V. Turkar","doi":"10.1109/InGARSS48198.2020.9358974","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358974","url":null,"abstract":"The most commonly present noise in Polarimetric Synthetic Aperture Radar (POLSAR) images is the Speckle Noise. This paper focuses on the removal of Speckle from SAR images using morphological operations like opening and closing which are based on the principles of erosion and dilation. A quantitative analysis of the image quality after processing with morphological operations is carried out using the most used, full reference and no reference quality metrics. The full reference quality metrics considered are Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The no reference quality metrics considered are Blind/Reference less Image Spatial Quality Evaluator (BRISQUE), Natural Image Quality Evaluator (NIQE), and Perception based Image Quality Evaluator (PIQE). The technique is focused around preserving point targets while removing noise. The results of proposed filters are compared with the existing filters. It is observed that the proposed technique can reduce the speckle significantly.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"514 1","pages":"126-129"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79636692","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 : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358931
Kavitha Devireddy, K. Sreeteja, Yaseen, Santhosh Kumar Veerlapati, C. Keerthi Chandra, Naveen Kumar Perumalla
Ionosphere is one of the largest sources of error for single frequency GNSS (Global Navigation Satellite Systems) users. The IRI-Plas is the widely used ionospheric and plasmaspheric climatic model for estimating VTEC (Vertical Total Electron Content) globally. This paper focuses on the performance of the IRI-Plas-2017 model with ingestion of GIM-TEC (Global Ionospheric Maps) input option at two low latitude stations, Hyderabad (Lat:17.2°N; Lon:78.5°E) and Bangalore (Lat: 12.9°N; Lon: 77.6°E) over the Indian region. The predicted TEC due to the model is compared with GPS TEC (Global Positioning System).The analysis is carried out for 7th, 8th and 9th September 2017 (Pre storm, Storm and post storm days). In this work, Symmetric Kullbacke Leibler Distance (SKLD), Cross Correlation (CC) coefficient and the metric norm (L2N) methods are used to evaluate the performance of IRI-Plas-TEC (with and without TEC input) with GPS TEC. From the results it is observed that TEC predicted by the assimilation option produced smaller estimation errors and substantial improvement of the model performance for ionospheric disturbances.
{"title":"Comparison of VTEC due to GPS and assimilation of the IRI-Plas model during a geomagnetic storm condition over Indian region","authors":"Kavitha Devireddy, K. Sreeteja, Yaseen, Santhosh Kumar Veerlapati, C. Keerthi Chandra, Naveen Kumar Perumalla","doi":"10.1109/InGARSS48198.2020.9358931","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358931","url":null,"abstract":"Ionosphere is one of the largest sources of error for single frequency GNSS (Global Navigation Satellite Systems) users. The IRI-Plas is the widely used ionospheric and plasmaspheric climatic model for estimating VTEC (Vertical Total Electron Content) globally. This paper focuses on the performance of the IRI-Plas-2017 model with ingestion of GIM-TEC (Global Ionospheric Maps) input option at two low latitude stations, Hyderabad (Lat:17.2°N; Lon:78.5°E) and Bangalore (Lat: 12.9°N; Lon: 77.6°E) over the Indian region. The predicted TEC due to the model is compared with GPS TEC (Global Positioning System).The analysis is carried out for 7th, 8th and 9th September 2017 (Pre storm, Storm and post storm days). In this work, Symmetric Kullbacke Leibler Distance (SKLD), Cross Correlation (CC) coefficient and the metric norm (L2N) methods are used to evaluate the performance of IRI-Plas-TEC (with and without TEC input) with GPS TEC. From the results it is observed that TEC predicted by the assimilation option produced smaller estimation errors and substantial improvement of the model performance for ionospheric disturbances.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"42 1","pages":"166-169"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76470983","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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