In satellite remote sensing of the earth, the sensors in the satellite look through a layer of atmosphere. The atmosphere in itself is made of a number of particles, gases and its nature is variable because of its constituents and seasonal patterns. When the electromagnetic energy encounters anything, even a tiny object like a molecule of a gas, one of the three reactions occurs; the radiation will either be reflected off the object, be absorbed by it or it could be transmitted through the object. It is therefore essential to understand the effects of the atmosphere on electromagnetic radiation travelling from the sun to the earth and back to the sensor through the atmosphere. With the proper analysis, it becomes possible to eliminate the effect atmosphere has on satellite imagery. Ozone is one constituent of atmosphere that has been monitored through different satellite missions as a part of understanding the atmosphere. However, the ozone records are not complete in time and space. Thus, the goal of this study was to create a continuous data set of ozone both in time and space starting from 1972/1/1 at all locations around the earth. The gaps that existed in the original data have been filled using appropriate interpolation algorithms. The uncertainty in the interpolated data was then calculated using the interpolation algorithms to fill synthetic gaps that were created in the region where original data existed. Comparison of the original data and interpolated data provided a mean for estimating the uncertainty of the interpolated data. The uncertainty from the interpolation added to the sensor uncertainty present in the data provided the final uncertainty in the ozone data. The change that occurs in the transmittance due to the interpolated ozone density data compared to the transmittance due to original data is also calculated. Finally the effect of the change in transmittance on the ability to predict ground reflectance is also calculated. For a spatial gap as big as 32 latitude x 40 longitude, the uncertainty in interpolated ozone density is less than 2% and, with the addition of sensor uncertainty, it is less than 3.5%. In case of temporal gaps, the interpolation uncertainty for a gap of a month was found to be 5.37%. And with the addition of inherent sensor uncertainty the final uncertainty is 5.79%. The change in transmittance of Landsat 7 band 2 due to the interpolated data is less than 0.15% for a spatial gap of 32 latitude x 40 longitude. In case of a temporal gap of 1 month, the change in transmittance of Landsat 7 band 2 is less than 0.4%. Finally, the change in the ability to predict ground reflectance due to the interpolated ozone data is calculated. The change in the prediction of ground reflectance due to spatially interpolated data compared to original was less than 0.4%. The temporally interpolated data changed the prediction ability of ground reflectance by less than 1.6% compared to the original ozone data.
","abstract_html":"<p>In satellite remote sensing of the earth, the sensors in the satellite look through a layer of atmosphere. The atmosphere in itself is made of a number of particles, gases and its nature is variable because of its constituents and seasonal patterns. When the electromagnetic energy encounters anything, even a tiny object like a molecule of a gas, one of the three reactions occurs; the radiation will either be reflected off the object, be absorbed by it or it could be transmitted through the object. It is therefore essential to understand the effects of the atmosphere on electromagnetic radiation travelling from the sun to the earth and back to the sensor through the atmosphere. With the proper analysis, it becomes possible to eliminate the effect atmosphere has on satellite imagery. Ozone is one constituent of atmosphere that has been monitored through different satellite missions as a part of understanding the atmosphere. However, the ozone records are not complete in time and space. Thus, the goal of this study was to create a continuous data set of ozone both in time and space starting from 1972/1/1 at all locations around the earth. The gaps that existed in the original data have been filled using appropriate interpolation algorithms. The uncertainty in the interpolated data was then calculated using the interpolation algorithms to fill synthetic gaps that were created in the region where original data existed. Comparison of the original data and interpolated data provided a mean for estimating the uncertainty of the interpolated data. The uncertainty from the interpolation added to the sensor uncertainty present in the data provided the final uncertainty in the ozone data. The change that occurs in the transmittance due to the interpolated ozone density data compared to the transmittance due to original data is also calculated. Finally the effect of the change in transmittance on the ability to predict ground reflectance is also calculated. For a spatial gap as big as 32 latitude x 40 longitude, the uncertainty in interpolated ozone density is less than 2% and, with the addition of sensor uncertainty, it is less than 3.5%. In case of temporal gaps, the interpolation uncertainty for a gap of a month was found to be 5.37%. And with the addition of inherent sensor uncertainty the final uncertainty is 5.79%. The change in transmittance of Landsat 7 band 2 due to the interpolated data is less than 0.15% for a spatial gap of 32 latitude x 40 longitude. In case of a temporal gap of 1 month, the change in transmittance of Landsat 7 band 2 is less than 0.4%. Finally, the change in the ability to predict ground reflectance due to the interpolated ozone data is calculated. The change in the prediction of ground reflectance due to spatially interpolated data compared to original was less than 0.4%. The temporally interpolated data changed the prediction ability of ground reflectance by less than 1.6% compared to the original ozone data.</p>","abstract_has_math":false,"creators":["Karanjit, Sudeep"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis - University Access Only","degree_discipline":"Electrical Engineering and Computer Science","degree_department":null,"school":null,"contributors":["Dennis Helder","Larry Leigh"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-01-01T08:00:00Z","date_published":"2012-01-01T08:00:00Z","updated_at":"2026-07-24T04:29:00Z","subjects":["Atmospheric Sciences","Electrical and Computer Engineering"],"languages":[],"rights":["In Copyright - Educational Use Permitted
http://rightsstatements.org/vocab/InC-EDU/1.0/
In Copyright - Educational Use Permitted
http://rightsstatements.org/vocab/InC-EDU/1.0/
In satellite remote sensing of the earth, the sensors in the satellite look through a layer of atmosphere. The atmosphere in itself is made of a number of particles, gases and its nature is variable because of its constituents and seasonal patterns. When the electromagnetic energy encounters anything, even a tiny object like a molecule of a gas, one of the three reactions occurs; the radiation will either be reflected off the object, be absorbed by it or it could be transmitted through the object. It is therefore essential to understand the effects of the atmosphere on electromagnetic radiation travelling from the sun to the earth and back to the sensor through the atmosphere. With the proper analysis, it becomes possible to eliminate the effect atmosphere has on satellite imagery. Ozone is one constituent of atmosphere that has been monitored through different satellite missions as a part of understanding the atmosphere. However, the ozone records are not complete in time and space. Thus, the goal of this study was to create a continuous data set of ozone both in time and space starting from 1972/1/1 at all locations around the earth. The gaps that existed in the original data have been filled using appropriate interpolation algorithms. The uncertainty in the interpolated data was then calculated using the interpolation algorithms to fill synthetic gaps that were created in the region where original data existed. Comparison of the original data and interpolated data provided a mean for estimating the uncertainty of the interpolated data. The uncertainty from the interpolation added to the sensor uncertainty present in the data provided the final uncertainty in the ozone data. The change that occurs in the transmittance due to the interpolated ozone density data compared to the transmittance due to original data is also calculated. Finally the effect of the change in transmittance on the ability to predict ground reflectance is also calculated. For a spatial gap as big as 32 latitude x 40 longitude, the uncertainty in interpolated ozone density is less than 2% and, with the addition of sensor uncertainty, it is less than 3.5%. In case of temporal gaps, the interpolation uncertainty for a gap of a month was found to be 5.37%. And with the addition of inherent sensor uncertainty the final uncertainty is 5.79%. The change in transmittance of Landsat 7 band 2 due to the interpolated data is less than 0.15% for a spatial gap of 32 latitude x 40 longitude. In case of a temporal gap of 1 month, the change in transmittance of Landsat 7 band 2 is less than 0.4%. Finally, the change in the ability to predict ground reflectance due to the interpolated ozone data is calculated. The change in the prediction of ground reflectance due to spatially interpolated data compared to original was less than 0.4%. The temporally interpolated data changed the prediction ability of ground reflectance by less than 1.6% compared to the original ozone data.
"]},{"key":"dc:title","label":"Title","values":["Creation of a Continuous Ozone Dataset from 1972 Onwards and its Effect on Remote Sensing"]}]}],"canonical_facts":{"dc:contributor":["Dennis Helder","Larry Leigh"],"dc:creator":["Karanjit, Sudeep"],"dc:date.available":["2017-07-26T07:00:00Z"],"dc:description.abstract":["In satellite remote sensing of the earth, the sensors in the satellite look through a layer of atmosphere. The atmosphere in itself is made of a number of particles, gases and its nature is variable because of its constituents and seasonal patterns. When the electromagnetic energy encounters anything, even a tiny object like a molecule of a gas, one of the three reactions occurs; the radiation will either be reflected off the object, be absorbed by it or it could be transmitted through the object. It is therefore essential to understand the effects of the atmosphere on electromagnetic radiation travelling from the sun to the earth and back to the sensor through the atmosphere. With the proper analysis, it becomes possible to eliminate the effect atmosphere has on satellite imagery. Ozone is one constituent of atmosphere that has been monitored through different satellite missions as a part of understanding the atmosphere. However, the ozone records are not complete in time and space. Thus, the goal of this study was to create a continuous data set of ozone both in time and space starting from 1972/1/1 at all locations around the earth. The gaps that existed in the original data have been filled using appropriate interpolation algorithms. The uncertainty in the interpolated data was then calculated using the interpolation algorithms to fill synthetic gaps that were created in the region where original data existed. Comparison of the original data and interpolated data provided a mean for estimating the uncertainty of the interpolated data. The uncertainty from the interpolation added to the sensor uncertainty present in the data provided the final uncertainty in the ozone data. The change that occurs in the transmittance due to the interpolated ozone density data compared to the transmittance due to original data is also calculated. Finally the effect of the change in transmittance on the ability to predict ground reflectance is also calculated. For a spatial gap as big as 32 latitude x 40 longitude, the uncertainty in interpolated ozone density is less than 2% and, with the addition of sensor uncertainty, it is less than 3.5%. In case of temporal gaps, the interpolation uncertainty for a gap of a month was found to be 5.37%. And with the addition of inherent sensor uncertainty the final uncertainty is 5.79%. The change in transmittance of Landsat 7 band 2 due to the interpolated data is less than 0.15% for a spatial gap of 32 latitude x 40 longitude. In case of a temporal gap of 1 month, the change in transmittance of Landsat 7 band 2 is less than 0.4%. Finally, the change in the ability to predict ground reflectance due to the interpolated ozone data is calculated. The change in the prediction of ground reflectance due to spatially interpolated data compared to original was less than 0.4%. The temporally interpolated data changed the prediction ability of ground reflectance by less than 1.6% compared to the original ozone data.
"],"dc:identifier":["https://openprairie.sdstate.edu/etd/1331"],"dc:rights":["In Copyright - Educational Use Permitted
http://rightsstatements.org/vocab/InC-EDU/1.0/