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Full-Text Articles in Physical Sciences and Mathematics
Machine Learning Approach To Retrieving Physical Variables From Remotely Sensed Data, Fazlul Shahriar
Machine Learning Approach To Retrieving Physical Variables From Remotely Sensed Data, Fazlul Shahriar
Dissertations, Theses, and Capstone Projects
Scientists from all over the world make use of remotely sensed data from hundreds of satellites to better understand the Earth. However, physical measurements from an instrument is sometimes missing either because the instrument hasn't been launched yet or the design of the instrument omitted a particular spectral band. Measurements received from the instrument may also be corrupt due to malfunction in the detectors on the instrument. Fortunately, there are machine learning techniques to estimate the missing or corrupt data. Using these techniques we can make use of the available data to its full potential.
We present work on four …
Machine Learning Algorithms For Automated Satellite Snow And Sea Ice Detection, George Bonev
Machine Learning Algorithms For Automated Satellite Snow And Sea Ice Detection, George Bonev
Dissertations, Theses, and Capstone Projects
The continuous mapping of snow and ice cover, particularly in the arctic and poles, are critical to understanding the earth and atmospheric science. Much of the world's sea ice and snow covers the most inhospitable places, making measurements from satellite-based remote sensors essential. Despite the wealth of data from these instruments many challenges remain. For instance, remote sensing instruments reside on-board different satellites and observe the earth at different portions of the electromagnetic spectrum with different spatial footprints. Integrating and fusing this information to make estimates of the surface is a subject of active research.
In response to these challenges, …
A Comparison Of Modis/Viirs Cloud Masks Over Ice-Bearing River: On Achieving Consistent Cloud Masking And Improved River Ice Mapping, Simon Kraatz, Reza Khanbilvardi, Peter Romanov
A Comparison Of Modis/Viirs Cloud Masks Over Ice-Bearing River: On Achieving Consistent Cloud Masking And Improved River Ice Mapping, Simon Kraatz, Reza Khanbilvardi, Peter Romanov
Publications and Research
The capability of frequently and accurately monitoring ice on rivers is important, since it may be possible to timely identify ice accumulations corresponding to ice jams. Ice jams are dam-like structures formed from arrested ice floes, and may cause rapid flooding. To inform on this potential hazard, the CREST River Ice Observing System (CRIOS) produces ice cover maps based on MODIS and VIIRS overpass data at several locations, including the Susquehanna River. CRIOS uses the respective platform’s automatically produced cloud masks to discriminate ice/snow covered grid cells from clouds. However, since cloud masks are produced using each instrument’s data, and …
Improved Viirs And Modis Sst Imagery, Irina Gladkova, Alexander Ignatov, Fazlul Shahriar, Yury Kihai, Don Hillger, Boris Petrenko
Improved Viirs And Modis Sst Imagery, Irina Gladkova, Alexander Ignatov, Fazlul Shahriar, Yury Kihai, Don Hillger, Boris Petrenko
Publications and Research
Moderate Resolution Imaging Spectroradiometers (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) radiometers, flown onboard Terra/Aqua and Suomi National Polar-orbiting Partnership (S-NPP)/Joint Polar Satellite System (JPSS) satellites, are capable of providing superior sea surface temperature (SST) imagery. However, the swath data of these multi-detector sensors are subject to several artifacts including bow-tie distortions and striping, and require special pre-processing steps. VIIRS additionally does two irreversible data reduction steps onboard: pixel aggregation (to reduce resolution changes across the swath) and pixel deletion, which complicate both bow-tie correction and destriping. While destriping was addressed elsewhere, this paper describes an algorithm, adopted in …