K-sharp: A segmented regression approach for image sharpening and normalization
In recent decades, Earth Observation (EO) satellite missions have improved in spatial resolution and revisit times. These missions, traditionally government-funded, utilize state-of-the-art technology and rigorous instrument calibration, with each mission costing millions of dollars. Recently, nano-...
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ftkingabdullahun:oai:repository.kaust.edu.sa:10754/693298 2024-01-07T09:47:08+01:00 K-sharp: A segmented regression approach for image sharpening and normalization Aragon Solorio, Bruno Jose Luis Cawse-Nicholson, Kerry Hulley, Glynn Houborg, Rasmus Fisher, Joshua B. 2023-07-26 http://hdl.handle.net/10754/693298 https://doi.org/10.1016/j.srs.2023.100095 unknown Elsevier BV https://linkinghub.elsevier.com/retrieve/pii/S2666017223000202 Aragon, B., Cawse-Nicholson, K., Hulley, G., Houborg, R., & Fisher, J. B. (2023). K-sharp: A segmented regression approach for image sharpening and normalization. Science of Remote Sensing, 100095. https://doi.org/10.1016/j.srs.2023.100095 doi:10.1016/j.srs.2023.100095 2666-0172 Science of Remote Sensing 100095 http://hdl.handle.net/10754/693298 Article 2023 ftkingabdullahun https://doi.org/10.1016/j.srs.2023.100095 2023-12-09T20:20:28Z In recent decades, Earth Observation (EO) satellite missions have improved in spatial resolution and revisit times. These missions, traditionally government-funded, utilize state-of-the-art technology and rigorous instrument calibration, with each mission costing millions of dollars. Recently, nano-satellites known as CubeSats are presenting a cost-effective option for EO; their capacity of working as a constellation has brought an unprecedented opportunity for EO in terms of achievable spatial and temporal resolutions, albeit at the cost of decreased accuracy and cross-sensor consistency. As such, CubeSat datasets often require post-calibration approaches before using them for scientific applications. K-sharp is a relatively simple, data-agnostic machine learning approach that combines K-means and partial least squares regression to derive relationships between two sets of images for normalization. This study used Planet's four-band CubeSat imagery to sharpen day-coincident Landsat 8 normalized difference vegetation index, albedo, and the first short-wave infrared (SWIR) band from 30 m to 3 m spatial resolution (it should be noted that the four-band CubeSat product does not include the first SWIR band, and that the calculation of albedo is not directly possible from this product). K-sharp was tested over agricultural, savanna, rainforest, and tundra sites with and without atmospheric correction. Our model reproduced surface conditions with an average r2 of 0.88 (rMAE = 11.39%) across all study sites and target variables when compared against the original Landsat 8 data. These results showcase the promising potential of K-sharp in generating precise, CubeSat-derived datasets with high radiometric quality, which can be incorporated into agricultural or ecological applications to enhance their decision-making process at fine spatial scales. This study was supported by the National Aeronautics and Space Administration (NASA), United States, the NASA Postdoctoral Program at the Jet Propulsion Laboratory, ... Article in Journal/Newspaper Tundra King Abdullah University of Science and Technology: KAUST Repository Science of Remote Sensing 8 100095 |
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King Abdullah University of Science and Technology: KAUST Repository |
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ftkingabdullahun |
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In recent decades, Earth Observation (EO) satellite missions have improved in spatial resolution and revisit times. These missions, traditionally government-funded, utilize state-of-the-art technology and rigorous instrument calibration, with each mission costing millions of dollars. Recently, nano-satellites known as CubeSats are presenting a cost-effective option for EO; their capacity of working as a constellation has brought an unprecedented opportunity for EO in terms of achievable spatial and temporal resolutions, albeit at the cost of decreased accuracy and cross-sensor consistency. As such, CubeSat datasets often require post-calibration approaches before using them for scientific applications. K-sharp is a relatively simple, data-agnostic machine learning approach that combines K-means and partial least squares regression to derive relationships between two sets of images for normalization. This study used Planet's four-band CubeSat imagery to sharpen day-coincident Landsat 8 normalized difference vegetation index, albedo, and the first short-wave infrared (SWIR) band from 30 m to 3 m spatial resolution (it should be noted that the four-band CubeSat product does not include the first SWIR band, and that the calculation of albedo is not directly possible from this product). K-sharp was tested over agricultural, savanna, rainforest, and tundra sites with and without atmospheric correction. Our model reproduced surface conditions with an average r2 of 0.88 (rMAE = 11.39%) across all study sites and target variables when compared against the original Landsat 8 data. These results showcase the promising potential of K-sharp in generating precise, CubeSat-derived datasets with high radiometric quality, which can be incorporated into agricultural or ecological applications to enhance their decision-making process at fine spatial scales. This study was supported by the National Aeronautics and Space Administration (NASA), United States, the NASA Postdoctoral Program at the Jet Propulsion Laboratory, ... |
format |
Article in Journal/Newspaper |
author |
Aragon Solorio, Bruno Jose Luis Cawse-Nicholson, Kerry Hulley, Glynn Houborg, Rasmus Fisher, Joshua B. |
spellingShingle |
Aragon Solorio, Bruno Jose Luis Cawse-Nicholson, Kerry Hulley, Glynn Houborg, Rasmus Fisher, Joshua B. K-sharp: A segmented regression approach for image sharpening and normalization |
author_facet |
Aragon Solorio, Bruno Jose Luis Cawse-Nicholson, Kerry Hulley, Glynn Houborg, Rasmus Fisher, Joshua B. |
author_sort |
Aragon Solorio, Bruno Jose Luis |
title |
K-sharp: A segmented regression approach for image sharpening and normalization |
title_short |
K-sharp: A segmented regression approach for image sharpening and normalization |
title_full |
K-sharp: A segmented regression approach for image sharpening and normalization |
title_fullStr |
K-sharp: A segmented regression approach for image sharpening and normalization |
title_full_unstemmed |
K-sharp: A segmented regression approach for image sharpening and normalization |
title_sort |
k-sharp: a segmented regression approach for image sharpening and normalization |
publisher |
Elsevier BV |
publishDate |
2023 |
url |
http://hdl.handle.net/10754/693298 https://doi.org/10.1016/j.srs.2023.100095 |
genre |
Tundra |
genre_facet |
Tundra |
op_relation |
https://linkinghub.elsevier.com/retrieve/pii/S2666017223000202 Aragon, B., Cawse-Nicholson, K., Hulley, G., Houborg, R., & Fisher, J. B. (2023). K-sharp: A segmented regression approach for image sharpening and normalization. Science of Remote Sensing, 100095. https://doi.org/10.1016/j.srs.2023.100095 doi:10.1016/j.srs.2023.100095 2666-0172 Science of Remote Sensing 100095 http://hdl.handle.net/10754/693298 |
op_doi |
https://doi.org/10.1016/j.srs.2023.100095 |
container_title |
Science of Remote Sensing |
container_volume |
8 |
container_start_page |
100095 |
_version_ |
1787429105686282240 |