SUREHYP: An Open Source Python Package for Preprocessing Hyperion Radiance Data and Retrieving Surface Reflectance
Surface reflectance is an essential product from remote sensing Earth observations critical for a wide variety of applications, including consistent land cover mapping and change, and estimation of vegetation attributes. From 2000 to 2017 the Earth Observing-1 Hyperion instrument acquired the first...
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| Published in | Sensors (Basel, Switzerland) Vol. 22; no. 23; p. 9205 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
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MDPI AG
26.11.2022
MDPI |
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| Online Access | Get full text |
| ISSN | 1424-8220 1424-8220 |
| DOI | 10.3390/s22239205 |
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| Abstract | Surface reflectance is an essential product from remote sensing Earth observations critical for a wide variety of applications, including consistent land cover mapping and change, and estimation of vegetation attributes. From 2000 to 2017 the Earth Observing-1 Hyperion instrument acquired the first satellite based hyperspectral image archive from space resulting in over 83,138 publicly available images. Hyperion imagery however requires significant preprocessing to derive surface reflectance. SUREHYP is a Python package designed to process batches of Hyperion images, bringing together a number of published algorithms and methods to correct at sensor radiance and derive surface reflectance. In this paper, we present the SUREHYP workflow and demonstrate its application on Hyperion imagery. Results indicate SUREHYP produces flat terrain surface reflectance results comparable to commercially available software, with reflectance values for the whole spectral range almost entirely within 10% of the software’s over a reference target, yet it is publicly available and open source, allowing the exploitation of this valuable hyperspectral archive on a global scale. |
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| AbstractList | Surface reflectance is an essential product from remote sensing Earth observations critical for a wide variety of applications, including consistent land cover mapping and change, and estimation of vegetation attributes. From 2000 to 2017 the Earth Observing-1 Hyperion instrument acquired the first satellite based hyperspectral image archive from space resulting in over 83,138 publicly available images. Hyperion imagery however requires significant preprocessing to derive surface reflectance. SUREHYP is a Python package designed to process batches of Hyperion images, bringing together a number of published algorithms and methods to correct at sensor radiance and derive surface reflectance. In this paper, we present the SUREHYP workflow and demonstrate its application on Hyperion imagery. Results indicate SUREHYP produces flat terrain surface reflectance results comparable to commercially available software, with reflectance values for the whole spectral range almost entirely within 10% of the software’s over a reference target, yet it is publicly available and open source, allowing the exploitation of this valuable hyperspectral archive on a global scale. Surface reflectance is an essential product from remote sensing Earth observations critical for a wide variety of applications, including consistent land cover mapping and change, and estimation of vegetation attributes. From 2000 to 2017 the Earth Observing-1 Hyperion instrument acquired the first satellite based hyperspectral image archive from space resulting in over 83,138 publicly available images. Hyperion imagery however requires significant preprocessing to derive surface reflectance. SUREHYP is a Python package designed to process batches of Hyperion images, bringing together a number of published algorithms and methods to correct at sensor radiance and derive surface reflectance. In this paper, we present the SUREHYP workflow and demonstrate its application on Hyperion imagery. Results indicate SUREHYP produces flat terrain surface reflectance results comparable to commercially available software, with reflectance values for the whole spectral range almost entirely within 10% of the software's over a reference target, yet it is publicly available and open source, allowing the exploitation of this valuable hyperspectral archive on a global scale.Surface reflectance is an essential product from remote sensing Earth observations critical for a wide variety of applications, including consistent land cover mapping and change, and estimation of vegetation attributes. From 2000 to 2017 the Earth Observing-1 Hyperion instrument acquired the first satellite based hyperspectral image archive from space resulting in over 83,138 publicly available images. Hyperion imagery however requires significant preprocessing to derive surface reflectance. SUREHYP is a Python package designed to process batches of Hyperion images, bringing together a number of published algorithms and methods to correct at sensor radiance and derive surface reflectance. In this paper, we present the SUREHYP workflow and demonstrate its application on Hyperion imagery. Results indicate SUREHYP produces flat terrain surface reflectance results comparable to commercially available software, with reflectance values for the whole spectral range almost entirely within 10% of the software's over a reference target, yet it is publicly available and open source, allowing the exploitation of this valuable hyperspectral archive on a global scale. |
| Audience | Academic |
| Author | Miraglio, Thomas Coops , Nicholas C. |
| AuthorAffiliation | Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada |
| AuthorAffiliation_xml | – name: Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada |
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| Cites_doi | 10.1007/s12517-012-0584-8 10.1201/9781315164151 10.3390/rs12183062 10.1117/1.JRS.14.016515 10.3390/rs9080834 10.1016/j.solener.2019.05.048 10.1109/TGRS.2003.813206 10.1016/j.isprsjprs.2018.01.017 10.1080/01431161.2012.715776 10.5589/m06-014 10.1016/j.envsoft.2016.03.015 10.1016/j.envsoft.2018.11.004 10.3390/rs11182072 10.3390/rs10020219 10.1016/S0038-092X(01)00054-8 10.1109/TGRS.2003.813214 10.1029/98JD02006 10.1080/01431161.2010.532175 10.1109/TGRS.2004.840720 10.1080/14498596.2018.1485120 10.1016/j.oregeorev.2018.07.017 10.1016/j.jqsrt.2017.03.004 10.1364/AO.37.004004 10.1109/ICCV.2011.6126544 10.1016/S0034-4257(98)00064-9 10.1016/j.jsames.2021.103460 10.1016/j.rse.2010.02.012 10.1080/0143116021000026779 10.3390/rs70608107 10.1109/MGRS.2019.2904706 10.1080/01431161.2017.1354265 10.3390/rs10020157 10.3390/rs1030184 10.1109/TGRS.2003.815018 10.1117/1.JRS.8.083645 10.1016/0034-4257(88)90019-3 10.1016/j.rse.2015.09.004 10.1080/01431161.2018.1483087 10.3390/rs70302942 10.1016/j.rse.2011.11.026 10.20944/preprints201809.0119.v3 10.1016/j.envsoft.2017.06.006 10.1016/j.rse.2011.10.007 |
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| SubjectTerms | Algorithms atmospheric correction batch processing Datasets Earth, Planet Household Articles Imagery, Psychotherapy Methods Neighborhoods Remote sensing satellite Satellites Sensors Software |
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| Title | SUREHYP: An Open Source Python Package for Preprocessing Hyperion Radiance Data and Retrieving Surface Reflectance |
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