Cited by 292Remote Sensing in AgricultureCalibration and Measurement TechniquesSatellite Image Processing and Photogrammetry

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The 50-year Landsat collection 2 archive

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Abstract

The Landsat global consolidated data archive now exceeds 50 years. In recognition of the need for consistently processed data across the Landsat satellite series, the United States Geological Survey (USGS) initiated collection-based processing of the entire archive that was processed as Collection 1 in 2016. In preparation for the data from the now successfully launched Landsat 9, the USGS reprocessed the Landsat archive as Collection 2 in 2020. This paper describes the rationale for, and the contents and advancements provided by Collection 2, and highlights the differences between the Collection 1 and Collection 2 products. Notably, the Collection 2 products have improved geolocation and, for the first time, the USGS provides a global inventory of Level 2 surface reflectance and surface temperature products. Also for the first time, the USGS used a commercial cloud computing architecture via Amazon Web Services (AWS) to efficiently process the archive and enable direct cloud access of the Landsat products. The paper concludes with discussion of likely improvements expected in Collection 3 in preparation for the Landsat Next mission that is planned for launch in the early 2030s.

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Research works printed but not linked: 95

  1. Barsi, 2014, Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration, Rem. Sens., 6, 11607, 10.3390/rs61111607 · DOI
  2. Brooks, 2018, Using window regression to gap-fill Landsat ETM+ post SLC-Off data, Rem. Sens., 10, 1502, 10.3390/rs10101502 · DOI
  3. Brown, 2020, Lessons learned implementing an operational continuous United States national land change monitoring capability: the land change monitoring, assessment, and projection (LCMAP) approach, Remote Sens. Environ., 238, 10.1016/j.rse.2019.111356 · DOI
  4. Chavez, 1996, Image-based atmospheric corrections—revisited and improved, Photogramm. Eng. Rem. Sens., 62, 1025
  5. Claverie, 2018, The Harmonized Landsat and Sentinel-2 surface reflectance data set, Rem. Sens. Environ., 219, 145, 10.1016/j.rse.2018.09.002 · DOI · Paper page
  6. Cook, 2014, Development of an operational calibration methodology for the Landsat thermal data archive and initial testing of the atmospheric compensation component of a land surface temperature (LST) product from the archive, Rem. Sens., 6, 11244, 10.3390/rs61111244 · DOI
  7. Doxani, 2023, Atmospheric Correction Inter-comparison eXercise, ACIX-II Land—an assessment of atmospheric correction processors for Landsat 8 and Sentinel-2 over land, Remote Sens. Environ., 285, 10.1016/j.rse.2022.113412 · DOI
  8. Dwyer, 2018, Analysis ready data: enabling analysis of the Landsat archive, Rem. Sens., 10, 1363, 10.3390/rs10091363 · DOI
  9. Egorov, 2023, Generation and comprehensive validation of 30 m conterminous United States Landsat percent tree cover and forest cover loss annual products, Sci. Remote Sens., 7
  10. Fahnestock, 2016, Rapid large-area mapping of ice flow using Landsat 8, Remote Sens. Environ., 185, 84, 10.1016/j.rse.2015.11.023 · DOI
  11. Feng, 2013, Global surface reflectance products from Landsat: assessment using coincident MODIS observations, Remote Sens. Environ., 134, 276, 10.1016/j.rse.2013.02.031 · DOI
  12. Foga, 2017, Cloud detection algorithm comparison and validation for operational Landsat data products, Remote Sens. Environ., 194, 379, 10.1016/j.rse.2017.03.026 · DOI
  13. Franks, 2020, The new Landsat collection-2 digital elevation model, Rem. Sens., 12, 3909, 10.3390/rs12233909 · DOI
  14. Gelaro, 2017, The Modern-Era Retrospective analysis for research and applications, version 2 (MERRA-2), J. Clim., 30, 5419, 10.1175/JCLI-D-16-0758.1 · DOI
  15. Gerace, 2020, Towards an operational, split window-derived surface temperature product for the thermal infrared sensors onboard Landsat 8 and 9, Rem. Sens., 12, 224, 10.3390/rs12020224 · DOI
  16. Gerace, 2017, Derivation and validation of the stray light correction algorithm for the Thermal Infrared Sensor onboard Landsat 8, Remote Sens. Environ., 191, 246, 10.1016/j.rse.2017.01.029 · DOI
  17. Gross, 2022, Initial cross-calibration of Landsat 8 and Landsat 9 using the simultaneous underfly event, Rem. Sens., 14, 2418, 10.3390/rs14102418 · DOI
  18. Gutman, 2013, Assessment of the NASA–USGS global land Survey (GLS) datasets, Remote Sens. Environ., 134, 249, 10.1016/j.rse.2013.02.026 · DOI
  19. Hansen, 2008, A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin, Remote Sens. Environ., 112, 2495, 10.1016/j.rse.2007.11.012 · DOI
  20. Helder, 2004, Landsat-5 Thematic Mapper reflective-band radiometric stability, IEEE Trans. Geosci. Rem. Sens., 42, 2730, 10.1109/TGRS.2004.839088 · DOI
  21. Hook, 2004, In-flight validation and recovery of water surface temperature with Landsat-5 thermal infrared data using an automated high-altitude lake validation site at Lake Tahoe, IEEE Trans. Geosci. Rem. Sens., 42, 2767, 10.1109/TGRS.2004.839092 · DOI
  22. Hulley, 2022, Validation and quality assessment of the ECOSTRESS level-2 land surface temperature and emissivity product, IEEE Trans. Geosci. Rem. Sens., 60, 1, 10.1109/TGRS.2021.3079879 · DOI
  23. Hulley, 2015, The ASTER global emissivity dataset (ASTER GED): mapping earth's emissivity at 100 meter spatial scale, Geophys. Res. Lett., 42, 7966, 10.1002/2015GL065564 · DOI
  24. Irish, 2006, Characterization of the Landsat-7 ETM+ automated cloud-cover assessment (ACCA) algorithm, Photogramm. Eng. Rem. Sens., 72, 1179, 10.14358/PERS.72.10.1179 · DOI
  25. Johnson, 2010, The 2009 cropland data layer, Photogramm. Eng. Rem. Sens., 76, 1201
  26. Ju, 2012, Continental-scale validation of MODIS-based and LEDAPS Landsat ETM+ atmospheric correction methods, Remote Sens. Environ., 122, 175, 10.1016/j.rse.2011.12.025 · DOI
  27. Justice, 2002, An overview of MODIS Land data processing and product status, Remote Sens. Environ., 83, 3, 10.1016/S0034-4257(02)00084-6 · DOI
  28. Justice, 1998, The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research, IEEE Trans. Geosci. Rem. Sens., 36, 1228, 10.1109/36.701075 · DOI
  29. Kaita, 2022, Landsat 9 cross calibration under-fly of Landsat 8: planning, and execution, Rem. Sens., 14, 5414, 10.3390/rs14215414 · DOI
  30. Kotchenova, 2006, Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: path radiance, Appl. Opt., 45, 6762, 10.1364/AO.45.006762 · DOI
  31. Kovalskyy, 2013, The global availability of Landsat 5 TM and Landsat 7 ETM+ land surface observations and implications for global 30m Landsat data product generation, Remote Sens. Environ., 130, 280, 10.1016/j.rse.2012.12.003 · DOI
  32. Kovalskyy, 2015, A one-year Landsat 8 conterminous United States study of cirrus and non-cirrus clouds, Rem. Sens., 7, 564, 10.3390/rs70100564 · DOI
  33. Laraby, 2018, Uncertainty estimation method and Landsat 7 global validation for the Landsat surface temperature product, Remote Sens. Environ., 216, 472, 10.1016/j.rse.2018.06.026 · DOI
  34. Li, 2017, A global analysis of Sentinel-2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring, Rem. Sens., 9, 902, 10.3390/rs9090902 · DOI
  35. Li, 2017, Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m reflective wavelength bands to Sentinel-2 20-m resolution, Rem. Sens., 9, 755, 10.3390/rs9070755 · DOI
  36. Li, 2022, Cloud and cloud shadow detection for optical satellite imagery: features, algorithms, validation, and prospects, ISPRS J. Photogrammetry Remote Sens., 188, 89, 10.1016/j.isprsjprs.2022.03.020 · DOI
  37. Liang, 2001, Atmospheric correction of Landsat ETM+ land surface imagery, I. Methods. IEEE Trans. Geosci. Remote Sens., 39, 2490, 10.1109/36.964986 · DOI
  38. Liu, 2021, Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020 - iMap World 1.0, Remote Sens. Environ., 258, 10.1016/j.rse.2021.112364 · DOI
  39. Loveland, 2012, Landsat—building a strong future, Remote Sens. Environ., 122, 22, 10.1016/j.rse.2011.09.022 · DOI
  40. Maiersperger, 2013, Characterizing LEDAPS surface reflectance products by comparisons with AERONET, field spectrometer, and MODIS data, Remote Sens. Environ., 136, 1, 10.1016/j.rse.2013.04.007 · DOI
  41. Malakar, 2018, An operational land surface temperature product for Landsat thermal data: methodology and validation, IEEE Trans. Geosci. Rem. Sens., 56, 5717, 10.1109/TGRS.2018.2824828 · DOI
  42. Markham, 2004, Landsat sensor performance: history and current status, IEEE Trans. Geosci. Rem. Sens., 42, 2691, 10.1109/TGRS.2004.840720 · DOI
  43. Markham, 2014, Landsat-8 operational land imager radiometric calibration and stability, Rem. Sens., 6, 12275, 10.3390/rs61212275 · DOI
  44. Markham, 2004, Landsat-7 ETM+ on-orbit reflective-band radiometric stability and absolute calibration, IEEE Trans. Geosci. Rem. Sens., 42, 2810, 10.1109/TGRS.2004.836389 · DOI
  45. Masek, 2006, A Landsat surface reflectance dataset for North America, 1990–2000, IEEE Trans. Geosci. Remote Sens. Lett., 3, 68, 10.1109/LGRS.2005.857030 · DOI
  46. Masek, 2020, Landsat 9: empowering open science and applications through continuity, Remote Sens. Environ., 248, 10.1016/j.rse.2020.111968 · DOI
  47. Mishra, 2016, Continuous calibration improvement: Landsat 5 through Landsat 8, Remote Sens. Environ., 185, 7, 10.1016/j.rse.2016.07.032 · DOI
  48. Montanaro, 2014, Stray light artifacts in imagery from the Landsat 8 thermal infrared sensor, Rem. Sens., 6, 10435, 10.3390/rs61110435 · DOI
  49. Nazeer, 2014, Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment, Int. J. Rem. Sens., 35, 6271, 10.1080/01431161.2014.951742 · DOI
  50. Ouaidrari, 1999, Operational atmospheric correction of Landsat TM data, Remote Sens. Environ., 70, 4, 10.1016/S0034-4257(99)00054-1 · DOI
  51. Pahlevan, 2017, Landsat 8 remote sensing reflectance (Rrs) products: evaluations, intercomparisons, and enhancements, Remote Sens. Environ., 190, 289, 10.1016/j.rse.2016.12.030 · DOI
  52. Qiu, 2018, Making Landsat time series consistent: evaluating and improving Landsat analysis ready data, Rem. Sens., 11, 51, 10.3390/rs11010051 · DOI
  53. Qiu, 2019, Fmask 4.0: improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery, Remote Sens. Environ., 231, 10.1016/j.rse.2019.05.024 · DOI
  54. Qiu, 2020, Cirrus clouds that adversely affect Landsat 8 images: what are they and how to detect them?, Remote Sens. Environ., 246, 10.1016/j.rse.2020.111884 · DOI
  55. Rengarajan, 2020, Harmonizing the Landsat ground reference with the Sentinel-2 global reference image using space-based bundle adjustment, Rem. Sens., 12, 3132, 10.3390/rs12193132 · DOI
  56. Reuter, 2015, The thermal infrared sensor (TIRS) on Landsat 8: design overview and pre-launch characterization, Rem. Sens., 7, 1135, 10.3390/rs70101135 · DOI
  57. Roger, 2022, Aerosol models from the AERONET database—application to surface reflectance validation, Atmos. Meas. Tech., 15, 1123, 10.5194/amt-15-1123-2022 · DOI
  58. Roy, 2002, The MODIS Land Product quality assessment approach, Remote Sens. Environ., 83, 62, 10.1016/S0034-4257(02)00087-1 · DOI
  59. Roy, 2010, Web-enabled Landsat data (WELD): Landsat ETM+ composited mosaics of the conterminous United States, Remote Sens. Environ., 114, 35, 10.1016/j.rse.2009.08.011 · DOI
  60. Roy, 2014, Landsat-8: science and product vision for terrestrial global change research, Remote Sens. Environ., 145, 154, 10.1016/j.rse.2014.02.001 · DOI
  61. Roy, 2016, A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance, Remote Sens. Environ., 176, 255, 10.1016/j.rse.2016.01.023 · DOI
  62. Roy, 2019, Landsat-8 and Sentinel-2 burned area mapping-A combined sensor multi-temporal change detection approach, Rem. Sens. Environ., 231, 10.1016/j.rse.2019.111254 · DOI
  63. Saunier, 2022, Sen2Like: paving the way towards harmonization and fusion of optical data, Rem. Sens., 14, 3855, 10.3390/rs14163855 · DOI
  64. Scambos, 2018, Ultralow surface temperatures in East Antarctica from satellite thermal infrared mapping: the coldest places on Earth, Geophys. Res. Lett., 45, 6124, 10.1029/2018GL078133 · DOI
  65. Schaaf, 2002, First operational BRDF, albedo nadir reflectance products from MODIS, Remote Sens. Environ., 83, 135, 10.1016/S0034-4257(02)00091-3 · DOI
  66. Schaepman-Strub, 2006, Reflectance quantities in optical remote sensing-definitions and case studies, Remote Sens. Environ., 103, 27, 10.1016/j.rse.2006.03.002 · DOI
  67. Schott, 2012, Thermal infrared radiometric calibration of the entire Landsat 4, 5, and 7 archive (1982–2010), Remote Sens. Environ., 122, 41, 10.1016/j.rse.2011.07.022 · DOI
  68. Shlien, 1979, Geometric correction, registration, and resampling of Landsat imagery, Can. J. Rem. Sens., 5, 74, 10.1080/07038992.1979.10854986 · DOI
  69. Skakun, 2019, Validation of the LaSRC cloud detection algorithm for Landsat 8 images, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 12, 2439, 10.1109/JSTARS.2019.2894553 · DOI
  70. Skakun, 2022, Cloud Mask Intercomparison eXercise (CMIX): an evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2, Remote Sens. Environ., 274, 10.1016/j.rse.2022.112990 · DOI
  71. Storey, 2016, A note on the temporary misregistration of landsat-8 operational land imager (OLI) and sentinel-2 multi spectral instrument (MSI) imagery, Remote Sens. Environ., 186, 121, 10.1016/j.rse.2016.08.025 · DOI
  72. Storey, 2004, Landsat-5 bumper-mode geometric correction, IEEE Trans. Geosci. Rem. Sens., 42, 2695, 10.1109/TGRS.2004.836390 · DOI
  73. Storey, 2019, Bundle adjustment using space-based triangulation method for improving the Landsat global ground reference, Rem. Sens., 11, 1640, 10.3390/rs11141640 · DOI
  74. Tanré, 1990, Technical note Description of a computer code to simulate the satellite signal in the solar spectrum: the 5S code, Int. J. Rem. Sens., 11, 659, 10.1080/01431169008955048 · DOI
  75. Toivonen, 2006, Across-path DN gradient in Landsat TM imagery of Amazonian forests: a challenge for image interpretation and mosaicking, Remote Sens. Environ., 100, 550, 10.1016/j.rse.2005.11.006 · DOI
  76. Tonooka, 2005, Accurate atmospheric correction of ASTER thermal infrared imagery using the WVS method, IEEE Trans. Geosci. Rem. Sens., 43, 2778, 10.1109/TGRS.2005.857886 · DOI
  77. Vermote, 2016, Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product, Remote Sens. Environ., 185, 46, 10.1016/j.rse.2016.04.008 · DOI
  78. Vuolo, 2015, Comparison of the Landsat Surface Reflectance Climate Data Record (CDR) and manually atmospherically corrected data in a semi-arid European study area, Int. J. Appl. Earth Obs. Geoinf., 42, 1
  79. Wan, 1996, A generalized split-window algorithm for retrieving land-surface temperature from space, IEEE Trans. Geosci. Rem. Sens., 34, 892, 10.1109/36.508406 · DOI
  80. Wang, 2018, Capturing rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products, Remote Sens. Environ., 207, 50, 10.1016/j.rse.2018.02.001 · DOI
  81. Wang, 2020, Spatial–spectral radial basis function-based interpolation for Landsat ETM+ SLC-off image gap filling, IEEE Trans. Geosci. Rem. Sens., 59, 7901, 10.1109/TGRS.2020.3038878 · DOI
  82. Wickham, 2021, Thematic accuracy assessment of the NLCD 2016 land cover for the conterminous United States, Remote Sens. Environ., 257, 10.1016/j.rse.2021.112357 · DOI
  83. Wolfe, 1998, MODIS land data storage, gridding, and compositing methodology: level 2 grid, IEEE Trans. Geosci. Rem. Sens., 36, 1324, 10.1109/36.701082 · DOI
  84. Wulder, 2019, Current status of Landsat program, science, and applications, Remote Sens. Environ., 225, 127, 10.1016/j.rse.2019.02.015 · DOI
  85. Wulder, 2022, Fifty years of Landsat science and impacts, Remote Sens. Environ., 280, 10.1016/j.rse.2022.113195 · DOI
  86. Wulder, 2016, The global Landsat archive—status, consolidation, and direction, Remote Sens. Environ., 185, 271, 10.1016/j.rse.2015.11.032 · DOI
  87. Yan, 2021, Improving Landsat Multispectral Scanner (MSS) geolocation by least-squares-adjustment based time-series co-registration, Remote Sens. Environ., 252, 10.1016/j.rse.2020.112181 · DOI
  88. Yan, 2020, Spatially and temporally complete Landsat reflectance time series modelling: the fill-and-fit approach, Rem. Sens. Environ., 241, 10.1016/j.rse.2020.111718 · DOI
  89. Zhai, 2022, Conterminous United States Landsat-8 top of atmosphere and surface reflectance tasseled cap transformation coefficients, Remote Sens. Environ., 274, 10.1016/j.rse.2022.112992 · DOI
  90. Zhang, 2016, Landsat 5 Thematic Mapper reflectance and NDVI 27-year time series inconsistencies due to satellite orbit change, Remote Sens. Environ., 186, 217, 10.1016/j.rse.2016.08.022 · DOI
  91. Zhang, 2018, Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences, Remote Sens. Environ., 215, 482, 10.1016/j.rse.2018.04.031 · DOI
  92. Zhang, 2023, Demonstration of large area land cover classification with a one dimensional convolutional neural network applied to single pixel temporal metric percentiles, Remote Sens. Environ., 295, 10.1016/j.rse.2023.113653 · DOI
  93. Zhu, 2015, Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images, Remote Sens. Environ., 159, 269, 10.1016/j.rse.2014.12.014 · DOI
  94. Zhu, 2012, Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sens. Environ., 118, 83, 10.1016/j.rse.2011.10.028 · DOI
  95. Zhu, 2019, Benefits of the free and open Landsat data policy, Remote Sens. Environ., 224, 382, 10.1016/j.rse.2019.02.016 · DOI

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Metadata

Authors
Crawford, C. J., Roy, D. P., Arab, S., Barnes, C., Vermote, E., Hulley, G., Gerace, A., Choate, M., Engebretson, C., Micijevic, E., Schmidt, G., Anderson, C., Anderson, M., Bouchard, M., Cook, B., Dittmeier, R., Howard, D., Jenkerson, C., Kim, M., Kleyians, T., Maiersperger, T., Mueller, C., Neigh, C., Owen, L., Page, B., Pahlevan, N., Rengarajan, R., Roger, J.-C., Sayler, K., Scaramuzza, P., Skakun, S., Yan, L., Zhang, H. K., Zhu, Z., & Zahn, S.
Affiliations
  • United States Geological Survey (US)
  • Michigan State University (US)
  • Goddard Space Flight Center (US)
  • California Institute of Technology (US)
  • Rochester Institute of Technology (US)
  • Agricultural Research Service (US)
  • University of Maryland, College Park (US)
  • South Dakota State University (US)
  • University of Connecticut (US)
Corresponding author
Crawford, Christopher J.
Venue
Science of Remote Sensing
Publisher
Elsevier BV
Published
2023-12
Volume
8
Pages
100103
ISSN
2666-0172 (ISSN-L)
OpenAlex
W4386840203
Cited by
292
References
0
Citations by year
2026: 92 · 2025: 114 · 2024: 83 · 2023: 3
FWCI
47.23
Type
journal article
Language
en
Open access
Locations
  • Science of Remote Sensing (publishedVersion, cc-by)
  • HAL (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Aeronautics and Space Administration (140G0121D001)
  • U.S. Geological Survey
  • Michigan State University (G12PC00069)
Topics
  • Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
  • Physical Sciences › Engineering › Aerospace Engineering › Calibration and Measurement Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Satellite Image Processing and Photogrammetry
Keywords
Landsat Collection 2, Landsat 9, cloud computing, Amazon Web Services, satellite data archive

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CoChat. (2026). Citation check: The 50-year Landsat collection 2 archive. https://app.cochat.ai/papers/10.1016/j.srs.2023.100103
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Crawford, C. J., Roy, D. P., Arab, S., Barnes, C., Vermote, E., Hulley, G., Gerace, A., Choate, M., Engebretson, C., Micijevic, E., Schmidt, G., Anderson, C., Anderson, M., Bouchard, M., Cook, B., Dittmeier, R., Howard, D., Jenkerson, C., Kim, M., . . . Zahn, S. (2023). The 50-year Landsat collection 2 archive. Science of Remote Sensing. https://doi.org/10.1016/j.srs.2023.100103