Special Issue "Fifty Years of Landsat"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: 30 June 2022.

Special Issue Editor

Dr. Prasad S. Thenkabail
E-Mail Website1 Website2
Guest Editor
Research Geographer-15, U. S. Geological Survey (USGS), USGS Western Geographic Science Center (WGSC), 2255, N. Gemini Dr., Flagstaff, AZ 86001, USA
Interests: hyperspectral remote sensing, remote sensing expertise in a number of areas including: (a) global croplands, (b) agriculture, (c) water resources, (d) wetlands, (e) droughts, (f) land use/land cover, (g) forestry, (h) natural resources management, (i) environments, (j) vegetation, and (k) characterization of large river basins and deltas
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Special Issue Information

Dear Colleagues,

The very name Landsat signifies Earth Remote Sensing. The first Landsat, Landsat-1 (then known as the Earth Resources Technology Satellite or ERTS-1), was launched on 23 July 1972. As its early name indicates, it was conceived as a technology demonstration satellite. However, it soon became apparent that this novel technology was invaluable to the study of planet Earth—so much so that Landsat and its successors have become an indispensable technology to study the entire planet over space and time, repetitively, objectively, and in multiple spectral, spatial, radiometric, and temporal resolutions, as evidenced in the thousands of publications over the years on myriad applications, such as land use land cover change (LULCC), climate studies, agriculture, forestry, water, droughts, and floods. Ever since July 1972, Landsat has gone through a colorful history and numerous travails but maintained its top position in Land remote sensing. On 11 February 2013, Landsat-8 was successfully launched and is currently in operation. Landsat-9 is expected to be launched later this year (2021), in time to celebrate the 50 years of Landsat’s legendary history.

The early Landsat versions (Landsat 1–5) demonstrated the immense value of high resolution (30 m or better) satellite remote sensing in understanding, modeling, mapping, and monitoring applications on planet Earth. An early significant success came through projects such as the Large Area Crop Inventory Experiment (LACIE) to determine the global wheat yield using Landsat-1 multispectral scanner (MSS) data. This experience led to Agriculture and Resource Inventory Surveys through the Aerospace Remote Sensing (AgRISTARS) program designed to address the technical issues defined by LACIE, to investigate other portions of the electromagnetic spectrum, and to expand the technology to several key commercial crops in important agricultural areas worldwide (Macdonald, 1984). Ever since, Landsat data have been used worldwide. Landsat was joined in its early years by the Satellite pour l’ Observation de la Terre (SPOT) series of France, first launched in 1984, and the Indian Remote Sensing Satellite (IRS) series of India, first launched in 1988. The failure of Landsat-6 during launch and the scan line issues of Landsat-7 caused some significant difficulties in the 1990s. However, four factors led to a gigantic leap in the use of Landsat data as we entered the 21st century, and these were (1) Landsat data continuity mission, later Landsat-8 (Loveland and Irons, 2016, Radcliff and Carlowicz, 2021); (2) web-enabled (free) Landsat data access for the entire world (Woodcock et al., 2008); (3) new processing methods and approaches that looked at Landsat data in terms of every pixel (Roy et al., 2014); and (4) cloud computing along with machine learning and artificial intelligence (Thenkabail et al., 2021).

When Landsat-9 is launched later this year, it will represent a landmark of 50 years of Landsat imaging and will usher in a new era in satellite remote sensing. This new era involves satellite sensor-based petabyte-scale big data, machine learning/deep learning, artificial intelligence, and the Internet of Things (IoT) that will, for example, usher in new tools to, for example, gather reference training and validation data from mobile apps and cloud computing. Data will be acquired from hundreds and thousands of mini- and microsatellites or continuously observing telescopes for any place and time in the world in every possible hyperspatial, hyperspectral, and hypertemporal mode. Remote sensing will not only become ubiquitous but democratized and cut across multiple disciplines of subject matter expertise, with data science, big data, coding, and computing on the cloud.

In the above context, I am inviting papers for a Special Issue on Landsat’s 50 years of legacy. Every possible type of papers is welcome, but each of them must be linked to Landsat science in one way or another. You are specifically encouraged to submit articles on the following topics:

  1. History and legacy of Landsat’s 50 years;
  2. Landsat science cutting across multiple applications;
  3. Landsat data calibration and validations including cross-sensor calibrations;
  4. Landsat science compared to science from other satellite sensors;
  5. Global as well as local studies;
  6. Strengths and limitations of Landsat data in various applications;
  7. Future of Landsat data;
  8. Comparing Landsat to studies from present and future generation of sensors;
  9. Other Landsat-related studies.

Dr. Prasad Thenkabail
Guest Editor


Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest.

Macdonald, 1984. A summary of the history of the development of automated remote sensing for agricultural applications. IEEE Transactions on Geoscience and Remote Sensing. 22(6): 473-482.

Loveland, T.R. and Irons, J.R. 2016. Landsat 8: The plans, the reality, and the legacy, Remote Sensing of Environment, 185: 1-6. https://0-doi-org.brum.beds.ac.uk/10.1016/j.rse.2016.07.033.

Radcliff, M., Carlowicz, M. 2021. ”Landsat: Continuing the Legacy,” NASA Earth Observatory, 1April 2021, URL: https://earthobservatory.nasa.gov/blogs/earthmatters/2021/04/01/landsat-continuing-the-legacy/?src=eoa-blogs

Roy, D.P., Wulder, M.A., Loveland, T.R., Woodcock C.E., Allen, R.G., Anderson, M.C.,  Helder, D., Irons, J.R., Johnson, D.M., Kennedy, R., Scambos, T.A., Schaaf, C.B., Schott, J.R., Sheng, Y., Vermote, E.F., Belward, A.S., Bindschadler, R., Cohen, W.B., Gao, F., Hipple, J.D., Hostert, P., Huntington, J., Justice, C.O., Kilic, A., Kovalskyy, V., Lee, Z.P., Lymburner, L., Masek, J.G., McCorkel, J., Shuai, Y., Trezza, R., Vogelmann, J., Wynne, R.H., Zhu, Z. 2014. Landsat-8: Science and product vision for terrestrial global change research, Remote Sensing of Environment, 145:154-172, ISSN 0034-4257, https://0-doi-org.brum.beds.ac.uk/10.1016/j.rse.2014.02.001.

Thenkabail, P.S., Teluguntla, P., Xiong, J., Oliphant, A., Congalton, R., Ozdogan, M., Gumma, M.K., Tilton, J., Giri, C., Milesi, C., Phalke, A., Massey, M., Yadav, K., Milesi, C., Sankey, T., Zhong, Y., Aneece, Y., Foley, D. 2021. Global Cropland Extent Product at 30m (GCEP30) derived using Landsat Satellite Time-series Data for the Year 2015 through Multiple Machine Learning Algorithms on Google Earth Engine (GEE) Cloud. Research Paper #, United States Geological Survey (USGS). In press. IP-119164.

Woodcock, C.E., Allen, A., Anderson, M., Belward, A., Bindschadler, R., Cohen, W., Gao, F., Goward, S.N., Helder, D., Helmer, E., Nemani, R., Oreopoulos, L., Schott, J., Thenkabail, P.S., Vermote, E.F., Vogelmann, J., Wulder, M.W. 2008. SCIENCE. VOL 320: 1011.

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  • Landsat
  • Remote Sensing
  • Land
  • Water
  • Planet Earth

Published Papers (1 paper)

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Evapotranspiration Estimation with the S-SEBI Method from Landsat 8 Data against Lysimeter Measurements at the Barrax Site, Spain
Remote Sens. 2021, 13(18), 3686; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183686 - 15 Sep 2021
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Evapotranspiration (ET) is a variable of the climatic system and hydrological cycle that plays an important role in biosphere–atmosphere–hydrosphere interactions. In this paper, remote sensing-based ET estimates with the simplified surface energy balance index (S-SEBI) model using Landsat 8 data were compared with [...] Read more.
Evapotranspiration (ET) is a variable of the climatic system and hydrological cycle that plays an important role in biosphere–atmosphere–hydrosphere interactions. In this paper, remote sensing-based ET estimates with the simplified surface energy balance index (S-SEBI) model using Landsat 8 data were compared with in situ lysimeter measurements for different land covers (Grass, Wheat, Barley, and Vineyard) at the Barrax site, Spain, for the period 2014–2018. Daily estimates produced superior performance than hourly estimates in all the land covers, with an average difference of 12% and 15% for daily and hourly ET estimates, respectively. Grass and Vineyard showed the best performance, with an RMSE of 0.10 mm/h and 0.09 mm/h and 1.11 mm/day and 0.63 mm/day, respectively. Thus, the S-SEBI model is able to retrieve ET from Landsat 8 data with an average RMSE for daily ET of 0.86 mm/day. Some model uncertainties were also analyzed, and we concluded that the overpass of the Landsat missions represents neither the maximum daily ET nor the average daily ET, which contributes to an increase in errors in the estimated ET. However, the S-SEBI model can be used to operationally retrieve ET from agriculture sites with good accuracy and sufficient variation between pixels, thus being a suitable option to be adopted into operational ET remote sensing programs for irrigation scheduling or other purposes. Full article
(This article belongs to the Special Issue Fifty Years of Landsat)
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