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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: closed (31 December 2023) | Viewed by 13861

Special Issue Editor


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Guest Editor
Senior Scientist (ST), 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

References:

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://doi.org/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://doi.org/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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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (4 papers)

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Research

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23 pages, 9396 KiB  
Article
Random Forest Classification of Multitemporal Landsat 8 Spectral Data and Phenology Metrics for Land Cover Mapping in the Sonoran and Mojave Deserts
by Madeline Melichar, Kamel Didan, Armando Barreto-Muñoz, Jennifer N. Duberstein, Eduardo Jiménez Hernández, Theresa Crimmins, Haiquan Li, Myles Traphagen, Kathryn A. Thomas and Pamela L. Nagler
Remote Sens. 2023, 15(5), 1266; https://doi.org/10.3390/rs15051266 - 25 Feb 2023
Cited by 4 | Viewed by 4069
Abstract
Geospatial data and tools evolve as new technologies are developed and landscape change occurs over time. As a result, these data may become outdated and inadequate for supporting critical habitat-related work across the international boundary in the Sonoran and Mojave Deserts Bird Conservation [...] Read more.
Geospatial data and tools evolve as new technologies are developed and landscape change occurs over time. As a result, these data may become outdated and inadequate for supporting critical habitat-related work across the international boundary in the Sonoran and Mojave Deserts Bird Conservation Region (BCR 33) due to the area’s complex vegetation communities and the discontinuity in data availability across the United States (US) and Mexico (MX) border. This research aimed to produce the first 30 m continuous land cover map of BCR 33 by prototyping new methods for desert vegetation classification using the Random Forest (RF) machine learning (ML) method. The developed RF classification model utilized multitemporal Landsat 8 Operational Land Imager spectral and vegetation index data from the period of 2013–2020, and phenology metrics tailored to capture the unique growing seasons of desert vegetation. Our RF model achieved an overall classification F-score of 0.80 and an overall accuracy of 91.68%. Our results portrayed the vegetation cover at a much finer resolution than existing land cover maps from the US and MX portions of the study area, allowing for the separation and identification of smaller habitat pockets, including riparian communities, which are critically important for desert wildlife and are often misclassified or nonexistent in current maps. This early prototyping effort serves as a proof of concept for the ML and data fusion methods that will be used to generate the final high-resolution land cover map of the entire BCR 33 region. Full article
(This article belongs to the Special Issue Fifty Years of Landsat)
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29 pages, 6162 KiB  
Article
Optimizing Landsat Next Shortwave Infrared Bands for Crop Residue Characterization
by Brian T. Lamb, Philip E. Dennison, W. Dean Hively, Raymond F. Kokaly, Guy Serbin, Zhuoting Wu, Philip W. Dabney, Jeffery G. Masek, Michael Campbell and Craig S. T. Daughtry
Remote Sens. 2022, 14(23), 6128; https://doi.org/10.3390/rs14236128 - 03 Dec 2022
Cited by 4 | Viewed by 1991
Abstract
This study focused on optimizing the placement of shortwave infrared (SWIR) bands for pixel-level estimation of fractional crop residue cover (fR) for the upcoming Landsat Next mission. We applied an iterative wavelength shift approach to a database of crop residue [...] Read more.
This study focused on optimizing the placement of shortwave infrared (SWIR) bands for pixel-level estimation of fractional crop residue cover (fR) for the upcoming Landsat Next mission. We applied an iterative wavelength shift approach to a database of crop residue field spectra collected in Beltsville, Maryland, USA (n = 916) and computed generalized two- and three-band spectral indices for all wavelength combinations between 2000 and 2350 nm, then used these indices to model field-measured fR. A subset of the full dataset with a Normalized Difference Vegetation Index (NDVI) < 0.3 threshold (n = 643) was generated to evaluate green vegetation impacts on fR estimation. For the two-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized normalized difference using 2226 nm and 2263 nm bands produced the top fR estimation performance (R2 = 0.8222; RMSE = 0.1296). These findings were similar to the established two-band Shortwave Infrared Normalized Difference Residue Index (SINDRI) (R2 = 0.8145; RMSE = 0.1324). Performance of the two-band generalized normalized difference and SINDRI decreased for the full-NDVI dataset (R2 = 0.5865 and 0.4144, respectively). For the three-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized ratio-based index with a 2031–2085–2216 nm band combination, closely matching established Cellulose Absorption Index (CAI) bands, was top performing (R2 = 0.8397; RMSE = 0.1231). Three-band indices with CAI-type wavelengths maintained top fR estimation performance for the full-NDVI dataset with a 2036–2111–2217 nm band combination (R2 = 0.7581; RMSE = 0.1548). The 2036–2111–2217 nm band combination was also top performing in fR estimation (R2 = 0.8690; RMSE = 0.0970) for an additional analysis assessing combined green vegetation cover and surface moisture effects. Our results indicate that a three-band configuration with band centers and wavelength tolerances of 2036 nm (±5 nm), 2097 nm (±14 nm), and 2214 (±11 nm) would optimize Landsat Next SWIR bands for fR estimation. Full article
(This article belongs to the Special Issue Fifty Years of Landsat)
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18 pages, 3710 KiB  
Article
Evapotranspiration Estimation with the S-SEBI Method from Landsat 8 Data against Lysimeter Measurements at the Barrax Site, Spain
by José Antonio Sobrino, Nájila Souza da Rocha, Drazen Skoković, Pâmela Suélen Käfer, Ramón López-Urrea, Juan Carlos Jiménez-Muñoz and Silvia Beatriz Alves Rolim
Remote Sens. 2021, 13(18), 3686; https://doi.org/10.3390/rs13183686 - 15 Sep 2021
Cited by 9 | Viewed by 2481
Abstract
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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Review

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26 pages, 2379 KiB  
Review
Long-Term Wetland Monitoring Using the Landsat Archive: A Review
by Quentin Demarquet, Sébastien Rapinel, Simon Dufour and Laurence Hubert-Moy
Remote Sens. 2023, 15(3), 820; https://doi.org/10.3390/rs15030820 - 31 Jan 2023
Cited by 4 | Viewed by 2949
Abstract
Wetlands, which provide multiple functions and ecosystem services, have decreased and been degraded worldwide for several decades due to human activities and climate change. Managers and scientists need tools to characterize and monitor wetland areas, structure, and functions in the long term and [...] Read more.
Wetlands, which provide multiple functions and ecosystem services, have decreased and been degraded worldwide for several decades due to human activities and climate change. Managers and scientists need tools to characterize and monitor wetland areas, structure, and functions in the long term and at regional and global scales and assess the effects of planning policies on their conservation status. The Landsat earth observation program has collected satellite images since 1972, which makes it the longest global earth observation record with respect to remote sensing. In this review, we describe how Landsat data have been used for long-term (≥20 years) wetland monitoring. A total of 351 articles were analyzed based on 5 topics and 22 attributes that address long-term wetland monitoring and Landsat data analysis issues. Results showed that (1) the open access Landsat archive successfully highlights changes in wetland areas, structure, and functions worldwide; (2) recent progress in artificial intelligence (AI) and machine learning opens new prospects for analyzing the Landsat archive; (3) most unexplored wetlands can be investigated using the Landsat archive; (4) new cloud-computing tools enable dense Landsat times-series to be processed over large areas. We recommend that future studies focus on changes in wetland functions using AI methods along with cloud computing. This review did not include reports and articles that do not mention the use of Landsat imagery. Full article
(This article belongs to the Special Issue Fifty Years of Landsat)
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