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Special Issue "Science of Landsat Analysis Ready Data"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 October 2018

Special Issue Editor

Guest Editor
Dr. Zhe Zhu

Department of Geosciences, Texas Tech University, Lubbock, TX, USA
Website | E-Mail
Interests: remote sensing of forest, clouds, urban, and land cover/land use; time series analysis; change detection; climate change

Special Issue Information

Dear Colleagues,

In October 2017, United States Geological Survey (USGS) will release the first version of Landsat Analysis Ready Data (ARD) for the conterminous United States (CONUS) (1982-2017) using Collection 1 Landsat data (https://landsat.usgs.gov/ard), which includes Landsat 4-5 Thematic Mapper (TM) Tier 1 data, Landsat 7 Enhanced Thematic Mapper Plus (ETM) Tier 1 data, and Landsat 8 Operational Land Imager (OLI)/ Thermal Infrared Sensor (TIRS) Tier 1/Tier 2 data. ARD for Alaska and Hawaii will be processed and will be available after CONUS ARD is complete, and eventually global Landsat ARD and Landsat 1-5 Multispectral Sensor (MSS) ARD will also be included in the Landsat ARD products. Landsat ARD are consistently processed to the highest scientific standards and level of processing required for time series analysis. This is another big step after the conversion of all pre-collection data into Collection 1 data. The release of Landsat ARD will make Landsat data much easier to be applied for time series analysis and will open doors in many scientific applications.

This is a very exciting moment for Landsat data users, and we would like to invite you to submit articles about your recent research with respect to the following topics; review articles covering one or more of these topics are also very welcome:

  • Current status and planned/operational Landsat ARD products
  • Specifications and characteristics of Landsat ARD
  • Evaluation of geometric and radiometric accuracies of Landsat ARD
  • Data inter-calibration and creation of long consistent time series (e.g., combination with Sentinel-2)
  • Combined used of Landsat ARD and other sensor data (e.g., Sentinel-2, LIDAR, microwaves, thermal scanners) and fusion approaches.
  • Suitability of Landsat ARD for LCLU mapping and LCLU change detection
  • Suitability of Landsat ARD for assessing vegetation dynamics (phenology, trend, and disturbance)
  • Suitability of Landsat ARD for hazards and disaster monitoring
  • Tools and algorithms for visualizing and analysing Landsat ARD

Dr. Zhe Zhu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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
  • Analysis Ready Data
  • Time Series Analysis
  • Scientific Applications
  • Product

Published Papers (5 papers)

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Research

Open AccessFeature PaperArticle Implications of Pixel Quality Flags on the Observation Density of a Continental Landsat Archive
Remote Sens. 2018, 10(10), 1570; https://doi.org/10.3390/rs10101570
Received: 31 July 2018 / Revised: 26 September 2018 / Accepted: 28 September 2018 / Published: 1 October 2018
PDF Full-text (9038 KB) | HTML Full-text | XML Full-text
Abstract
Pixel quality (PQ) products delivered with Analysis Ready Data (ARD) provide users with information about the conditions of the surface, atmosphere, and sensor at the time of acquisition. Knowing whether an observation was affected by clouds or sensor saturation is crucial when selecting
[...] Read more.
Pixel quality (PQ) products delivered with Analysis Ready Data (ARD) provide users with information about the conditions of the surface, atmosphere, and sensor at the time of acquisition. Knowing whether an observation was affected by clouds or sensor saturation is crucial when selecting data to include in automated analysis, as imperfect or erroneous observations are undesirable for most applications. There is, however, a certain rate of commission error in cloud detection, and saturation may not affect all spectral bands at a time, which can lead to suitable observations being excluded. This can have a substantial impact on the amount of data available for analysis. To understand how different surface types can affect cloud commission and saturation, we analyzed cloud and per-band saturation PQ flags for 31 years of Landsat data within Digital Earth Australia. Areas showing substantial reduction in observation density compared to their surroundings were investigated to characterize how specific surface types impact on the temporal density of observations deemed desirable. Using Fmask 3.2 by way of example, our approach demonstrates a method that can be applied to summarize the characteristics of cloud-screening algorithms and sensor saturation. Results indicate that cloud commission and sensor saturation rates show specific characteristics depending on the targets under observation. This potentially leads to an imbalance in data availability driven by surface type in a given study area. Based on our findings, the level of detail in PQ flags delivered with ARD is pivotal in maximizing the potential of EO data. Full article
(This article belongs to the Special Issue Science of Landsat Analysis Ready Data)
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Open AccessFeature PaperArticle Using Window Regression to Gap-Fill Landsat ETM+ Post SLC-Off Data
Remote Sens. 2018, 10(10), 1502; https://doi.org/10.3390/rs10101502
Received: 31 July 2018 / Revised: 6 September 2018 / Accepted: 18 September 2018 / Published: 20 September 2018
PDF Full-text (7434 KB) | HTML Full-text | XML Full-text
Abstract
The continued development of algorithms using multitemporal Landsat data creates opportunities to develop and adapt imputation algorithms to improve the quality of that data as part of preprocessing. One example is de-striping Enhanced Thematic Mapper Plus (ETM+, Landsat 7) images acquired after the
[...] Read more.
The continued development of algorithms using multitemporal Landsat data creates opportunities to develop and adapt imputation algorithms to improve the quality of that data as part of preprocessing. One example is de-striping Enhanced Thematic Mapper Plus (ETM+, Landsat 7) images acquired after the Scan Line Corrector failure in 2003. In this study, we apply window regression, an algorithm that was originally designed to impute low-quality Moderate Resolution Imaging Spectroradiometer (MODIS) data, to Landsat Analysis Ready Data from 2014–2016. We mask Operational Land Imager (OLI; Landsat 8) image stacks from five study areas with corresponding ETM+ missing data layers, using these modified OLI stacks as inputs. We explored the algorithm’s parameter space, particularly window size in the spatial and temporal dimensions. Window regression yielded the best accuracy (and moderately long computation time) with a large spatial radius (a 7 × 7 pixel window) and a moderate temporal radius (here, five layers). In this case, root mean square error for deviations from the observed reflectance ranged from 3.7–7.6% over all study areas, depending on the band. Second-order response surface analysis suggested that a 15 × 15 pixel window, in conjunction with a 9-layer temporal window, may produce the best accuracy. Compared to the neighborhood similar pixel interpolator gap-filling algorithm, window regression yielded slightly better accuracy on average. Because it relies on no ancillary data, window regression may be used to conveniently preprocess stacks for other data-intensive algorithms. Full article
(This article belongs to the Special Issue Science of Landsat Analysis Ready Data)
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Open AccessArticle Analysis Ready Data: Enabling Analysis of the Landsat Archive
Remote Sens. 2018, 10(9), 1363; https://doi.org/10.3390/rs10091363
Received: 1 August 2018 / Revised: 21 August 2018 / Accepted: 25 August 2018 / Published: 28 August 2018
PDF Full-text (8021 KB) | HTML Full-text | XML Full-text
Abstract
Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and
[...] Read more.
Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor quality observations flagged so that they can be excluded. The United States Geological Survey (USGS) has processed all of the Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) archive over the conterminous United States (CONUS), Alaska, and Hawaii, into Landsat ARD. The ARD are available to significantly reduce the burden of pre-processing on users of Landsat data. Provision of pre-prepared ARD is intended to make it easier for users to produce Landsat-based maps of land cover and land-cover change and other derived geophysical and biophysical products. The ARD are provided as tiled, georegistered, top of atmosphere and atmospherically corrected products defined in a common equal area projection, accompanied by spatially explicit quality assessment information, and appropriate metadata to enable further processing while retaining traceability of data provenance. Full article
(This article belongs to the Special Issue Science of Landsat Analysis Ready Data)
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Open AccessFeature PaperArticle Large-Area Gap Filling of Landsat Reflectance Time Series by Spectral-Angle-Mapper Based Spatio-Temporal Similarity (SAMSTS)
Remote Sens. 2018, 10(4), 609; https://doi.org/10.3390/rs10040609
Received: 9 March 2018 / Revised: 7 April 2018 / Accepted: 12 April 2018 / Published: 14 April 2018
Cited by 2 | PDF Full-text (79315 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Landsat time series commonly contain missing observations, i.e., gaps, due to the orbit and sensing geometry, data acquisition strategy, and cloud contamination. A spectral-angle-mapper (SAM) based spatio-temporal similarity (SAMSTS) gap-filling algorithm is presented that is designed to fill small and large area gaps
[...] Read more.
Landsat time series commonly contain missing observations, i.e., gaps, due to the orbit and sensing geometry, data acquisition strategy, and cloud contamination. A spectral-angle-mapper (SAM) based spatio-temporal similarity (SAMSTS) gap-filling algorithm is presented that is designed to fill small and large area gaps in Landsat data, using one year or less of data and without using other satellite data. Each gap pixel is filled by an alternative similar pixel that is located in a non-missing region of the image. The alternative similar pixel locations are identified by comparison of reflectance time series using a SAM metric revised to be adaptive to missing observations. A time series segmentation-and-clustering approach is used to increase the search efficiency. The SAMSTS algorithm is demonstrated using six months of Landsat 8 Operational Land Imager (OLI) reflectance time series over three 150 × 150 km (5000 × 5000 30 m pixels) areas in California, Minnesota and Kansas. The three areas contain different land cover types, especially crops that have different phenology and abrupt changes due to agricultural harvesting, which make gap filling challenging. Fillings on simulated gaps, which are equivalent to 36% of 5000 × 5000 images in each test area, are presented. The gap filling accuracy is assessed quantitatively, and the SAMSTS algorithm is shown to perform better than the simple closest temporal pixel substitution gap filling approach and the sinusoidal harmonic model-based gap filling approach. The SAMSTS algorithm provides gap-filled data with five-band reflective-wavelength root-mean-square differences less the 0.02, which is comparable to the OLI reflectance calibration accuracy. Full article
(This article belongs to the Special Issue Science of Landsat Analysis Ready Data)
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Open AccessFeature PaperArticle Demonstration of Percent Tree Cover Mapping Using Landsat Analysis Ready Data (ARD) and Sensitivity with Respect to Landsat ARD Processing Level
Remote Sens. 2018, 10(2), 209; https://doi.org/10.3390/rs10020209
Received: 22 December 2017 / Revised: 16 January 2018 / Accepted: 27 January 2018 / Published: 31 January 2018
Cited by 5 | PDF Full-text (9673 KB) | HTML Full-text | XML Full-text
Abstract
The recently available Landsat Analysis Ready Data (ARD) are provided as top of atmosphere (TOA) and atmospherically corrected (surface) reflectance tiled products and are designed to make the U.S. Landsat archive for the United States straightforward to use. In this study, the utility
[...] Read more.
The recently available Landsat Analysis Ready Data (ARD) are provided as top of atmosphere (TOA) and atmospherically corrected (surface) reflectance tiled products and are designed to make the U.S. Landsat archive for the United States straightforward to use. In this study, the utility of ARD for 30 m percent tree cover mapping is demonstrated and the impact of different ARD processing levels on mapping accuracy examined. Five years of Landsat 5 and 7 ARD over 12 tiles encompassing Washington State are considered using an established bagged regression tree methodology and training data derived from Goddard LiDAR Hyperspectral & Thermal Imager (G-LiHT) data. Sensitivity to the amount of training data is examined with increasing mapping accuracy observed as more training data are used. Four processing levels of ARD are considered independently and the mapped results are compared: (i) TOA ARD; (ii) surface ARD; (iii) bidirectional reflectance distribution function (BRDF) adjusted atmospherically corrected ARD; and (iv) weekly composited BRDF adjusted atmospherically corrected ARD. The atmospherically corrected ARD provide marginally the highest mapping accuracies, although accuracy differences are negligible among the four (≤0.07% RMSE) when modest amounts of training data are used. The TOA ARD provide the most accurate maps compared to the other input data when only small amounts of training data are used, and the least accurate maps otherwise. The results are illustrated and the implications discussed. Full article
(This article belongs to the Special Issue Science of Landsat Analysis Ready Data)
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