Special Issue "Science of Landsat Analysis Ready Data"

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

Deadline for manuscript submissions: closed (30 June 2019).

Special Issue Editor

Dr. Zhe Zhu
E-Mail Website
Guest Editor
Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269-4087, USA
Interests: remote sensing of forests; urban and clouds; land cover and land use change; time series analysis
Special Issues and Collections in MDPI journals

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

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Keywords

  • Landsat
  • Analysis Ready Data
  • Time Series Analysis
  • Scientific Applications
  • Product

Published Papers (9 papers)

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Editorial

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Open AccessEditorial
Science of Landsat Analysis Ready Data
by Zhe Zhu
Remote Sens. 2019, 11(18), 2166; https://doi.org/10.3390/rs11182166 - 18 Sep 2019
Cited by 1
Abstract
The free and open policy of Landsat data in 2008 completely changed the way that Landsat data was analyzed and used, particularly for applications such as time series analysis. Nine years later, the United States Geological Survey (USGS) released the first version of [...] Read more.
The free and open policy of Landsat data in 2008 completely changed the way that Landsat data was analyzed and used, particularly for applications such as time series analysis. Nine years later, the United States Geological Survey (USGS) released the first version of Landsat Analysis Ready Data (ARD) for the United States, which was another milestone in Landsat history. The Landsat time series is so convenient and easy to use and has triggered science that was not possible a few decades ago. In this Editorial, we review the current status of Landsat ARD, introduce scientific studies of Landsat ARD from this special issue, and discuss global Landsat ARD. Full article
(This article belongs to the Special Issue Science of Landsat Analysis Ready Data)

Research

Jump to: Editorial

Open AccessArticle
Implementation of a Satellite Based Inland Water Algal Bloom Alerting System Using Analysis Ready Data
Remote Sens. 2019, 11(24), 2954; https://doi.org/10.3390/rs11242954 - 10 Dec 2019
Abstract
Water managers need tools to assist in the management of ever increasing algal bloom problems over wide spatial areas to complement sparse and declining in situ monitoring networks. Optical methods employing satellite data offer rapid and widespread coverage for early detection of bloom [...] Read more.
Water managers need tools to assist in the management of ever increasing algal bloom problems over wide spatial areas to complement sparse and declining in situ monitoring networks. Optical methods employing satellite data offer rapid and widespread coverage for early detection of bloom events. The advent of the Analysis Ready Data (ARD) and Open Data Cube concepts offer the means to lower the technical challenges confronting managers, allowing them to adopt satellite tools. Exploiting Landsat ARD integrated into the Digital Earth Australia data cube, we developed a prototype algal bloom alerting tool for the state of New South Wales, Australia. A visualization portal allows managers to gain insights into bloom status across the state as a whole and to further investigate spatial patterns in bloom alerts at an individual water body basis. To complement this we also proposed an algal alert system for trial based on chlorophyll and TSM levels which requires further testing. The system was able to retrieve the status of 444 water bodies across the state and outputs from the visualization system are presented. Time series of image acquisitions during an intense bloom in one lake are used to demonstrate the potential of the system. We discuss the implications for further development and operationalisation including the potential for augmentation with alternative algorithms and incorporation of other sensor ARD data to improve both temporal and spectral resolutions. Full article
(This article belongs to the Special Issue Science of Landsat Analysis Ready Data)
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Open AccessEditor’s ChoiceArticle
Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) Observation Coverage over the Conterminous United States and Implications for Terrestrial Monitoring
Remote Sens. 2019, 11(4), 447; https://doi.org/10.3390/rs11040447 - 21 Feb 2019
Cited by 7
Abstract
The Landsat Analysis Ready Data (ARD) are designed to make the U.S. Landsat archive straightforward to use. In this paper, the availability of the Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) ARD over the conterminous [...] Read more.
The Landsat Analysis Ready Data (ARD) are designed to make the U.S. Landsat archive straightforward to use. In this paper, the availability of the Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) ARD over the conterminous United States (CONUS) are quantified for a 36-year period (1 January 1982 to 31 December 2017). Complex patterns of ARD availability occur due to the satellite orbit and sensor geometry, cloud, sensor acquisition and health issues and because of changing relative orientation of the ARD tiles with respect to the Landsat orbit paths. Quantitative per-pixel and summary ARD tile results are reported. Within the CONUS, the average annual number of non-cloudy observations in each 150 × 150 km ARD tile varies from 0.53 to 16.80 (Landsat 4 TM), 11.08 to 22.83 (Landsat 5 TM), 9.73 to 21.72 (Landsat 7 ETM+) and 14.23 to 30.07 (all three sensors). The annual number was most frequently only 2 to 4 Landsat 4 TM observations (36% of the CONUS tiles), increasing to 14 to 16 Landsat 5 TM observations (26% of tiles), 12 to 14 Landsat 7 ETM+ observations (31% of tiles) and 18 to 20 observations (23% of tiles) when considering all three sensors. The most frequently observed ARD tiles were in the arid south-west and in certain mountain rain shadow regions and the least observed tiles were in the north-east, around the Great Lakes and along parts of the north-west coast. The quality of time series algorithm results is expected to be reduced at ARD tiles with low reported availability. The smallest annual number of cloud-free observations for the Landsat 5 TM are over ARD tile h28v04 (northern New York state), for Landsat 7 ETM+ are over tile h25v07 (Ohio and Pennsylvania) and for Landsat 4 TM are over tile h22v08 (northern Indiana). The greatest annual number of cloud-free observations for the Landsat 5 TM and 7 ETM+ ARD are over southern California ARD tile h04v11 and for the Landsat 4 TM are over southern Arizona tile h06v13. The reported results likely overestimate the number of good surface observations because shadows and cirrus clouds were not considered. Implications of the findings for terrestrial monitoring and future ARD research are discussed. Full article
(This article belongs to the Special Issue Science of Landsat Analysis Ready Data)
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Open AccessArticle
Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data
Remote Sens. 2019, 11(1), 51; https://doi.org/10.3390/rs11010051 - 29 Dec 2018
Cited by 9
Abstract
Recently, the United States Geological Survey (USGS) has released a new dataset, called Landsat Analysis Ready Data (ARD), which is designed specifically for facilitating time series analysis. In this study, we evaluated the temporal consistency of this new dataset and recommended several processing [...] Read more.
Recently, the United States Geological Survey (USGS) has released a new dataset, called Landsat Analysis Ready Data (ARD), which is designed specifically for facilitating time series analysis. In this study, we evaluated the temporal consistency of this new dataset and recommended several processing streamlines for improving data consistency. Specifically, we examined the impacts of data resampling, cloud/cloud shadow detection, Bidirectional Reflectance Distribution Function (BRDF) correction, and topographic correction on the temporal consistency of the Landsat Time Series (LTS). We have four major observations. First, single-resampled data (ARD) are generally more consistent than double-resampled data (re-projected Collection 1 data), but the difference is very minor. Second, the improved cloud and cloud shadow detection approach (e.g., Fmask 4.0 vs. 3.3) moderately increased data consistency. Third, BRDF correction contributed the most in making LTS consistent. Finally, we corrected the topographic effects by using several widely used algorithms, including Sun-Canopy-Sensor (SCS), a semiempirical SCS (SCS+C), and Illumination Correction (IC) algorithms, however they were found to have very limited or even negative impacts on the consistency of LTS. Therefore, we recommend using Landsat ARD with the improved cloud and cloud shadow detection approach (Fmask 4.0), and with BRDF correction for routine time series analysis. Full article
(This article belongs to the Special Issue Science of Landsat Analysis Ready Data)
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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 - 01 Oct 2018
Cited by 4
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 - 20 Sep 2018
Cited by 3
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 - 28 Aug 2018
Cited by 39
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 - 14 Apr 2018
Cited by 12
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 - 31 Jan 2018
Cited by 17
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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