Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

Li, Zhongbin; Zhang, Hankui K.; Roy, David P.; Yan, Lin; Huang, Haiyan; Li, Jian

doi:10.3390/rs9070755

Cited by 30 publications

(10 citation statements)

References 63 publications

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“…This means that the ARD are not resampled twice. This is advantageous as resampling degrades the geometric fidelity, either by introducing pixel shifts (for nearest neighbor resampling), or by smoothing the data (e.g., bilinear resampling) and/or introducing artefacts at high contrast edges (e.g., cubic convolution resampling) [14,15]. The utility of reprojecting the data directly into Albers is illustrated in Figure 1 that shows (top) ARD data (i.e., resampled once) and (middle) the equivalent data nearest neighbor resampled from Collection 1 UTM into the Albers projection (i.e., resampled twice).…”

Section: Ard Projectionmentioning

confidence: 99%

Analysis Ready Data: Enabling Analysis of the Landsat Archive

et al. 2018

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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.

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Section: Ard Projectionmentioning

confidence: 99%

Analysis Ready Data: Enabling Analysis of the Landsat Archive

et al. 2018

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“…The Sentinel-2 and the Landsat-8 data are registered to sub-pixel precision using affine transformations derived with a robust matching algorithm developed for this purpose [45] and via a least-squares adjustment among different orbits to reduce sensitivity to missing and cloudy data typically found when registering only individual tiles and images [46]. Research to downscale Landsat 8 30 m data to 20 m Sentinel 2 MSI resolution is underway [47] but currently the MSI 20 m data are upscaled to 30 m by bilinear resampling and the Landsat 8 Collection1 data are also bilinear resampled.…”

Section: Landsat-8 and Sentinel-2 Burned Area Mappingmentioning

confidence: 99%

Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

et al. 2018

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Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable.

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“…Temporal enhancement is a key requirement, but spatial enhancement is another aspect of Landsat that could be improved for time series applications. Enhancement of spatial resolution has been carried out mostly based on data fusion methods [4][5][6][7]. Studies have also shown that data fusion can lead to improvements in quantitative remote sensing applications such as land cover [4,8,9].…”

Section: Introductionmentioning

confidence: 99%

Landsat Super-Resolution Enhancement Using Convolution Neural Networks and Sentinel-2 for Training

et al. 2018

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Landsat is a fundamental data source for understanding historical change and its effect on environmental processes. In this research we test shallow and deep convolution neural networks (CNNs) for Landsat image super-resolution enhancement, trained using Sentinel-2, in three study sites representing boreal forest, tundra, and cropland/woodland environments. The analysis sought to assess baseline performance and determine the capacity for spatial and temporal extension of the trained CNNs. This is not a data fusion approach and a high-resolution image is only needed to train the CNN. Results show improvement with the deeper network generally achieving better results. For spatial and temporal extension, the deep CNN performed the same or better than the shallow CNN, but at greater computational cost. Results for temporal extension were influenced by change potentiality reducing the performance difference between the shallow and deep CNN. Visual examination revealed sharper images regarding land cover boundaries, linear features, and within-cover textures. The results suggest that spatial enhancement of the Landsat archive is feasible, with optimal performance where CNNs can be trained and applied within the same spatial domain. Future research will assess the enhancement on time series and associated land cover applications.

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Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

Cited by 30 publications

References 63 publications

Analysis Ready Data: Enabling Analysis of the Landsat Archive

Analysis Ready Data: Enabling Analysis of the Landsat Archive

Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

Landsat Super-Resolution Enhancement Using Convolution Neural Networks and Sentinel-2 for Training

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