Landsat-8 Operational Land Imager Design, Characterization and Performance

Knight, Edward J.; Kvaran, Geir

doi:10.3390/rs61110286

Cited by 211 publications

(134 citation statements)

References 9 publications

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“…On orbit characterization covers the spatial and the radiometric performances of the sensor. However, most on orbit radiometric characterizations use calibration data sets, as opposed to natural ground targets [35], and spatial characterization uses Earth targets as well as stars and moon shots [33,36]. On the other hand, empirical analysis of new sensors based on natural surface targets provides fitness for use results for future applications.…”

Section: Introductionmentioning

confidence: 99%

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Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

et al. 2016

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Land cover and land use maps derived from satellite remote sensing imagery are critical to support biodiversity and conservation, especially over large areas. With its 10 m to 20 m spatial resolution, Sentinel-2 is a promising sensor for the detection of a variety of landscape features of ecological relevance. However, many components of the ecological network are still smaller than the 10 m pixel, i.e., they are sub-pixel targets that stretch the sensor’s resolution to its limit. This paper proposes a framework to empirically estimate the minimum object size for an accurate detection of a set of structuring landscape foreground/background pairs. The developed method combines a spectral separability analysis and an empirical point spread function estimation for Sentinel-2. The same approach was also applied to Landsat-8 and SPOT-5 (Take 5), which can be considered as similar in terms of spectral definition and spatial resolution, respectively. Results show that Sentinel-2 performs consistently on both aspects. A large number of indices have been tested along with the individual spectral bands and target discrimination was possible in all but one case. Overall, results for Sentinel-2 highlight the critical importance of a good compromise between the spatial and spectral resolution. For instance, the Sentinel-2 roads detection limit was of 3 m and small water bodies are separable with a diameter larger than 11 m. In addition, the analysis of spectral mixtures draws attention to the uneven sensitivity of a variety of spectral indices. The proposed framework could be implemented to assess the fitness for purpose of future sensors within a large range of applications.

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Section: Introductionmentioning

confidence: 99%

“…Pre-launch and on orbit characterization of spaceborne sensors are the first step towards verifying that the technical specifications are met with a focus on calibrating the sensor and assessing the continuity with previous missions [33,34]. On orbit characterization covers the spatial and the radiometric performances of the sensor.…”

Section: Introductionmentioning

confidence: 99%

Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

et al. 2016

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“…For comparisons and evaluation of the S1 snow products, we derived snow maps from terrain corrected images of the Landsat-7 and Landsat-8 [22] missions using the visible and shortwave infrared channels with 30 m pixel size. The Landsat data were downloaded from USGS as L1T data being radiometrically calibrated and rectified in UTM projection.…”

Section: Introductionmentioning

confidence: 99%

Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR

et al. 2016

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Abstract:The Sentinel satellite constellation series, developed and operated by the European Space Agency, represents the dedicated space component of the European Copernicus program, committed to long-term operational services in environment, climate and security. We developed, tested and evaluated an algorithm for generating maps of snowmelt area from C-band synthetic aperture radar (SAR) data of the Sentinel-1 mission. For snowmelt classification, a change detection method is applied, using multitemporal dual-polarized SAR data acquired in Interferometric Wide swath (IW) mode, the basic operation mode over land surfaces. Of particular benefit for wet snow retrievals are the high instrument stability, the high spatial resolution across the 250 km wide swath, and the short revisit time. In order to study the impact of polarization, we generated maps of melting snow using data of the VV-polarized channel, the VH-polarized channel and a combined VV-and VH-based channel using a weighting function that accounts for effects of the local incidence angle. Comparisons are performed with snow maps derived from Landsat images over study areas in the Alps and in Iceland. The pixel-by-pixel comparisons show good agreement between the snow products of the two sensors, with the best performance for retrievals based on the combined (VV and VH) channel and a minor decline for the VH-based product. The VV-based snowmelt extent product shows a drop-off in quality over areas with steep terrain because of the decreasing backscatter contrast of snow-covered versus snow-free surfaces on fore-slopes. The investigations demonstrate the excellent capability of the Sentinel-1 mission for operational monitoring of snowmelt areas.

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“…These features must be considered when working with the relative calibration techniques described herein. The OLI has three on-board radiometric calibration devices-lamps, a shutter and solar diffuser panels [6]. For this paper, the solar diffuser panels are the most important to consider.…”

Section: Landsat 8 and The Operational Land Imager (Oli)mentioning

confidence: 99%

“…Within the focal plane array, the 14 FPMs are staggered, as illustrated in Figure 1a. The visible/near-infrared (VNIR) and panchromatic (PAN) bands (1-5 and 8) utilize silicon p-intrinsic-n (SiPIN) detectors, while the SWIR bands (6,7,9) use mercury-cadmium-telluride (HgCdTe) detectors for imaging [3]. Single and double backup detector arrays are present in each FPM for each SiPIN and HgCdTe band, respectively [4,5].…”

Section: Landsat 8 and The Operational Land Imager (Oli)mentioning

confidence: 99%

Radiometric Non-Uniformity Characterization and Correction of Landsat 8 OLI Using Earth Imagery-Based Techniques

et al. 2014

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Landsat 8 is the first satellite in the Landsat mission to acquire spectral imagery of the Earth using pushbroom sensor instruments. As a result, there are almost 70,000 unique detectors on the Operational Land Imager (OLI) alone to monitor. Due to minute variations in manufacturing and temporal degradation, every detector will exhibit a different behavior when exposed to uniform radiance, causing a noticeable striping artifact in collected imagery. Solar collects using the OLI's on-board solar diffuser panels are the primary method of characterizing detector level non-uniformity. This paper reports on an approach for using a side-slither maneuver to estimate relative detector gains within each individual focal plane module (FPM) in the OLI. A method to characterize cirrus band detector-level non-uniformity using deep convective clouds (DCCs) is also presented. These approaches are discussed, and then, correction results are compared with the diffuser-based method. Detector relative gain stability is assessed using the side-slither technique. Side-slither relative gains were found to correct streaking in test imagery with quality comparable to diffuser-based gains (within 0.005% for VNIR/PAN; 0.01% for SWIR) and identified a 0.5% temporal drift over a year. The DCC technique provided relative gains that visually decreased striping over the operational calibration in many images.

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Landsat-8 Operational Land Imager Design, Characterization and Performance

Cited by 211 publications

References 9 publications

Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR

Radiometric Non-Uniformity Characterization and Correction of Landsat 8 OLI Using Earth Imagery-Based Techniques

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