The Landsat Data Continuity Mission Operational Land Imager (OLI) sensor

Markham, Brian L.; Knight, Edward J.; Canova, Brent P.; Donley, Eric; Kvaran, Geir; Lee, Kenton; Barsi, Julia A.; Pedelty, J. A.; Dabney, Philip W.; Irons, James R.

doi:10.1109/igarss.2012.6351961

Cited by 4 publications

(3 citation statements)

References 3 publications

(2 reference statements)

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“…However, it also faces some difficulty in co-registering the data of multiple spectral bands, because of the varying sensitivities of the detectors [18]. Second, the Landsat-8 OLI sensor not only keeps the traditional settings of band designations in geometric resolution (30 m), scene size (170 km × 183 km) and revisit cycle (16-day), but also adds to the heritage bands (see Table 1) with new bands, such as coastal and aerosol studies (Band 1: ultra-blue), cirrus cloud detection (Band 9) and a quality assessment band, and adjusts the wavelength of each band [20]. It is worthwhile to note that the spectral range of Landsat-8 OLI is narrower than its predecessor, Landsat-7 ETM+ (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors

2013

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Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are currently operational for routine Earth observation. There are substantial differences between instruments onboard both satellites. The enhancements achieved with Landsat-8 refer to the scanning technology (replacing of whisk-broom scanners with two separate push-broom OLI and TIRS scanners), an extended number of spectral bands (two additional bands provided) and narrower bandwidths. Therefore, cross-comparative analysis is very necessary for the combined use of multi-decadal Landsat imagery. In this study, 3,311 independent sample points of four major land cover types (primary forest, unplanted cropland, swidden cultivation and water body) were used to compare the spectral bands of ETM+ and OLI. Eight sample plots with different land cover types were manually selected for comparison with the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), the Land Surface Water Index (LSWI) and the Normalized Burn Ratio (NBR). These indices were calculated with six pairs of ETM+ and OLI cloud-free images, which were acquired over the border area of Myanmar, Laos and Thailand just two days apart, when Landsat-8 achieved operational obit. Comparative results showed that: (1) the average surface reflectance of each band differed slightly, but with a high degree of similarities between both sensors. In comparison with ETM+, the OLI had higher values for the near-infrared band for vegetative land cover types, but lower values for non-vegetative types. The new sensor had lower values for the shortwave infrared (2.11-2.29 µm) band for

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Section: Introductionmentioning

confidence: 99%

Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors

2013

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“…The Landsat-8 has an Operational Land Imager, which is abbreviated as OLI (hereafter, the OLI sensor on-board the Landsat-8 satellite is written as Landsat-8/OLI), and a Thermal Infrared Sensor on-board. The OLI sensor was designed by the Ball Aerospace and Technology Corporation, and it includes 9 bands covering the visible, near-infrared and short-wave infrared portions of the spectrum [15]. The OLI has spatial and spectral characteristics similar to those of the Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+), but it also includes some enhancements.…”

Section: Oli Imagerymentioning

confidence: 99%

Cross-Calibration of GF-1/WFV over a Desert Site Using Landsat-8/OLI Imagery and ZY-3/TLC Data

Yang

Zhong

Lv³

et al. 2015

Remote Sensing

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Abstract:The wide field of view (WFV) is an optical imaging sensor on-board the Gao Fen 1 (GF-1). The WFV lacks an on-board calibrator, so on-orbit radiometric calibration is required. Zhong et al. proposed a method for cross-calibrating the charge-coupled device on-board the Chinese Huan Jing 1 (HJ-1/CCD) that can be applied to the GF-1/WFV. However, the accuracy is limited because of the wider radiometric dynamic range and the higher spatial resolution of the GF-1/WFV. Therefore, Landsat-8 Operational Land Imager (OLI) imagery with a radiometric resolution similar to that of the GF-1/WFV and DEM extracted from ZY-3 three-line array panchromatic camera (TLC) with a higher spatial resolution were used. A calibration site with uniform surface material and a natural topographic variation was selected, and a model of this site's bidirectional reflectance distribution function (BRDF) was developed. The model has excellent agreement with the real situation, as shown by the comparison of the simulations to the actual OLI surface reflectance. Then, the model was used to calibrate the WFV. Compared with the TOA reflectance from synchronized Landsat-8/OLI images, all errors calculated with the calibration coefficients retrieved in this paper are less than 5%, much less than the errors

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“…Landsat-8 has an Operational Land Imager (OLI) and a Thermal Infrared Sensor (TIRS) onboard. The Landsat-8/OLI is designed by Ball Aerospace and Technology Corporation (BATC) and it includes nine bands covering the visible, near-infrared, and short-wave infrared portions of the spectrum [10].…”

Section: Datamentioning

confidence: 99%

Radiometric Cross-Calibration of GF-4 in Multispectral Bands

Yang

Zhong

et al. 2017

Remote Sensing

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Abstract:The GaoFen-4 (GF-4), launched at the end of December 2015, is China's first high-resolution geostationary optical satellite. A panchromatic and multispectral sensor (PMS) is onboard the GF-4 satellite. Unfortunately, the GF-4 has no onboard calibration assembly, so on-orbit radiometric calibration is required. Like the charge-coupled device (CCD) onboard HuanJing-1 (HJ) or the wide field of view sensor (WFV) onboard GaoFen-1 (GF-1), GF-4 also has a wide field of view, which provides challenges for cross-calibration with narrow field of view sensors, like the Landsat series. A new technique has been developed and used to calibrate HJ-1/CCD and GF-1/WFV, which is verified viable. The technique has three key steps: (1) calculate the surface using the bi-directional reflectance distribution function (BRDF) characterization of a site, taking advantage of its uniform surface material and natural topographic variation using Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) imagery and digital elevation model (DEM) products; (2) calculate the radiance at the top-of-the atmosphere (TOA) with the simulated surface reflectance using the atmosphere radiant transfer model; and (3) fit the calibration coefficients with the TOA radiance and corresponding Digital Number (DN) values of the image. This study attempts to demonstrate the technique is also feasible to calibrate GF-4 multispectral bands. After fitting the calibration coefficients using the technique, extensive validation is conducted by cross-validation using the image pairs of GF-4/PMS and Landsat-8/OLI with similar transit times and close view zenith. The validation result indicates a higher accuracy and frequency than that given by the China Centre for Resources Satellite Data and Application (CRESDA) using vicarious calibration. The study shows that the new technique is also quite feasible for GF-4 multispectral bands as a routine long-term procedure.

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The Landsat Data Continuity Mission Operational Land Imager (OLI) sensor

Cited by 4 publications

References 3 publications

Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors

Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors

Cross-Calibration of GF-1/WFV over a Desert Site Using Landsat-8/OLI Imagery and ZY-3/TLC Data

Radiometric Cross-Calibration of GF-4 in Multispectral Bands

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