Landsat-8 Operational Land Imager (OLI) Radiometric Performance On-Orbit

Morfitt, Ron; Barsi, Julia A.; Levy, Raviv; Markham, Brian L.; Micijevic, Esad; Ong, Lawrence; Scaramuzza, Pat; Vanderwerff, Kelly

doi:10.3390/rs70202208

Cited by 145 publications

(101 citation statements)

References 14 publications

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“…The OLI imagery has seven multi-spectral bands in VIS-SWIR region at 30-m spatial resolution [40]. In comparison with other Landsat systems (TM and ETM+), the OLI has a new "deep blue" band, which may provide additional information about soil properties in this study.…”

Section: Satellite Imagery and Preprocessingmentioning

confidence: 99%

Remote Sensing of Soil Alkalinity and Salinity in the Wuyu’er-Shuangyang River Basin, Northeast China

et al. 2016

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Abstract:The Songnen Plain of the Northeast China is one of the three largest soda saline-alkali regions worldwide. To better understand soil alkalinization and salinization in this important agricultural region, it is vital to explore the distribution and variation of soil alkalinity and salinity in space and time. This study examined soil properties and identified the variables to extract soil alkalinity and salinity via physico-chemical, statistical, spectral, and image analysis. The physico-chemical and statistical results suggested that alkaline soils, coming from the main solute Na 2 CO 3 and NaHCO 3 in parent rocks, characterized the study area. The pH and electric conductivity (EC ) were correlated with both narrow band and broad band reflectance. For soil pH, the sensitive bands were in short wavelength (VIS) and the band with the highest correlation was 475 nm (r = 0.84). For soil EC, the sensitive bands were also in VIS and the band with the highest correlation was 354 nm (r = 0.84). With the stepwise regression, it was found that the pH was sensitive to reflectance of OLI band 2 and band 6, while the EC was only sensitive to band 1. The R 2 Adj (0.73 and 0.72) and root mean square error (RMSE) (0.98 and 1.07 dS/m) indicated that, the two stepwise regression models could estimate soil alkalinity and salinity with a considerable accuracy. Spatial distributions of soil alkalinity and salinity were mapped from the OLI image with the RMSE of 1.01 and 0.64 dS/m, respectively. Soil alkalinity was related to salinity but most soils in the study area were non-saline soils. The area of alkaline soils was 44.46% of the basin. Highly alkaline soils were close to the Zhalong wetland and downstream of rivers, which could become a severe concern for crop productivity in this area.

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Section: Satellite Imagery and Preprocessingmentioning

confidence: 99%

Remote Sensing of Soil Alkalinity and Salinity in the Wuyu’er-Shuangyang River Basin, Northeast China

et al. 2016

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“…It is important to study continuity among sensors from the same series of satellites by considering of the important parameters including sensor characteristics, over-pass time, scanning system, angular affect and etc. A few studies related to the continuity of Landsat 8 have been conducted, including preflight and post-launch calibrations [28][29][30][31][32][33]. Most of these studies have shown that the continuity between the Landsat-8 OLI and Landsat-7 ETM+ is good.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

She

Zhang

et al. 2015

Remote Sensing

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Landsat 8, the most recently launched satellite of the series, promises to maintain the continuity of Landsat 7. However, in addition to subtle differences in sensor characteristics and vegetation index (VI) generation algorithms, VIs respond differently to the seasonality of the various types of vegetation cover. The purpose of this study was to elucidate the effects of these variations on VIs between Operational Land Imager (OLI) and Enhanced Thematic Mapper Plus (ETM+). Ground spectral data for vegetation were used to simulate the Landsat at-senor broadband reflectance, with consideration of sensor band-pass differences. Three band-geometric VIs (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI)) and two band-transformation VIs (Vegetation Index based on the Universal Pattern Decomposition method (VIUPD), Tasseled Cap Transformation Greenness (TCG)) were tested to evaluate the performance of various VI generation algorithms in relation to multi-sensor continuity. Six vegetation types were included to evaluate the continuity in different vegetation types. Four pairs of data during four seasons were selected to evaluate continuity with respect to seasonal variation. The simulated data showed that OLI largely inherits the band-pass characteristics of ETM+. Overall, the continuity of band-transformation derived VIs was higher than OPEN ACCESSRemote Sens. 2015, 7 13486 band-geometry derived VIs. VI continuity was higher in the three forest types and the shrubs in the relatively rapid growth periods of summer and autumn, but lower for the other two non-forest types (grassland and crops) during the same periods.

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“…10 OLI is the most advanced radiometer ever flown on a Landsat platform, with SNRs roughly an order of magnitude higher than the predecessor Enhanced Thematic Mapper Plus (ETMþ) instrument of Landsat-7 and 12-bit rather than 8-bit digital resolution. 11 The SNRs reported for OLI or ETMþ, however, are based on radiances typical of land observations. To assess potential OLI performance over oceans, the sensor noise model 11 was applied to typical radiances (Ltyp) interpolated from values reported in Ref.…”

Section: Data and Sensor Characteristicsmentioning

confidence: 99%

Ocean color measurements with the Operational Land Imager on Landsat-8: implementation and evaluation in SeaDAS

Franz

Bailey

Kuring

et al. 2015

J. Appl. Remote Sens

133

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Abstract. The Operational Land Imager (OLI) is a multispectral radiometer hosted on the recently launched Landsat8 satellite. OLI includes a suite of relatively narrow spectral bands at 30 m spatial resolution in the visible to shortwave infrared, which makes it a potential tool for ocean color radiometry: measurement of the reflected spectral radiance upwelling from beneath the ocean surface that carries information on the biogeochemical constituents of the upper ocean euphotic zone. To evaluate the potential of OLI to measure ocean color, processing support was implemented in Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), which is an open-source software package distributed by NASA for processing, analysis, and display of ocean remote sensing measurements from a variety of spaceborne multispectral radiometers. Here we describe the implementation of OLI processing capabilities within SeaDAS, including support for various methods of atmospheric correction to remove the effects of atmospheric scattering and absorption and retrieve the spectral remote sensing reflectance (Rrs; sr −1 ). The quality of the retrieved Rrs imagery will be assessed, as will the derived water column constituents, such as the concentration of the phytoplankton pigment chlorophyll a.

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Landsat-8 Operational Land Imager (OLI) Radiometric Performance On-Orbit

Cited by 145 publications

References 14 publications

Remote Sensing of Soil Alkalinity and Salinity in the Wuyu’er-Shuangyang River Basin, Northeast China

Remote Sensing of Soil Alkalinity and Salinity in the Wuyu’er-Shuangyang River Basin, Northeast China

Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

Ocean color measurements with the Operational Land Imager on Landsat-8: implementation and evaluation in SeaDAS

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