Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity.

By David P. Roy, V. Kovalskyy, Hankui K. Zhang, Eric F. Vermote, Lin Yan, S. Kumar, A. Egorov. Remote Sensing of Environment, 2016

1,121 citations recorded by the source corpus.

At over 40 years, the Landsat satellites provide the longest temporal record of space-based land surface observations, and the successful 2013 launch of the Landsat-8 is continuing this legacy. Ideally, the Landsat data record should be consistent over the Landsat sensor series. The Landsat-8 Operational Land Imager (OLI) has improved calibration, signal to noise characteristics, higher 12-bit radiometric resolution, and spectrally narrower wavebands than the previous Landsat-7 Enhanced Thematic Mapper (ETM+). Reflective wavelength differences between the two Landsat sensors depend also on the surface reflectance and atmospheric state which are difficult to model comprehensively. The orbit and sensing geometries of the Landsat-8 OLI and Landsat-7 ETM+ provide swath edge overlapping paths sensed only one day apart. The overlap regions are sensed in alternating backscatter and forward scattering orientations so Landsat bi-directional reflectance effects are evident but approximately balanced between the two sensors when large amounts of time series data are considered. Taking advantage of this configuration a total of 59 million 30m corresponding sensor observations extracted from 6,317 Landsat-7 ETM+ and Landsat-8 OLI images acquired over three winter and three summer months for all the conterminous United States (CONUS) are compared. Results considering different stages of cloud and saturation filtering, and filtering to reduce one day surface state differences, demonstrate the importance of appropriate per-pixel data screening. Top of atmosphere (TOA) and atmospherically corrected surface reflectance for the spectrally corresponding visible, near infrared and shortwave infrared bands, and derived normalized difference vegetation index (NDVI), are compared and their differences quantified. On average the OLI TOA reflectance is greater than the ETM+ TOA reflectance for all bands, with greatest differences in the near-infrared (NIR) and the shortwave infrared bands due to the quite different spectral response functions between the sensors. The atmospheric correction reduces the mean difference in the NIR and shortwave infrared but increases the mean difference in the visible bands. Regardless of whether TOA or surface reflectance are used to generate NDVI, on average, for vegetated soil and vegetation surfaces (0 ≤ NDVI ≤ 1), the OLI NDVI is greater than the ETM+ NDVI. Statistical functions to transform between the comparable sensor bands and sensor NDVI values are presented so that the user community may apply them in their own research to improve temporal continuity between the Landsat-7 ETM+ and Landsat-8 OLI sensor data. The transformation functions were developed using ordinary least squares (OLS) regression and were fit quite reliably (r 2 values >0.7 for the reflectance data and >0.9 for the NDVI data, p-values <0.0001).

Subfields: Aerospace Engineering, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/53707930deda223165c15e0a4f49ac86d86ab53e

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Citations in the corpus

Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review (2020, background): “projection, and spatial extent (tile); (2) atmospheric correction; (3) cloud mask; (4) adjustments to represent the response from a common spectral bandpass and nadir BRDF-normalization keying off a single, global and constant BRDF shape [110,142]; (5) performance of geographic co-registration to wa”
Current status of Landsat program, science, and applications (2019, methodology): “…et al., 2014) have allowed subsequent development of applied maps of condition and change (Gómez et al., 2012; Hansen et al., 2014; Hermosilla et al., 2015a, 2015b; Schroeder et al., 2011; White et al., 2011; White et al., 2014; Yan and Roy, 2014, 2016; Griffiths et al., 2018; Roy and Yan, 2018).”
Analysis Ready Data: Enabling Analysis of the Landsat Archive (2018, background): “For example, in a study examining approximately 60 million Landsat 7 ETM+ observations extracted from three winter and three summer months over the CONUS more than 10% of the blue and red band Landsat 7 ETM+ pixel values were saturated [42].”
Land Cover Classification using Google Earth Engine and Random Forest Classifier - The Role of Image Composition (2020, neutral): “To focus on the effect of image selection on land cover classification, we minimized all other effects encompassing for example, those arising from the differences between RS sensors, for example, the difference between Landsat OLI and previous Landsat sensors [51], the effect of preprocessing (e.”
A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring (2017, background): “These include inter-sensor calibration [32,33], statistical examination of among-sensor spectral band-pass differences [19,34,35] and characterization of surface reflectance anisotropy [36,37].”
A survival guide to Landsat preprocessing. (2017, background): “…analysis spans different sensors (e.g., Landsat 7 ETM+ and Landsat 8 OLI) since systematic biases appear among their surface reflectance C o n C e p ts & s yn th e s is values, and these biases can be magnified in products such as the normalized difference vegetation index (NDVI; Roy et al. 2016).”
Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications (2017, background): “However, the OLI data are reported to have some differences when compared with previous Landsat sensors (Holden and Woodcock, 2016; Roy et al., 2016; Zhu et al., 2016a).”
Land cover 2.0 (2018, methodology): “In narrow ﬁ eld of view data, such as Landsat or Sentinel-2 (Gascon et al. 2017), these e ﬀ ects have been minimized using statistical correction methods (Toivonen et al. 2006; Hansen et al. 2008) and more recently using more physically-based methods (Roy et al. 2016b, 2017).”