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).