How do different sensors perform across the electromagnetic spectrum? This question bears practical importance when we want to combine data acquired by different sensors. I thought it would be interesting and fun to do a simulation of how different common sensors see the same feature.

We could in principle do this using subsets of images of the same region captured by different sensors, but it is actually easier to compare them using a given spectral signature, the reflectance (or emittance) of a certain material as a function of wavelength.

I therefore went to the Aster spectral library and downloaded several datasets corresponding to different spectral signatures. In the following example, we use that of common lawn grass:

Spectral signature of lawn grass.
Spectral signature of lawn grass. Source: ASTER spectral library.

How do Landsat 7 ETM+, landsat 8 OLI and Sentinel 2A MSI “see” this grass? To answer this question we need to know the shape of the actual relative sensor responses as a function of wavelength. These are technical data that can be found in the documentation for the sensors, and can for example be downloaded from this site.

The data about the spectral signature of grass and the relative spectral responses of the instruments are expressed slightly differently in these files: micrometers vs. nanometers of wavelength; different intervals of the data, etc. Therefore a bit of fiddling with the data was necessary to put everything into a comparable form. I did all that using an eclectic mixture of free software tools (as usual!). In this case, I mainly used Perl Data Language and Generic Mapping Tools to interpolate the one-dimensional files onto a regular wavelength interval (I used sample1d for that). Everything was neatly glued together using Perl scripts and charts were created with gnuplot.

We can see that the responses of these three instruments in the visible and near-infrared parts of the spectrum are somewhat different:

Spectral response of Landsat 7 ETM+ in the visible and near infrared.
Spectral response of Landsat 7 ETM+ in the visible and near infrared. Data from Aster Spectral Library and Landsat Program.
Spectral response of Landsat 8 OLI in the visible and near infrared.
Spectral response of Landsat 8 OLI in the visible and near infrared. Data from Aster Spectral library and Landsat program.
Spectral response of Sentinel 2 MSI in the visible and near infrared.
Spectral response of Sentinel 2 MSI in the visible and near infrared. Data from ESA Copernicus Sentinel program.

Or, if we make a somewhat crowded packing of all three in one figure:

Spectral responses of Landsat 7 ETM+, Landsat 8 OLI and Sentinel 2 MSI in the visible and near infrared.
Spectral responses of Landsat 7 ETM+, Landsat 8 OLI and Sentinel 2 MSI in the visible and near infrared. Data from ASTER Spectral library, Landsat Program and ESA Copernicus Sentinel Program.

I deliberately omitted the bands 5, 6 and 7 of Sentinel 2 that capture the vegetation ramp to avoid clutter. Also since Landsat sensors do not have this feature there would be nothing to actually compare. We see that although the bands are judiciously designed to capture vegetation, all three sensors capture slightly different versions of the same feature.

To make the comparison, I derived the averaged reflectance of lawn grass, as expressed in the spectral signature, weighted by the relative spectral response of each sensor in each band. I then calculated the reflectance for each band and also for fun calculated the Normalized Vegetation Index:

Average band reflectance (percent) for grass using weighted sensor sensitivities

ETM+
B: 3.87
G: 7.86
R: 4.51
IR: 38.54
NDVI: 0.79

OLI
B: 3.93
G: 8.88
R: 4.67
IR: 34.03
NDVI: 0.76

MSI
B: 3.30
G: 8.39
R: 4.16
IR: 32.56
NDVI: 0.77

We see that values are very similar and differ only in a negligible way. The differences noted above would possibly dissapear in a real case under the uncertainty introduced by atmospheric effects and their correction. This is good news, but we have to remember that we have chosen a very simple spectral signature to study (that of grass). We cannot rule out that a more complicated signature could possibly fare worse, introducing artifacts and discrepancies in the results. In fact, small but significant differences in spectral responses have been found that may require due attention in critical applications (see for example this paper by Mandanici and Bitelli )

Finally, in a whim of informality, I allowed myself to do some “magicks” and create fake pixels out of these curves that would show what the sensors would display on an image. I did this by rescaling the reflectances to the range 0-255, allowing either the green or the infrarred take the value 255 for natural (R,G,B – RGB) and false color (IR,R,G – RGB), respectively, while adjusting the other two channels proportionately:

Simulated grass pixel, as potentially seen by ETM+, OLI and MSI.
Simulated grass pixel, as potentially seen by ETM+, OLI and MSI.
Simulated grass pixel, as potentially seen by ETM+, OLI and MSI. False color.
Simulated grass pixel, as potentially seen by ETM+, OLI and MSI. False color.

Obviously, very much similar tones, although not quite!

Thanks for reading!

References

Mandanici, E.; Bitelli, G. Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use. Remote Sens. 2016, 8, 1014.

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