Landsat 8 has been online for a couple of months now, and the images look incredible. While all of the bands from previous Landsat mission...
Landsat 8 has been online for a couple of months now, and the images look incredible. While all of the bands from previous Landsat missions are still incorporated, there are a couple of new ones, such as the coastal blue band water penetration/aerosol detection and the cirrus cloud band for cloud masking and other applications. Here’s a rundown of some common band combinations applied to Landsat 8, displayed as a red, green, blue (RGB):
| Natural Color | 4 3 2 |
| False Color (urban) | 7 6 4 |
| Color Infrared (vegetation) | 5 4 3 |
| Agriculture | 6 5 2 |
| Atmospheric Penetration | 7 6 5 |
| Healthy Vegetation | 5 6 2 |
| Land/Water | 5 6 4 |
| Natural With Atmospheric Removal | 7 5 3 |
| Shortwave Infrared | 7 5 4 |
| Vegetation Analysis | 6 5 4 |
Here’s how the new bands from Landsat 8 line up with Landsat 7:
Landsat 7
|
Landsat 8
| ||||
Band Name
|
Bandwidth (µm)
|
Resolution (m)
|
Band Name
|
Bandwidth (µm)
|
Resolution (m)
|
Band 1 Coastal
|
0.43 – 0.45
|
30
| |||
Band 1 Blue
|
0.45 – 0.52
|
30
|
Band 2 Blue
|
0.45 – 0.51
|
30
|
Band 2 Green
|
0.52 – 0.60
|
30
|
Band 3 Green
|
0.53 – 0.59
|
30
|
Band 3 Red
|
0.63 – 0.69
|
30
|
Band 4 Red
|
0.64 – 0.67
|
30
|
Band 4 NIR
|
0.77 – 0.90
|
30
|
Band 5 NIR
|
0.85 – 0.88
|
30
|
Band 5 SWIR 1
|
1.55 – 1.75
|
30
|
Band 6 SWIR 1
|
1.57 – 1.65
|
30
|
Band 7 SWIR 2
|
2.09 – 2.35
|
30
|
Band 7 SWIR 2
|
2.11 – 2.29
|
30
|
Band 8 Pan
|
0.52 – 0.90
|
15
|
Band 8 Pan
|
0.50 – 0.68
|
15
|
Band 9 Cirrus
|
1.36 – 1.38
|
30
| |||
Band 6 TIR
|
10.40 – 12.50
|
30/60
|
Band 10 TIRS 1
|
10.6 – 11.19
|
100
|
Band 11 TIRS 2
|
11.5 – 12.51
|
100
| |||
For the most part, the bands line up with what we’re used to, with some minor tweaking of the spectral ranges. The thermal infrared band from Landsat 7 is now split into two bands for Landsat 8. Whereas before you had one thermal band that was acquired at 60 m resolution (and resampled to 30 m) now you have increased spectral resolution at the cost of spatial resolution. It wouldn’t be remote sensing without trade-offs, right?
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