Judul : Band Combinations for Landsat 8
link : Band Combinations for Landsat 8
Band Combinations for Landsat 8
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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