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I was expecting the Landsat images to be something like satellite view in Google maps. I realize that close-up views in Google maps are collected by airplanes, not satellites. I'm not expecting to see individual cars and houses in the Landsat images, but the images I looked at were very disappointing. The ones I downloaded were TIFF files, 8k x 8k (or sometimes 16k x 16k), 16-bit grayscale, covering large areas (~100km square), and if I zoomed-in the image quickly became blurry. With photos that I take with a digital camera that are only 2k x 2k, I can zoom in quite a lot and see a lot of detail. The Landsat images seem to be much larger than they need to be for the level of detail they provide. EDIT: To give an example, here's San Francisco that I zoomed-in on and cropped: http://imgur.com/IoRFgCu You can see that zooming more will not make it better. Is that the best we get from Landsat? Also, why does the download contain a dozen files (named xxxB1.TIF, xxxB2.TIF, xxxB3.TIF, etc.) that seem to cover the same area? |
Most of the imagery you're looking at is probably from Landsat 7, which launched in 1999 and is still (partially) functional and active today. (Edit: scratch that, it looks to be all very recent Landsat 8 data.)
Due to the need to have comparable datasets over multiple generations of satellites, newer platforms (e.g. the recently launched Landsat Data Continuity Mission, a.k.a. Landsat 8) deliberately target similar spatial resolutions as the older platforms (This is a good thing!).
Next, Landsat as a program is focused on uses like landcover classification, monitoring broad changes over time, etc. There's a higher-resolution 15m pan-chromatic band on the past few generations, but the real value is in the lower-resolution (30m, 60m, or 100m) multispectral bands. The visible bands are just the tip of the iceberg, and they actually aren't the most useful for the "main" purposes of landsat (land cover classification, etc).
For many uses cases in remote sensing, we're much more interested in having good spectral coverage and a long record of changes than in having very high spatial resolution. Landcover classification has always been a key use case for the Landsat missions. Most of the information is in the spectral signature of a given pixel. (Some recent methods use texture, etc, but it's less useful than you'd think.) There's a trade-off between the wavelength being observed, the spatial resolution, and the exposure time. Because all bands need to be acquired in a similar amount of time, longer wavelength bands (e.g. mid-far infrared) will always have a lower spatial resolution than panchromatic or visual bands. (The transparency of the atmosphere is actually the main player here, but I'll skip the details.)
The Landsat program has been invaluable in documenting and understanding human, ecologic, geologic, and hydrologic changes over the past >30 years. We don't have anything comparable! It's really, really incredible imagery.