Sentinel-1A level-1 high resolution GRD data have 20X22m resolution (rgXaz)with 5X1 number of looks. While Landsat-8 data have 30m resolution. My question is how can I compare the resolution differences between them? In other words, which one of them has higher resolution? When I digitize a lake size from the two sensors, lake size from Landsat-8 is always greater than Sentine-1A. Note that its a same lake; images were taken on the same acquisition date or with not much of time difference. What could be the reasons behind for differences in the lake sizes? Is it because of resolution difference? If yes, How can I interpret the results?


This is a bit of an apples and oranges comparison. The Sentinel-1A sensor is an active radar system carrying a C-band synthetic aperture radar array. Whereas, Landsat 8 is a passive spectral system with 16-bit radiometric resolution across 0.43 - 2.29 micrometers (excluding the 100m2 IR bands). The characteristics of the sensors will dictate the feature resolution of the objects being detected.

Because the Landsat 8 sensor is carrying a blue edge band (0.43 - 0.45 um), it is likely able to discriminate water/wetland features more effectively than the backscatter signal from Sentinel-1A. This would account for the differences in feature size, regardless of the on the ground cell resolution differences. If you want to use radar SAR data for water/wetland classification it would be better to use L-band data from ALOS PALSAR. This would provide much better discrimination than Sentinel-1A C-band data.

I would have to add here that you really need to consider a classification algorithm. It is impossible to visually Elucidate information contained in both of these data. There could very well be information contained in the data that is providing feature discrimination that is not readily apparent in a visual assessment. The difference in the results could be associated with user bias and not information content. This is particularity relevant in a multivariate context where there may be very relevant information, contained across multiple bands that are not being used in and RGB composite, comprising the image backdrop being used in heads up digitizing. You are effectively throwing away data. In the radar data there is likely a pixel gradient that is not apparent in your visual interpretation. The image can also be seriously compromised based on the type of stretch used in display.

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    @SONAM WANGCHUK, but you are completely ignoring feature discrimination based on the actual data. SAR C-band data does not discriminate water as well as L-band whereas, the Landsat 8 blue-edge band with 16-bit radiometric resolution has the ability to discriminate water features very accurately. The increased discrimination sensitivity of Landsat 8 more than makes up for the differences in resolution differences. It is not all about resolution, you also have to factor in separability and specific feature detection of a given sensor! – Jeffrey Evans Apr 1 '16 at 19:07
  • Actually, lake surface was extracted using NDWI index (not RGB). Do you think it could have influenced the lake size? Will there be any difference between the lake areas from RGB and NDWI? @Jeffrey Evans – SONAM WANGCHUK Apr 1 '16 at 20:08
  • Yes, because you are using a specific index that is transforming the data from multivarite to univarite. As stated previously, you are throwing data away. The NDWI was developed pre-Landsat 8 and does not utilize the blue edge band which is very relevant to water/wetland gradients. – Jeffrey Evans Apr 1 '16 at 20:15

Based on the information you provided, Sentinel-1A has a higher spatial resolution. Each pixel from the Sentinel is 20m x 22m, while each pixel from Landsat 8 is 30m x 30m. For a 100m x 100m area, there would be approximately 25 pixels in the Sentinal image and approximately 9 pixels in the Landsat image.

If you're classifying the image, your analysis may be yielding a larger area from the Landsat image because of the mixed pixel problem. Pixels on the edge of the lake cover some lake and some land. But when you classify the image, you force it to pick one or the other. The smaller spatial resolution from the Sentinel image would minimize this problem. Because of the larger Landsat pixel size, there is likely a larger area mistakenly classified as lake when it is really land.

If you're manually digitizing the image, I don't have a quick answer.

EDIT: Sentinel is an active sensor, which is going to change how you interpret the data. If you're just looking for the lake area, I would use a passive sensor and not an active sensor. The lake water will have very low reflectance across all Landsat bands.

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    you are not taking into account that Sentinel-1A is an active SAR sensor and Landsat 8 is a passive spectral sensor. The differences in sensor characteristics can directly account for differences in feature representation and thus resulting feature size. – Jeffrey Evans Apr 1 '16 at 17:22
  • Edited, thanks. I'm not familiar with Sentinel. An active sensor seems like overkill for this application. – Christopher Apr 1 '16 at 18:30
  • It is not at all overkill. There is a large body of literature on water and wetland classification using radar sensors. There is a great global effort, out of NASA-JPL, for wetland classification using ALOS PALSAR L-band data. – Jeffrey Evans Apr 1 '16 at 18:37
  • It's more esoteric at least. It's far more likely that someone will know how to classify a Landsat image than interpret radar backscatter. Given OP's level of understanding (or lack thereof), Landsat seems like a more feasible option. – Christopher Apr 1 '16 at 19:24
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    Sentinel-1A level 1 data is processed into "Single Look Complex" and "Ground Range Detected" data which is quite interpretable and usable in this type of analysis. Dealing with direct interpretation of backscatter data would be an issue with Level-0 processing. – Jeffrey Evans Apr 1 '16 at 19:46

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