Wiki source code of 3.5.3. Sentinel-1 products

Version 7.1 by Claudio Navacchi on 2018/10/03 11:52
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1 Index
2
3 * Processing chain
4 ** Preprocessing
5 ** Parameters
6 ** Products
7 * Examples
8 ** Monthly mean (MMEN)
9 ** Data composite (M-COMP/S-COMP)
10 ** Surface soil moisture (SSM)
11 * References
12
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14 == __**Processing chain**__ ==
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16 The processing chainline of the //SAR Geophysical Retrieval Toolbox (SGRT)// \autocite{naeimi2016geophysical} allows to use Sentinel-1A and Sentinel-1B raw data (Level 1) hosted by EODC and produce higher level products, such as \textbf{preprocessed datasets} (Level 2), \textbf{parameter datasets} (Level 3) and finally value-added \textbf{product datasets} (Level 4) like surface soil moisture (the naming follows the SGRT terminology).
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18 === __**Preprocessing**__ ===
19
20 === __**Parameters**__ ===
21
22 === __**Products**__ ===
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24
25 == __**Examples**__ ==
26
27 === __**Data composite (M-COMP/S-COMP)**__ ===
28
29 [[**Data composites** >>https://www.eodc.eu/sentinel-1-data-composites/]]are created by the Remote Sensing Group of TU Wien. A fully customized **pre-processing** chain is applied to the Sentinel-1 Level 1 SAR data, including calibration, georeferencing, terrain correction, format conversion and compression, quality control, etc. The resulting **backscatter data** are used and delivered as **combinations** of either **different** **polarisations** (e.g. V/V + V/H) and **timely averages** (e.g. monthly or seasonal). Additionally, **false colour composites** are created (e.g. RGB combinations of different polarisations at different times of year). Due to the nature of their visual impression, these composites nicely show the characteristics of the sensor to identify different types of land cover.
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32 (% style="text-align:center" %)
33 [[image:image-20180807074415-1.png||height="226" width="1000"]]
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35 Left: Landsat-8 RGB image of London area, UK. Middle: False colour composite (RGB) of Sentinel-1 backscatter data over London area, UK. Red band shows mean of backscatter (VH) during summer (Jun-Jul-Aug), Blue band shows mean of backscatter (VH) during winter (Dec-Jan-Feb), and Green band shows the ratio of the Red and Blue band (mean_summer/mean_winter). Right: False colour composite (RGB) of Sentinel-1 backscatter data over London area, UK. Red band shows mean of backscatter (VV) during winter (Dec-Jan-Feb), Blue band shows mean of backscatter (VH) during winter (Dec-Jan-Feb), and Green band shows the ratio of the Red and Blue band (mean_summer/mean_winter). This composite highlights the variation of backscatter due to different polarizations.
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38 === __**Monthly mean (MMEN)**__ ===
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40 The **mean products **consits of a single dataset representing the average value per pixel over a defined period. These can be generated at five, ten or monthly intervalls. These higher level products are valuable as single acquisition dates contain abundant noise.
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42 (% style="text-align:center" %)
43 [[image:MMean.jpg]]
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48 === __**Surface soil moisture (SSM)**__ ===
49
50 === __**References**__ ===
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53 ^^1^^ Naeimi, Vahid et al. (2016). “Geophysical parameters retrieval from sentinel-1 SAR data: a case study for high performance computing at EODC”. In: //Proceedings of the 24th High Performance Computing Symposium.// Society for Computer Simulation International, p. 10.
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