3.4.1.3. Products

Version 2.1 by Tuan Le on 2019/05/26 13:03

Products

SGRT products are derived based on single (i.e. one timestamp) or aggregated (over a specific timespan) preprocessed data.  It has to be noted, that the radiometric terrain flattened GMR is a relatively new representation of backscatter and is therefore kept at the preprocessing level at the moment. A list of higher-level products is given below:

  • Surface Soil Moisture (SSM): The TU Wien soil moisture retrieval algorithm for Sentinel-1 can be found in 3. SSM shows the soil moisture content of the upper soil layer (in percentage), being an indispensable parameter for agriculture, drought and vegetation monitoring and climate change research.
  • Surface Soil Moisture Noise (SSM-NOISE): SSM-NOISE reflects the noise level, or in other words, the reliability of the soil moisture estimation.
  • Water (WATER): WATER shows water bodies.
  • Wetness (WWS): Wetness is a combined product containing information of SSM and WATER and depicts a classification of water bodies, wet areas and non-wet areas.
  • Seasonal RGB Composite (S-COMP): Building upon monthly or seasonal means, these composites convey changes in land cover by setting each band of an RGB image to a mean covering different timespans of a year. This often includes a variation of polarisations for each channel/band. Due to the nature of their visual impression, these composites nicely show the characteristics of the sensor to identify different types of land cover.

Parameters

Parameter products build upon a timestack of preprocessed (SIG0, GMR, PLIA) data or other higher-level data products  (WWS, SSM) and aim to aggregate a product over time using different statistics. One can specify the temporal interval of aggregation:

  • a user-defined timespan (temporal flag "T")
  • seasonally (temporal flag "S")
  • monthly (temporal flag "M")
  • daily, i.e. aggregation of a given amount of days (temporal flag "D")

For a detailed description of the product naming convention and concatenation of the herein presented parts see .....  Available statistical measures are given below:

  • Minimum ("MIN")
  • Maximum ("MAX")
  • Mean ("MEN")
  • Standard Deviation ("STD")
  • Percentiles (5th, 10th, 25th, 50th (median), 75th, 90th, 95th) ("P05",  "P10", ...)
  • Frequency (probability of a certain class within the given time period) ("FRQ")

Examples

SIG0 and GMR comparison

sig0_gam0_merge.png

Backscatter images show the Stubaier Alpen, the Wipptal and Innsbruck at 10m spatial sampling. The Austrian border separating Austria from Italy is marked in red. The acquisition was performed on 03.06.2017. Left: SIG0 backscatter image.   Right: Radiometric terrain flattened GMR backscatter image. It is clearly visible, that terrain effects are less dominant or even completely removed when comparing GMR with respect to SIG0. Moreover, important land cover types become more distinctive, for instance, wet snow at higher elevated areas.

Monthly mean (MMEN)

MMean.jpg

Exemplary depiction of a scene covering the northern part of Vienna at 10m spatial sampling. These higher level products are valuable as single acquisition dates contain abundant noise. Especially agricultural areas convey a nice impression.

Surface soil moisture (SSM)

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Sentinel-1 SSM product at 500m spatial sampling over Austria. The depicted acquisition ranges from 02.08.2016 to 10.08.2016. One can identify dry regions in the eastern part and wet regions in the center part. Water bodies, mountains and cities are depicted as white, since a soil moisture retrieval can not be performed for this types of land cover (at the moment). Additionally, forest regions also do not yield a reliable estimate of soil moisture, but have not been masked out due to the lack of a forest mask.

Seasonal composite (S-COMP)

image-20180807074415-1.png

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. (data composites)

 

References

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.

2 Small, David (2011). "Flattening gamma: Radiometric terrain correction for SAR imagery". In: IEEE Transactions on Geoscience and Remote Sensing 49.8, pp. 3081-3093.

3Gruber, Alexander et al. (2013). “Potential of Sentinel-1 for high-resolution soil moisture monitoring”. In: Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International. IEEE, pp. 4030–4033.