Difference between revisions of "RECOMMENDATIONS"

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[[GFMS | [Home]]]
 
[[GFMS | [Home]]]
 
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While [[RECOMMENDATIONS#False alarms | '''false alarms''']] draw the attention of users unnecessarily and thus could create frustration and mistrust in the product, [[RECOMMENDATIONS#Missed alarms | '''missed alarms''']] on the other hand would lead to situation where the flood event is not detected and leaves the users without notice, possibly losing time for reaction measures.
 
While [[RECOMMENDATIONS#False alarms | '''false alarms''']] draw the attention of users unnecessarily and thus could create frustration and mistrust in the product, [[RECOMMENDATIONS#Missed alarms | '''missed alarms''']] on the other hand would lead to situation where the flood event is not detected and leaves the users without notice, possibly losing time for reaction measures.
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==False alarms==
 
==False alarms==
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As opposite, floods occurring in:
 
As opposite, floods occurring in:
 
* '''urban areas'''
 
* '''urban areas'''
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* '''densely vegetated sites'''
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* '''densely vegetated areas'''
  
 
or under weather conditions featuring:
 
or under weather conditions featuring:
 
* '''strong winds'''
 
* '''strong winds'''
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* '''heavy rain'''  
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* '''heavy rainfall'''  
  
 
bear the danger of missed alarms.
 
bear the danger of missed alarms.
 
In particular, wind and heavy rainfall are hard-to-spot dynamic process as they roughen water surfaces and hence undermine the initial assumption of low backscatter due to specular reflection on smooth water surfaces.
 
In particular, wind and heavy rainfall are hard-to-spot dynamic process as they roughen water surfaces and hence undermine the initial assumption of low backscatter due to specular reflection on smooth water surfaces.
  
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static - Effects bound to static ground surface characteristics like e.g. land cover or shadowing, are addressed by the Exclusion Mask
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== How to tackle false/missed alarms with the GFMS==
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This discuss the abovementioned issues of SAR-based water mapping which potentially cannot be directly solved by the proposed flood detection algorithms using only NRT-available backscatter information and hence indication need on pixel level for potential misclassification due to reduced sensitivity. <br>
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The aforementioned challenges are the classified into '''Static effects''' and '''Dynamic effects''' with the first bound to the ground surface, land cover or topography, and the latter resulting from meteorological dynamics. This is also done with the perspective on a performant global NRT processing. <br>
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As a note: <u>static</u> is understood here with respect to the GFMS reprocessing cycle, i.e., static layers remain unchanged during NRT processing, but they might be updated in the course of a reprocessing after evolution activities.
  
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We grouped these reasons and defined four types:
 
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1. Sentinel-1 does not receive sufficiently strong signals from the ground surface to distinguish a flooded from a non-flooded surface. In such a case, we encounter No-Sensitivity to detect water surfaces.
 
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2. Sentinel-1 senses the ground, but backscatter from the non-flooded surface is in general so low as to be indistinguishable from backscatter from smooth open water. Here we encounter water-look-alikes due to a Low Backscatter signature of the ground.
 
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3. The Sentinel-1 signals are heavily distorted by terrain effects, effectively enhancing the noise and signal disturbances to such a degree to that it becomes larger than the change in backscatter due to potential flooding. As this problem generally occurs over areas with strong topography, we refer to this with Topographic Distortions.
 
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4. Sentinel-1 receives no signals from certain regions of the land surface due to Radar Shadows casted by mountains, high vegetation canopies or anthropogenic structures.
 
  
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and dynamic effects SAR backscatter pixels that during sensing were likely to be influenced by meteorological conditions like wind, soil dryness or freeze, or wet snow cover, are flagged by the dynamic Advisory Flags
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'''Static effects''' bound to ground surface characteristics like:
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* land cover (e.g., '''flat impervious areas''', '''urban areas''', '''densely vegetated areas''')
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* shadowing ('''radar shadowing''')
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are addressed by the '''[[Exclusion Mask]]'''. <br>
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Pixels that could not be classified by the SAR sensor into flood area, permanent/seasonal water body, and non-water area, are highlighted in this product layer as no-data pixels.
  
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strong winds, rainfall, as well as the presence of wet snow, frost and dry soils.
 
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Unfortunately, all these factors are difficult to capture because of their extremely dynamic nature and high spatial heterogeneity. Therefore, wind, rainfall, temperature, and snow data coming from sparsely distributed meteorological stations and/or numerical weather prediction with its much coarser resolution are hardly suited to capture the exact situation at the time of the S-1 acquisition and are not ideal for providing the required advisory flags. Optical remote sensing satellites such as MODIS or Sentinel-3 would provide sufficient spatial details, but neither do optical data depict the environmental conditions as seen by Sentinel-1, nor do they provide timely observations at all times due to cloud cover and poor lightning conditions. Therefore, the only robust and applicable source for the environmental advisory flags are microwave remote sensing data.
 
  
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'''Dynamic effects''' triggered by weather conditions
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* meteorological features ('''strong wind''', '''heavy rainfall''')
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* meteorological-induced state of the soil ('''soil dryness''', '''frozen ground''', or '''wet snow''')
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are flagged by the dynamic '''[[Advisory Flags]]'''. The '''[[Advisory Flags]]''' indicate locations where the SAR data might be disturbed by such processes during the acquisition, but leaves the flood and water extent layers unmasked.
  
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[[File:figure14.jpg]]
 
  
  
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During the snow accumulation period, dry snow- and icepacks are almost transparent to microwaves. As a result, the SAR signal penetrates the snow/icepack up to several meters and the main contribution to the backscattering is from the snow–ground interface (Rott and Mätzler, 1987). During the melting period, however, the increase of the amount of free liquid water inside the snow and ice bodies causes high dielectric losses, thereby increasing the absorption coefficient, featuring very low backscatter. In addition, the occurrence of meltwater puddles might change the backscattering behaviour of the surface, leading to components with specular microwave reflection, further decreasing the received amplitude at the sensor. Such patches of very low backscatter from those combined effects act easily as water-look-alikes and are source of false alarms.
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Both layers are delivered with the '''[[PRODUCTS | other flood product layers]]''' and aim at improving the reliability, usefulness and user acceptance of the GFMS product. <br>
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Similarly, frozen soils with no free liquid water components in the upper soil layers show very low backscatter signatures, appearing from the radar perspective as quasi-dry soils.
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The design of this mask/flag-system recognises also the User perspective: with our approach, we provide a simple '''[[Exclusion Mask]]''' indicating all the pixels that could not be classified by the input Sentinel-1 data, consulting statistical parameters from the data cube as well as auxiliary datasets. <br>
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Wind – Advisory Flag 2
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The pixels addressed by '''[[Exclusion Mask]]''' thus can be directly discarded as no-data, leaving the interpretation of the produced flood extent and the (adjacent) no-data-gaps to the users, who commonly know best their area-of-interest. We believe that users are have in general good skill to deal with no-data-gaps, as long as the general reliability of the product is assured.
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Wind over flood surfaces can lead to missed alarms, as it roughens water surfaces and hence undermine the initial assumption of low backscatter due to specular reflection on smooth water surfaces. For the detection of wind, we make use of the backscatter signature of permanent water bodies inferred from time series within the Sentinel-1 data cube archive.  
 
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We examine the NRT 20m Sentinel-1 backscatter data over permanent water bodies whether it deviates from a calm water signature. In the case of a roughened water surface, it is most likely to observe increased backscatter that it is indicative of strong winds, which in turn are likely to roughen nearby water surfaces over the flooded areas too.
 
  
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The '''[[Advisory Flags]]''' layer aims to raise awareness that meteorological processes comprising wind or frozen conditions might impair the water body detection. <br>
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As the '''[[Advisory Flags]]''' can only be retrieved at a coarser resolution, we do not forward the information of the flags to the masking or to the '''[[Exclusion Mask]]'''. <br>
 
As coming in the form of the additional layer, it should guide the users when interpreting the product, allowing additional insight on its local reliability at the time of Sentinel-1 acquisition.
 
As coming in the form of the additional layer, it should guide the users when interpreting the product, allowing additional insight on its local reliability at the time of Sentinel-1 acquisition.
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[[GFMS | [Home]]]
 
[[GFMS | [Home]]]

Latest revision as of 14:11, 4 March 2021

caption

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In general, we can distinguish between effects leading to both over- and underestimation of detected flood extent (related to false alarms and missed alarms, respectively).

While false alarms draw the attention of users unnecessarily and thus could create frustration and mistrust in the product, missed alarms on the other hand would lead to situation where the flood event is not detected and leaves the users without notice, possibly losing time for reaction measures.

False alarms

The here mentioned water-look-alikes would yield false alarms:

  • very dry or sandy soils
  • frozen ground
  • wet snow
  • flat impervious areas (e.g. smooth tarmac covers as airfields or roads)

These surfaces and artifacts usually feature very low backscatter signatures and appear thus as water-look-alikes in SAR imagery, rendering the water and flood mapping a difficult task.
Another common effect in SAR remote sensing is radar shadowing, which appear over strong terrain (especially at the far-range section of the SAR image) as well as in the vicinity of high objects above the ground, like high buildings and along forest borders.

Missed alarms

As opposite, floods occurring in:

  • urban areas
  • densely vegetated areas

or under weather conditions featuring:

  • strong winds
  • heavy rainfall

bear the danger of missed alarms. In particular, wind and heavy rainfall are hard-to-spot dynamic process as they roughen water surfaces and hence undermine the initial assumption of low backscatter due to specular reflection on smooth water surfaces.

How to tackle false/missed alarms with the GFMS

This discuss the abovementioned issues of SAR-based water mapping which potentially cannot be directly solved by the proposed flood detection algorithms using only NRT-available backscatter information and hence indication need on pixel level for potential misclassification due to reduced sensitivity.

The aforementioned challenges are the classified into Static effects and Dynamic effects with the first bound to the ground surface, land cover or topography, and the latter resulting from meteorological dynamics. This is also done with the perspective on a performant global NRT processing.
As a note: static is understood here with respect to the GFMS reprocessing cycle, i.e., static layers remain unchanged during NRT processing, but they might be updated in the course of a reprocessing after evolution activities.


Static effects bound to ground surface characteristics like:

  • land cover (e.g., flat impervious areas, urban areas, densely vegetated areas)
  • shadowing (radar shadowing)

are addressed by the Exclusion Mask.
Pixels that could not be classified by the SAR sensor into flood area, permanent/seasonal water body, and non-water area, are highlighted in this product layer as no-data pixels.


Dynamic effects triggered by weather conditions

  • meteorological features (strong wind, heavy rainfall)
  • meteorological-induced state of the soil (soil dryness, frozen ground, or wet snow)

are flagged by the dynamic Advisory Flags. The Advisory Flags indicate locations where the SAR data might be disturbed by such processes during the acquisition, but leaves the flood and water extent layers unmasked.


Both layers are delivered with the other flood product layers and aim at improving the reliability, usefulness and user acceptance of the GFMS product.
The design of this mask/flag-system recognises also the User perspective: with our approach, we provide a simple Exclusion Mask indicating all the pixels that could not be classified by the input Sentinel-1 data, consulting statistical parameters from the data cube as well as auxiliary datasets.
The pixels addressed by Exclusion Mask thus can be directly discarded as no-data, leaving the interpretation of the produced flood extent and the (adjacent) no-data-gaps to the users, who commonly know best their area-of-interest. We believe that users are have in general good skill to deal with no-data-gaps, as long as the general reliability of the product is assured.

The Advisory Flags layer aims to raise awareness that meteorological processes comprising wind or frozen conditions might impair the water body detection.
As the Advisory Flags can only be retrieved at a coarser resolution, we do not forward the information of the flags to the masking or to the Exclusion Mask.
As coming in the form of the additional layer, it should guide the users when interpreting the product, allowing additional insight on its local reliability at the time of Sentinel-1 acquisition.

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