Hi all!
The first version of i.sentinel.mask is now available and can be installed from the official svn repository.
i.sentinel.mask is a module for cloud and shadow masks computation and it is the first step of my GSoC project.
Thanks to those who contributed with their suggestions during this first period of coding!
I will continue keeping the community constantly updated on the progress of the module [0][1].
__NOTE: This email correspondence and any attachments to and from this sender is subject to the Freedom of Information Act (FOIA) and may be disclosed to third parties.__
Trying to reinstall and getting:
Downloading precompiled GRASS Addons <i.sentinel.mask>…
ERROR: Extension <i.sentinel.mask> not found
Either Roberta has started looking at it already or it is a local problem. I will try to chase it down here before I go on vacation. Either way, I appreciate the work Roberta continues to put into this. Thanks again Roberta!
__NOTE: This email correspondence and any attachments to and from this sender is subject to the Freedom of Information Act (FOIA) and may be disclosed to third parties.__
Trying to reinstall and getting:
Downloading precompiled GRASS Addons <i.sentinel.mask>…
ERROR: Extension <i.sentinel.mask> not found
Either Roberta has started looking at it already or it is a local problem. I will try to chase it down here before I go on vacation. Either way, I appreciate the work Roberta continues to put into this. Thanks again Roberta!
__NOTE: This email correspondence and any attachments to and from this sender is subject to the Freedom of Information Act (FOIA) and may be disclosed to third parties.__
Hi all!
The first version of i.sentinel.mask is now available and can be installed from the official svn repository.
i.sentinel.mask is a module for cloud and shadow masks computation and it is the first step of my GSoC project.
Thank you, Roberta. Great job !
One question: ISTR that you mentioned that the input to this module has to be atmospherically corrected images, i.e. land surface reflectance. Is this true ? If yes, then this needs to be mentioned in the man page.
Hi all!
The first version of i.sentinel.mask is now available and can be installed from the official svn repository.
i.sentinel.mask is a module for cloud and shadow masks computation and it is the first step of my GSoC project.
Thank you, Roberta. Great job !
Thank you!
One question: ISTR that you mentioned that the input to this module has to be atmospherically corrected images, i.e. land surface reflectance. Is this true ? If yes, then this needs to be mentioned in the man page.
Hi all!
The first version of i.sentinel.mask is now available and can be installed from the official svn repository.
i.sentinel.mask is a module for cloud and shadow masks computation and it is the first step of my GSoC project.
Thank you, Roberta. Great job !
Thank you!
One question: ISTR that you mentioned that the input to this module has to be atmospherically corrected images, i.e. land surface reflectance. Is this true ? If yes, then this needs to be mentioned in the man page.
Hi Stefan!
Certainly applying the topographic correction could be very useful to avoid misclassification.
I did several tests with i.topo.corr but I always got strange results especially in areas with steeper terrain.
I also tested the topographic correction tool implemented in arcsi [0] but again the results were not satisfactory at all. This is why I have not further investigated this issue within my PhD research.
In order to remove misclassifications, e.g. due to topographic shadows, I implemented the shadow mask cleaning procedure. It intersects the rough shadow mask with the cloud mask that is shifted according to the sun position. In this way, all the areas identified as shadows without a corresponding cloud are removed from the final shadow mask.
I’m still very interested in investigating the topographic correction so if you have any suggestion please, let me know!!
Hi all!
The first version of i.sentinel.mask is now available and can be installed from the official svn repository.
i.sentinel.mask is a module for cloud and shadow masks computation and it is the first step of my GSoC project.
Thank you, Roberta. Great job !
Thank you!
One question: ISTR that you mentioned that the input to this module has to be atmospherically corrected images, i.e. land surface reflectance. Is this true ? If yes, then this needs to be mentioned in the man page.
In order to remove misclassifications, e.g. due to topographic shadows, I implemented the shadow mask cleaning procedure. It intersects the rough shadow mask with the cloud mask that is shifted according to the sun position. In this way, all the areas identified as shadows without a corresponding cloud are removed from the final shadow mask.
Maybe it could be made clearer in the manual that the module detects cloud shadows, not any shadows.
i.topo.corr performed quite well for us in the Norwegian mountains with minnaert method…
What options did you try in i.topo.corr?
Cheers
Stefan
···
Hi Stefan!
Certainly applying the topographic correction could be very useful to avoid misclassification.
I did several tests with i.topo.corr but I always got strange results especially in areas with steeper terrain.
I also tested the topographic correction tool implemented in arcsi [0] but again the results were not satisfactory at all. This is why I have not further investigated this issue within my PhD research.
In order to remove misclassifications, e.g. due to topographic shadows, I implemented the shadow mask cleaning procedure. It intersects the rough shadow mask with the cloud mask that is shifted according to the sun position. In this way, all the areas identified as shadows without a corresponding cloud are removed from the final shadow mask.
I’m still very interested in investigating the topographic correction so if you have any suggestion please, let me know!!
Hi all!
The first version of i.sentinel.mask is now available and can be installed from the official svn repository.
i.sentinel.mask is a module for cloud and shadow masks computation and it is the first step of my GSoC project.
Thank you, Roberta. Great job !
Thank you!
One question: ISTR that you mentioned that the input to this module has to be atmospherically corrected images, i.e. land surface reflectance. Is this true ? If yes, then this needs to be mentioned in the man page.
thank you for your effort. I tried i.sentinel.mask just now with one Sentinel-2 granule. I have considerably better results with Level 2A Sentinel product, topographically and atmospherically corrected from Level 1C by sen2cor with default settings.
Regarding strange results of i.topo.corr, I also had a problem few months ago. In the first pass, if the resolution of DEM raster differs from current region settings, you can create strange illumination model. The solution was to set region from the DEM (e.g. with 25 m resolution in case of EU-DEM), then to create illumination model with “i.topo.corr -i …”), restore region settings (e.g. with 30 m for Landsat) and finally to apply topo correction in the second pass.
For some unknown reason, i.topo.corr did not change region settings automatically, despite you can find “The illumination model (cos_i) with flag -i uses the actual region as limits and the resolution of the elevation map.” in its manual (Notes section).
Subject: Re: [GRASS-dev] New GRASS addon i.sentinel.mask is now available (GSoC 2018 project)
Hi Stefan!
Certainly applying the topographic correction could be very useful to avoid misclassification.
I did several tests with i.topo.corr but I always got strange results especially in areas with steeper terrain.
I also tested the topographic correction tool implemented in arcsi [0] but again the results were not satisfactory at all. This is why I have not further investigated this issue within my PhD research.
In order to remove misclassifications, e.g. due to topographic shadows, I implemented the shadow mask cleaning procedure. It intersects the rough shadow mask with the cloud mask that is shifted according to the sun position. In this way, all the areas identified as shadows without a corresponding cloud are removed from the final shadow mask.
I’m still very interested in investigating the topographic correction so if you have any suggestion please, let me know!!
Hi all!
The first version of i.sentinel.mask is now available and can be installed from the official svn repository.
i.sentinel.mask is a module for cloud and shadow masks computation and it is the first step of my GSoC project.
Thank you, Roberta. Great job !
Thank you!
One question: ISTR that you mentioned that the input to this module has to be atmospherically corrected images, i.e. land surface reflectance. Is this true ? If yes, then this needs to be mentioned in the man page.
thank you for your effort. I tried i.sentinel.mask just now with one Sentinel-2 granule. I have considerably better results with Level 2A Sentinel product, topographically and atmospherically corrected from Level 1C by sen2cor with default settings.
Thank you so much for testing i.sentinel.mask! If it is possible I would be very interested in seeing your results.
Regarding strange results of i.topo.corr, I also had a problem few months ago. In the first pass, if the resolution of DEM raster differs from current region settings, you can create strange illumination model. The solution was to set region from the DEM (e.g. with 25 m resolution in case of EU-DEM), then to create illumination model with “i.topo.corr -i …”), restore region settings (e.g. with 30 m for Landsat) and finally to apply topo correction in the second pass.
For some unknown reason, i.topo.corr did not change region settings automatically, despite you can find “The illumination model (cos_i) with flag -i uses the actual region as limits and the resolution of the elevation map.” in its manual (Notes section).
Interesting! I will test i.topo.corr as you suggest.
Subject: Re: [GRASS-dev] New GRASS addon i.sentinel.mask is now available (GSoC 2018 project)
Hi Stefan!
Certainly applying the topographic correction could be very useful to avoid misclassification.
I did several tests with i.topo.corr but I always got strange results especially in areas with steeper terrain.
I also tested the topographic correction tool implemented in arcsi [0] but again the results were not satisfactory at all. This is why I have not further investigated this issue within my PhD research.
In order to remove misclassifications, e.g. due to topographic shadows, I implemented the shadow mask cleaning procedure. It intersects the rough shadow mask with the cloud mask that is shifted according to the sun position. In this way, all the areas identified as shadows without a corresponding cloud are removed from the final shadow mask.
I’m still very interested in investigating the topographic correction so if you have any suggestion please, let me know!!
Hi all!
The first version of i.sentinel.mask is now available and can be installed from the official svn repository.
i.sentinel.mask is a module for cloud and shadow masks computation and it is the first step of my GSoC project.
Thank you, Roberta. Great job !
Thank you!
One question: ISTR that you mentioned that the input to this module has to be atmospherically corrected images, i.e. land surface reflectance. Is this true ? If yes, then this needs to be mentioned in the man page.