SENTINEL - Revision history

⚠️Griembauer: /* Atmospheric correction with Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI) */ Update of arcsi installation guide

2021-02-17T13:50:00Z

Atmospheric correction with Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI): Update of arcsi installation guide

← Older revision		Revision as of 13:50, 17 February 2021
Line 280:		Line 280:
	Aerosol Optical Thickness (AOT) is an important parameter in atmospheric correction models such as 6S. In some regions of the world it has been found that a constant can be used. However, for most regions a constant is not viable as the atmosphere is very variable. Incorrect parametrization for AOT can lead to large errors in atmospheric correction. ARCSI provides a method of deriving AOT from an image by using a dark object subtraction (DOS) to estimate surface reflectance in the blue channel. Taking this estimated reflectance as input, 6S is then numerically inverted to identify an AOT value which provides a surface reflectance value as close to that estimated from DOS as possible.		Aerosol Optical Thickness (AOT) is an important parameter in atmospheric correction models such as 6S. In some regions of the world it has been found that a constant can be used. However, for most regions a constant is not viable as the atmosphere is very variable. Incorrect parametrization for AOT can lead to large errors in atmospheric correction. ARCSI provides a method of deriving AOT from an image by using a dark object subtraction (DOS) to estimate surface reflectance in the blue channel. Taking this estimated reflectance as input, 6S is then numerically inverted to identify an AOT value which provides a surface reflectance value as close to that estimated from DOS as possible.

	Instructions to install the software can be found at: https://~~spectraldifferences~~.~~wordpress~~.~~com/software~~/, but you can also have it through a docker image from: https://github.com/mundialis/docker-arcsi (for a usage example, see below).		Instructions to install the software can be found at: https://arcsi.remotesensing.info/, but you can also have it through a docker image from: https://github.com/mundialis/docker-arcsi (for a usage example, see below).

	''Important'': The resulting atmospherically corrected satellite data are stored in '''KEA format''' (extended HDF) and can be converted with <tt>gdal_translate</tt> but ONLY if GDAL was compiled with http://www.kealib.org/ support or the kealib GDAL-plugin is installed!		''Important'': The resulting atmospherically corrected satellite data are stored in '''KEA format''' (extended HDF) and can be converted with <tt>gdal_translate</tt> but ONLY if GDAL was compiled with http://www.kealib.org/ support or the kealib GDAL-plugin is installed!

Neteler: + Atmospheric correction with Maja (Draft)

2020-12-19T11:31:15Z

+ Atmospheric correction with Maja (Draft)

@@ Line 437: / Line 437: @@
 Example here
 -->
 ===Estimate indices===

Neteler: /* Atmospheric correction with Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI) */

2020-08-19T14:07:07Z

Atmospheric correction with Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI)

← Older revision		Revision as of 14:07, 19 August 2020
Line 282:		Line 282:
	Instructions to install the software can be found at: https://spectraldifferences.wordpress.com/software/, but you can also have it through a docker image from: https://github.com/mundialis/docker-arcsi (for a usage example, see below).		Instructions to install the software can be found at: https://spectraldifferences.wordpress.com/software/, but you can also have it through a docker image from: https://github.com/mundialis/docker-arcsi (for a usage example, see below).

	''Important'': The resulting atmospherically corrected satellite data are stored in '''KEA format''' (extended HDF) and can be converted with <tt>gdal_translate</tt> but ONLY if GDAL was compiled with http://www.kealib.org/ support!		''Important'': The resulting atmospherically corrected satellite data are stored in '''KEA format''' (extended HDF) and can be converted with <tt>gdal_translate</tt> but ONLY if GDAL was compiled with http://www.kealib.org/ support or the kealib GDAL-plugin is installed!

	{\| class="wikitable sortable"		{\| class="wikitable sortable"

Neteler: /* Atmospheric correction with Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI) */

2020-08-19T13:20:51Z

Atmospheric correction with Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI)

← Older revision		Revision as of 13:20, 19 August 2020
Line 282:		Line 282:
	Instructions to install the software can be found at: https://spectraldifferences.wordpress.com/software/, but you can also have it through a docker image from: https://github.com/mundialis/docker-arcsi (for a usage example, see below).		Instructions to install the software can be found at: https://spectraldifferences.wordpress.com/software/, but you can also have it through a docker image from: https://github.com/mundialis/docker-arcsi (for a usage example, see below).

	Important: The resulting atmospherically corrected satellite data are stored in '''KEA format''' (extended HDF) and can be converted with <tt>gdal_translate</tt> but ONLY if GDAL was compiled with http://www.kealib.org/ support!		''Important'': The resulting atmospherically corrected satellite data are stored in '''KEA format''' (extended HDF) and can be converted with <tt>gdal_translate</tt> but ONLY if GDAL was compiled with http://www.kealib.org/ support!

	{\| class="wikitable sortable"		{\| class="wikitable sortable"
Line 399:		Line 399:
	--dem ${S2IMG}.SAFE/srtm_21_05_utm17 --minaot 0.05 --maxaot 0.6 --simpledos		--dem ${S2IMG}.SAFE/srtm_21_05_utm17 --minaot 0.05 --maxaot 0.6 --simpledos
	</source>		</source>

			All resulting files are stored in KEA format.

	==== ARCSI: usage example using docker ====		==== ARCSI: usage example using docker ====
Line 428:		Line 430:
	--dem /dem/${DEM} --demnodata 0 --minaot 0.05 --maxaot 0.6		--dem /dem/${DEM} --demnodata 0 --minaot 0.05 --maxaot 0.6
	</source>		</source>

			All resulting files are stored in KEA format.

	<!--		<!--

Neteler: +note about KEA format

2020-08-19T10:37:31Z

+note about KEA format

← Older revision		Revision as of 10:37, 19 August 2020
Line 281:		Line 281:

	Instructions to install the software can be found at: https://spectraldifferences.wordpress.com/software/, but you can also have it through a docker image from: https://github.com/mundialis/docker-arcsi (for a usage example, see below).		Instructions to install the software can be found at: https://spectraldifferences.wordpress.com/software/, but you can also have it through a docker image from: https://github.com/mundialis/docker-arcsi (for a usage example, see below).

			Important: The resulting atmospherically corrected satellite data are stored in '''KEA format''' (extended HDF) and can be converted with <tt>gdal_translate</tt> but ONLY if GDAL was compiled with http://www.kealib.org/ support!

	{\| class="wikitable sortable"		{\| class="wikitable sortable"

Neteler: /* ARCSI: usage example using docker */

2020-08-19T07:04:19Z

ARCSI: usage example using docker

← Older revision		Revision as of 07:04, 19 August 2020
Line 418:		Line 418:

	# run ARCSI (we use volume mapping to make S2 and DEM visible inside the docker container)		# run ARCSI (we use volume mapping to make S2 and DEM visible inside the docker container)
			# produce CLOUD and Surface Reflectance results, use DOSAOTSGL for AOT estimation
	docker run -it --rm -v ${MY_S2_PATH}:/data -v ${MY_DEM_PATH}:/dem mundialis/arcsi \		docker run -it --rm -v ${MY_S2_PATH}:/data -v ${MY_DEM_PATH}:/dem mundialis/arcsi \
	arcsi.py --sensor sen2 -i /data/${S2IMG}.SAFE/$XML.xml -o /data/${S2IMG}.SAFE/output \		arcsi.py --sensor sen2 -i /data/${S2IMG}.SAFE/$XML.xml -o /data/${S2IMG}.SAFE/output \
	--tmpath /tmp -f KEA --stats -p CLOUDS ~~RAD~~ DOSAOTSGL SREF \		--tmpath /tmp -f KEA --stats -p CLOUDS DOSAOTSGL SREF \
	--aeroimg /opt/conda/share/arcsi/WorldAerosolParams.kea \		--aeroimg /opt/conda/share/arcsi/WorldAerosolParams.kea \
	--atmosimg /opt/conda/share/arcsi/WorldAtmosphereParams.kea \		--atmosimg /opt/conda/share/arcsi/WorldAtmosphereParams.kea \

Neteler: /* Atmospheric correction with Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI) */

2020-08-18T11:17:51Z

Atmospheric correction with Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI)

← Older revision		Revision as of 11:17, 18 August 2020
Line 274:		Line 274:
	To apply atmospheric correction to the remaining bands, only the last line in the 6S parameters file (i.e., the sensor band) needs to be changed. The other parameters will remain the same.		To apply atmospheric correction to the remaining bands, only the last line in the 6S parameters file (i.e., the sensor band) needs to be changed. The other parameters will remain the same.

	===Atmospheric correction with ~~[https://rsgislib.org/arcsi/~~ Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI)]===		===Atmospheric correction with Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI)===

	The Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI) software was developed at Aberystwyth University, under contract from the Norwegian Space Centre and allows automatic correction to Top Of Atmosphere (TOA) or Surface Reflectance (SREF). The conversion to Surface Reflectance uses the 6S code, accessed through the [http://py6s.rtwilson.com/ Py6S] Python interface. It was originally developed for Landsat data but it also supports Sentinel 2 satellites.		The Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI, https://github.com/remotesensinginfo/arcsi) software was developed at Aberystwyth University, under contract from the Norwegian Space Centre and allows automatic correction to Top Of Atmosphere (TOA) or Surface Reflectance (SREF). The conversion to Surface Reflectance uses the 6S code, accessed through the [http://py6s.rtwilson.com/ Py6S] Python interface. It was originally developed for Landsat data but it also supports Sentinel 2 satellites.

	Aerosol Optical Thickness (AOT) is an important parameter in atmospheric correction models such as 6S. In some regions of the world it has been found that a constant can be used. However, for most regions a constant is not viable as the atmosphere is very variable. Incorrect ~~parameterization~~ for AOT can lead to large errors in atmospheric correction. ARCSI provides a method of deriving AOT from an image by using a dark object subtraction (DOS) to estimate surface reflectance in the blue channel. Taking this estimated reflectance as input, 6S is then numerically inverted to identify an AOT value which provides a surface reflectance value as close to that estimated from DOS as possible.		Aerosol Optical Thickness (AOT) is an important parameter in atmospheric correction models such as 6S. In some regions of the world it has been found that a constant can be used. However, for most regions a constant is not viable as the atmosphere is very variable. Incorrect parametrization for AOT can lead to large errors in atmospheric correction. ARCSI provides a method of deriving AOT from an image by using a dark object subtraction (DOS) to estimate surface reflectance in the blue channel. Taking this estimated reflectance as input, 6S is then numerically inverted to identify an AOT value which provides a surface reflectance value as close to that estimated from DOS as possible.

	Instructions to install the software can be found at: https://spectraldifferences.wordpress.com/software/, but you can also have it through a docker image from: https://github.com/mundialis/docker-arcsi (for a usage example, see below).		Instructions to install the software can be found at: https://spectraldifferences.wordpress.com/software/, but you can also have it through a docker image from: https://github.com/mundialis/docker-arcsi (for a usage example, see below).

Neteler: added ARCSI Product table

2020-08-18T09:08:33Z

added ARCSI Product table

← Older revision		Revision as of 09:08, 18 August 2020
Line 281:		Line 281:

	Instructions to install the software can be found at: https://spectraldifferences.wordpress.com/software/, but you can also have it through a docker image from: https://github.com/mundialis/docker-arcsi (for a usage example, see below).		Instructions to install the software can be found at: https://spectraldifferences.wordpress.com/software/, but you can also have it through a docker image from: https://github.com/mundialis/docker-arcsi (for a usage example, see below).

			{\| class="wikitable sortable"
			! ARCSI Product !! Definition
			\|-
			\| CLEARSKY \|\| Identify large continuous areas without cloud, providing an image mask
			\|-
			\| CLOUDS \|\| Perform a classification to mask cloud and cloud shadow
			\|-
			\| DDVAOT \|\| Dark Dense Vegetation Aerosol Optical Thickness – used for atmospheric correction
			\|-
			\| DEM \|\| Extract and reproject the inputted DEM for the image
			\|-
			\| DOS \|\| Perform a dark object subtraction to retrieve an estimate of surface reflectance
			\|-
			\| DOSAOT \|\| Estimate the Aerosol Optical Thickness (AOT) for the scene
			\|-
			\| DOSAOTSGL \|\| Estimate a single value of Aerosol Optical Thickness (AOT) for the scene
			\|-
			\| FOOTPRINT \|\| Extract the image data footprint as a shapefile
			\|-
			\| METADATA \|\| Create a metadata file with key information from the input image and information calculated during the analysis
			\|-
			\| RAD \|\| At sensor radiance image
			\|-
			\| SATURATE \|\| Mask of saturated pixels
			\|-
			\| SHARP \|\| Sharpen the 20 m image bands to 10 m using a local correlation filter
			\|-
			\| SREF \|\| Using the 6S atmospheric model calculate the surface reflectance pixel value for the scene
			\|-
			\| STDSREF \|\| Calculate standardised reflectance, normalising for the sun angle and topography (i.e., topographic correction)
			\|-
			\| THERMAL \|\| Calculate thermal product (not for Sentinel-2)
			\|-
			\| TOA \|\| At sensor reflectance (top of atmosphere; TOA)
			\|-
			\| TOPOSHADOW \|\| Produce a mask for topographic shadow using the DEM
			\|}

			''Sources:''
			* "Developing a standards and automated production for Sentinel-2 Analysis ready Data - SD1707" ([http://sciencesearch.defra.gov.uk/Default.aspx?Menu=Menu&Module=More&Location=None&Completed=0&ProjectID=19836 ref])
			* https://groups.google.com/g/rsgislib-support/c/CQ_S4BRuE3Y


	==== ARCSI: usage examples with DOS1 and DOSAOTSGL corrections ====		==== ARCSI: usage examples with DOS1 and DOSAOTSGL corrections ====

Neteler: update to ARCSI 3.8.1

2020-08-17T17:35:04Z

update to ARCSI 3.8.1

← Older revision		Revision as of 17:35, 17 August 2020
Line 368:		Line 368:
	MY_S2_PATH=/path/to/S2_data/		MY_S2_PATH=/path/to/S2_data/
	MY_DEM_PATH=/path/to/DEM_data/		MY_DEM_PATH=/path/to/DEM_data/

			## ESA XML name:
			XML=MTD_MSIL1C.xml
			## USGS (modified XML name!):
			# XML=$(echo $S2IMG \| sed 's+PRD_MSIL1C+MTD_SAFL1C+g')

	# run ARCSI (we use volume mapping to make S2 and DEM visible inside the docker container)		# run ARCSI (we use volume mapping to make S2 and DEM visible inside the docker container)
	docker run -i -t -v ${MY_S2_PATH}:/data -v ${MY_DEM_PATH}:/dem ~~arcsi~~/arcsi \		docker run -it --rm -v ${MY_S2_PATH}:/data -v ${MY_DEM_PATH}:/dem mundialis/arcsi \
	arcsi.py --sensor sen2 -i /data/${S2IMG}.SAFE/~~MTD_MSIL1C~~.xml -o /data/${S2IMG}.SAFE/output \		arcsi.py --sensor sen2 -i /data/${S2IMG}.SAFE/$XML.xml -o /data/${S2IMG}.SAFE/output \
	--tmpath /tmp -f KEA --stats -p CLOUDS RAD DOSAOTSGL SREF \		--tmpath /tmp -f KEA --stats -p CLOUDS RAD DOSAOTSGL SREF \
	--aeroimg /opt/conda/share/arcsi/WorldAerosolParams.kea \		--aeroimg /opt/conda/share/arcsi/WorldAerosolParams.kea \
	--atmosimg /opt/conda/share/arcsi/WorldAtmosphereParams.kea \		--atmosimg /opt/conda/share/arcsi/WorldAtmosphereParams.kea \
	--dem /dem/${DEM} --minaot 0.05 --maxaot 0.6		--dem /dem/${DEM} --demnodata 0 --minaot 0.05 --maxaot 0.6
	</source>		</source>

Neteler: +Jena GRASS GIS Workshop

2020-06-14T21:17:27Z

+Jena GRASS GIS Workshop

← Older revision		Revision as of 21:17, 14 June 2020
Line 438:		Line 438:

	==Further reading==		==Further reading==
	IMAGICO.DE blog
	* [http://blog.imagico.de/sentinel-2-having-a-first-look-at-the-data-part-1/ First look at Sentinel data]		* Jena GRASS GIS Workshop:
	* [http://blog.imagico.de/who-is-moving-the-cheese-again/ Who is moving the cheese again?]		** [https://training.gismentors.eu/grass-gis-workshop-jena/units/20.html Unit 20 - Sentinel downloader]
			** [https://training.gismentors.eu/grass-gis-workshop-jena/units/21.html Unit 21 - Sentinel spatio-temporal]
			** [https://training.gismentors.eu/grass-gis-workshop-jena/units/22.html Unit 22 - Spatio-temporal scripting]
			** [https://training.gismentors.eu/grass-gis-workshop-jena/units/23.html Unit 23 - Spatio-temporal parallelization]

			IMAGICO.DE blog:
			* [http://blog.imagico.de/sentinel-2-having-a-first-look-at-the-data-part-1/ First look at Sentinel data] (2016)
			* [http://blog.imagico.de/who-is-moving-the-cheese-again/ Who is moving the cheese again?] (2016)

	[[Category: Documentation]]		[[Category: Documentation]]