{"id":1432,"date":"2021-01-04T18:49:00","date_gmt":"2021-01-04T17:49:00","guid":{"rendered":"https:\/\/hannes.enjoys.it\/blog\/?p=1432"},"modified":"2022-07-25T20:51:11","modified_gmt":"2022-07-25T18:51:11","slug":"satellite-composite-of-earth-2020","status":"publish","type":"post","link":"https:\/\/hannes.enjoys.it\/blog\/2021\/01\/satellite-composite-of-earth-2020\/","title":{"rendered":"Satellite composite of Earth 2020"},"content":{"rendered":"\n

A follow-up to Average Earth from Space 2018<\/a> with a how-to. For each day of 2020 I took one global true color image of the whole planet and merged them together by using the most typical color per pixel. You can see cloud patterns in astonishing detail, global wind, permafrost (careful, white can be ice and\/or clouds here) and more. Scroll to the bottom for interactive full resolution viewers.<\/p>\n\n\n\n

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Basically we will want to overlay one satellite image per day into one image for the whole year. You need two things: The images and the GDAL suite of geospatial processing tools.<\/p>\n\n\n\n

Imagery<\/h2>\n\n\n\n

You can get a daily satellite composite of (almost) the whole earth from NASA. For example of the Soumi NPP \/ VIIRS<\/a> instrument. Check it out at WorldView<\/a>.<\/p>\n\n\n\n

You can download those images via Global Imagery Browse Services (GIBS)<\/a>.<\/p>\n\n\n\n

As the API I used two years ago is gone, Joshua Stevens was so nice to share code he used previously<\/a>. It was easy to adapt:<\/p>\n\n\n\n

set -e\nset -u\n\n# run like: $ bash gibs_viirs.sh 2020-10-05\n# you get: VIIRS_SNPP_CorrectedReflectance_TrueColor-2020-10-05.tif\n# in ~15 minutes and at ~600 megabytes for 32768x16384 pixels\n\n# based on https:\/\/gist.github.com\/jscarto\/6c0413f4820ed5141744e96e19f31205\n\n# https:\/\/gibs.earthdata.nasa.gov\/wmts\/epsg4326\/best\/1.0.0\/WMTSCapabilities.xml\n# VIIRS_SNPP_CorrectedReflectance_TrueColor is not served as PNG by GIBS\n# so this is using JPEG tiles as source\n\n# -outsize 65536 32768 took ~50 minutes\n# -outsize 32768 16384 took ~15 minutes\n# you can run multiple instances at once without issues to reduce total time\n\n# TODO probably should be using a less detailed tileset than 250m to put\n# less stress on the server...!\n\nlayer=VIIRS_SNPP_CorrectedReflectance_TrueColor\ncaldate=$1  # 2020-09-09\ntilelevel=8  # 8 is the highest for 250m, see Capa -> 163840 81920 would be the full outsize\n# 2022 says: Dude, check what gdal says for \"Input file size is x, y\" and then compare it to the outsize. Use the tilelevel that gives 2x the outsize, that seems to be what's needed\n\n# ready? let's go!\ngdal_translate \\\n-outsize 32768 16384 \\\n-projwin -180 90 180 -90 \\\n-of GTIFF \\\n-co TILED=YES \\\n-co COMPRESS=DEFLATE \\\n-co PREDICTOR=2 \\\n-co NUM_THREADS=ALL_CPUS \\\n\"<GDAL_WMS>\n <Service name=\\\"TMS\\\">\n <ServerUrl>https:\/\/gibs.earthdata.nasa.gov\/wmts\/epsg4326\/best\/\"${layer}\"\/default\/\"${caldate}\"\/250m\/\\${z}\/\\${y}\/\\${x}.jpg<\/ServerUrl>\n<\/Service>\n<DataWindow>\n <UpperLeftX>-180.0<\/UpperLeftX><!-- makes sense -->\n <UpperLeftY>90<\/UpperLeftY><!-- makes sense -->\n <LowerRightX>396.0<\/LowerRightX><!-- wtf -->\n <LowerRightY>-198<\/LowerRightY><!-- wtf -->\n <TileLevel>\"${tilelevel}\"<\/TileLevel>\n <TileCountX>2<\/TileCountX>\n <TileCountY>1<\/TileCountY>\n <YOrigin>top<\/YOrigin>\n<\/DataWindow>\n<Projection>EPSG:4326<\/Projection>\n<BlockSizeX>512<\/BlockSizeX><!-- correct for VIIRS_SNPP_CorrectedReflectance_TrueColor-->\n<BlockSizeY>512<\/BlockSizeY><!-- correct for VIIRS_SNPP_CorrectedReflectance_TrueColor -->\n<BandsCount>3<\/BandsCount>\n<\/GDAL_WMS>\" \\\n${layer}-${caldate}.tif<\/code><\/pre>\n\n\n\n
As this was no scientific project, please note that I have spent no time checking e. g.:<\/p>\n\n\n\n