Getting information from raster datasets
From now, we assume you have a GDAL-enabled shell, and the current working directory is the one where you have downloaded the sample datasets, as indicated in the Pre-requisites: GDAL installation and workshop sample dataset download section.
Utilities demonstrated
Scanning a folder for GDAL datasets
First, make sure you are in the directory containing the workshop datasets (the exact location depends on your setup):
$ cd gdal_cli_workshop_data-master
# Windows
$ cd /c/gdal/gdal_cli_workshop_data-master
Now let's use gdal dataset identify in recursive mode in the current directory:
$ gdal dataset identify -r .
Output:
./S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml: SENTINEL2
./S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/INSPIRE.xml: GML
./S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/QI_DATA/MSK_DETFOO_B12.jp2: JP2OpenJPEG
./S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/QI_DATA/MSK_DETFOO_B05.jp2: JP2OpenJPEG
./S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/QI_DATA/MSK_QUALIT_B8A.jp2: JP2OpenJPEG
./S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/QI_DATA/MSK_QUALIT_B05.jp2: JP2OpenJPEG
./S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/QI_DATA/MSK_DETFOO_B07.jp2: JP2OpenJPEG
./S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/QI_DATA/MSK_QUALIT_B08.jp2: JP2OpenJPEG
./S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/QI_DATA/MSK_DETFOO_B02.jp2: JP2OpenJPEG
./S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/QI_DATA/MSK_DETFOO_B09.jp2: JP2OpenJPEG
[ ... snip ... ]
./S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/HTML/banner_2.png: PNG
./S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/HTML/star_bg.jpg: JPEG
./S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/HTML/banner_1.png: PNG
./S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/HTML/banner_3.png: PNG
./S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/manifest.safe: GML
[ ... snip ... ]
We see 3 main Sentinel 2 products, the JPEG-2000 files with the data, and a bunch of auxiliary files, most of them being "noise".
Raster dataset with subdatasets
Let's get information on one of the Sentinel 2 datasets
$ gdal raster info S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml
Output:
Driver short name and long name. It is generally a good idea to consult the driver documentation page to learn about the peculiarities of each format. Here we are using Sentinel-2.
Driver: SENTINEL2/Sentinel 2
Metadata that applies at the dataset level
Metadata:
AOT_QUANTIFICATION_VALUE=1000.0
AOT_QUANTIFICATION_VALUE_UNIT=none
AOT_RETRIEVAL_ACCURACY=0.0
AOT_RETRIEVAL_METHOD=SEN2COR_DDV
BOA_QUANTIFICATION_VALUE=10000
BOA_QUANTIFICATION_VALUE_UNIT=none
CAST_SHADOW_PERCENTAGE=0.020978
CLOUDY_PIXEL_OVER_LAND_PERCENTAGE=13.652931
CLOUD_COVERAGE_ASSESSMENT=13.622445
CLOUD_SHADOW_PERCENTAGE=4.0E-6
DATATAKE_1_DATATAKE_SENSING_START=2026-04-23T09:40:29.024Z
DATATAKE_1_DATATAKE_TYPE=INS-NOBS
DATATAKE_1_ID=GS2B_20260423T094029_047681_N05.12
DATATAKE_1_SENSING_ORBIT_DIRECTION=DESCENDING
DATATAKE_1_SENSING_ORBIT_NUMBER=36
DATATAKE_1_SPACECRAFT_NAME=Sentinel-2B
DEGRADED_ANC_DATA_PERCENTAGE=0.0
DEGRADED_MSI_DATA_PERCENTAGE=0
FOOTPRINT=POLYGON((20.815513868354657 45.0636441557092, 20.738388385926616 45.0825860720989, 20.7383279976147 45.08243722604747, 20.73829168579047 45.08244616120928, 20.737841954045535 45.081338607396354, 20.723616416649268 45.08479331137292, 20.723749295718594 45.08512101907749, 20.420208979129363 45.15414241241193, 20.420325912036265 45.15443445656253, 20.4201440484806 45.154474815657785, 20.42019397077344 45.15459941811133, 20.41993843797668 45.15465628545975, 20.420062047711312 45.154964985110986, 20.141998695588608 45.217031549439376, 20.141951168063247 45.21691119363821, 20.140959170987895 45.21713316759646, 20.14095913434592 45.217133074779504, 20.140956302668084 45.21713370924307, 20.14068749523566 45.21645339695486, 19.828665356777663 45.28183133788763, 19.82877859125412 45.282121770659984, 19.82873392014264 45.282130804492965, 19.828808772594694 45.28232269102338, 19.828661421501813 45.28235252832233, 19.828817354085736 45.282752449475936, 19.723826624134375 45.30404156688893, 19.70695108472353 46.0462596089832, 21.126167237224053 46.05350473573164, 21.124259208669503 45.193517377663945, 21.08029382801478 45.086817339340286, 21.071227534895563 45.06492790336747, 20.815513868354657 45.0636441557092))
FORMAT_CORRECTNESS=PASSED
GENERAL_QUALITY=PASSED
GENERATION_TIME=2026-04-23T11:57:14.000000Z
GEOMETRIC_QUALITY=PASSED
GRANULE_MEAN_AOT=0.06598
GRANULE_MEAN_WV=0.75046
HIGH_PROBA_CLOUDS_PERCENTAGE=0.001836
L2A_QUALITY=PASSED
MEDIUM_PROBA_CLOUDS_PERCENTAGE=0.009661
NODATA_PIXEL_PERCENTAGE=9.990737
NOT_VEGETATED_PERCENTAGE=28.988278
OZONE_SOURCE=AUX_ECMWFT
OZONE_VALUE=416.167104
PREVIEW_GEO_INFO=Not applicable
PREVIEW_IMAGE_URL=Not applicable
PROCESSING_BASELINE=05.12
PROCESSING_LEVEL=Level-2A
PRODUCT_DOI=https://doi.org/10.5270/S2_-znk9xsj
PRODUCT_START_TIME=2026-04-23T09:40:29.024Z
PRODUCT_STOP_TIME=2026-04-23T09:40:29.024Z
PRODUCT_TYPE=S2MSI2A
PRODUCT_URI=S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE
RADIATIVE_TRANSFER_ACCURACY=0.0
RADIOMETRIC_QUALITY=PASSED
REFERENCE_BAND=B4
REFLECTANCE_CONVERSION_U=0.991700831171221
SATURATED_DEFECTIVE_PIXEL_PERCENTAGE=0.0
SENSOR_QUALITY=PASSED
SNOW_ICE_PERCENTAGE=0.0
SPECIAL_VALUE_NODATA=0
SPECIAL_VALUE_SATURATED=65535
THIN_CIRRUS_PERCENTAGE=13.610949
UNCLASSIFIED_PERCENTAGE=0.243549
VEGETATION_PERCENTAGE=56.460464
WATER_PERCENTAGE=0.664281
WATER_VAPOUR_RETRIEVAL_ACCURACY=0.0
WVP_QUANTIFICATION_VALUE=1000.0
WVP_QUANTIFICATION_VALUE_UNIT=cm
And "subdatasets"
Subdatasets:
SUBDATASET_1_NAME=SENTINEL2_L2A:S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml:10m:EPSG_32634
SUBDATASET_1_DESC=Bands B2, B3, B4, B8, AOT, WVP with 10m resolution, UTM 34N
SUBDATASET_2_NAME=SENTINEL2_L2A:S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml:20m:EPSG_32634
SUBDATASET_2_DESC=Bands B5, B6, B7, B8A, B11, B12, AOT, CLD, SCL, SNW, WVP with 20m resolution, UTM 34N
SUBDATASET_3_NAME=SENTINEL2_L2A:S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml:60m:EPSG_32634
SUBDATASET_3_DESC=Bands B1, B9, AOT, CLD, SCL, SNW, WVP with 60m resolution, UTM 34N
SUBDATASET_4_NAME=SENTINEL2_L2A:S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml:TCI:EPSG_32634
SUBDATASET_4_DESC=True color image, UTM 34N
Subdatasets can be thought as sub-products or raster layers of a container file.
The value of a SUBDATASET_xxx_NAME key can be used as valid GDAL dataset name.
Text information on a raster dataset
So for example let's open the 10m resolution bands with:
$ gdal raster info SENTINEL2_L2A:S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml:10m:EPSG_32634
Output:
File(s) composing the dataset:
Files: S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml
S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/MTD_TL.xml
S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/IMG_DATA/R10m/T34TDR_20260423T094029_B04_10m.jp2
S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/IMG_DATA/R10m/T34TDR_20260423T094029_B03_10m.jp2
S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/IMG_DATA/R10m/T34TDR_20260423T094029_B02_10m.jp2
S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/IMG_DATA/R10m/T34TDR_20260423T094029_AOT_10m.jp2
S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/IMG_DATA/R10m/T34TDR_20260423T094029_WVP_10m.jp2
Dimensions in pixel: first value is the width (number of columns), second value is the height (number of rows):
Size is 10980, 10980
Information about the Coordinate Reference System (CRS), also often informally called "projection":
Coordinate Reference System:
- name: WGS 84 / UTM zone 34N
- ID: EPSG:32634
- type: Projected
- projection type: UTM zone 34N, Transverse Mercator
- units: metre
Information how the axis of the dataset maps to the axis of the coordinate reference system. Here the first axis of the data, which is always the X/width dimension for GDAL rasters maps to the first axis of the CRS which is Eastings, and the second axis of the raster, which is always the Y/height dimension maps to the second axis of the CRS which is Northings.
Data axis to CRS axis mapping: 1,2
Coordinates of the top-left corner of the raster, X value first, Y value second:
Origin = (399960.000000000000000,5100000.000000000000000)
Size of a pixel in CRS units (here meters), with X value first, Y value second:
Pixel Size = (10.000000000000000,-10.000000000000000)
Image Structure Metadata is format dependent and gives information on the
compression method, internal organization with the INTERLEAVING keyword
(pixel versus band interleaving. cf https://gdal.org/en/stable/user/raster_data_model.html#multiband-pixel-organization-interleave-metadata-item), etc.
Image Structure Metadata:
COMPRESSION=JPEG2000
Coordinates of the corner and center. First tuple of values is expressed in CRS, so here as eastings, northings as this is a projected CRS. Second tuple is their corresponding value in the geographic CRS underlying the projected CRS, here WGS 84 as longitude, latitude in degree, minute and decimal seconds.
Corner Coordinates:
Upper Left ( 399960.000, 5100000.000) ( 19d42'25.02"E, 46d 2'46.53"N)
Lower Left ( 399960.000, 4990200.000) ( 19d43'45.90"E, 45d 3'29.49"N)
Upper Right ( 509760.000, 5100000.000) ( 21d 7'34.20"E, 46d 3'12.62"N)
Lower Right ( 509760.000, 4990200.000) ( 21d 7'26.31"E, 45d 3'54.69"N)
Center ( 454860.000, 5045100.000) ( 20d25'17.86"E, 45d33'28.71"N)
Followed by band specific information
Band 1 Block=128x128 Type=UInt16, ColorInterp=Red
Description = B4, central wavelength 665 nm
Overviews: 5490x5490, 2745x2745, 1373x1373
Metadata:
BANDNAME=B4
BANDWIDTH=30
BANDWIDTH_UNIT=nm
WAVELENGTH=665
WAVELENGTH_UNIT=nm
SOLAR_IRRADIANCE=1512.79
SOLAR_IRRADIANCE_UNIT=W/m2/um
BOA_ADD_OFFSET=-1000
Image Structure Metadata:
NBITS=15
Imagery:
CENTRAL_WAVELENGTH_UM=0.665
FWHM_UM=0.030
[ ... snip ...]
Block corresponds to the smallest unit GDAL can access pixel values. It is typically either a whole line or a set of few lines called "strip", or a rectangular (almost always a square) called a "tile".
The data type UInt16 is a 16-bit unsigned integer value, so for values between 0 and
65535, here actually restricted to 0-32767 given the NBITS=15 metadata.
Overviews correspond to image pyramids, i.e. reduced resolution versions of the full resolution raster. They are generally automatically used by GDAL for processing occurring at a reduced resolution.
The Imagery metadata domain contains a few metadata items whose meaning is normalized across drivers, whereas the main (default) domain is driver specific and may contain anything.
CENTRAL_WAVELENGTH_UM corresponds to the Central Wavelength in micrometers and
FWHM_UM to the Full-width half-maximum (FWHM) value in micrometers.
There are various options that can be used to customize the default output of
gdal raster info. You can get them by asking for auto-completion suggestions
by adding dash dash and pressing the TAB key twice.
$ gdal raster info SENTINEL2_L2A:S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml:10m:EPSG_32634 --<TAB><TAB>
Output:
--approx-stats --hist --list-mdd --no-ct --no-mask --open-option --subdataset
--checksum --input --metadata-domain --no-fl --no-md --output-format
--crs-format --input-format --min-max --no-gcp --no-nodata --stats
For example statistics:
$ gdal raster info SENTINEL2_L2A:S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml:10m:EPSG_32634 --stats
Answer "y" and validate.
Output:
Band 1 Block=128x128 Type=UInt16, ColorInterp=Red
Description = B4, central wavelength 665 nm
Min=0.000 Max=17143.000
Minimum=0.000, Maximum=17143.000, Mean=1439.120, StdDev=582.568
[... snip ... ]
Metadata:
[... snip ... ]
STATISTICS_MINIMUM=0
STATISTICS_MAXIMUM=17143
STATISTICS_MEAN=1439.1200572908
STATISTICS_STDDEV=582.56825507515
STATISTICS_VALID_PERCENT=100
Here it would seem that all pixels are valid, but the Sentinel 2 driver does
not report a NoData / missing value for the dataset, hence if you open the
dataset with qgis S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml
you will see that values at 0 actually correspond to a NoData value. Something
to keep in mind and take into account in further processing.
Short version
Do you find gdal raster info too long ?
You can use gdal info or even gdal !
Beware those short versions only accept the --format option, which is common
between gdal raster info and gdal vector info
Getting information as JSON
Text output is friendly for humans, but no so much for machine processing.
$ gdal raster info SENTINEL2_L2A:S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml:10m:EPSG_32634 --format=json
Output (trimmed to keep only one band)
{
"description":"SENTINEL2_L2A:S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml:10m:EPSG_32634",
"driverShortName":"SENTINEL2",
"driverLongName":"Sentinel 2",
"files":[
"S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml",
"S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/MTD_TL.xml",
"S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/IMG_DATA/R10m/T34TDR_20260423T094029_B04_10m.jp2",
"S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/IMG_DATA/R10m/T34TDR_20260423T094029_B03_10m.jp2",
"S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/IMG_DATA/R10m/T34TDR_20260423T094029_B02_10m.jp2",
"S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/IMG_DATA/R10m/T34TDR_20260423T094029_B08_10m.jp2",
"S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/IMG_DATA/R10m/T34TDR_20260423T094029_AOT_10m.jp2",
"S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/GRANULE/L2A_T34TDR_A047681_20260423T094113/IMG_DATA/R10m/T34TDR_20260423T094029_WVP_10m.jp2"
],
"size":[
10980,
10980
],
"coordinateSystem":{
"wkt":"PROJCRS[\"WGS 84 / UTM zone 34N\",\n BASEGEOGCRS[\"WGS 84\",\n DATUM[\"World Geodetic System 1984\",\n ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n LENGTHUNIT[\"metre\",1]]],\n PRIMEM[\"Greenwich\",0,\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n ID[\"EPSG\",4326]],\n CONVERSION[\"UTM zone 34N\",\n METHOD[\"Transverse Mercator\",\n ID[\"EPSG\",9807]],\n PARAMETER[\"Latitude of natural origin\",0,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8801]],\n PARAMETER[\"Longitude of natural origin\",21,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8802]],\n PARAMETER[\"Scale factor at natural origin\",0.9996,\n SCALEUNIT[\"unity\",1],\n ID[\"EPSG\",8805]],\n PARAMETER[\"False easting\",500000,\n LENGTHUNIT[\"metre\",1],\n ID[\"EPSG\",8806]],\n PARAMETER[\"False northing\",0,\n LENGTHUNIT[\"metre\",1],\n ID[\"EPSG\",8807]]],\n CS[Cartesian,2],\n AXIS[\"easting\",east,\n ORDER[1],\n LENGTHUNIT[\"metre\",1]],\n AXIS[\"northing\",north,\n ORDER[2],\n LENGTHUNIT[\"metre\",1]],\n ID[\"EPSG\",32634]]",
"dataAxisToSRSAxisMapping":[
1,
2
]
},
"geoTransform":[
399960.0,
10.0,
0.0,
5100000.0,
0.0,
-10.0
],
"metadata":{
"":{
"AOT_QUANTIFICATION_VALUE":"1000.0",
"AOT_QUANTIFICATION_VALUE_UNIT":"none",
"AOT_RETRIEVAL_ACCURACY":"0.0",
"AOT_RETRIEVAL_METHOD":"SEN2COR_DDV",
"BOA_QUANTIFICATION_VALUE":"10000",
"BOA_QUANTIFICATION_VALUE_UNIT":"none",
"CAST_SHADOW_PERCENTAGE":"0.020978",
"CLOUDY_PIXEL_OVER_LAND_PERCENTAGE":"13.652931",
"CLOUD_COVERAGE_ASSESSMENT":"13.622445",
"CLOUD_SHADOW_PERCENTAGE":"4.0E-6",
"DATATAKE_1_DATATAKE_SENSING_START":"2026-04-23T09:40:29.024Z",
"DATATAKE_1_DATATAKE_TYPE":"INS-NOBS",
"DATATAKE_1_ID":"GS2B_20260423T094029_047681_N05.12",
"DATATAKE_1_SENSING_ORBIT_DIRECTION":"DESCENDING",
"DATATAKE_1_SENSING_ORBIT_NUMBER":"36",
"DATATAKE_1_SPACECRAFT_NAME":"Sentinel-2B",
"DEGRADED_ANC_DATA_PERCENTAGE":"0.0",
"DEGRADED_MSI_DATA_PERCENTAGE":"0",
"FORMAT_CORRECTNESS":"PASSED",
"GENERAL_QUALITY":"PASSED",
"GENERATION_TIME":"2026-04-23T11:57:14.000000Z",
"GEOMETRIC_QUALITY":"PASSED",
"GRANULE_MEAN_AOT":"0.06598",
"GRANULE_MEAN_WV":"0.75046",
"HIGH_PROBA_CLOUDS_PERCENTAGE":"0.001836",
"L2A_QUALITY":"PASSED",
"MEDIUM_PROBA_CLOUDS_PERCENTAGE":"0.009661",
"NODATA_PIXEL_PERCENTAGE":"9.990737",
"NOT_VEGETATED_PERCENTAGE":"28.988278",
"OZONE_SOURCE":"AUX_ECMWFT",
"OZONE_VALUE":"416.167104",
"PREVIEW_GEO_INFO":"Not applicable",
"PREVIEW_IMAGE_URL":"Not applicable",
"PROCESSING_BASELINE":"05.12",
"PROCESSING_LEVEL":"Level-2A",
"PRODUCT_DOI":"https://doi.org/10.5270/S2_-znk9xsj",
"PRODUCT_START_TIME":"2026-04-23T09:40:29.024Z",
"PRODUCT_STOP_TIME":"2026-04-23T09:40:29.024Z",
"PRODUCT_TYPE":"S2MSI2A",
"PRODUCT_URI":"S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE",
"RADIATIVE_TRANSFER_ACCURACY":"0.0",
"RADIOMETRIC_QUALITY":"PASSED",
"REFERENCE_BAND":"B4",
"REFLECTANCE_CONVERSION_U":"0.991700831171221",
"SATURATED_DEFECTIVE_PIXEL_PERCENTAGE":"0.0",
"SENSOR_QUALITY":"PASSED",
"SNOW_ICE_PERCENTAGE":"0.0",
"SPECIAL_VALUE_NODATA":"0",
"SPECIAL_VALUE_SATURATED":"65535",
"THIN_CIRRUS_PERCENTAGE":"13.610949",
"UNCLASSIFIED_PERCENTAGE":"0.243549",
"VEGETATION_PERCENTAGE":"56.460464",
"WATER_PERCENTAGE":"0.664281",
"WATER_VAPOUR_RETRIEVAL_ACCURACY":"0.0",
"WVP_QUANTIFICATION_VALUE":"1000.0",
"WVP_QUANTIFICATION_VALUE_UNIT":"cm"
},
"IMAGE_STRUCTURE":{
"COMPRESSION":"JPEG2000"
}
},
"cornerCoordinates":{
"upperLeft":[
399960.0,
5100000.0
],
"lowerLeft":[
399960.0,
4990200.0
],
"lowerRight":[
509760.0,
4990200.0
],
"upperRight":[
509760.0,
5100000.0
],
"center":[
454860.0,
5045100.0
]
},
"wgs84Extent":{
"type":"Polygon",
"coordinates":[
[
[
19.7069511,
46.0462596
],
[
19.7294164,
45.0581917
],
[
21.1239745,
45.0651927
],
[
21.1261672,
46.0535047
],
[
19.7069511,
46.0462596
]
]
]
},
"bands":[
{
"band":1,
"block":[
128,
128
],
"type":"UInt16",
"colorInterpretation":"Red",
"description":"B4, central wavelength 665 nm",
"min":0.0,
"max":17143.0,
"overviews":[
{
"size":[
5490,
5490
]
},
{
"size":[
2745,
2745
]
},
{
"size":[
1373,
1373
]
}
],
"metadata":{
"":{
"BANDNAME":"B4",
"BANDWIDTH":"30",
"BANDWIDTH_UNIT":"nm",
"WAVELENGTH":"665",
"WAVELENGTH_UNIT":"nm",
"SOLAR_IRRADIANCE":"1512.79",
"SOLAR_IRRADIANCE_UNIT":"W/m2/um",
"BOA_ADD_OFFSET":"-1000"
},
"IMAGE_STRUCTURE":{
"NBITS":"15"
},
"IMAGERY":{
"CENTRAL_WAVELENGTH_UM":"0.665",
"FWHM_UM":"0.030"
}
}
}
],
"stac":{
"proj:shape":[
10980,
10980
],
"proj:wkt2":"PROJCRS[\"WGS 84 / UTM zone 34N\",\n BASEGEOGCRS[\"WGS 84\",\n DATUM[\"World Geodetic System 1984\",\n ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n LENGTHUNIT[\"metre\",1]]],\n PRIMEM[\"Greenwich\",0,\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n ID[\"EPSG\",4326]],\n CONVERSION[\"UTM zone 34N\",\n METHOD[\"Transverse Mercator\",\n ID[\"EPSG\",9807]],\n PARAMETER[\"Latitude of natural origin\",0,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8801]],\n PARAMETER[\"Longitude of natural origin\",21,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8802]],\n PARAMETER[\"Scale factor at natural origin\",0.9996,\n SCALEUNIT[\"unity\",1],\n ID[\"EPSG\",8805]],\n PARAMETER[\"False easting\",500000,\n LENGTHUNIT[\"metre\",1],\n ID[\"EPSG\",8806]],\n PARAMETER[\"False northing\",0,\n LENGTHUNIT[\"metre\",1],\n ID[\"EPSG\",8807]]],\n CS[Cartesian,2],\n AXIS[\"easting\",east,\n ORDER[1],\n LENGTHUNIT[\"metre\",1]],\n AXIS[\"northing\",north,\n ORDER[2],\n LENGTHUNIT[\"metre\",1]],\n ID[\"EPSG\",32634]]",
"proj:epsg":32634,
"proj:projjson":{
"$schema":"https://proj.org/schemas/v0.7/projjson.schema.json",
"type":"ProjectedCRS",
"name":"WGS 84 / UTM zone 34N",
"base_crs":{
"type":"GeographicCRS",
"name":"WGS 84",
"datum":{
"type":"GeodeticReferenceFrame",
"name":"World Geodetic System 1984",
"ellipsoid":{
"name":"WGS 84",
"semi_major_axis":6378137,
"inverse_flattening":298.257223563
}
},
"coordinate_system":{
"subtype":"ellipsoidal",
"axis":[
{
"name":"Geodetic latitude",
"abbreviation":"Lat",
"direction":"north",
"unit":"degree"
},
{
"name":"Geodetic longitude",
"abbreviation":"Lon",
"direction":"east",
"unit":"degree"
}
]
},
"id":{
"authority":"EPSG",
"code":4326
}
},
"conversion":{
"name":"UTM zone 34N",
"method":{
"name":"Transverse Mercator",
"id":{
"authority":"EPSG",
"code":9807
}
},
"parameters":[
{
"name":"Latitude of natural origin",
"value":0,
"unit":"degree",
"id":{
"authority":"EPSG",
"code":8801
}
},
{
"name":"Longitude of natural origin",
"value":21,
"unit":"degree",
"id":{
"authority":"EPSG",
"code":8802
}
},
{
"name":"Scale factor at natural origin",
"value":0.9996,
"unit":"unity",
"id":{
"authority":"EPSG",
"code":8805
}
},
{
"name":"False easting",
"value":500000,
"unit":"metre",
"id":{
"authority":"EPSG",
"code":8806
}
},
{
"name":"False northing",
"value":0,
"unit":"metre",
"id":{
"authority":"EPSG",
"code":8807
}
}
]
},
"coordinate_system":{
"subtype":"Cartesian",
"axis":[
{
"name":"Easting",
"abbreviation":"",
"direction":"east",
"unit":"metre"
},
{
"name":"Northing",
"abbreviation":"",
"direction":"north",
"unit":"metre"
}
]
},
"id":{
"authority":"EPSG",
"code":32634
}
},
"proj:transform":[
10.0,
0.0,
399960.0,
0.0,
-10.0,
5100000.0
],
"raster:bands":[
{
"data_type":"uint16"
},
{
"data_type":"uint16"
},
{
"data_type":"uint16"
},
{
"data_type":"uint16"
},
{
"data_type":"uint16"
},
{
"data_type":"uint16"
}
],
"eo:bands":[
{
"name":"b1",
"description":"B4, central wavelength 665 nm",
"common_name":"red"
},
{
"name":"b2",
"description":"B3, central wavelength 560 nm",
"common_name":"green"
},
{
"name":"b3",
"description":"B2, central wavelength 490 nm",
"common_name":"blue"
},
{
"name":"b4",
"description":"B8, central wavelength 842 nm",
"common_name":"nir"
},
{
"name":"b5",
"description":"AOT, Aerosol Optical Thickness map (at 550nm)"
},
{
"name":"b6",
"description":"WVP, Scene-average Water Vapour map"
}
]
}
}
We can for example extract only the colour interpretation of the first band by combining with the very powerful jq JSON command line processing utility
$ gdal raster info SENTINEL2_L2A:S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml:10m:EPSG_32634 --format=json | jq ".bands[0].colorInterpretation"
Note
jq uses 0-based array indexing.
Output:
"Red"
Exercise
Extract the CRS code using jq.
(hint)
Hint
Look at STAC related metadata in the JSON output.
Invoking Algorithms from Python
See How to use gdal CLI algorithms from Python.
Start Python:
$ python
And run:
from osgeo import gdal
help(gdal.alg)
NAME
gdal.alg
SUBMODULES
dataset
driver
mdim
raster
vector
vsi
help(gdal.alg.raster.info)
Help on function info:
info(
input_format: Optional[Union[List[str], dict, str]] = None,
open_option: Optional[Union[List[str], dict, str]] = None,
input: Optional[Union[List[gdal.Dataset], List[str], List[os.PathLike[str]]]] = None,
output_format: Optional[str] = None,
min_max: Optional[bool] = None,
stats: Optional[bool] = None,
approx_stats: Optional[bool] = None,
hist: Optional[bool] = None,
no_gcp: Optional[bool] = None,
no_md: Optional[bool] = None,
no_ct: Optional[bool] = None,
no_fl: Optional[bool] = None,
checksum: Optional[bool] = None,
list_mdd: Optional[bool] = None,
metadata_domain: Optional[str] = None,
no_nodata: Optional[bool] = None,
no_mask: Optional[bool] = None,
subdataset: Optional[int] = None,
crs_format: Optional[str] = None,
progress: Optional[Callable[[float, str, object], bool]] = None,
**kwargs
)
Return information on a raster dataset.
Consult https://gdal.org/programs/gdal_raster_info.html for more details.
Parameters
----------
input_format: Optional[Union[List[str], dict, str]]=None
Input formats
open_option: Optional[Union[List[str], dict, str]]=None
Open options
input: Union[List[gdal.Dataset], List[str], List[os.PathLike[str]]]
Aliases: dataset
Input raster dataset
output_format: Optional[str]=None
Aliases: format
Output format
min_max: Optional[bool]=None
Compute minimum and maximum value
stats: Optional[bool]=None
Retrieve or compute statistics, using all pixels
approx_stats: Optional[bool]=None
Retrieve or compute statistics, using a subset of pixels
hist: Optional[bool]=None
Retrieve or compute histogram
no_gcp: Optional[bool]=None
Suppress ground control points list printing
no_md: Optional[bool]=None
Suppress metadata printing
no_ct: Optional[bool]=None
Suppress color table printing
no_fl: Optional[bool]=None
Suppress file list printing
checksum: Optional[bool]=None
Compute pixel checksum
list_mdd: Optional[bool]=None
Aliases: list_metadata_domains
List all metadata domains available for the dataset
metadata_domain: Optional[str]=None
Report metadata for the specified domain. 'all' can be used to report metadata in all domains
Output parameters
-----------------
output_string: str
Output string, in which the result is placed
Let's try it:
>>> gdal.alg.raster.info(input='SENTINEL2_L2A:S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml:10m:EPSG_32634')
<osgeo.gdal.Algorithm; proxy of <Swig Object of type 'GDALAlgorithmHS *' at 0x7feb83e38cb0> >
So that's return an Algorithm object. Let's extract its output:
from osgeo import gdal
with gdal.alg.raster.info(input='SENTINEL2_L2A:S2B_MSIL2A_20260423T094029_N0512_R036_T34TDR_20260423T115714.SAFE/MTD_MSIL2A.xml:10m:EPSG_32634', min_max=True) as alg:
print(alg.Output()['bands'][0])
After a few seconds:
{'band': 1, 'block': [128, 128], 'type': 'UInt16', 'colorInterpretation': 'Red', 'description': 'B4, central wavelength 665 nm', 'computedMin': 0.0, 'computedMax': 17143.0, 'overviews': [{'size': [5490, 5490]}, {'size': [2745, 2745]}, {'size': [1373, 1373]}], 'metadata': {'': {'BANDNAME': 'B4', 'BANDWIDTH': '30', 'BANDWIDTH_UNIT': 'nm', 'WAVELENGTH': '665', 'WAVELENGTH_UNIT': 'nm', 'SOLAR_IRRADIANCE': '1512.79', 'SOLAR_IRRADIANCE_UNIT': 'W/m2/um', 'BOA_ADD_OFFSET': '-1000'}, 'IMAGE_STRUCTURE': {'NBITS': '15'}, 'IMAGERY': {'CENTRAL_WAVELENGTH_UM': '0.665', 'FWHM_UM': '0.030'}}}
To exit Python and return to the shell, type:
exit()
Getting pixel value
gdal raster pixel-info <dataset> <X> <Y>
By default, the X and Y positional arguments are in column,row coordinate space
(--position-crs=pixel). It is also possible to specify them in the CRS of
the dataset (--position-crs=dataset), or an explicit CRS.
For example, getting pixel values at Timișoara center (45.7558° N, 21.2322° E):
$ gdal raster pixel-info \
SENTINEL2_L2A:S2B_MSIL2A_20260423T094029_N0512_R036_T34TER_20260423T115714.SAFE/MTD_MSIL2A.xml:10m:EPSG_32634 \
21.2322 45.7558 --position-crs EPSG:4326
{
"type":"FeatureCollection",
"crs":{
"type":"name",
"properties":{
"name":"urn:ogc:def:crs:EPSG::32634"
}
},
"features":[
{
"type":"Feature",
"properties":{
"input_coordinate":[
21.232199999999999,
45.755800000000001
],
"column":1807.8714361003731,
"line":3305.8006131011061,
"bands":[
{
"band_number":1,
"raw_value":2490,
"unscaled_value":2490.0,
"files":[
"S2B_MSIL2A_20260423T094029_N0512_R036_T34TER_20260423T115714.SAFE\/GRANULE\/L2A_T34TER_A047681_20260423T094113\/IMG_DATA\/R10m\/T34TER_20260423T094029_B04_10m.jp2"
]
},
"[... snip ...]",
{
"band_number":6,
"raw_value":927,
"unscaled_value":927.0,
"files":[
"S2B_MSIL2A_20260423T094029_N0512_R036_T34TER_20260423T115714.SAFE\/GRANULE\/L2A_T34TER_A047681_20260423T094113\/IMG_DATA\/R10m\/T34TER_20260423T094029_WVP_10m.jp2"
]
}
]
},
"geometry":{
"type":"Point",
"coordinates":[
518058.71436100372,
5066941.9938689889
]
}
}
]
}