Data access and discovery#
Context#
We will be using Long Term Statistics (1999-2019) product provided by the Copernicus Global Land Service over Lombardia and access them through S3-compatible storage. We will also explore Sentinel-2 Cloud-Optimised Dataset online through SpatioTemporal Asset Catalogs (STAC).
Setup#
This episode uses the following main Python packages:
s3fs [S3FsDTeam16]
pystac-client [pystacDTeam24]
odc-stac [ODCDTeam24]
rioxarray [SBR+22]
matplotlib [Hun07]
cartopy [MetOffice15]
Please install these packages if not already available in your Python environment.
Packages#
In this episode, Python packages are imported when we start to use them. However, for best software practices, we recommend you to install and import all the necessary libraries at the top of your Jupyter notebook.
Introduction to the Long Term statistics#
CGLS LTS are computed over a time span of 20 years aggregated over separate 10-day periods (month/01,month/11, month/21). For each date the long term minimum, maximum, mean, median and standard deviation are computed.
S3-compatible Object Storage to access online data#
Up to now we have downloaded data locally and then opened with Xarray open_dataset
. When willing to manipulate large amount of data, this approach is not optimal (since it requires a lot of unnecessary local downloads). Sharing data online as Object Storage allows for data sharing and access to much larger amounts of data.
One of the most popular methods to access online remote data is through Amazon Simple Storage Service (S3) and you don’t necessarily need to use Amazon services to benefit from S3 object storage. Many other providers offer S3-compatible object storage that can be accessed in a very similar way.
Below we will be accessing online the NDVI Long Term Statistics from Copernicus Land Service that we have publicly stored in OpenStack Object storage (Swift).
import s3fs
import xarray as xr
fs = s3fs.S3FileSystem(anon=True,
client_kwargs={
'endpoint_url': 'https://object-store.cloud.muni.cz'
})
Tip
The parameter anon
is for anonymous
and is set to True
because the data we have stored at https://object-store.cloud.muni.cz
is public
List files and folders in existing buckets#
Instead of organizing files in various folders, object storage systems store files in a flat organization of containers (called “buckets”).
fs.ls('foss4g-data')
['foss4g-data/CGLS_LTS_1999_2019',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia',
'foss4g-data/test']
fs.ls('foss4g-data/CGLS_LTS_1999_2019_Lombardia')
['foss4g-data/CGLS_LTS_1999_2019_Lombardia/',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0101_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0111_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0121_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0201_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0211_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0221_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0301_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0311_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0321_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0401_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0411_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0421_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0501_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0511_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0521_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0601_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0611_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0621_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0701_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0711_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0721_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0801_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0811_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0821_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0901_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0911_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0921_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1001_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1011_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1021_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1101_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1111_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1121_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1201_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1211_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1221_GLOBE_VGT-PROBAV_V3.0.1.nc']
Access remote files from S3-compatible Object Storage#
s3path = 's3://foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0101_GLOBE_VGT-PROBAV_V3.0.1.nc'
LTS = xr.open_dataset(fs.open(s3path))
LTS
<xarray.Dataset> Size: 3MB Dimensions: (lon: 331, lat: 235) Coordinates: * lon (lon) float64 3kB 8.5 8.509 8.518 8.527 ... 11.42 11.43 11.44 11.45 * lat (lat) float64 2kB 46.7 46.69 46.68 46.67 ... 44.63 44.62 44.61 Data variables: crs |S1 1B ... min (lat, lon) float64 622kB ... median (lat, lon) float64 622kB ... max (lat, lon) float64 622kB ... mean (lat, lon) float64 622kB ... stdev (lat, lon) float64 622kB ... nobs (lat, lon) float32 311kB ... Attributes: (12/19) Conventions: CF-1.6 parent_identifier: urn:cgls:global:ndvi_stats_all identifier: urn:cgls:global:ndvi_stats_all:NDVI-LTS_1999-2019-0... long_name: Normalized Difference Vegetation Index title: Normalized Difference Vegetation Index: Long Term S... product_version: V3.0.1 ... ... source: Derived from EO satellite imagery processing_mode: Offline references: https://land.copernicus.eu/global/products/ndvi copyright: Copernicus Service information 2021 archive_facility: VITO history: 2021-03-01 - Processing line NDVI LTS
LTS.sel(lat=45.88, lon=8.63, method='nearest')['min'].values
array(0.264)
Warning
The same dataset can be available from different locations e.g. CGLS distributor VITO, Zenodo, S3-compatible OpenStack Object storage (Swift), etc. How do you know if it corresponds to the very same dataset? You cannot know except if the datasets have a persistent identifier such as a Digital Object Identifier. It is therefore recommended 1) to be extra careful about where you get your datasets, and 2) to double check that the content is exactly what you expect (for instance, you can perform basic quality checks).
Access multiple remote files#
s3path = 's3://foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_*.nc'
remote_files = fs.glob(s3path)
remote_files
Show code cell output
['foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0101_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0111_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0121_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0201_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0211_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0221_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0301_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0311_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0321_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0401_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0411_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0421_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0501_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0511_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0521_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0601_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0611_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0621_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0701_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0711_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0721_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0801_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0811_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0821_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0901_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0911_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-0921_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1001_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1011_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1021_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1101_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1111_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1121_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1201_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1211_GLOBE_VGT-PROBAV_V3.0.1.nc',
'foss4g-data/CGLS_LTS_1999_2019_Lombardia/AOI_c_gls_NDVI-LTS_1999-2019-1221_GLOBE_VGT-PROBAV_V3.0.1.nc']
We need to add a time dimension to concatenate data. For this, we define a function that will be called for each remote file (via the preprocess
parameter of Xarray open_mfdataset
.)
from datetime import datetime
def preprocess(ds):
t = datetime.strptime(ds.attrs['identifier'].split(':')[-1].split('_')[1].replace('1999-', ''), "%Y-%m%d")
return(ds.assign_coords(time=t).expand_dims('time'))
Xarray open_mfdataset
allows opening multiple files at the same time.
# Iterate through remote_files to create a fileset
fileset = [fs.open(file) for file in remote_files]
When opening remote files, you can also select the variables you wish to analyze.
LTS = xr.open_mfdataset(fileset, combine='nested', concat_dim=['time'], preprocess=preprocess,
decode_coords="all")
LTS
<xarray.Dataset> Size: 123MB Dimensions: (time: 36, lat: 235, lon: 331) Coordinates: * lon (lon) float64 3kB 8.5 8.509 8.518 8.527 ... 11.42 11.43 11.44 11.45 * lat (lat) float64 2kB 46.7 46.69 46.68 46.67 ... 44.63 44.62 44.61 crs |S1 1B b'' * time (time) datetime64[ns] 288B 2019-01-01 2019-01-11 ... 2019-12-21 Data variables: min (time, lat, lon) float64 22MB dask.array<chunksize=(1, 235, 331), meta=np.ndarray> median (time, lat, lon) float64 22MB dask.array<chunksize=(1, 235, 331), meta=np.ndarray> max (time, lat, lon) float64 22MB dask.array<chunksize=(1, 235, 331), meta=np.ndarray> mean (time, lat, lon) float64 22MB dask.array<chunksize=(1, 235, 331), meta=np.ndarray> stdev (time, lat, lon) float64 22MB dask.array<chunksize=(1, 235, 331), meta=np.ndarray> nobs (time, lat, lon) float32 11MB dask.array<chunksize=(1, 235, 331), meta=np.ndarray> Attributes: (12/19) Conventions: CF-1.6 parent_identifier: urn:cgls:global:ndvi_stats_all identifier: urn:cgls:global:ndvi_stats_all:NDVI-LTS_1999-2019-0... long_name: Normalized Difference Vegetation Index title: Normalized Difference Vegetation Index: Long Term S... product_version: V3.0.1 ... ... source: Derived from EO satellite imagery processing_mode: Offline references: https://land.copernicus.eu/global/products/ndvi copyright: Copernicus Service information 2021 archive_facility: VITO history: 2021-03-01 - Processing line NDVI LTS
Visualize maximum NDVI for January 2015#
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
fig = plt.figure(1, figsize=[20, 10])
# We're using cartopy and are plotting in PlateCarree projection
# (see documentation on cartopy)
ax = plt.subplot(1, 1, 1, projection=ccrs.PlateCarree())
#ax.set_extent([5, 15, 40, 50], crs=ccrs.PlateCarree()) # lon1 lon2 lat1 lat2
ax.coastlines(resolution='10m')
ax.gridlines(draw_labels=True)
LTS.sel(time="2015-01-01", method="nearest")["max"].plot(ax=ax, vmin=-1, vmax=1, transform=ccrs.PlateCarree(), cmap="RdYlGn")
# One way to customize your title
plt.title("Maximum NDVI over Lombardia", fontsize=18)
Text(0.5, 1.0, 'Maximum NDVI over Lombardia')
/home/runner/micromamba/envs/geohack/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_coastline.zip
warnings.warn(f'Downloading: {url}', DownloadWarning)
Tip
If you use one of xarray’s open methods such as xarray.open_dataset to load netCDF files with the default engine, it is recommended to use decode_coords=”all”. This will load the grid mapping variable into coordinates for compatibility with rioxarray. See rioxarray documentation.
Preparing and discover online datasets#
With the plethora of cloud storage, there are many available online datasets. To ease the preparation and discovery of such datasets, we describe emerging community-driven initiatives promoting standards suited to both geospatial and geoscience communities. Most of the material below is adapted from a previous Pangeo 101 training [GAF2)].
Analysis Ready, cloud optimized data (ARCO)#
When analyzing data at scale, the data format used is key. For years, the main data format was netCDF e.g. Network Common Data Form but with the use of cloud computing and interest in Open Science, different formats are often more suitable.
Formats for analyzing data from the cloud are refered to as “Analysis Ready, Cloud Optimized” data formats or in short ARCO. Find further info about ARCO datasets in [SAH+22].
What is “Analysis Ready”?
Think in terms of “Datasets” not “data files”
No need for tedious homogenizing / cleaning setup guides
Curated and cataloged
What is “Cloud Optimized”?
Compatible with object storage e.g. access via HTTP
Supports lazy access and intelligent subsetting
Integrates with high-level analysis libraries and distributed frameworks
Instead of having a big dataset, ARCO datasets are chunked appropriately for analysis and have rich metadata (See Figure 1).
Fig 1. Example of an ARCO dataset. Source: [GAF2)].
Spatio Temporal Asset Catalogs (STAC)#
The STAC specification is a common language to describe geospatial information, so it can more easily be worked with, indexed, and discovered. The development of the STAC specification has simplified and standardized access to satellite datasets. This specification allows users to easily access metadata and raw data, and filter data based on attributes such as satellite type, cloud cover, area of interest, and dates.
Why STAC?#
Each provider has its own catalog and interface (APIs).
Every time you want to access a new catalog, you need to change your program.
We have lots of data providers and each with a bespoke interface.
It is becoming quickly difficult for programmers who need to design a new data connector each time.
Features#
STAC catalogs are extremely simple.
They are composed of three layers:
Catalogs
Collections
Items
STAC is very popular for Earth Observation satellite imagery.
For instance it can be used to access Sentinel-2 in AWS (see Figure 3).
Fig 3. Example of STAC collection of Sentinel-2 images hosted in AWS. Source: [GAF2)].
Python packages to search items from STAC catalogs#
pystac-client
interacts with SpatioTemporal Asset Catalogs (STAC) to discover and access geospatial data easily.odc-stac
is based on Open Data Cube (ODC) and facilitates the management and analysis of large volumes of data accessible from STAC.
from pystac_client import Client
from odc.stac import load
import odc.geo
Search for Items#
Let’s check if items are available for given combination of collection, date, geometry, query, etc.
# use publically available stac link such as
client = Client.open("https://earth-search.aws.element84.com/v1")
# ID of the collection
collection = "sentinel-2-l2a"
# Geometry of AOI e.g. small area over Lombardia (Italy)
geometry = {
"coordinates": [
[
[
9.621052726784058,
45.863683047884365
],
[
9.621052726784058,
45.72725813387305
],
[
9.874045082377933,
45.72725813387305
],
[
9.874045082377933,
45.863683047884365
],
[
9.621052726784058,
45.863683047884365
]
]
],
"type": "Polygon",
}
Search items for a specific date#
# Specific Date
date_YYMMDD = "2023-01-01"
# run pystac client search to see available dataset
search = client.search(
collections=[collection], intersects=geometry, datetime=date_YYMMDD
)
# spit out data as GeoJSON dictionary
print(search.item_collection_as_dict())
Show code cell output
{'type': 'FeatureCollection', 'features': [{'type': 'Feature', 'stac_version': '1.0.0', 'id': 'S2B_32TNR_20230101_0_L2A', 'properties': {'created': '2023-01-01T15:38:00.242Z', 'platform': 'sentinel-2b', 'constellation': 'sentinel-2', 'instruments': ['msi'], 'eo:cloud_cover': 93.50211, 'proj:epsg': 32632, 'mgrs:utm_zone': 32, 'mgrs:latitude_band': 'T', 'mgrs:grid_square': 'NR', 'grid:code': 'MGRS-32TNR', 'view:sun_azimuth': 166.183161249214, 'view:sun_elevation': 20.248337225593005, 's2:degraded_msi_data_percentage': 0.0022, 's2:nodata_pixel_percentage': 0, 's2:saturated_defective_pixel_percentage': 0, 's2:dark_features_percentage': 2.043006, 's2:cloud_shadow_percentage': 4.12275, 's2:vegetation_percentage': 0.054559, 's2:not_vegetated_percentage': 0.154664, 's2:water_percentage': 0.038135, 's2:unclassified_percentage': 0.00642, 's2:medium_proba_clouds_percentage': 7.874592, 's2:high_proba_clouds_percentage': 84.83994, 's2:thin_cirrus_percentage': 0.787582, 's2:snow_ice_percentage': 0.078354, 's2:product_type': 'S2MSI2A', 's2:processing_baseline': '05.09', 's2:product_uri': 'S2B_MSIL2A_20230101T102339_N0509_R065_T32TNR_20230101T113717.SAFE', 's2:generation_time': '2023-01-01T11:37:17.000000Z', 's2:datatake_id': 'GS2B_20230101T102339_030407_N05.09', 's2:datatake_type': 'INS-NOBS', 's2:datastrip_id': 'S2B_OPER_MSI_L2A_DS_2BPS_20230101T113717_S20230101T102333_N05.09', 's2:granule_id': 'S2B_OPER_MSI_L2A_TL_2BPS_20230101T113717_A030407_T32TNR_N05.09', 's2:reflectance_conversion_factor': 1.03412690929912, 'datetime': '2023-01-01T10:28:12.809000Z', 's2:sequence': '0', 'earthsearch:s3_path': 's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A', 'earthsearch:payload_id': 'roda-sentinel2/workflow-sentinel2-to-stac/315a1af58a55f3166923303539c25ff8', 'earthsearch:boa_offset_applied': True, 'processing:software': {'sentinel2-to-stac': '0.1.0'}, 'updated': '2023-01-01T15:38:00.242Z'}, 'geometry': {'type': 'Polygon', 'coordinates': [[[8.999754387288686, 46.05356536925373], [10.418890992989095, 46.04475738929435], [10.394241393041778, 45.056757746793146], [8.999758655880777, 45.06526899586078], [8.999754387288686, 46.05356536925373]]]}, 'links': [{'rel': 'self', 'type': 'application/geo+json', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/items/S2B_32TNR_20230101_0_L2A'}, {'rel': 'canonical', 'href': 's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/S2B_32TNR_20230101_0_L2A.json', 'type': 'application/json'}, {'rel': 'license', 'href': 'https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice'}, {'rel': 'derived_from', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l1c/items/S2B_32TNR_20230101_0_L1C', 'type': 'application/geo+json'}, {'rel': 'parent', 'type': 'application/json', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a'}, {'rel': 'collection', 'type': 'application/json', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a'}, {'rel': 'root', 'type': 'application/json', 'href': 'https://earth-search.aws.element84.com/v1'}, {'rel': 'thumbnail', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/items/S2B_32TNR_20230101_0_L2A/thumbnail'}], 'assets': {'aot': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/AOT.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Aerosol optical thickness (AOT)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}, 'blue': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/B02.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Blue (band 2) - 10m', 'eo:bands': [{'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'coastal': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/B01.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Coastal aerosol (band 1) - 60m', 'eo:bands': [{'name': 'coastal', 'common_name': 'coastal', 'description': 'Coastal aerosol (band 1)', 'center_wavelength': 0.443, 'full_width_half_max': 0.027}], 'gsd': 60, 'proj:shape': [1830, 1830], 'proj:transform': [60, 0, 499980, 0, -60, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 60, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'granule_metadata': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/granule_metadata.xml', 'type': 'application/xml', 'roles': ['metadata']}, 'green': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/B03.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Green (band 3) - 10m', 'eo:bands': [{'name': 'green', 'common_name': 'green', 'description': 'Green (band 3)', 'center_wavelength': 0.56, 'full_width_half_max': 0.045}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/B08.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'NIR 1 (band 8) - 10m', 'eo:bands': [{'name': 'nir', 'common_name': 'nir', 'description': 'NIR 1 (band 8)', 'center_wavelength': 0.842, 'full_width_half_max': 0.145}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir08': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/B8A.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'NIR 2 (band 8A) - 20m', 'eo:bands': [{'name': 'nir08', 'common_name': 'nir08', 'description': 'NIR 2 (band 8A)', 'center_wavelength': 0.865, 'full_width_half_max': 0.033}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir09': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/B09.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'NIR 3 (band 9) - 60m', 'eo:bands': [{'name': 'nir09', 'common_name': 'nir09', 'description': 'NIR 3 (band 9)', 'center_wavelength': 0.945, 'full_width_half_max': 0.026}], 'gsd': 60, 'proj:shape': [1830, 1830], 'proj:transform': [60, 0, 499980, 0, -60, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 60, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'red': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/B04.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Red (band 4) - 10m', 'eo:bands': [{'name': 'red', 'common_name': 'red', 'description': 'Red (band 4)', 'center_wavelength': 0.665, 'full_width_half_max': 0.038}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge1': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/B05.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Red edge 1 (band 5) - 20m', 'eo:bands': [{'name': 'rededge1', 'common_name': 'rededge', 'description': 'Red edge 1 (band 5)', 'center_wavelength': 0.704, 'full_width_half_max': 0.019}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge2': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/B06.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Red edge 2 (band 6) - 20m', 'eo:bands': [{'name': 'rededge2', 'common_name': 'rededge', 'description': 'Red edge 2 (band 6)', 'center_wavelength': 0.74, 'full_width_half_max': 0.018}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge3': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/B07.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Red edge 3 (band 7) - 20m', 'eo:bands': [{'name': 'rededge3', 'common_name': 'rededge', 'description': 'Red edge 3 (band 7)', 'center_wavelength': 0.783, 'full_width_half_max': 0.028}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'scl': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/SCL.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Scene classification map (SCL)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint8', 'spatial_resolution': 20}], 'roles': ['data', 'reflectance']}, 'swir16': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/B11.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'SWIR 1 (band 11) - 20m', 'eo:bands': [{'name': 'swir16', 'common_name': 'swir16', 'description': 'SWIR 1 (band 11)', 'center_wavelength': 1.61, 'full_width_half_max': 0.143}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'swir22': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/B12.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'SWIR 2 (band 12) - 20m', 'eo:bands': [{'name': 'swir22', 'common_name': 'swir22', 'description': 'SWIR 2 (band 12)', 'center_wavelength': 2.19, 'full_width_half_max': 0.242}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'thumbnail': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/thumbnail.jpg', 'type': 'image/jpeg', 'title': 'Thumbnail image', 'roles': ['thumbnail']}, 'tileinfo_metadata': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/tileinfo_metadata.json', 'type': 'application/json', 'roles': ['metadata']}, 'visual': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/TCI.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'True color image', 'eo:bands': [{'name': 'red', 'common_name': 'red', 'description': 'Red (band 4)', 'center_wavelength': 0.665, 'full_width_half_max': 0.038}, {'name': 'green', 'common_name': 'green', 'description': 'Green (band 3)', 'center_wavelength': 0.56, 'full_width_half_max': 0.045}, {'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'roles': ['visual']}, 'wvp': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/WVP.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Water vapour (WVP)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'unit': 'cm', 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}, 'aot-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/AOT.jp2', 'type': 'image/jp2', 'title': 'Aerosol optical thickness (AOT)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}, 'blue-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B02.jp2', 'type': 'image/jp2', 'title': 'Blue (band 2) - 10m', 'eo:bands': [{'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'coastal-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B01.jp2', 'type': 'image/jp2', 'title': 'Coastal aerosol (band 1) - 60m', 'eo:bands': [{'name': 'coastal', 'common_name': 'coastal', 'description': 'Coastal aerosol (band 1)', 'center_wavelength': 0.443, 'full_width_half_max': 0.027}], 'gsd': 60, 'proj:shape': [1830, 1830], 'proj:transform': [60, 0, 499980, 0, -60, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 60, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'green-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B03.jp2', 'type': 'image/jp2', 'title': 'Green (band 3) - 10m', 'eo:bands': [{'name': 'green', 'common_name': 'green', 'description': 'Green (band 3)', 'center_wavelength': 0.56, 'full_width_half_max': 0.045}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B08.jp2', 'type': 'image/jp2', 'title': 'NIR 1 (band 8) - 10m', 'eo:bands': [{'name': 'nir', 'common_name': 'nir', 'description': 'NIR 1 (band 8)', 'center_wavelength': 0.842, 'full_width_half_max': 0.145}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir08-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B8A.jp2', 'type': 'image/jp2', 'title': 'NIR 2 (band 8A) - 20m', 'eo:bands': [{'name': 'nir08', 'common_name': 'nir08', 'description': 'NIR 2 (band 8A)', 'center_wavelength': 0.865, 'full_width_half_max': 0.033}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir09-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B09.jp2', 'type': 'image/jp2', 'title': 'NIR 3 (band 9) - 60m', 'eo:bands': [{'name': 'nir09', 'common_name': 'nir09', 'description': 'NIR 3 (band 9)', 'center_wavelength': 0.945, 'full_width_half_max': 0.026}], 'gsd': 60, 'proj:shape': [1830, 1830], 'proj:transform': [60, 0, 499980, 0, -60, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 60, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'red-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B04.jp2', 'type': 'image/jp2', 'title': 'Red (band 4) - 10m', 'eo:bands': [{'name': 'red', 'common_name': 'red', 'description': 'Red (band 4)', 'center_wavelength': 0.665, 'full_width_half_max': 0.038}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge1-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B05.jp2', 'type': 'image/jp2', 'title': 'Red edge 1 (band 5) - 20m', 'eo:bands': [{'name': 'rededge1', 'common_name': 'rededge', 'description': 'Red edge 1 (band 5)', 'center_wavelength': 0.704, 'full_width_half_max': 0.019}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge2-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B06.jp2', 'type': 'image/jp2', 'title': 'Red edge 2 (band 6) - 20m', 'eo:bands': [{'name': 'rededge2', 'common_name': 'rededge', 'description': 'Red edge 2 (band 6)', 'center_wavelength': 0.74, 'full_width_half_max': 0.018}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge3-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B07.jp2', 'type': 'image/jp2', 'title': 'Red edge 3 (band 7) - 20m', 'eo:bands': [{'name': 'rededge3', 'common_name': 'rededge', 'description': 'Red edge 3 (band 7)', 'center_wavelength': 0.783, 'full_width_half_max': 0.028}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'scl-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/SCL.jp2', 'type': 'image/jp2', 'title': 'Scene classification map (SCL)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint8', 'spatial_resolution': 20}], 'roles': ['data', 'reflectance']}, 'swir16-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B11.jp2', 'type': 'image/jp2', 'title': 'SWIR 1 (band 11) - 20m', 'eo:bands': [{'name': 'swir16', 'common_name': 'swir16', 'description': 'SWIR 1 (band 11)', 'center_wavelength': 1.61, 'full_width_half_max': 0.143}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'swir22-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B12.jp2', 'type': 'image/jp2', 'title': 'SWIR 2 (band 12) - 20m', 'eo:bands': [{'name': 'swir22', 'common_name': 'swir22', 'description': 'SWIR 2 (band 12)', 'center_wavelength': 2.19, 'full_width_half_max': 0.242}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'visual-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/TCI.jp2', 'type': 'image/jp2', 'title': 'True color image', 'eo:bands': [{'name': 'red', 'common_name': 'red', 'description': 'Red (band 4)', 'center_wavelength': 0.665, 'full_width_half_max': 0.038}, {'name': 'green', 'common_name': 'green', 'description': 'Green (band 3)', 'center_wavelength': 0.56, 'full_width_half_max': 0.045}, {'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'roles': ['visual']}, 'wvp-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/WVP.jp2', 'type': 'image/jp2', 'title': 'Water vapour (WVP)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'unit': 'cm', 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}}, 'bbox': [8.999754387288686, 45.056757746793146, 10.418890992989095, 46.05356536925373], 'stac_extensions': ['https://stac-extensions.github.io/processing/v1.1.0/schema.json', 'https://stac-extensions.github.io/mgrs/v1.0.0/schema.json', 'https://stac-extensions.github.io/grid/v1.0.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/eo/v1.0.0/schema.json', 'https://stac-extensions.github.io/raster/v1.1.0/schema.json', 'https://stac-extensions.github.io/projection/v1.0.0/schema.json'], 'collection': 'sentinel-2-l2a'}]}
Search items for a specific month#
# Complete month
date_YYMM = "2023-01"
# run pystac client search to see available dataset
search = client.search(
collections=[collection], intersects=geometry, datetime=date_YYMM
)
# spit out data as GeoJSON dictionary
print(search.item_collection_as_dict())
# loop through each item
for item in search.items_as_dicts():
print(item)
Show code cell output
{'type': 'FeatureCollection', 'features': [{'type': 'Feature', 'stac_version': '1.0.0', 'id': 'S2B_32TNR_20230131_0_L2A', 'properties': {'created': '2023-01-31T19:20:02.816Z', 'platform': 'sentinel-2b', 'constellation': 'sentinel-2', 'instruments': ['msi'], 'eo:cloud_cover': 0.112465, 'proj:epsg': 32632, 'mgrs:utm_zone': 32, 'mgrs:latitude_band': 'T', 'mgrs:grid_square': 'NR', 'grid:code': 'MGRS-32TNR', 'view:sun_azimuth': 162.471016303525, 'view:sun_elevation': 25.273284914117596, 's2:degraded_msi_data_percentage': 0.0026, 's2:nodata_pixel_percentage': 3e-05, 's2:saturated_defective_pixel_percentage': 0, 's2:dark_features_percentage': 9.818108, 's2:cloud_shadow_percentage': 0.002522, 's2:vegetation_percentage': 31.585425, 's2:not_vegetated_percentage': 51.599735, 's2:water_percentage': 2.399037, 's2:unclassified_percentage': 0.003381, 's2:medium_proba_clouds_percentage': 0.00499, 's2:high_proba_clouds_percentage': 0.010272, 's2:thin_cirrus_percentage': 0.097203, 's2:snow_ice_percentage': 4.479326, 's2:product_type': 'S2MSI2A', 's2:processing_baseline': '05.09', 's2:product_uri': 'S2B_MSIL2A_20230131T102149_N0509_R065_T32TNR_20230131T131849.SAFE', 's2:generation_time': '2023-01-31T13:18:49.000000Z', 's2:datatake_id': 'GS2B_20230131T102149_030836_N05.09', 's2:datatake_type': 'INS-NOBS', 's2:datastrip_id': 'S2B_OPER_MSI_L2A_DS_2BPS_20230131T131849_S20230131T102549_N05.09', 's2:granule_id': 'S2B_OPER_MSI_L2A_TL_2BPS_20230131T131849_A030836_T32TNR_N05.09', 's2:reflectance_conversion_factor': 1.03138686673961, 'datetime': '2023-01-31T10:28:11.279000Z', 's2:sequence': '0', 'earthsearch:s3_path': 's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A', 'earthsearch:payload_id': 'roda-sentinel2/workflow-sentinel2-to-stac/7734c7b274348e5d6d4c53ea8a2e5b86', 'earthsearch:boa_offset_applied': True, 'processing:software': {'sentinel2-to-stac': '0.1.0'}, 'updated': '2023-01-31T19:20:02.816Z'}, 'geometry': {'type': 'Polygon', 'coordinates': [[[8.999754387288686, 46.05356536925373], [10.418890992989095, 46.04475738929435], [10.394241393041778, 45.056757746793146], [8.999758655880777, 45.06526899586078], [8.999754387288686, 46.05356536925373]]]}, 'links': [{'rel': 'self', 'type': 'application/geo+json', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/items/S2B_32TNR_20230131_0_L2A'}, {'rel': 'canonical', 'href': 's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/S2B_32TNR_20230131_0_L2A.json', 'type': 'application/json'}, {'rel': 'license', 'href': 'https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice'}, {'rel': 'derived_from', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l1c/items/S2B_32TNR_20230131_0_L1C', 'type': 'application/geo+json'}, {'rel': 'parent', 'type': 'application/json', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a'}, {'rel': 'collection', 'type': 'application/json', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a'}, {'rel': 'root', 'type': 'application/json', 'href': 'https://earth-search.aws.element84.com/v1'}, {'rel': 'thumbnail', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/items/S2B_32TNR_20230131_0_L2A/thumbnail'}], 'assets': {'aot': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/AOT.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Aerosol optical thickness (AOT)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}, 'blue': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B02.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Blue (band 2) - 10m', 'eo:bands': [{'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'coastal': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B01.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Coastal aerosol (band 1) - 60m', 'eo:bands': [{'name': 'coastal', 'common_name': 'coastal', 'description': 'Coastal aerosol (band 1)', 'center_wavelength': 0.443, 'full_width_half_max': 0.027}], 'gsd': 60, 'proj:shape': [1830, 1830], 'proj:transform': [60, 0, 499980, 0, -60, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 60, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'granule_metadata': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/granule_metadata.xml', 'type': 'application/xml', 'roles': ['metadata']}, 'green': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B03.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Green (band 3) - 10m', 'eo:bands': [{'name': 'green', 'common_name': 'green', 'description': 'Green (band 3)', 'center_wavelength': 0.56, 'full_width_half_max': 0.045}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B08.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'NIR 1 (band 8) - 10m', 'eo:bands': [{'name': 'nir', 'common_name': 'nir', 'description': 'NIR 1 (band 8)', 'center_wavelength': 0.842, 'full_width_half_max': 0.145}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir08': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B8A.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'NIR 2 (band 8A) - 20m', 'eo:bands': [{'name': 'nir08', 'common_name': 'nir08', 'description': 'NIR 2 (band 8A)', 'center_wavelength': 0.865, 'full_width_half_max': 0.033}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir09': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B09.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'NIR 3 (band 9) - 60m', 'eo:bands': [{'name': 'nir09', 'common_name': 'nir09', 'description': 'NIR 3 (band 9)', 'center_wavelength': 0.945, 'full_width_half_max': 0.026}], 'gsd': 60, 'proj:shape': [1830, 1830], 'proj:transform': [60, 0, 499980, 0, -60, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 60, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'red': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B04.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Red (band 4) - 10m', 'eo:bands': [{'name': 'red', 'common_name': 'red', 'description': 'Red (band 4)', 'center_wavelength': 0.665, 'full_width_half_max': 0.038}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge1': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B05.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Red edge 1 (band 5) - 20m', 'eo:bands': [{'name': 'rededge1', 'common_name': 'rededge', 'description': 'Red edge 1 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'reflectance']}, 'rededge3': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B07.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Red edge 3 (band 7) - 20m', 'eo:bands': [{'name': 'rededge3', 'common_name': 'rededge', 'description': 'Red edge 3 (band 7)', 'center_wavelength': 0.783, 'full_width_half_max': 0.028}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'scl': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/SCL.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Scene classification map (SCL)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, 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20m', 'eo:bands': [{'name': 'swir16', 'common_name': 'swir16', 'description': 'SWIR 1 (band 11)', 'center_wavelength': 1.61, 'full_width_half_max': 0.143}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'swir22': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/B12.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'SWIR 2 (band 12) - 20m', 'eo:bands': [{'name': 'swir22', 'common_name': 'swir22', 'description': 'SWIR 2 (band 12)', 'center_wavelength': 2.19, 'full_width_half_max': 0.242}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'thumbnail': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/thumbnail.jpg', 'type': 'image/jpeg', 'title': 'Thumbnail image', 'roles': ['thumbnail']}, 'tileinfo_metadata': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/tileinfo_metadata.json', 'type': 'application/json', 'roles': ['metadata']}, 'visual': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/TCI.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'True color image', 'eo:bands': [{'name': 'red', 'common_name': 'red', 'description': 'Red (band 4)', 'center_wavelength': 0.665, 'full_width_half_max': 0.038}, {'name': 'green', 'common_name': 'green', 'description': 'Green (band 3)', 'center_wavelength': 0.56, 'full_width_half_max': 0.045}, {'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'roles': ['visual']}, 'wvp': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230101_0_L2A/WVP.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Water vapour (WVP)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'unit': 'cm', 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}, 'aot-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/AOT.jp2', 'type': 'image/jp2', 'title': 'Aerosol optical thickness (AOT)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}, 'blue-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B02.jp2', 'type': 'image/jp2', 'title': 'Blue (band 2) - 10m', 'eo:bands': [{'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'coastal-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B01.jp2', 'type': 'image/jp2', 'title': 'Coastal aerosol (band 1) - 60m', 'eo:bands': [{'name': 'coastal', 'common_name': 'coastal', 'description': 'Coastal aerosol (band 1)', 'center_wavelength': 0.443, 'full_width_half_max': 0.027}], 'gsd': 60, 'proj:shape': [1830, 1830], 'proj:transform': [60, 0, 499980, 0, -60, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 60, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'green-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B03.jp2', 'type': 'image/jp2', 'title': 'Green (band 3) - 10m', 'eo:bands': [{'name': 'green', 'common_name': 'green', 'description': 'Green (band 3)', 'center_wavelength': 0.56, 'full_width_half_max': 0.045}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B08.jp2', 'type': 'image/jp2', 'title': 'NIR 1 (band 8) - 10m', 'eo:bands': [{'name': 'nir', 'common_name': 'nir', 'description': 'NIR 1 (band 8)', 'center_wavelength': 0.842, 'full_width_half_max': 0.145}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir08-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B8A.jp2', 'type': 'image/jp2', 'title': 'NIR 2 (band 8A) - 20m', 'eo:bands': [{'name': 'nir08', 'common_name': 'nir08', 'description': 'NIR 2 (band 8A)', 'center_wavelength': 0.865, 'full_width_half_max': 0.033}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir09-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B09.jp2', 'type': 'image/jp2', 'title': 'NIR 3 (band 9) - 60m', 'eo:bands': [{'name': 'nir09', 'common_name': 'nir09', 'description': 'NIR 3 (band 9)', 'center_wavelength': 0.945, 'full_width_half_max': 0.026}], 'gsd': 60, 'proj:shape': [1830, 1830], 'proj:transform': [60, 0, 499980, 0, -60, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 60, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'red-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B04.jp2', 'type': 'image/jp2', 'title': 'Red (band 4) - 10m', 'eo:bands': [{'name': 'red', 'common_name': 'red', 'description': 'Red (band 4)', 'center_wavelength': 0.665, 'full_width_half_max': 0.038}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge1-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B05.jp2', 'type': 'image/jp2', 'title': 'Red edge 1 (band 5) - 20m', 'eo:bands': [{'name': 'rededge1', 'common_name': 'rededge', 'description': 'Red edge 1 (band 5)', 'center_wavelength': 0.704, 'full_width_half_max': 0.019}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge2-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B06.jp2', 'type': 'image/jp2', 'title': 'Red edge 2 (band 6) - 20m', 'eo:bands': [{'name': 'rededge2', 'common_name': 'rededge', 'description': 'Red edge 2 (band 6)', 'center_wavelength': 0.74, 'full_width_half_max': 0.018}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge3-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B07.jp2', 'type': 'image/jp2', 'title': 'Red edge 3 (band 7) - 20m', 'eo:bands': [{'name': 'rededge3', 'common_name': 'rededge', 'description': 'Red edge 3 (band 7)', 'center_wavelength': 0.783, 'full_width_half_max': 0.028}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'scl-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/SCL.jp2', 'type': 'image/jp2', 'title': 'Scene classification map (SCL)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint8', 'spatial_resolution': 20}], 'roles': ['data', 'reflectance']}, 'swir16-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B11.jp2', 'type': 'image/jp2', 'title': 'SWIR 1 (band 11) - 20m', 'eo:bands': [{'name': 'swir16', 'common_name': 'swir16', 'description': 'SWIR 1 (band 11)', 'center_wavelength': 1.61, 'full_width_half_max': 0.143}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'swir22-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/B12.jp2', 'type': 'image/jp2', 'title': 'SWIR 2 (band 12) - 20m', 'eo:bands': [{'name': 'swir22', 'common_name': 'swir22', 'description': 'SWIR 2 (band 12)', 'center_wavelength': 2.19, 'full_width_half_max': 0.242}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'visual-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/TCI.jp2', 'type': 'image/jp2', 'title': 'True color image', 'eo:bands': [{'name': 'red', 'common_name': 'red', 'description': 'Red (band 4)', 'center_wavelength': 0.665, 'full_width_half_max': 0.038}, {'name': 'green', 'common_name': 'green', 'description': 'Green (band 3)', 'center_wavelength': 0.56, 'full_width_half_max': 0.045}, {'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'roles': ['visual']}, 'wvp-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/1/0/WVP.jp2', 'type': 'image/jp2', 'title': 'Water vapour (WVP)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'unit': 'cm', 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}}, 'bbox': [8.999754387288686, 45.056757746793146, 10.418890992989095, 46.05356536925373], 'stac_extensions': ['https://stac-extensions.github.io/processing/v1.1.0/schema.json', 'https://stac-extensions.github.io/mgrs/v1.0.0/schema.json', 'https://stac-extensions.github.io/grid/v1.0.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/eo/v1.0.0/schema.json', 'https://stac-extensions.github.io/raster/v1.1.0/schema.json', 'https://stac-extensions.github.io/projection/v1.0.0/schema.json'], 'collection': 'sentinel-2-l2a'}
Search items for a date range#
# Date range
date_range = "2023-01-10/2023-01-20"
# run pystac client search to see available dataset
search = client.search(
collections=[collection], intersects=geometry, datetime=date_range
)
# spit out data as GeoJSON dictionary
print(search.item_collection_as_dict())
# loop through each item
for item in search.items_as_dicts():
print(item)
Show code cell output
{'type': 'FeatureCollection', 'features': [{'type': 'Feature', 'stac_version': '1.0.0', 'id': 'S2A_32TNR_20230116_0_L2A', 'properties': {'created': '2023-01-16T16:58:50.344Z', 'platform': 'sentinel-2a', 'constellation': 'sentinel-2', 'instruments': ['msi'], 'eo:cloud_cover': 95.510781, 'proj:epsg': 32632, 'mgrs:utm_zone': 32, 'mgrs:latitude_band': 'T', 'mgrs:grid_square': 'NR', 'grid:code': 'MGRS-32TNR', 'view:sun_azimuth': 164.233149382057, 'view:sun_elevation': 21.990654775921797, 's2:degraded_msi_data_percentage': 0.0075, 's2:nodata_pixel_percentage': 0, 's2:saturated_defective_pixel_percentage': 0, 's2:dark_features_percentage': 1.055385, 's2:cloud_shadow_percentage': 3.343091, 's2:vegetation_percentage': 0, 's2:not_vegetated_percentage': 0.000202, 's2:water_percentage': 0.007004, 's2:unclassified_percentage': 4e-05, 's2:medium_proba_clouds_percentage': 47.175184, 's2:high_proba_clouds_percentage': 46.2935, 's2:thin_cirrus_percentage': 2.042097, 's2:snow_ice_percentage': 0.083497, 's2:product_type': 'S2MSI2A', 's2:processing_baseline': '05.09', 's2:product_uri': 'S2A_MSIL2A_20230116T102351_N0509_R065_T32TNR_20230116T135854.SAFE', 's2:generation_time': '2023-01-16T13:58:54.000000Z', 's2:datatake_id': 'GS2A_20230116T102351_039530_N05.09', 's2:datatake_type': 'INS-NOBS', 's2:datastrip_id': 'S2A_OPER_MSI_L2A_DS_2APS_20230116T135854_S20230116T102349_N05.09', 's2:granule_id': 'S2A_OPER_MSI_L2A_TL_2APS_20230116T135854_A039530_T32TNR_N05.09', 's2:reflectance_conversion_factor': 1.03390495472955, 'datetime': '2023-01-16T10:28:10.371000Z', 's2:sequence': '0', 'earthsearch:s3_path': 's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2A_32TNR_20230116_0_L2A', 'earthsearch:payload_id': 'roda-sentinel2/workflow-sentinel2-to-stac/39ce3760ba19612f7ea3b75482a255f7', 'earthsearch:boa_offset_applied': True, 'processing:software': {'sentinel2-to-stac': '0.1.0'}, 'updated': '2023-01-16T16:58:50.344Z'}, 'geometry': {'type': 'Polygon', 'coordinates': [[[8.999754387288686, 46.05356536925373], [10.418890992989095, 46.04475738929435], [10.394241393041778, 45.056757746793146], [8.999758655880777, 45.06526899586078], [8.999754387288686, 46.05356536925373]]]}, 'links': [{'rel': 'self', 'type': 'application/geo+json', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/items/S2A_32TNR_20230116_0_L2A'}, {'rel': 'canonical', 'href': 's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2A_32TNR_20230116_0_L2A/S2A_32TNR_20230116_0_L2A.json', 'type': 'application/json'}, {'rel': 'license', 'href': 'https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice'}, {'rel': 'derived_from', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l1c/items/S2A_32TNR_20230116_0_L1C', 'type': 'application/geo+json'}, {'rel': 'parent', 'type': 'application/json', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a'}, {'rel': 'collection', 'type': 'application/json', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a'}, {'rel': 'root', 'type': 'application/json', 'href': 'https://earth-search.aws.element84.com/v1'}, {'rel': 'thumbnail', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/items/S2A_32TNR_20230116_0_L2A/thumbnail'}], 'assets': {'aot': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2A_32TNR_20230116_0_L2A/AOT.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Aerosol optical thickness (AOT)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}, 'blue': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2A_32TNR_20230116_0_L2A/B02.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Blue (band 2) - 10m', 'eo:bands': [{'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'coastal': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2A_32TNR_20230116_0_L2A/B01.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Coastal aerosol (band 1) - 60m', 'eo:bands': [{'name': 'coastal', 'common_name': 'coastal', 'description': 'Coastal aerosol (band 1)', 'center_wavelength': 0.443, 'full_width_half_max': 0.027}], 'gsd': 60, 'proj:shape': [1830, 1830], 'proj:transform': [60, 0, 499980, 0, -60, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 60, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'granule_metadata': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2A_32TNR_20230116_0_L2A/granule_metadata.xml', 'type': 'application/xml', 'roles': ['metadata']}, 'green': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2A_32TNR_20230116_0_L2A/B03.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Green (band 3) - 10m', 'eo:bands': [{'name': 'green', 'common_name': 'green', 'description': 'Green (band 3)', 'center_wavelength': 0.56, 'full_width_half_max': 0.045}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2A_32TNR_20230116_0_L2A/B08.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'NIR 1 (band 8) - 10m', 'eo:bands': [{'name': 'nir', 'common_name': 'nir', 'description': 'NIR 1 (band 8)', 'center_wavelength': 0.842, 'full_width_half_max': 0.145}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir08': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2A_32TNR_20230116_0_L2A/B8A.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'NIR 2 (band 8A) - 20m', 'eo:bands': [{'name': 'nir08', 'common_name': 'nir08', 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20m', 'eo:bands': [{'name': 'swir16', 'common_name': 'swir16', 'description': 'SWIR 1 (band 11)', 'center_wavelength': 1.61, 'full_width_half_max': 0.143}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'swir22': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230111_0_L2A/B12.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'SWIR 2 (band 12) - 20m', 'eo:bands': [{'name': 'swir22', 'common_name': 'swir22', 'description': 'SWIR 2 (band 12)', 'center_wavelength': 2.19, 'full_width_half_max': 0.242}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'thumbnail': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230111_0_L2A/thumbnail.jpg', 'type': 'image/jpeg', 'title': 'Thumbnail image', 'roles': ['thumbnail']}, 'tileinfo_metadata': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230111_0_L2A/tileinfo_metadata.json', 'type': 'application/json', 'roles': ['metadata']}, 'visual': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230111_0_L2A/TCI.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'True color image', 'eo:bands': [{'name': 'red', 'common_name': 'red', 'description': 'Red (band 4)', 'center_wavelength': 0.665, 'full_width_half_max': 0.038}, {'name': 'green', 'common_name': 'green', 'description': 'Green (band 3)', 'center_wavelength': 0.56, 'full_width_half_max': 0.045}, {'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'roles': ['visual']}, 'wvp': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230111_0_L2A/WVP.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Water vapour (WVP)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'unit': 'cm', 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}, 'aot-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/AOT.jp2', 'type': 'image/jp2', 'title': 'Aerosol optical thickness (AOT)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}, 'blue-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/B02.jp2', 'type': 'image/jp2', 'title': 'Blue (band 2) - 10m', 'eo:bands': [{'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'coastal-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/B01.jp2', 'type': 'image/jp2', 'title': 'Coastal aerosol (band 1) - 60m', 'eo:bands': [{'name': 'coastal', 'common_name': 'coastal', 'description': 'Coastal aerosol (band 1)', 'center_wavelength': 0.443, 'full_width_half_max': 0.027}], 'gsd': 60, 'proj:shape': [1830, 1830], 'proj:transform': [60, 0, 499980, 0, -60, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 60, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'green-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/B03.jp2', 'type': 'image/jp2', 'title': 'Green (band 3) - 10m', 'eo:bands': [{'name': 'green', 'common_name': 'green', 'description': 'Green (band 3)', 'center_wavelength': 0.56, 'full_width_half_max': 0.045}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/B08.jp2', 'type': 'image/jp2', 'title': 'NIR 1 (band 8) - 10m', 'eo:bands': [{'name': 'nir', 'common_name': 'nir', 'description': 'NIR 1 (band 8)', 'center_wavelength': 0.842, 'full_width_half_max': 0.145}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir08-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/B8A.jp2', 'type': 'image/jp2', 'title': 'NIR 2 (band 8A) - 20m', 'eo:bands': [{'name': 'nir08', 'common_name': 'nir08', 'description': 'NIR 2 (band 8A)', 'center_wavelength': 0.865, 'full_width_half_max': 0.033}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir09-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/B09.jp2', 'type': 'image/jp2', 'title': 'NIR 3 (band 9) - 60m', 'eo:bands': [{'name': 'nir09', 'common_name': 'nir09', 'description': 'NIR 3 (band 9)', 'center_wavelength': 0.945, 'full_width_half_max': 0.026}], 'gsd': 60, 'proj:shape': [1830, 1830], 'proj:transform': [60, 0, 499980, 0, -60, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 60, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'red-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/B04.jp2', 'type': 'image/jp2', 'title': 'Red (band 4) - 10m', 'eo:bands': [{'name': 'red', 'common_name': 'red', 'description': 'Red (band 4)', 'center_wavelength': 0.665, 'full_width_half_max': 0.038}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge1-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/B05.jp2', 'type': 'image/jp2', 'title': 'Red edge 1 (band 5) - 20m', 'eo:bands': [{'name': 'rededge1', 'common_name': 'rededge', 'description': 'Red edge 1 (band 5)', 'center_wavelength': 0.704, 'full_width_half_max': 0.019}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge2-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/B06.jp2', 'type': 'image/jp2', 'title': 'Red edge 2 (band 6) - 20m', 'eo:bands': [{'name': 'rededge2', 'common_name': 'rededge', 'description': 'Red edge 2 (band 6)', 'center_wavelength': 0.74, 'full_width_half_max': 0.018}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge3-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/B07.jp2', 'type': 'image/jp2', 'title': 'Red edge 3 (band 7) - 20m', 'eo:bands': [{'name': 'rededge3', 'common_name': 'rededge', 'description': 'Red edge 3 (band 7)', 'center_wavelength': 0.783, 'full_width_half_max': 0.028}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'scl-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/SCL.jp2', 'type': 'image/jp2', 'title': 'Scene classification map (SCL)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint8', 'spatial_resolution': 20}], 'roles': ['data', 'reflectance']}, 'swir16-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/B11.jp2', 'type': 'image/jp2', 'title': 'SWIR 1 (band 11) - 20m', 'eo:bands': [{'name': 'swir16', 'common_name': 'swir16', 'description': 'SWIR 1 (band 11)', 'center_wavelength': 1.61, 'full_width_half_max': 0.143}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'swir22-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/B12.jp2', 'type': 'image/jp2', 'title': 'SWIR 2 (band 12) - 20m', 'eo:bands': [{'name': 'swir22', 'common_name': 'swir22', 'description': 'SWIR 2 (band 12)', 'center_wavelength': 2.19, 'full_width_half_max': 0.242}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'visual-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/TCI.jp2', 'type': 'image/jp2', 'title': 'True color image', 'eo:bands': [{'name': 'red', 'common_name': 'red', 'description': 'Red (band 4)', 'center_wavelength': 0.665, 'full_width_half_max': 0.038}, {'name': 'green', 'common_name': 'green', 'description': 'Green (band 3)', 'center_wavelength': 0.56, 'full_width_half_max': 0.045}, {'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'roles': ['visual']}, 'wvp-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/11/0/WVP.jp2', 'type': 'image/jp2', 'title': 'Water vapour (WVP)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'unit': 'cm', 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}}, 'bbox': [8.999754387288686, 45.056757746793146, 10.418890992989095, 46.05356536925373], 'stac_extensions': ['https://stac-extensions.github.io/grid/v1.0.0/schema.json', 'https://stac-extensions.github.io/eo/v1.0.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/raster/v1.1.0/schema.json', 'https://stac-extensions.github.io/mgrs/v1.0.0/schema.json', 'https://stac-extensions.github.io/processing/v1.1.0/schema.json', 'https://stac-extensions.github.io/projection/v1.0.0/schema.json'], 'collection': 'sentinel-2-l2a'}
Search items for a specific date and cloud cover and vegetation conditions#
# additional filters as per metadata
date_YYMM = "2023-01"
filters = {
"eo:cloud_cover":{"lt":0.2},
"s2:vegetation_percentage": {"gt": 25}
}
# run pystac client search to see available dataset
search = client.search(collections=[collection], intersects=geometry , query=filters ,datetime=date_YYMM) #bbox=tas_bbox
#spit out data as GeoJSON dictionary
print(search.item_collection_as_dict())
# loop through each item
for item in search.items_as_dicts():
print(item)
Show code cell output
{'type': 'FeatureCollection', 'features': [{'type': 'Feature', 'stac_version': '1.0.0', 'id': 'S2B_32TNR_20230131_0_L2A', 'properties': {'created': '2023-01-31T19:20:02.816Z', 'platform': 'sentinel-2b', 'constellation': 'sentinel-2', 'instruments': ['msi'], 'eo:cloud_cover': 0.112465, 'proj:epsg': 32632, 'mgrs:utm_zone': 32, 'mgrs:latitude_band': 'T', 'mgrs:grid_square': 'NR', 'grid:code': 'MGRS-32TNR', 'view:sun_azimuth': 162.471016303525, 'view:sun_elevation': 25.273284914117596, 's2:degraded_msi_data_percentage': 0.0026, 's2:nodata_pixel_percentage': 3e-05, 's2:saturated_defective_pixel_percentage': 0, 's2:dark_features_percentage': 9.818108, 's2:cloud_shadow_percentage': 0.002522, 's2:vegetation_percentage': 31.585425, 's2:not_vegetated_percentage': 51.599735, 's2:water_percentage': 2.399037, 's2:unclassified_percentage': 0.003381, 's2:medium_proba_clouds_percentage': 0.00499, 's2:high_proba_clouds_percentage': 0.010272, 's2:thin_cirrus_percentage': 0.097203, 's2:snow_ice_percentage': 4.479326, 's2:product_type': 'S2MSI2A', 's2:processing_baseline': '05.09', 's2:product_uri': 'S2B_MSIL2A_20230131T102149_N0509_R065_T32TNR_20230131T131849.SAFE', 's2:generation_time': '2023-01-31T13:18:49.000000Z', 's2:datatake_id': 'GS2B_20230131T102149_030836_N05.09', 's2:datatake_type': 'INS-NOBS', 's2:datastrip_id': 'S2B_OPER_MSI_L2A_DS_2BPS_20230131T131849_S20230131T102549_N05.09', 's2:granule_id': 'S2B_OPER_MSI_L2A_TL_2BPS_20230131T131849_A030836_T32TNR_N05.09', 's2:reflectance_conversion_factor': 1.03138686673961, 'datetime': '2023-01-31T10:28:11.279000Z', 's2:sequence': '0', 'earthsearch:s3_path': 's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A', 'earthsearch:payload_id': 'roda-sentinel2/workflow-sentinel2-to-stac/7734c7b274348e5d6d4c53ea8a2e5b86', 'earthsearch:boa_offset_applied': True, 'processing:software': {'sentinel2-to-stac': '0.1.0'}, 'updated': '2023-01-31T19:20:02.816Z'}, 'geometry': {'type': 'Polygon', 'coordinates': [[[8.999754387288686, 46.05356536925373], [10.418890992989095, 46.04475738929435], [10.394241393041778, 45.056757746793146], [8.999758655880777, 45.06526899586078], [8.999754387288686, 46.05356536925373]]]}, 'links': [{'rel': 'self', 'type': 'application/geo+json', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/items/S2B_32TNR_20230131_0_L2A'}, {'rel': 'canonical', 'href': 's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/S2B_32TNR_20230131_0_L2A.json', 'type': 'application/json'}, {'rel': 'license', 'href': 'https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice'}, {'rel': 'derived_from', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l1c/items/S2B_32TNR_20230131_0_L1C', 'type': 'application/geo+json'}, {'rel': 'parent', 'type': 'application/json', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a'}, {'rel': 'collection', 'type': 'application/json', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a'}, {'rel': 'root', 'type': 'application/json', 'href': 'https://earth-search.aws.element84.com/v1'}, {'rel': 'thumbnail', 'href': 'https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/items/S2B_32TNR_20230131_0_L2A/thumbnail'}], 'assets': {'aot': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/AOT.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Aerosol optical thickness (AOT)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}, 'blue': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B02.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Blue (band 2) - 10m', 'eo:bands': [{'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'coastal': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B01.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Coastal aerosol (band 1) - 60m', 'eo:bands': [{'name': 'coastal', 'common_name': 'coastal', 'description': 'Coastal aerosol (band 1)', 'center_wavelength': 0.443, 'full_width_half_max': 0.027}], 'gsd': 60, 'proj:shape': [1830, 1830], 'proj:transform': [60, 0, 499980, 0, -60, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 60, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'granule_metadata': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/granule_metadata.xml', 'type': 'application/xml', 'roles': ['metadata']}, 'green': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B03.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Green (band 3) - 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10m', 'eo:bands': [{'name': 'red', 'common_name': 'red', 'description': 'Red (band 4)', 'center_wavelength': 0.665, 'full_width_half_max': 0.038}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge1': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B05.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Red edge 1 (band 5) - 20m', 'eo:bands': [{'name': 'rededge1', 'common_name': 'rededge', 'description': 'Red edge 1 (band 5)', 'center_wavelength': 0.704, 'full_width_half_max': 0.019}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge2': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B06.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Red edge 2 (band 6) - 20m', 'eo:bands': [{'name': 'rededge2', 'common_name': 'rededge', 'description': 'Red edge 2 (band 6)', 'center_wavelength': 0.74, 'full_width_half_max': 0.018}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge3': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B07.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Red edge 3 (band 7) - 20m', 'eo:bands': [{'name': 'rededge3', 'common_name': 'rededge', 'description': 'Red edge 3 (band 7)', 'center_wavelength': 0.783, 'full_width_half_max': 0.028}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'scl': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/SCL.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Scene classification map (SCL)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint8', 'spatial_resolution': 20}], 'roles': ['data', 'reflectance']}, 'swir16': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B11.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'SWIR 1 (band 11) - 20m', 'eo:bands': [{'name': 'swir16', 'common_name': 'swir16', 'description': 'SWIR 1 (band 11)', 'center_wavelength': 1.61, 'full_width_half_max': 0.143}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'swir22': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/B12.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'SWIR 2 (band 12) - 20m', 'eo:bands': [{'name': 'swir22', 'common_name': 'swir22', 'description': 'SWIR 2 (band 12)', 'center_wavelength': 2.19, 'full_width_half_max': 0.242}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'thumbnail': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/thumbnail.jpg', 'type': 'image/jpeg', 'title': 'Thumbnail image', 'roles': ['thumbnail']}, 'tileinfo_metadata': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/tileinfo_metadata.json', 'type': 'application/json', 'roles': ['metadata']}, 'visual': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/TCI.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'True color image', 'eo:bands': [{'name': 'red', 'common_name': 'red', 'description': 'Red (band 4)', 'center_wavelength': 0.665, 'full_width_half_max': 0.038}, {'name': 'green', 'common_name': 'green', 'description': 'Green (band 3)', 'center_wavelength': 0.56, 'full_width_half_max': 0.045}, {'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'roles': ['visual']}, 'wvp': {'href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NR/2023/1/S2B_32TNR_20230131_0_L2A/WVP.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'title': 'Water vapour (WVP)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'unit': 'cm', 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}, 'aot-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/AOT.jp2', 'type': 'image/jp2', 'title': 'Aerosol optical thickness (AOT)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}, 'blue-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/B02.jp2', 'type': 'image/jp2', 'title': 'Blue (band 2) - 10m', 'eo:bands': [{'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'coastal-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/B01.jp2', 'type': 'image/jp2', 'title': 'Coastal aerosol (band 1) - 60m', 'eo:bands': [{'name': 'coastal', 'common_name': 'coastal', 'description': 'Coastal aerosol (band 1)', 'center_wavelength': 0.443, 'full_width_half_max': 0.027}], 'gsd': 60, 'proj:shape': [1830, 1830], 'proj:transform': [60, 0, 499980, 0, -60, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 60, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'green-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/B03.jp2', 'type': 'image/jp2', 'title': 'Green (band 3) - 10m', 'eo:bands': [{'name': 'green', 'common_name': 'green', 'description': 'Green (band 3)', 'center_wavelength': 0.56, 'full_width_half_max': 0.045}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/B08.jp2', 'type': 'image/jp2', 'title': 'NIR 1 (band 8) - 10m', 'eo:bands': [{'name': 'nir', 'common_name': 'nir', 'description': 'NIR 1 (band 8)', 'center_wavelength': 0.842, 'full_width_half_max': 0.145}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir08-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/B8A.jp2', 'type': 'image/jp2', 'title': 'NIR 2 (band 8A) - 20m', 'eo:bands': [{'name': 'nir08', 'common_name': 'nir08', 'description': 'NIR 2 (band 8A)', 'center_wavelength': 0.865, 'full_width_half_max': 0.033}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'nir09-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/B09.jp2', 'type': 'image/jp2', 'title': 'NIR 3 (band 9) - 60m', 'eo:bands': [{'name': 'nir09', 'common_name': 'nir09', 'description': 'NIR 3 (band 9)', 'center_wavelength': 0.945, 'full_width_half_max': 0.026}], 'gsd': 60, 'proj:shape': [1830, 1830], 'proj:transform': [60, 0, 499980, 0, -60, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 60, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'red-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/B04.jp2', 'type': 'image/jp2', 'title': 'Red (band 4) - 10m', 'eo:bands': [{'name': 'red', 'common_name': 'red', 'description': 'Red (band 4)', 'center_wavelength': 0.665, 'full_width_half_max': 0.038}], 'gsd': 10, 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge1-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/B05.jp2', 'type': 'image/jp2', 'title': 'Red edge 1 (band 5) - 20m', 'eo:bands': [{'name': 'rededge1', 'common_name': 'rededge', 'description': 'Red edge 1 (band 5)', 'center_wavelength': 0.704, 'full_width_half_max': 0.019}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge2-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/B06.jp2', 'type': 'image/jp2', 'title': 'Red edge 2 (band 6) - 20m', 'eo:bands': [{'name': 'rededge2', 'common_name': 'rededge', 'description': 'Red edge 2 (band 6)', 'center_wavelength': 0.74, 'full_width_half_max': 0.018}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'rededge3-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/B07.jp2', 'type': 'image/jp2', 'title': 'Red edge 3 (band 7) - 20m', 'eo:bands': [{'name': 'rededge3', 'common_name': 'rededge', 'description': 'Red edge 3 (band 7)', 'center_wavelength': 0.783, 'full_width_half_max': 0.028}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'scl-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/SCL.jp2', 'type': 'image/jp2', 'title': 'Scene classification map (SCL)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint8', 'spatial_resolution': 20}], 'roles': ['data', 'reflectance']}, 'swir16-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/B11.jp2', 'type': 'image/jp2', 'title': 'SWIR 1 (band 11) - 20m', 'eo:bands': [{'name': 'swir16', 'common_name': 'swir16', 'description': 'SWIR 1 (band 11)', 'center_wavelength': 1.61, 'full_width_half_max': 0.143}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'swir22-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/B12.jp2', 'type': 'image/jp2', 'title': 'SWIR 2 (band 12) - 20m', 'eo:bands': [{'name': 'swir22', 'common_name': 'swir22', 'description': 'SWIR 2 (band 12)', 'center_wavelength': 2.19, 'full_width_half_max': 0.242}], 'gsd': 20, 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'scale': 0.0001, 'offset': -0.1}], 'roles': ['data', 'reflectance']}, 'visual-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/TCI.jp2', 'type': 'image/jp2', 'title': 'True color image', 'eo:bands': [{'name': 'red', 'common_name': 'red', 'description': 'Red (band 4)', 'center_wavelength': 0.665, 'full_width_half_max': 0.038}, {'name': 'green', 'common_name': 'green', 'description': 'Green (band 3)', 'center_wavelength': 0.56, 'full_width_half_max': 0.045}, {'name': 'blue', 'common_name': 'blue', 'description': 'Blue (band 2)', 'center_wavelength': 0.49, 'full_width_half_max': 0.098}], 'proj:shape': [10980, 10980], 'proj:transform': [10, 0, 499980, 0, -10, 5100000], 'roles': ['visual']}, 'wvp-jp2': {'href': 's3://sentinel-s2-l2a/tiles/32/T/NR/2023/1/31/0/WVP.jp2', 'type': 'image/jp2', 'title': 'Water vapour (WVP)', 'proj:shape': [5490, 5490], 'proj:transform': [20, 0, 499980, 0, -20, 5100000], 'raster:bands': [{'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 20, 'unit': 'cm', 'scale': 0.001, 'offset': 0}], 'roles': ['data', 'reflectance']}}, 'bbox': [8.999754387288686, 45.056757746793146, 10.418890992989095, 46.05356536925373], 'stac_extensions': ['https://stac-extensions.github.io/processing/v1.1.0/schema.json', 'https://stac-extensions.github.io/eo/v1.0.0/schema.json', 'https://stac-extensions.github.io/view/v1.0.0/schema.json', 'https://stac-extensions.github.io/grid/v1.0.0/schema.json', 'https://stac-extensions.github.io/projection/v1.0.0/schema.json', 'https://stac-extensions.github.io/raster/v1.1.0/schema.json', 'https://stac-extensions.github.io/mgrs/v1.0.0/schema.json'], 'collection': 'sentinel-2-l2a'}
load the data in xarray format#
data = load(search.items(), geopolygon=geometry, groupby="solar_day", chunks={})
data
<xarray.Dataset> Size: 201MB Dimensions: (y: 1535, x: 1982, time: 1) Coordinates: * y (y) float64 12kB 5.079e+06 5.079e+06 ... 5.064e+06 5.064e+06 * x (x) float64 16kB 5.482e+05 5.482e+05 ... 5.68e+05 5.68e+05 spatial_ref int32 4B 32632 * time (time) datetime64[ns] 8B 2023-01-31T10:28:11.279000 Data variables: (12/32) aot (time, y, x) uint16 6MB dask.array<chunksize=(1, 1535, 1982), meta=np.ndarray> blue (time, y, x) uint16 6MB dask.array<chunksize=(1, 1535, 1982), meta=np.ndarray> coastal (time, y, x) uint16 6MB dask.array<chunksize=(1, 1535, 1982), meta=np.ndarray> green (time, y, x) uint16 6MB dask.array<chunksize=(1, 1535, 1982), meta=np.ndarray> nir (time, y, x) uint16 6MB dask.array<chunksize=(1, 1535, 1982), meta=np.ndarray> nir08 (time, y, x) uint16 6MB dask.array<chunksize=(1, 1535, 1982), meta=np.ndarray> ... ... rededge3-jp2 (time, y, x) uint16 6MB dask.array<chunksize=(1, 1535, 1982), meta=np.ndarray> scl-jp2 (time, y, x) uint8 3MB dask.array<chunksize=(1, 1535, 1982), meta=np.ndarray> swir16-jp2 (time, y, x) uint16 6MB dask.array<chunksize=(1, 1535, 1982), meta=np.ndarray> swir22-jp2 (time, y, x) uint16 6MB dask.array<chunksize=(1, 1535, 1982), meta=np.ndarray> visual-jp2 (time, y, x) float32 12MB dask.array<chunksize=(1, 1535, 1982), meta=np.ndarray> wvp-jp2 (time, y, x) uint16 6MB dask.array<chunksize=(1, 1535, 1982), meta=np.ndarray>
create the index without considering scale or offset#
ndvi = (data.nir - data.red) / (data.nir + data.red)
Quickly visualize the generated vegetation index#
ndvi.where(ndvi <= 1.).plot()
<matplotlib.collections.QuadMesh at 0x7fd47bde5e20>
Use native projection to plot on geographical map#
proj = ccrs.epsg(ndvi.spatial_ref.values)
fig = plt.figure(1, figsize=[20, 10])
# We're using cartopy and are plotting in PlateCarree projection
# (see documentation on cartopy)
ax = plt.subplot(1, 1, 1, projection=proj)
ax.coastlines(resolution='10m')
ax.gridlines(draw_labels=True)
ndvi.plot(ax=ax, transform=proj, vmax=1, cmap="RdYlGn")
# One way to customize your title
plt.title("NDVI", fontsize=18)
Text(0.5, 1.0, 'NDVI')
Reproject to EPSG 4326#
import rioxarray
ndvi.rio.write_crs(ndvi.spatial_ref.values, inplace=True)
<xarray.DataArray (time: 1, y: 1535, x: 1982)> Size: 24MB dask.array<truediv, shape=(1, 1535, 1982), dtype=float64, chunksize=(1, 1535, 1982), chunktype=numpy.ndarray> Coordinates: * y (y) float64 12kB 5.079e+06 5.079e+06 ... 5.064e+06 5.064e+06 * x (x) float64 16kB 5.482e+05 5.482e+05 ... 5.68e+05 5.68e+05 * time (time) datetime64[ns] 8B 2023-01-31T10:28:11.279000 spatial_ref int64 8B 0
reprojected = ndvi.rio.reproject("EPSG:4326")
reprojected
<xarray.DataArray (time: 1, y: 1212, x: 2226)> Size: 22MB array([[[1.79769313e+308, 1.79769313e+308, 1.79769313e+308, ..., 1.79769313e+308, 1.79769313e+308, 1.79769313e+308], [1.79769313e+308, 1.79769313e+308, 1.79769313e+308, ..., 1.79769313e+308, 1.79769313e+308, 1.79769313e+308], [1.79769313e+308, 1.79769313e+308, 1.79769313e+308, ..., 1.79769313e+308, 1.79769313e+308, 1.79769313e+308], ..., [1.79769313e+308, 1.79769313e+308, 1.79769313e+308, ..., 1.79769313e+308, 1.79769313e+308, 1.79769313e+308], [1.79769313e+308, 1.79769313e+308, 1.79769313e+308, ..., 1.79769313e+308, 1.79769313e+308, 1.79769313e+308], [1.79769313e+308, 1.79769313e+308, 1.79769313e+308, ..., 1.79769313e+308, 1.79769313e+308, 1.79769313e+308]]]) Coordinates: * x (x) float64 18kB 9.62 9.62 9.62 9.62 ... 9.876 9.876 9.876 * y (y) float64 10kB 45.87 45.87 45.87 45.86 ... 45.73 45.73 45.73 * time (time) datetime64[ns] 8B 2023-01-31T10:28:11.279000 spatial_ref int64 8B 0 Attributes: _FillValue: 1.7976931348623157e+308
reprojected = reprojected.rename(x='lon', y='lat')
Save into geotiff#
reprojected.rio.to_raster("ndvi.tiff")
open geotiff with rioxarray#
ndvi = xr.open_dataset("ndvi.tiff", engine="rasterio")
ndvi
<xarray.Dataset> Size: 22MB Dimensions: (band: 1, x: 2226, y: 1212) Coordinates: * band (band) int64 8B 1 * x (x) float64 18kB 9.62 9.62 9.62 9.62 ... 9.876 9.876 9.876 * y (y) float64 10kB 45.87 45.87 45.87 45.86 ... 45.73 45.73 45.73 spatial_ref int64 8B ... Data variables: band_data (band, y, x) float64 22MB ...
Visualize on a map#
fig = plt.figure(1, figsize=[20, 10])
# We're using cartopy and are plotting in PlateCarree projection
# (see documentation on cartopy)
ax = plt.subplot(1, 1, 1, projection=ccrs.PlateCarree())
ax.gridlines(draw_labels=True)
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.OCEAN)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.BORDERS, linestyle=':')
ax.add_feature(cfeature.LAKES, alpha=0.5)
ax.add_feature(cfeature.RIVERS)
ndvi.band_data.plot(ax=ax, transform=ccrs.PlateCarree(), vmax=1, cmap="RdYlGn")
#ax.set_extent([5.0, 13.0, 44, 50], crs=ccrs.PlateCarree()) # lon1 lon2 lat1 lat2
# One way to customize your title
plt.title("NDVI", fontsize=18)
Text(0.5, 1.0, 'NDVI')
/home/runner/micromamba/envs/geohack/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_land.zip
warnings.warn(f'Downloading: {url}', DownloadWarning)
/home/runner/micromamba/envs/geohack/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_ocean.zip
warnings.warn(f'Downloading: {url}', DownloadWarning)
/home/runner/micromamba/envs/geohack/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_admin_0_boundary_lines_land.zip
warnings.warn(f'Downloading: {url}', DownloadWarning)
/home/runner/micromamba/envs/geohack/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_lakes.zip
warnings.warn(f'Downloading: {url}', DownloadWarning)
/home/runner/micromamba/envs/geohack/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_rivers_lake_centerlines.zip
warnings.warn(f'Downloading: {url}', DownloadWarning)
- Access to remote dataset
- ARCO datasets
- STAC
References#
Basile Goussard, Ryan Abernathey, and Anne Fouilloux. The pangeo ecosystem. https://training.galaxyproject.org/training-material/topics/climate/tutorials/pangeo-notebook/slides.html#1, 2022 (accessed August 7, 2022).
Charles Stern, Ryan Abernathey, Joseph Hamman, Rachel Wegener, Chiara Lepore, Sean Harkins, and Alexander Merose. Pangeo forge: crowdsourcing analysis-ready, cloud optimized data production. Frontiers in Climate, 2022. URL: https://www.frontiersin.org/articles/10.3389/fclim.2021.782909, doi:10.3389/fclim.2021.782909.
Packages citation#
S. Hoyer and J. Hamman. Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software, 2017. URL: https://doi.org/10.5334/jors.148, doi:10.5334/jors.148.
ODC Development Team. odc-stac: Load STAC items into xarray Datasets. 2024. URL: opendatacube/odc-stac.
pystac Development Team. pystac-client: Python client for searching STAC APIs. 2024. URL: stac-utils/pystac-client.
S3Fs Development Team. S3Fs. 2016. URL: fsspec/s3fs.