Metadata
title: "E-TRAINEE Tutorial - Sentinel-2 data STAC access and time series processing"description: "This is a tutorial within the second theme of Module 1 of the E-TRAINEE course."
lastUpdate: 2025-03-23
authors: Andreas Mayr
Sentinel-2 data STAC access and time series processing¶
This Notebook illustrates how to query and download Sentinel-2 satellite imagery from a SpatioTemporal Asset Catalog (STAC) hosted on S3 object storage. Furthermore, it shows how to work with the data using xarray (which was introduced in a tutorial in theme 1), and how to perform tasks like subsetting, cloud masking, spectral index calculation, temporal aggregation, and trend analysis. A few simple processing steps let you explore the development of the Normalized Difference Vegetation Index (NDVI) in spring and summer, including e.g. the seasonal greening of mountain grasslands.
The Notebook heavily relies on the Python packages pystac-client, stackstac, and xarray. These packages are contained in the requirements file provided for the course (etrainee_m1.yml). Please see the instructions on the software page for setting up a Conda environment based on this file.
Parts of the Notebook use code from a tutorial provided here.
Query data via STAC¶
Import the most important packages.
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#from osgeo import gdal # In case of problems with importing rasterio, run 'mamba install gdal' and then import gdal like this first
import pystac_client
import stackstac
import pyproj
import xarray
import rioxarray
#from osgeo import gdal # In case of problems with importing rasterio, run 'mamba install gdal' and then import gdal like this first
import pystac_client
import stackstac
import pyproj
import xarray
import rioxarray
We use the pystac_client package to look for items from the Sentinel-2 L2A collection in a STAC catalog (named sentinel-2-l2a or similar, depending on endpoint). E.g., try the endpoint of the Copernicus Dataspace Ecosystem (CDSE) STAC catalog.
Alternatively, you could try element84’s search endpoint on Amazon Web Service (AWS) which works without credentials (i.e., no registration and authentication needed). However, this seems to raise problems with inconsistent band value offsets due to different processing baselines within the collection and/or the way this is handled by stackstac (see e.g. here).
CDSE secrets
For downloading from the CDSE endpoint you will need credentials ("secrets"). To generate the necessary credentials, you must have a registered account on dataspace.copernicus.eu. If you don’t have an account, you can find a registration link here. To obtain secrets, visit this page and log in with your CDSE account (here you can also see your transfer data limit and bandwidth limit). Create your own S3 credentials and save them somewhere; see this page for a more detailed description.
Using the os package, we can set environment variables to authenticate as a valid user for the S3 protocol.
The only variables you need to configure are:
AWS_ACCESS_KEY_IDAWS_SECRET_ACCESS_KEY
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import os
os.environ["GDAL_HTTP_TCP_KEEPALIVE"] = "YES"
os.environ["AWS_S3_ENDPOINT"] = "eodata.dataspace.copernicus.eu"
os.environ["AWS_ACCESS_KEY_ID"] = "your_access_key" # !
os.environ["AWS_SECRET_ACCESS_KEY"] = "your_secret_access_key" # !
os.environ["AWS_HTTPS"] = "YES"
os.environ["AWS_VIRTUAL_HOSTING"] = "FALSE"
os.environ["GDAL_HTTP_UNSAFESSL"] = "YES"
import os
os.environ["GDAL_HTTP_TCP_KEEPALIVE"] = "YES"
os.environ["AWS_S3_ENDPOINT"] = "eodata.dataspace.copernicus.eu"
os.environ["AWS_ACCESS_KEY_ID"] = "your_access_key" # !
os.environ["AWS_SECRET_ACCESS_KEY"] = "your_secret_access_key" # !
os.environ["AWS_HTTPS"] = "YES"
os.environ["AWS_VIRTUAL_HOSTING"] = "FALSE"
os.environ["GDAL_HTTP_UNSAFESSL"] = "YES"
Search items from the STAC catalog¶
Now connect to the STAC catalog and search items of the sentinel-2-l2a collection that cover (intersect) a point of interest or an area. As search criteria we use a maximum cloud cover (per scene) and a time period.
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#URL = "https://earth-search.aws.element84.com/v1"
URL = "https://stac.dataspace.copernicus.eu/v1/"
catalog = pystac_client.Client.open(URL)
#URL = "https://earth-search.aws.element84.com/v1"
URL = "https://stac.dataspace.copernicus.eu/v1/"
catalog = pystac_client.Client.open(URL)
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%%time
# Define a point of interest to intersect our query
lon, lat = 11.39, 47.26 # Innsbruck (Austria)
items = catalog.search(
intersects=dict(type="Point", coordinates=[lon, lat]),
collections=["sentinel-2-l2a"],
query = ['eo:cloud_cover<50'],
datetime="2021-04-01/2021-08-31"
).item_collection()
print(f'{len(items)} items found') # How many items in the catalog match these criteria?
%%time
# Define a point of interest to intersect our query
lon, lat = 11.39, 47.26 # Innsbruck (Austria)
items = catalog.search(
intersects=dict(type="Point", coordinates=[lon, lat]),
collections=["sentinel-2-l2a"],
query = ['eo:cloud_cover<50'],
datetime="2021-04-01/2021-08-31"
).item_collection()
print(f'{len(items)} items found') # How many items in the catalog match these criteria?
24 items found CPU times: total: 156 ms Wall time: 1.31 s
Let's have look at the items found by the search. You can expand them and inspect the assets and the metadata they contain (such as coordinate reference system and resolution of spectral bands).
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items
items
Out[5]:
- type "FeatureCollection"
features[] 24 items
0
- type "Feature"
- stac_version "1.1.0"
stac_extensions[] 15 items
- 0 "https://stac-extensions.github.io/eo/v2.0.0/schema.json"
- 1 "https://stac-extensions.github.io/raster/v2.0.0/schema.json"
- 2 "https://stac-extensions.github.io/sat/v1.0.0/schema.json"
- 3 "https://stac-extensions.github.io/projection/v2.0.0/schema.json"
- 4 "https://stac-extensions.github.io/grid/v1.1.0/schema.json"
- 5 "https://stac-extensions.github.io/view/v1.0.0/schema.json"
- 6 "https://stac-extensions.github.io/file/v2.1.0/schema.json"
- 7 "https://stac-extensions.github.io/processing/v1.2.0/schema.json"
- 8 "https://stac-extensions.github.io/alternate-assets/v1.2.0/schema.json"
- 9 "https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"
- 10 "https://stac-extensions.github.io/authentication/v1.1.0/schema.json"
- 11 "https://stac-extensions.github.io/classification/v2.0.0/schema.json"
- 12 "https://stac-extensions.github.io/product/v0.1.0/schema.json"
- 13 "https://cs-si.github.io/eopf-stac-extension/v1.2.0/schema.json"
- 14 "https://stac-extensions.github.io/storage/v2.0.0/schema.json"
- id "S2A_MSIL2A_20210821T101031_N0500_R022_T32TPT_20230120T102131"
geometry
- type "Polygon"
coordinates[] 1 items
0[] 12 items
0[] 2 items
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1[] 2 items
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properties
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- created "2023-02-18T23:05:08.041000Z"
- updated "2024-04-26T16:53:39.638902Z"
- datetime "2021-08-21T10:10:31.024000Z"
- platform "sentinel-2a"
- grid:code "MGRS-32TPT"
- published "2023-02-24T02:37:32.879968Z"
statistics
- water 0.776181
- nodata 8.875183
- dark_area 4.506517
- vegetation 63.35969
- thin_cirrus 4.394666
- cloud_shadow 3.6869319999999997
- unclassified 1.352611
- not_vegetated 9.031644
- high_proba_clouds 5.66865
- medium_proba_clouds 6.177029000000001
- saturated_defective 0.0
instruments[] 1 items
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auth:schemes
s3
- type "s3"
oidc
- type "openIdConnect"
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- end_datetime "2021-08-21T10:10:31.024Z"
- product:type "S2MSI2A"
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- constellation "sentinel-2"
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- eo:cloud_cover 16.24
- start_datetime "2021-08-21T10:10:31.024Z"
- sat:orbit_state "descending"
storage:schemes
cdse-s3
- title "Copernicus Data Space Ecosystem S3"
- platform "https://eodata.dataspace.copernicus.eu"
- description "This endpoint provides access to EO data which is stored on the Object Storage. A Global Service Load Balancer (GSLB) directs the request to either CloudFerro Cloud or OpenTelekom Cloud (OTC) S3 endpoint based on the location of the DNS resolver."
- requester_pays False
creodias-s3
- title "CREODIAS S3"
- platform "https://eodata.cloudferro.com"
- description "Comprehensive Earth Observation Data (EODATA) archive offered by CREODIAS as a commercial part of CDSE, designed to provide users with access to a vast repository of satellite data without predefined quota limits"
- requester_pays True
- eopf:datatake_id "GS2A_20210821T101031_032194_N05.00"
- processing:level "L2"
- view:sun_azimuth 155.028547977446
- eopf:datastrip_id "S2A_OPER_MSI_L2A_DS_S2RP_20230120T102131_S20210821T101418_N05.00"
- processing:version "05.00"
- product:timeliness "PT24H"
- sat:absolute_orbit 32194
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- view:sun_elevation 52.3641253600513
- processing:datetime "2023-01-20T10:21:31.000000Z"
- processing:facility "ESA"
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- view:incidence_angle 7.93273129599281
- product:timeliness_category "NRT"
- sat:platform_international_designator "2015-028A"
links[] 5 items
0
- rel "collection"
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- type "application/json"
1
- rel "parent"
- href "https://stac.dataspace.copernicus.eu/v1/collections/sentinel-2-l2a"
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- rel "root"
- href "https://stac.dataspace.copernicus.eu/v1/"
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- rel "self"
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- type "application/geo+json"
4
- rel "version-history"
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- type "application/json"
- title "Product history record from the CDSE traceability service"
assets
AOT_10m
- href "s3://eodata/Sentinel-2/MSI/L2A_N0500/2021/08/21/S2A_MSIL2A_20210821T101031_N0500_R022_T32TPT_20230120T102131.SAFE/GRANULE/L2A_T32TPT_A032194_20210821T101418/IMG_DATA/R10m/T32TPT_20210821T101031_AOT_10m.jp2"
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alternate
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statistics
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storage:schemes
cdse-s3
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creodias-s3
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statistics
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- collection "sentinel-2-l2a"
Let's see which area the items (from different orbits) cover.
In [48]:
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import geopandas as gpd
items_gdf = gpd.GeoDataFrame.from_features(items.to_dict(), crs="epsg:4326")
items_gdf.explore(column='sat:relative_orbit', style_kwds={"fill": False}, categorical=True)
import geopandas as gpd
items_gdf = gpd.GeoDataFrame.from_features(items.to_dict(), crs="epsg:4326")
items_gdf.explore(column='sat:relative_orbit', style_kwds={"fill": False}, categorical=True)
Out[48]:
Make this Notebook Trusted to load map: File -> Trust Notebook
Stack the items into a DataArray and define a subset¶
Use stackstac to turn those STAC items into a lazy xarray. Without specifying anything, this makes use of dask and reads the data in chunks, with one chunk shaped (1, 1, 1024, 1024) in the time, band, y, x dimensions (Read more about chunk sizes here, here, and here.). Using all the defaults, our data will be in its native coordinate reference system, at the finest resolution of all the assets. If this metadata is not set on all the items/assets or if you want a different CRS/resolution, specify this.
In [6]:
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%time stack = stackstac.stack(items, epsg=32632, resolution=10)
%time stack = stackstac.stack(items, epsg=32632, resolution=10)
CPU times: total: 156 ms Wall time: 143 ms
Now print info about this xarray.DataArray called 'stack'. Note that everything has run quickly so far, despite the large data volume covered by our query. Up to now, we are only working with metadata, no download or processing of any pixel values.
In [9]:
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stack
stack
Out[9]:
<xarray.DataArray 'stackstac-0c897e1e7d23a4b88e6617cf1192e0ff' (time: 24,
band: 36,
y: 11558,
x: 11560)> Size: 924GB
dask.array<fetch_raster_window, shape=(24, 36, 11558, 11560), dtype=float64, chunksize=(1, 1, 1024, 1024), chunktype=numpy.ndarray>
Coordinates: (12/53)
* time (time) datetime64[ns] 192B 2021-04...
id (time) <U60 6kB 'S2B_MSIL2A_202104...
* band (band) <U7 1kB 'AOT_10m' ... 'WVP_...
* x (x) float64 92kB 5.982e+05 ... 7.1...
* y (y) float64 92kB 5.303e+06 ... 5.1...
sat:relative_orbit (time) int32 96B 65 22 65 ... 65 22
... ...
title (band) <U37 5kB 'Aerosol optical t...
storage:refs object 8B {'creodias-s3', 'cdse-s3'}
bands (band) object 288B None None ... None
description (band) object 288B None None ... None
classification:classes (band) object 288B None None ... None
epsg int32 4B 32632
Attributes:
spec: RasterSpec(epsg=32632, bounds=(598150, 5187400, 713750, 5302...
crs: epsg:32632
transform: | 10.00, 0.00, 598150.00|\n| 0.00,-10.00, 5302980.00|\n| 0.0...
resolution: 10We are only interested in a relatively small area around our main point of interest (defined above for intersection). So we convert the coordinates of this point into the native CRS of the satellite data, add a buffer and then use this rectangle to slice ("clip") the stacked array spatially.
In [10]:
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x_utm, y_utm = pyproj.Proj(stack.crs)(lon, lat) # Re-project the geographic point coordinates to the sat data CRS (i.e. EPSG:32632)
buffer = 5000 # meters
x_utm, y_utm = pyproj.Proj(stack.crs)(lon, lat) # Re-project the geographic point coordinates to the sat data CRS (i.e. EPSG:32632)
buffer = 5000 # meters
Now we have a much smaller DataArray (with 1000 by 1000 cells in x/y) called 'stack_aoi'.
In [11]:
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stack_aoi = stack.loc[..., y_utm+buffer:y_utm-buffer, x_utm-buffer:x_utm+buffer]
stack_aoi
stack_aoi = stack.loc[..., y_utm+buffer:y_utm-buffer, x_utm-buffer:x_utm+buffer]
stack_aoi
Out[11]:
<xarray.DataArray 'stackstac-0c897e1e7d23a4b88e6617cf1192e0ff' (time: 24,
band: 36,
y: 1000, x: 1000)> Size: 7GB
dask.array<getitem, shape=(24, 36, 1000, 1000), dtype=float64, chunksize=(1, 1, 972, 575), chunktype=numpy.ndarray>
Coordinates: (12/53)
* time (time) datetime64[ns] 192B 2021-04...
id (time) <U60 6kB 'S2B_MSIL2A_202104...
* band (band) <U7 1kB 'AOT_10m' ... 'WVP_...
* x (x) float64 8kB 6.758e+05 ... 6.85...
* y (y) float64 8kB 5.242e+06 ... 5.23...
sat:relative_orbit (time) int32 96B 65 22 65 ... 65 22
... ...
title (band) <U37 5kB 'Aerosol optical t...
storage:refs object 8B {'creodias-s3', 'cdse-s3'}
bands (band) object 288B None None ... None
description (band) object 288B None None ... None
classification:classes (band) object 288B None None ... None
epsg int32 4B 32632
Attributes:
spec: RasterSpec(epsg=32632, bounds=(598150, 5187400, 713750, 5302...
crs: epsg:32632
transform: | 10.00, 0.00, 598150.00|\n| 0.00,-10.00, 5302980.00|\n| 0.0...
resolution: 10We want only the four 10-m bands and the Scene Classification (SCL) layer.
In [12]:
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#stack_aoi = stack_aoi.sel(band = ['blue', 'green', 'red', 'nir', 'scl']) # band names in the collection retreived via the element84/AWS endpoint
stack_aoi = stack_aoi.sel(band = ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'SCL_20m']) # band names in the collection retreived via the CDSE endpoint
stack_aoi
#stack_aoi = stack_aoi.sel(band = ['blue', 'green', 'red', 'nir', 'scl']) # band names in the collection retreived via the element84/AWS endpoint
stack_aoi = stack_aoi.sel(band = ['B02_10m', 'B03_10m', 'B04_10m', 'B08_10m', 'SCL_20m']) # band names in the collection retreived via the CDSE endpoint
stack_aoi
Out[12]:
<xarray.DataArray 'stackstac-0c897e1e7d23a4b88e6617cf1192e0ff' (time: 24,
band: 5,
y: 1000, x: 1000)> Size: 960MB
dask.array<getitem, shape=(24, 5, 1000, 1000), dtype=float64, chunksize=(1, 1, 972, 575), chunktype=numpy.ndarray>
Coordinates: (12/53)
* time (time) datetime64[ns] 192B 2021-04...
id (time) <U60 6kB 'S2B_MSIL2A_202104...
* band (band) <U7 140B 'B02_10m' ... 'SCL...
* x (x) float64 8kB 6.758e+05 ... 6.85...
* y (y) float64 8kB 5.242e+06 ... 5.23...
sat:relative_orbit (time) int32 96B 65 22 65 ... 65 22
... ...
title (band) <U37 740B 'Blue (band 2) - ...
storage:refs object 8B {'creodias-s3', 'cdse-s3'}
bands (band) object 40B None None ... None
description (band) object 40B None None ... None
classification:classes (band) object 40B None None ... None
epsg int32 4B 32632
Attributes:
spec: RasterSpec(epsg=32632, bounds=(598150, 5187400, 713750, 5302...
crs: epsg:32632
transform: | 10.00, 0.00, 598150.00|\n| 0.00,-10.00, 5302980.00|\n| 0.0...
resolution: 10Load data¶
Now we trigger the stacking of these selected bands for our area-of-interest by calling compute(), i.e. we load the data into memory. This may take some minutes but then enables us to analyse and plot the data faster. Alternatively (also for larger datasets), we could continue with the lazy array and in subsequent steps only load what is needed.
In [13]:
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%%time
data = stack_aoi.compute();
%%time
data = stack_aoi.compute();
CPU times: total: 4min 31s Wall time: 16min 25s
c:\Users\Andi\mambaforge\envs\etrainee_m1_25-1\Lib\site-packages\dask\array\numpy_compat.py:57: RuntimeWarning: invalid value encountered in divide x = np.divide(x1, x2, out) c:\Users\Andi\mambaforge\envs\etrainee_m1_25-1\Lib\site-packages\dask\_task_spec.py:741: RuntimeWarning: invalid value encountered in divide return self.func(*new_argspec) c:\Users\Andi\mambaforge\envs\etrainee_m1_25-1\Lib\site-packages\dask\_task_spec.py:741: RuntimeWarning: divide by zero encountered in divide return self.func(*new_argspec) c:\Users\Andi\mambaforge\envs\etrainee_m1_25-1\Lib\site-packages\dask\array\numpy_compat.py:57: RuntimeWarning: invalid value encountered in divide x = np.divide(x1, x2, out) c:\Users\Andi\mambaforge\envs\etrainee_m1_25-1\Lib\site-packages\dask\_task_spec.py:741: RuntimeWarning: invalid value encountered in divide return self.func(*new_argspec)
In [14]:
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data
data
Out[14]:
<xarray.DataArray 'stackstac-0c897e1e7d23a4b88e6617cf1192e0ff' (time: 24,
band: 5,
y: 1000, x: 1000)> Size: 960MB
array([[[[1.3528e+04, 1.2521e+04, 1.2416e+04, ..., 1.1580e+03,
1.1790e+03, 1.1710e+03],
[1.2551e+04, 1.2690e+04, 1.2697e+04, ..., 1.1720e+03,
1.1860e+03, 1.1850e+03],
[1.1873e+04, 1.2207e+04, 1.2695e+04, ..., 1.1870e+03,
1.1860e+03, 1.1880e+03],
...,
[1.1030e+03, 1.1210e+03, 1.3670e+03, ..., 2.2940e+03,
2.7770e+03, 3.2320e+03],
[1.1070e+03, 1.0640e+03, 1.1740e+03, ..., 2.7150e+03,
2.6980e+03, 3.0950e+03],
[1.2290e+03, 1.0570e+03, 1.0750e+03, ..., 2.8710e+03,
2.4590e+03, 2.3510e+03]],
[[1.3537e+04, 1.2488e+04, 1.2287e+04, ..., 1.1580e+03,
1.2030e+03, 1.1840e+03],
[1.2631e+04, 1.2695e+04, 1.2686e+04, ..., 1.1600e+03,
1.2660e+03, 1.2140e+03],
[1.1856e+04, 1.2240e+04, 1.2855e+04, ..., 1.1960e+03,
1.2610e+03, 1.2210e+03],
...
[3.5140e+03, 3.7280e+03, 3.4360e+03, ..., 4.3960e+03,
4.8440e+03, 5.7480e+03],
[3.6240e+03, 3.5140e+03, 3.4100e+03, ..., 4.0880e+03,
4.0420e+03, 4.2040e+03],
[3.0260e+03, 3.0700e+03, 3.3040e+03, ..., 4.2960e+03,
4.2720e+03, 3.4620e+03]],
[[2.0000e+00, 2.0000e+00, 2.0000e+00, ..., 4.0000e+00,
4.0000e+00, 4.0000e+00],
[2.0000e+00, 2.0000e+00, 2.0000e+00, ..., 4.0000e+00,
4.0000e+00, 4.0000e+00],
[2.0000e+00, 2.0000e+00, 2.0000e+00, ..., 4.0000e+00,
4.0000e+00, 4.0000e+00],
...,
[4.0000e+00, 4.0000e+00, 4.0000e+00, ..., 4.0000e+00,
4.0000e+00, 4.0000e+00],
[4.0000e+00, 4.0000e+00, 4.0000e+00, ..., 4.0000e+00,
4.0000e+00, 4.0000e+00],
[4.0000e+00, 4.0000e+00, 4.0000e+00, ..., 4.0000e+00,
4.0000e+00, 4.0000e+00]]]])
Coordinates: (12/53)
* time (time) datetime64[ns] 192B 2021-04...
id (time) <U60 6kB 'S2B_MSIL2A_202104...
* band (band) <U7 140B 'B02_10m' ... 'SCL...
* x (x) float64 8kB 6.758e+05 ... 6.85...
* y (y) float64 8kB 5.242e+06 ... 5.23...
sat:relative_orbit (time) int32 96B 65 22 65 ... 65 22
... ...
title (band) <U37 740B 'Blue (band 2) - ...
storage:refs object 8B {'creodias-s3', 'cdse-s3'}
bands (band) object 40B None None ... None
description (band) object 40B None None ... None
classification:classes (band) object 40B None None ... None
epsg int32 4B 32632
Attributes:
spec: RasterSpec(epsg=32632, bounds=(598150, 5187400, 713750, 5302...
crs: epsg:32632
transform: | 10.00, 0.00, 598150.00|\n| 0.00,-10.00, 5302980.00|\n| 0.0...
resolution: 10Try plotting some data (July 2021 only):
In [15]:
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data.sel(band='B08_10m').sel(time='2021-07').plot.imshow(col='time', robust=True, cbar_kwargs = {'label': 'B08 reflectance'})
data.sel(band='B08_10m').sel(time='2021-07').plot.imshow(col='time', robust=True, cbar_kwargs = {'label': 'B08 reflectance'})
Out[15]:
<xarray.plot.facetgrid.FacetGrid at 0x18bca291cd0>
Do you remember why the reflectance is not within 0-1 but has much larger values?
Cloud masking¶
We use the scene classification provided by ESA via the SCL band as a simple (but not the best) means to mask clouds and other problematic pixels.
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import dask.array as da
scl = data.sel(band=["SCL_20m"])
# Sentinel-2 Scene Classification Map: nodata, saturated/defective, dark, cloud shadow, cloud med. prob., cloud high prob., cirrus
invalid_data = da.isin(scl, [0, 1, 2, 3, 8, 9, 10])
valid_data = data.where(~invalid_data)
# Show some of the cloud masked data
valid_data.sel(band='B08_10m').sel(time='2021-07').plot.imshow(col='time', robust=True)
import dask.array as da
scl = data.sel(band=["SCL_20m"])
# Sentinel-2 Scene Classification Map: nodata, saturated/defective, dark, cloud shadow, cloud med. prob., cloud high prob., cirrus
invalid_data = da.isin(scl, [0, 1, 2, 3, 8, 9, 10])
valid_data = data.where(~invalid_data)
# Show some of the cloud masked data
valid_data.sel(band='B08_10m').sel(time='2021-07').plot.imshow(col='time', robust=True)
Out[17]:
<xarray.plot.facetgrid.FacetGrid at 0x18b801f8ec0>
Let's show cloud-masked RGB true color composites.
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valid_data.sel(time='2021-07').sel(band=['B04_10m', 'B03_10m', 'B02_10m']).plot.imshow(col='time', robust=True)
valid_data.sel(time='2021-07').sel(band=['B04_10m', 'B03_10m', 'B02_10m']).plot.imshow(col='time', robust=True)
Out[18]:
<xarray.plot.facetgrid.FacetGrid at 0x18bcc027dd0>
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# Compute the NDVI from the NIR (B8) and red (B4) bands
nir, red = valid_data.sel(band='B08_10m'), valid_data.sel(band='B04_10m')
NDVI = (nir - red) / (nir + red)
# Compute the NDVI from the NIR (B8) and red (B4) bands
nir, red = valid_data.sel(band='B08_10m'), valid_data.sel(band='B04_10m')
NDVI = (nir - red) / (nir + red)
NDVI is a new DataArray. Let's plot one month of NDVI rasters.
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NDVI.sel(time='2021-07').plot.imshow(col='time', cmap="PRGn", robust=True, cbar_kwargs = {'label': 'NDVI'})
NDVI.sel(time='2021-07').plot.imshow(col='time', cmap="PRGn", robust=True, cbar_kwargs = {'label': 'NDVI'})
Out[21]:
<xarray.plot.facetgrid.FacetGrid at 0x18b83808d40>
Write data to disk¶
If you want to select the NDVI raster closest to a specific date and export it as a GeoTIFF, just run this line:
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NDVI.sel(time='2021-07-15', method='nearest').rio.to_raster(r"C:\work\etrainee\stac_exports/S2-NDVI_midjuly.tif")
NDVI.sel(time='2021-07-15', method='nearest').rio.to_raster(r"C:\work\etrainee\stac_exports/S2-NDVI_midjuly.tif")
Now let's write all NDVI rasters of one selected month to a netCDF file (see also the xarray user guide for other formats and more details). We drop all non-index coordinates in this dataset.
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NDVI.sel(time='2021-07').reset_coords(drop=True).to_netcdf(r"C:\work\etrainee\stac_exports/S2-NDVI_2021-07.nc", engine="netcdf4")
NDVI.sel(time='2021-07').reset_coords(drop=True).to_netcdf(r"C:\work\etrainee\stac_exports/S2-NDVI_2021-07.nc", engine="netcdf4")
We can 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.
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xds = xarray.open_dataset(r"C:\work\etrainee\stac_exports/S2-NDVI_2021-07.nc", decode_coords="all")
xds
xds = xarray.open_dataset(r"C:\work\etrainee\stac_exports/S2-NDVI_2021-07.nc", decode_coords="all")
xds
Out[29]:
<xarray.Dataset> Size: 40MB
Dimensions: (time: 5, x: 1000, y: 1000)
Coordinates:
* time (time) datetime64[ns] 40B 202...
* x (x) float64 8kB 6.758e+05 ......
* y (y) float64 8kB 5.242e+06 ......
Data variables:
stackstac-0c897e1e7d23a4b88e6617cf1192e0ff (time, y, x) float64 40MB ...We can rename the variable(s) of a Dataset according to a dictionary. Let's do so if we want to be able to call NDVI easily as xds['NDVI'] in the following.
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xds = xds.rename(name_dict = {'stackstac-0c897e1e7d23a4b88e6617cf1192e0ff': 'NDVI'})
xds
xds = xds.rename(name_dict = {'stackstac-0c897e1e7d23a4b88e6617cf1192e0ff': 'NDVI'})
xds
Out[30]:
<xarray.Dataset> Size: 40MB
Dimensions: (time: 5, x: 1000, y: 1000)
Coordinates:
* time (time) datetime64[ns] 40B 2021-07-02T10:10:31.024000 ... 2021-07...
* x (x) float64 8kB 6.758e+05 6.758e+05 ... 6.858e+05 6.858e+05
* y (y) float64 8kB 5.242e+06 5.242e+06 ... 5.232e+06 5.232e+06
Data variables:
NDVI (time, y, x) float64 40MB ...In [31]:
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xds['NDVI'].plot.imshow(col='time', cmap="PRGn", robust=True, cbar_kwargs = {'label': 'NDVI'})
xds['NDVI'].plot.imshow(col='time', cmap="PRGn", robust=True, cbar_kwargs = {'label': 'NDVI'})
Out[31]:
<xarray.plot.facetgrid.FacetGrid at 0x18b858d1fd0>
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NDVI.sel(time='2021-07').interpolate_na(dim='time').plot(col='time', cmap="PRGn", robust=True, cbar_kwargs = {'label': 'NDVI'})
NDVI.sel(time='2021-07').interpolate_na(dim='time').plot(col='time', cmap="PRGn", robust=True, cbar_kwargs = {'label': 'NDVI'})
Out[32]:
<xarray.plot.facetgrid.FacetGrid at 0x18b874504d0>
Resampling¶
We resample/aggregate to one month temporal resolution by computing the mean for each month. Other aggregations (e.g. maximum) can be achieved similarly. Note how we can absolutely define the range of values to be displayed (important for visual comparisons).
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NDVI.resample(time='1ME').mean().plot(col='time', cmap="PRGn", robust=True, vmin=-0.2, vmax=0.6, cbar_kwargs = {'label': 'Monthly mean NDVI'})
NDVI.resample(time='1ME').mean().plot(col='time', cmap="PRGn", robust=True, vmin=-0.2, vmax=0.6, cbar_kwargs = {'label': 'Monthly mean NDVI'})
Out[33]:
<xarray.plot.facetgrid.FacetGrid at 0x18b89cced20>
Trend analysis¶
The xarrayutils package contains a convenient linear_trend() function. We use this to fit a linear least-squares trendline to our NDVI time series.
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from xarrayutils import linear_trend
NDVI_regressed = linear_trend(NDVI, 'time')
from xarrayutils import linear_trend
NDVI_regressed = linear_trend(NDVI, 'time')
Plot maps (with default colormaps) for the resulting slope of the estimated linear trend along with r-value (Pearson correlation coefficient), p-value (for a hypothesis test whose null hypothesis is that the slope is zero), and standard error (of the estimated slope). We use matplotlib syntax and functions to configure the plot.
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from matplotlib import pyplot as plt
fig, axes = plt.subplots(2,2, figsize=(15,10), sharex=True, sharey=True)
NDVI_regressed.slope.plot(robust=True, ax=axes[0,0])
NDVI_regressed.r_value.plot(robust=True, ax=axes[0,1])
NDVI_regressed.p_value.plot(robust=True, ax=axes[1,0])
NDVI_regressed.std_err.plot(robust=True, ax=axes[1,1])
for ax, param in zip(axes.flat, ['slope', 'r-value', 'p-value', 'std error']):
ax.set_title(f'NDVI trend 04-08/2021: {param}')
fig.tight_layout()
from matplotlib import pyplot as plt
fig, axes = plt.subplots(2,2, figsize=(15,10), sharex=True, sharey=True)
NDVI_regressed.slope.plot(robust=True, ax=axes[0,0])
NDVI_regressed.r_value.plot(robust=True, ax=axes[0,1])
NDVI_regressed.p_value.plot(robust=True, ax=axes[1,0])
NDVI_regressed.std_err.plot(robust=True, ax=axes[1,1])
for ax, param in zip(axes.flat, ['slope', 'r-value', 'p-value', 'std error']):
ax.set_title(f'NDVI trend 04-08/2021: {param}')
fig.tight_layout()
Create an interactive map to inspect the slope of the trend in more detail.
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import hvplot.xarray
import holoviews as hv
NDVI_regressed.slope.hvplot(x='x', y='y', aspect=1, clim=(-0.02,0.02), cmap='coolwarm', title='NDVI slope April - Aug')
import hvplot.xarray
import holoviews as hv
NDVI_regressed.slope.hvplot(x='x', y='y', aspect=1, clim=(-0.02,0.02), cmap='coolwarm', title='NDVI slope April - Aug')
Out[36]: