Stations Discovery + Match-Up¶
Given a SENTINEL-3 OLCI, i.e. S3A_OL_1_EFR____20251031T094707_20251031T095007_20251101T104938_0180_132_136_2160_PS1_O_NT_004 with specified attributes:
geometry,- [
properties.start_datetime,properties.end_datetime]
search all active AERONET stations, where:
Site_Latitude(Degrees),Site_Longitude(Degrees)ingeometry
for each discovered station, filter all acquisitions where:
properties.start_datetime- 1h <=Date(dd:mm:yyyy),Time(hh:mm:ss)<=properties.end_datetime+ 1h
In [7]:
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from httpx import (
Client,
Response
)
from pathlib import Path
from pystac import Item
from typing import Any
import json
import sys
out_dir: Path = Path('.')
with Client() as client:
response: Response = client.get('https://stac.dataspace.copernicus.eu/v1/collections/sentinel-3-olci-1-efr-ntc/items/S3A_OL_1_EFR____20251031T094707_20251031T095007_20251101T104938_0180_132_136_2160_PS1_O_NT_004')
item_str: str = response.text
item_dict = json.loads(item_str)
json.dump(item_dict, sys.stdout, indent=2)
from httpx import (
Client,
Response
)
from pathlib import Path
from pystac import Item
from typing import Any
import json
import sys
out_dir: Path = Path('.')
with Client() as client:
response: Response = client.get('https://stac.dataspace.copernicus.eu/v1/collections/sentinel-3-olci-1-efr-ntc/items/S3A_OL_1_EFR____20251031T094707_20251031T095007_20251101T104938_0180_132_136_2160_PS1_O_NT_004')
item_str: str = response.text
item_dict = json.loads(item_str)
json.dump(item_dict, sys.stdout, indent=2)
{
"id": "S3A_OL_1_EFR____20251031T094707_20251031T095007_20251101T104938_0180_132_136_2160_PS1_O_NT_004",
"bbox": [
-1.51876,
39.545,
18.484,
52.4535
],
"type": "Feature",
"links": [
{
"rel": "collection",
"type": "application/json",
"href": "https://stac.dataspace.copernicus.eu/v1/collections/sentinel-3-olci-1-efr-ntc"
},
{
"rel": "parent",
"type": "application/json",
"href": "https://stac.dataspace.copernicus.eu/v1/collections/sentinel-3-olci-1-efr-ntc"
},
{
"rel": "root",
"type": "application/json",
"href": "https://stac.dataspace.copernicus.eu/v1/"
},
{
"rel": "self",
"type": "application/geo+json",
"href": "https://stac.dataspace.copernicus.eu/v1/collections/sentinel-3-olci-1-efr-ntc/items/S3A_OL_1_EFR____20251031T094707_20251031T095007_20251101T104938_0180_132_136_2160_PS1_O_NT_004"
},
{
"rel": "version-history",
"href": "https://trace.dataspace.copernicus.eu/api/v1/traces/name/S3A_OL_1_EFR____20251031T094707_20251031T095007_20251101T104938_0180_132_136_2160_PS1_O_NT_004.SEN3.zip",
"type": "application/json",
"title": "Product history record from the CDSE traceability service"
},
{
"rel": "enclosure",
"href": "s3://eodata/Sentinel-3/OLCI/OL_1_EFR___/2025/10/31/S3A_OL_1_EFR____20251031T094707_20251031T095007_20251101T104938_0180_132_136_2160_PS1_O_NT_004.SEN3/",
"type": "application/x-directory",
"title": "S3 path to source directory",
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"s3"
],
"storage:refs": [
"cdse-s3",
"creodias-s3"
]
}
],
"assets": {
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"href": "https://download.dataspace.copernicus.eu/odata/v1/Products(464ae0c3-b8b0-46c0-a53c-f42188c413d4)/$value",
"roles": [
"data",
"metadata",
"archive"
],
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],
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"type": "application/zip",
"title": "Zipped product"
},
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],
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"auth:refs": [
"s3"
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],
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}
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"type": "image/jpeg",
"title": "Thumbnail",
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"auth:refs": [
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"auth:refs": [
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"storage:refs": [],
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}
},
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],
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],
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"roles": [
"data"
],
"alternate": {
"https": {
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"auth:refs": [
"oidc"
],
"storage:refs": [],
"alternate:name": "HTTPS"
}
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"file:size": 2593891,
"storage:refs": [
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"creodias-s3"
],
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},
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"type": "application/netcdf",
"roles": [
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],
"title": "Removed Pixels information used for SYN L1c reconstruction",
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"roles": [
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],
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"invalid": 4.0,
"cosmetic": 0.0,
"saturated": 0.0,
"duplicated": 23.0,
"saline_water": 21.0,
"tidal_region": 1.0,
"dubious_samples": 0.0,
"fresh_inland_water": 1.0
},
"instruments": [
"olci"
],
"auth:schemes": {
"s3": {
"type": "s3"
},
"oidc": {
"type": "openIdConnect",
"openIdConnectUrl": "https://identity.dataspace.copernicus.eu/auth/realms/CDSE/.well-known/openid-configuration"
}
},
"end_datetime": "2025-10-31T09:50:06.893754Z",
"product:type": "OL_1_EFR___",
"constellation": "sentinel-3",
"start_datetime": "2025-10-31T09:47:06.893754Z",
"sat:orbit_cycle": 132,
"sat:orbit_state": "descending",
"storage:schemes": {
"cdse-s3": {
"type": "custom-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 of both CloudFerro Cloud and OpenTelekom Cloud (OTC). See the [documentation](https://documentation.dataspace.copernicus.eu/APIs/S3.html) for more information, including how to get credentials.",
"requester_pays": false
},
"creodias-s3": {
"type": "custom-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
}
},
"processing:level": "L1",
"processing:version": "03.50",
"product:timeliness": "P1M",
"sat:absolute_orbit": 50544,
"sat:relative_orbit": 136,
"processing:datetime": "2025-11-01T10:50:32.000000Z",
"processing:software": {
"eometadatatool": "251021130925+dirty"
},
"eopf:origin_datetime": "2025-11-01T10:54:46.627000Z",
"product:timeliness_category": "NT",
"sat:platform_international_designator": "2016-011A"
},
"stac_extensions": [
"https://cs-si.github.io/eopf-stac-extension/v1.2.0/schema.json",
"https://stac-extensions.github.io/alternate-assets/v1.2.0/schema.json",
"https://stac-extensions.github.io/authentication/v1.1.0/schema.json",
"https://stac-extensions.github.io/eo/v2.0.0/schema.json",
"https://stac-extensions.github.io/file/v2.1.0/schema.json",
"https://stac-extensions.github.io/processing/v1.2.0/schema.json",
"https://stac-extensions.github.io/product/v1.0.0/schema.json",
"https://stac-extensions.github.io/projection/v2.0.0/schema.json",
"https://stac-extensions.github.io/sat/v1.1.0/schema.json",
"https://stac-extensions.github.io/storage/v2.0.0/schema.json",
"https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"
],
"stac_version": "1.1.0"
}
Execute the query_stations_from_parquet operation¶
In [8]:
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source_item: Item = Item.from_dict(item_dict)
cql2_filter = {
"op": "and",
"args": [
{
"op": "s_intersects",
"args": [
{"property": "geometry"},
source_item.geometry,
],
}
],
}
json.dump(cql2_filter, sys.stdout, indent=2)
source_item: Item = Item.from_dict(item_dict)
cql2_filter = {
"op": "and",
"args": [
{
"op": "s_intersects",
"args": [
{"property": "geometry"},
source_item.geometry,
],
}
],
}
json.dump(cql2_filter, sys.stdout, indent=2)
{
"op": "and",
"args": [
{
"op": "s_intersects",
"args": [
{
"property": "geometry"
},
{
"type": "Polygon",
"coordinates": [
[
[
-1.5187599999999999,
41.97070000000001
],
[
13.725899999999998,
39.545
],
[
18.484,
49.88549999999999
],
[
0.10018799999999999,
52.45349999999999
],
[
-1.5187599999999999,
41.97070000000001
]
]
]
}
]
}
]
}
In [9]:
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from pygeofilter_aeronet import (
DEFAULT_STATIONS_PARQUET_URL,
query_stations_from_parquet
)
from typing import List
_, resulting_items = query_stations_from_parquet(
cql2_filter=cql2_filter,
file_path=DEFAULT_STATIONS_PARQUET_URL
)
from pygeofilter_aeronet import (
DEFAULT_STATIONS_PARQUET_URL,
query_stations_from_parquet
)
from typing import List
_, resulting_items = query_stations_from_parquet(
cql2_filter=cql2_filter,
file_path=DEFAULT_STATIONS_PARQUET_URL
)
Prepare the Map screen¶
In [ ]:
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print(f"Found {len(resulting_items)} STAC Item(s) representing AERONET Station(s)")
for item in resulting_items:
print(item.properties)
print(f"Found {len(resulting_items)} STAC Item(s) representing AERONET Station(s)")
for item in resulting_items:
print(item.properties)
Found 114 STAC Item(s) representing AERONET Station(s)
In [11]:
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from folium import (
GeoJson,
LayerControl,
Map
)
from folium.plugins import Fullscreen
map: Map = Map()
layer_control = LayerControl(position="topright", collapsed=True)
fullscreen = Fullscreen()
GeoJson(
source_item,
name=source_item.id,
style_function=lambda f: {
"fillColor": "yellow",
"color": "red",
"weight": 2,
"fillOpacity": 0.3,
}
).add_to(map)
for item in resulting_items:
GeoJson(
item,
name=item.id,
style_function=lambda f: {
"fillColor": "yellow",
"color": "red",
"weight": 2,
"fillOpacity": 0.3,
}
).add_to(map)
layer_control.add_to(map)
fullscreen.add_to(map)
map.fit_bounds(map.get_bounds()) # type: ignore not to important for the demo
map
from folium import (
GeoJson,
LayerControl,
Map
)
from folium.plugins import Fullscreen
map: Map = Map()
layer_control = LayerControl(position="topright", collapsed=True)
fullscreen = Fullscreen()
GeoJson(
source_item,
name=source_item.id,
style_function=lambda f: {
"fillColor": "yellow",
"color": "red",
"weight": 2,
"fillOpacity": 0.3,
}
).add_to(map)
for item in resulting_items:
GeoJson(
item,
name=item.id,
style_function=lambda f: {
"fillColor": "yellow",
"color": "red",
"weight": 2,
"fillOpacity": 0.3,
}
).add_to(map)
layer_control.add_to(map)
fullscreen.add_to(map)
map.fit_bounds(map.get_bounds()) # type: ignore not to important for the demo
map
Out[11]:
Make this Notebook Trusted to load map: File -> Trust Notebook
search data for each discovered station¶
In [12]:
Copied!
from datetime import (
datetime,
timedelta,
timezone
)
from dateutil import parser
from geopandas import read_parquet
from geopandas.geodataframe import GeoDataFrame
from IPython.display import (
display,
Markdown
)
from pygeofilter_aeronet import aeronet_search
def _parse_date_from_properties(date_string_id: str) -> datetime:
return parser.parse(source_item.properties[date_string_id])
start_datetime = _parse_date_from_properties('start_datetime') - timedelta(hours=1)
end_datetime = _parse_date_from_properties('end_datetime') + timedelta(hours=1)
def _serialize_date(input_date: datetime) -> str:
dt_utc = input_date.astimezone(timezone.utc)
return dt_utc.isoformat().replace("+00:00", "Z")
map: Map = Map()
layer_control = LayerControl(position="topright", collapsed=True)
fullscreen = Fullscreen()
GeoJson(
source_item,
name=source_item.id,
style_function=lambda f: {
"fillColor": "yellow",
"color": "red",
"weight": 2,
"fillOpacity": 0.3,
}
).add_to(map)
DEFAULT_TIMEOUT: int = 5
FILTERING_SITES: List[str] = [ 'Lille', 'Paris', 'Rome_Tor_Vergata' ]
for item in resulting_items:
site_name: str = item.properties['aeronet:site_name']
if site_name in FILTERING_SITES:
cql2_filter = {
"op": "and",
"args": [
{"op": "eq", "args": [{"property": "site"}, site_name]},
{"op": "eq", "args": [{"property": "data_type"}, "AOD10"]},
{"op": "eq", "args": [{"property": "format"}, "csv"]},
{"op": "eq", "args": [{"property": "data_format"}, "daily-average"]},
{
"op": "t_after",
"args": [
{"property": "time"},
{"timestamp": _serialize_date(start_datetime)},
],
},
{
"op": "t_before",
"args": [
{"property": "time"},
{"timestamp": _serialize_date(end_datetime)},
],
}
],
}
try:
resulting_item: Item = aeronet_search(
cql2_filter=cql2_filter,
output_dir=out_dir,
timeout=DEFAULT_TIMEOUT
)
display(Markdown(f"### {site_name} results as Data Frame"))
geoparquet_file: str = resulting_item.get_assets()['geoparquet'].href
geoparquet_data: GeoDataFrame = read_parquet(geoparquet_file)
display(geoparquet_data)
geoparquet_data = geoparquet_data.dissolve(by='AERONET_Site')
GeoJson(
geoparquet_data.to_json(default=str),
name="Stac Item footprints",
style_function=lambda x: {"fillColor": "#00000000", "color": "#0000ff", "weight": 1},
control=True,
).add_to(map)
except Exception as e:
print(f"An error occurred while retrieving data from site {site_name}: {e}")
layer_control.add_to(map)
fullscreen.add_to(map)
map.fit_bounds(map.get_bounds()) # type: ignore not to important for the demo
map
from datetime import (
datetime,
timedelta,
timezone
)
from dateutil import parser
from geopandas import read_parquet
from geopandas.geodataframe import GeoDataFrame
from IPython.display import (
display,
Markdown
)
from pygeofilter_aeronet import aeronet_search
def _parse_date_from_properties(date_string_id: str) -> datetime:
return parser.parse(source_item.properties[date_string_id])
start_datetime = _parse_date_from_properties('start_datetime') - timedelta(hours=1)
end_datetime = _parse_date_from_properties('end_datetime') + timedelta(hours=1)
def _serialize_date(input_date: datetime) -> str:
dt_utc = input_date.astimezone(timezone.utc)
return dt_utc.isoformat().replace("+00:00", "Z")
map: Map = Map()
layer_control = LayerControl(position="topright", collapsed=True)
fullscreen = Fullscreen()
GeoJson(
source_item,
name=source_item.id,
style_function=lambda f: {
"fillColor": "yellow",
"color": "red",
"weight": 2,
"fillOpacity": 0.3,
}
).add_to(map)
DEFAULT_TIMEOUT: int = 5
FILTERING_SITES: List[str] = [ 'Lille', 'Paris', 'Rome_Tor_Vergata' ]
for item in resulting_items:
site_name: str = item.properties['aeronet:site_name']
if site_name in FILTERING_SITES:
cql2_filter = {
"op": "and",
"args": [
{"op": "eq", "args": [{"property": "site"}, site_name]},
{"op": "eq", "args": [{"property": "data_type"}, "AOD10"]},
{"op": "eq", "args": [{"property": "format"}, "csv"]},
{"op": "eq", "args": [{"property": "data_format"}, "daily-average"]},
{
"op": "t_after",
"args": [
{"property": "time"},
{"timestamp": _serialize_date(start_datetime)},
],
},
{
"op": "t_before",
"args": [
{"property": "time"},
{"timestamp": _serialize_date(end_datetime)},
],
}
],
}
try:
resulting_item: Item = aeronet_search(
cql2_filter=cql2_filter,
output_dir=out_dir,
timeout=DEFAULT_TIMEOUT
)
display(Markdown(f"### {site_name} results as Data Frame"))
geoparquet_file: str = resulting_item.get_assets()['geoparquet'].href
geoparquet_data: GeoDataFrame = read_parquet(geoparquet_file)
display(geoparquet_data)
geoparquet_data = geoparquet_data.dissolve(by='AERONET_Site')
GeoJson(
geoparquet_data.to_json(default=str),
name="Stac Item footprints",
style_function=lambda x: {"fillColor": "#00000000", "color": "#0000ff", "weight": 1},
control=True,
).add_to(map)
except Exception as e:
print(f"An error occurred while retrieving data from site {site_name}: {e}")
layer_control.add_to(map)
fullscreen.add_to(map)
map.fit_bounds(map.get_bounds()) # type: ignore not to important for the demo
map
2025-11-18 17:42:30.556 | SUCCESS | pygeofilter_aeronet:aeronet_search:248 - Query on https://aeronet.gsfc.nasa.gov successfully obtained data: 2025-11-18 17:42:30.561 | SUCCESS | pygeofilter_aeronet:aeronet_search:261 - Data saved to to CSV file: /home/stripodi/Documents/pygeofilter/pygeofilter-aeronet/docs/use_cases/3c413a12-ec5e-40b3-8b01-84031088fed7.csv 2025-11-18 17:42:30.573 | SUCCESS | pygeofilter_aeronet:aeronet_search:295 - Data saved to GeoParquet file: /home/stripodi/Documents/pygeofilter/pygeofilter-aeronet/docs/use_cases/3c413a12-ec5e-40b3-8b01-84031088fed7.parquet
Lille results as Data Frame¶
| AERONET_Site | Date(dd:mm:yyyy) | Time(hh:mm:ss) | Day_of_Year | AOD_1640nm | AOD_1020nm | AOD_870nm | AOD_865nm | AOD_779nm | AOD_675nm | ... | N[340-440_Angstrom_Exponent] | N[440-675_Angstrom_Exponent[Polar]] | Data_Quality_Level | AERONET_Instrument_Number | AERONET_Site_Name | Site_Latitude(Degrees) | Site_Longitude(Degrees) | Site_Elevation(m) | geometry | datetime | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Lille | 30:10:2025 | 12:00:00 | 303 | 0.294629 | 0.309954 | 0.303359 | -999.0 | -999.0 | 0.296156 | ... | 45 | 0 | lev10 | 1345 | Lille | 50.611667 | 3.141667 | 60.0 | POINT (3.14167 50.61167) | 2025-10-30 12:00:00 |
| 1 | Lille | 31:10:2025 | 12:00:00 | 304 | 0.546914 | 0.518839 | 0.499880 | -999.0 | -999.0 | 0.486531 | ... | 5 | 0 | lev10 | 1345 | Lille | 50.611667 | 3.141667 | 60.0 | POINT (3.14167 50.61167) | 2025-10-31 12:00:00 |
2 rows × 84 columns
2025-11-18 17:42:31.279 | SUCCESS | pygeofilter_aeronet:aeronet_search:248 - Query on https://aeronet.gsfc.nasa.gov successfully obtained data: 2025-11-18 17:42:31.282 | SUCCESS | pygeofilter_aeronet:aeronet_search:261 - Data saved to to CSV file: /home/stripodi/Documents/pygeofilter/pygeofilter-aeronet/docs/use_cases/a11b6ecb-67ef-4dd7-8f16-5bf84db188a4.csv 2025-11-18 17:42:31.293 | SUCCESS | pygeofilter_aeronet:aeronet_search:295 - Data saved to GeoParquet file: /home/stripodi/Documents/pygeofilter/pygeofilter-aeronet/docs/use_cases/a11b6ecb-67ef-4dd7-8f16-5bf84db188a4.parquet
Paris results as Data Frame¶
| AERONET_Site | Date(dd:mm:yyyy) | Time(hh:mm:ss) | Day_of_Year | AOD_1640nm | AOD_1020nm | AOD_870nm | AOD_865nm | AOD_779nm | AOD_675nm | ... | N[340-440_Angstrom_Exponent] | N[440-675_Angstrom_Exponent[Polar]] | Data_Quality_Level | AERONET_Instrument_Number | AERONET_Site_Name | Site_Latitude(Degrees) | Site_Longitude(Degrees) | Site_Elevation(m) | geometry | datetime | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Paris | 30:10:2025 | 12:00:00 | 303 | 0.153396 | 0.151924 | 0.148786 | -999.0 | -999.0 | 0.147329 | ... | 30 | 0 | lev10 | 718 | Paris | 48.846797 | 2.355508 | 50.0 | POINT (2.35551 48.8468) | 2025-10-30 12:00:00 |
| 1 | Paris | 31:10:2025 | 12:00:00 | 304 | 0.133803 | 0.132780 | 0.130804 | -999.0 | -999.0 | 0.133111 | ... | 7 | 0 | lev10 | 718 | Paris | 48.846797 | 2.355508 | 50.0 | POINT (2.35551 48.8468) | 2025-10-31 12:00:00 |
2 rows × 84 columns
2025-11-18 17:42:32.308 | SUCCESS | pygeofilter_aeronet:aeronet_search:248 - Query on https://aeronet.gsfc.nasa.gov successfully obtained data: 2025-11-18 17:42:32.311 | SUCCESS | pygeofilter_aeronet:aeronet_search:261 - Data saved to to CSV file: /home/stripodi/Documents/pygeofilter/pygeofilter-aeronet/docs/use_cases/b83ae5d3-9e3f-4546-b648-aa76aa4f66ba.csv 2025-11-18 17:42:32.320 | SUCCESS | pygeofilter_aeronet:aeronet_search:295 - Data saved to GeoParquet file: /home/stripodi/Documents/pygeofilter/pygeofilter-aeronet/docs/use_cases/b83ae5d3-9e3f-4546-b648-aa76aa4f66ba.parquet
Rome_Tor_Vergata results as Data Frame¶
| AERONET_Site | Date(dd:mm:yyyy) | Time(hh:mm:ss) | Day_of_Year | AOD_1640nm | AOD_1020nm | AOD_870nm | AOD_865nm | AOD_779nm | AOD_675nm | ... | N[340-440_Angstrom_Exponent] | N[440-675_Angstrom_Exponent[Polar]] | Data_Quality_Level | AERONET_Instrument_Number | AERONET_Site_Name | Site_Latitude(Degrees) | Site_Longitude(Degrees) | Site_Elevation(m) | geometry | datetime | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Rome_Tor_Vergata | 30:10:2025 | 12:00:00 | 303 | 0.626251 | 0.602303 | 0.591329 | -999.0 | -999.0 | 0.586273 | ... | 11 | 0 | lev10 | 1321 | Rome_Tor_Vergata | 41.83955 | 12.647333 | 130.0 | POINT (12.64733 41.83955) | 2025-10-30 12:00:00 |
| 1 | Rome_Tor_Vergata | 31:10:2025 | 12:00:00 | 304 | 0.062751 | 0.081665 | 0.088833 | -999.0 | -999.0 | 0.106361 | ... | 36 | 0 | lev10 | 1321 | Rome_Tor_Vergata | 41.83955 | 12.647333 | 130.0 | POINT (12.64733 41.83955) | 2025-10-31 12:00:00 |
2 rows × 84 columns
Out[12]:
Make this Notebook Trusted to load map: File -> Trust Notebook