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  • Level 1.0 data from the "Cart_Site" for AOD daily averages

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  • Level 2.0 data from the "Cart_Site" for AOD daily averages
    • Execute the operation
    • Visualize the results as Data Frame
    • Visualize results on Map screen
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pygeofilter AERONET API support for CQL2
  • How-to guides
  • Level 2.0 data from the "Cart_Site" for AOD daily averages
  • Edit on Terradue/pygeofilter-aeronet

Level 2.0 data from the "Cart_Site" for AOD daily averages¶

Define a cql2-json filter for:

https://aeronet.gsfc.nasa.gov/cgi-bin/print_web_data_v3?site=Cart_Site&year=2000&month=6&day=1&year2=2000&month2=6&day2=14&AOD20=1&AVG=20

In [1]:
Copied! 
from pathlib import Path
from pystac import Item

import json
import sys

out_dir: Path = Path('.')

cql2_filter = {
    "op": "and",
    "args": [
        {"op": "eq", "args": [{"property": "site"}, "Cart_Site"]},
        {"op": "eq", "args": [{"property": "data_type"}, "AOD20"]},
        {"op": "eq", "args": [{"property": "format"}, "csv"]},
        {"op": "eq", "args": [{"property": "data_format"}, "daily-average"]},
        {
            "op": "t_after",
            "args": [
                {"property": "time"},
                {"timestamp": "2000-06-01T00:00:00Z"},
            ],
        },
        {
            "op": "t_before",
            "args": [
                {"property": "time"},
                {"timestamp": "2000-06-14T23:59:59Z"},
            ],
        },
    ],
}

json.dump(cql2_filter, sys.stdout, indent=2)
from pathlib import Path from pystac import Item import json import sys out_dir: Path = Path('.') cql2_filter = { "op": "and", "args": [ {"op": "eq", "args": [{"property": "site"}, "Cart_Site"]}, {"op": "eq", "args": [{"property": "data_type"}, "AOD20"]}, {"op": "eq", "args": [{"property": "format"}, "csv"]}, {"op": "eq", "args": [{"property": "data_format"}, "daily-average"]}, { "op": "t_after", "args": [ {"property": "time"}, {"timestamp": "2000-06-01T00:00:00Z"}, ], }, { "op": "t_before", "args": [ {"property": "time"}, {"timestamp": "2000-06-14T23:59:59Z"}, ], }, ], } json.dump(cql2_filter, sys.stdout, indent=2)
{
  "op": "and",
  "args": [
    {
      "op": "eq",
      "args": [
        {
          "property": "site"
        },
        "Cart_Site"
      ]
    },
    {
      "op": "eq",
      "args": [
        {
          "property": "data_type"
        },
        "AOD20"
      ]
    },
    {
      "op": "eq",
      "args": [
        {
          "property": "format"
        },
        "csv"
      ]
    },
    {
      "op": "eq",
      "args": [
        {
          "property": "data_format"
        },
        "daily-average"
      ]
    },
    {
      "op": "t_after",
      "args": [
        {
          "property": "time"
        },
        {
          "timestamp": "2000-06-01T00:00:00Z"
        }
      ]
    },
    {
      "op": "t_before",
      "args": [
        {
          "property": "time"
        },
        {
          "timestamp": "2000-06-14T23:59:59Z"
        }
      ]
    }
  ]
}

Execute the search operation¶

In [2]:
Copied! 
from pygeofilter_aeronet import aeronet_search

item: Item = aeronet_search(
    cql2_filter=cql2_filter,
    output_dir=out_dir
)

json.dump(item.to_dict(), sys.stdout, indent=2)
from pygeofilter_aeronet import aeronet_search item: Item = aeronet_search( cql2_filter=cql2_filter, output_dir=out_dir ) json.dump(item.to_dict(), sys.stdout, indent=2)
2025-11-14 18:07:57.256 | SUCCESS  | pygeofilter_aeronet:aeronet_search:231 - Query on https://aeronet.gsfc.nasa.gov successfully obtained data:
2025-11-14 18:07:57.258 | SUCCESS  | pygeofilter_aeronet:aeronet_search:239 - Data saved to to CSV file: /home/stripodi/Documents/pygeofilter/pygeofilter-aeronet/docs/samples/845a1e47-b937-4ed6-8a8f-1524df33d9fd.csv
2025-11-14 18:07:57.266 | SUCCESS  | pygeofilter_aeronet:aeronet_search:270 - Data saved to GeoParquet file: /home/stripodi/Documents/pygeofilter/pygeofilter-aeronet/docs/samples/845a1e47-b937-4ed6-8a8f-1524df33d9fd.parquet

{
  "type": "Feature",
  "stac_version": "1.1.0",
  "stac_extensions": [],
  "id": "urn:uuid:845a1e47-b937-4ed6-8a8f-1524df33d9fd",
  "geometry": {
    "type": "Point",
    "coordinates": [
      -97.48639,
      36.60667
    ]
  },
  "bbox": [
    -97.48639,
    36.60667,
    -97.48639,
    36.60667
  ],
  "properties": {
    "datetime": "2025-11-14T18:07:57.296674Z"
  },
  "links": [
    {
      "rel": "related",
      "href": "https://aeronet.gsfc.nasa.gov/cgi-bin/print_web_data_v3?site=Cart_Site&AOD20=1&if_no_html=1&AVG=20&year=2000&month=6&day=1&hour=0&year2=2000&month2=6&day2=14&hour2=23",
      "type": "text/csv",
      "title": "AERONET Web Service search"
    }
  ],
  "assets": {
    "csv": {
      "href": "845a1e47-b937-4ed6-8a8f-1524df33d9fd.csv",
      "type": "text/csv",
      "description": "Search result - CVS Format"
    },
    "geoparquet": {
      "href": "845a1e47-b937-4ed6-8a8f-1524df33d9fd.parquet",
      "type": "application/vnd.apache.parquet",
      "description": "Search result - GeoParquet Format"
    }
  }
}

Visualize the results as Data Frame¶

In [3]:
Copied! 
from geopandas import read_parquet
from geopandas.geodataframe import GeoDataFrame

geoparquet_file: str = item.get_assets()['geoparquet'].href
geoparquet_data: GeoDataFrame = read_parquet(geoparquet_file)

geoparquet_data
from geopandas import read_parquet from geopandas.geodataframe import GeoDataFrame geoparquet_file: str = item.get_assets()['geoparquet'].href geoparquet_data: GeoDataFrame = read_parquet(geoparquet_file) geoparquet_data
Out[3]:
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 Cart_Site 31:05:2000 12:00:00 152 -999.0 0.035200 0.051679 -999.0 -999.0 0.061963 ... 34 0 lev20 99 Cart_Site 36.60667 -97.48639 318.0 POINT (-97.48639 36.60667) 2000-05-31 12:00:00
1 Cart_Site 01:06:2000 12:00:00 153 -999.0 0.049179 0.065103 -999.0 -999.0 0.075437 ... 13 0 lev20 99 Cart_Site 36.60667 -97.48639 318.0 POINT (-97.48639 36.60667) 2000-06-01 12:00:00
2 Cart_Site 04:06:2000 12:00:00 156 -999.0 0.110029 0.141054 -999.0 -999.0 0.200035 ... 2 0 lev20 99 Cart_Site 36.60667 -97.48639 318.0 POINT (-97.48639 36.60667) 2000-06-04 12:00:00
3 Cart_Site 05:06:2000 12:00:00 157 -999.0 0.031582 0.042280 -999.0 -999.0 0.046784 ... 55 0 lev20 99 Cart_Site 36.60667 -97.48639 318.0 POINT (-97.48639 36.60667) 2000-06-05 12:00:00
4 Cart_Site 06:06:2000 12:00:00 158 -999.0 0.053464 0.077199 -999.0 -999.0 0.101528 ... 25 0 lev20 99 Cart_Site 36.60667 -97.48639 318.0 POINT (-97.48639 36.60667) 2000-06-06 12:00:00
5 Cart_Site 07:06:2000 12:00:00 159 -999.0 0.041645 0.057704 -999.0 -999.0 0.073657 ... 16 0 lev20 99 Cart_Site 36.60667 -97.48639 318.0 POINT (-97.48639 36.60667) 2000-06-07 12:00:00
6 Cart_Site 08:06:2000 12:00:00 160 -999.0 0.026916 0.039999 -999.0 -999.0 0.051110 ... 38 0 lev20 99 Cart_Site 36.60667 -97.48639 318.0 POINT (-97.48639 36.60667) 2000-06-08 12:00:00
7 Cart_Site 09:06:2000 12:00:00 161 -999.0 0.086260 0.115657 -999.0 -999.0 0.163068 ... 7 0 lev20 99 Cart_Site 36.60667 -97.48639 318.0 POINT (-97.48639 36.60667) 2000-06-09 12:00:00
8 Cart_Site 11:06:2000 12:00:00 163 -999.0 0.027895 0.040069 -999.0 -999.0 0.048234 ... 6 0 lev20 99 Cart_Site 36.60667 -97.48639 318.0 POINT (-97.48639 36.60667) 2000-06-11 12:00:00
9 Cart_Site 12:06:2000 12:00:00 164 -999.0 0.079964 0.094614 -999.0 -999.0 0.109477 ... 9 0 lev20 99 Cart_Site 36.60667 -97.48639 318.0 POINT (-97.48639 36.60667) 2000-06-12 12:00:00
10 Cart_Site 13:06:2000 12:00:00 165 -999.0 0.090479 0.104427 -999.0 -999.0 0.113828 ... 16 0 lev20 99 Cart_Site 36.60667 -97.48639 318.0 POINT (-97.48639 36.60667) 2000-06-13 12:00:00

11 rows × 84 columns

Visualize results on Map screen¶

In [4]:
Copied! 
from folium import (
    GeoJson,
    LayerControl,
    Map
)
from folium.plugins import (
    Fullscreen
)
from IPython.display import (
    display,
    HTML
)

map: Map = Map()
layer_control = LayerControl(position="topright", collapsed=True)
fullscreen = Fullscreen()
style = {"fillColor": "#00000000", "color": "#0000ff", "weight": 1}

footprints: GeoJson = GeoJson(
    geoparquet_data.dissolve(by='AERONET_Site').to_json(default=str),
    name="Stac Item footprints",
    style_function=lambda x: style,
    control=True,
)

footprints.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 ) from IPython.display import ( display, HTML ) map: Map = Map() layer_control = LayerControl(position="topright", collapsed=True) fullscreen = Fullscreen() style = {"fillColor": "#00000000", "color": "#0000ff", "weight": 1} footprints: GeoJson = GeoJson( geoparquet_data.dissolve(by='AERONET_Site').to_json(default=str), name="Stac Item footprints", style_function=lambda x: style, control=True, ) footprints.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[4]:
Make this Notebook Trusted to load map: File -> Trust Notebook
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