Contents

Weather Data Access in Search

Contents

Weather Data Access in Search#

Short description

This notebook introduces the Weather access and refinement.

1 - Import spacesense object(s) and other dependencies#

[1]:

from spacesense import Client
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import json

import os
if "SS_API_KEY" not in os.environ:
    from getpass import getpass
    api_key = getpass('Enter your api key : ')
    os.environ["SS_API_KEY"] = api_key

2 - Define AOI and TOI#

[2]:

# Define the AOI
aoi = {
  "type": "FeatureCollection",
  "features": [
    {
      "id": "0",
      "type": "Feature",
      "properties": {},
      "geometry": {
        "type": "Polygon",
        "coordinates": [
          [
            [
              8.622499,
              39.831038
            ],
            [
              8.622499,
              39.827197
            ],
            [
              8.630311,
              39.827197
            ],
            [
              8.630311,
              39.831038
            ],
            [
              8.622499,
              39.831038
            ]
          ]
        ]
      }
    }
  ]
}

# Define TOI
start_date = "2021-02-01"
end_date = "2021-04-15"

# Get an instance of the SpaceSense Client object
client = Client(id="weather_search")

3 - Search Weather#

[3]:

# Retrieves some weather all S1 images corresponding to the aoi, start date, and end date
weather_variables = ["LAILOW", "PREC", "UWIND", "VWIND"]
res_weather = client.weather_search(aoi, start_date, end_date, variables=weather_variables)
df = res_weather.dataframe
df

[3]:

	date	lailow	prec	uwind	vwind
0	2021-02-01	1.725094	7.116849	4.864336	-2.266004
1	2021-02-02	1.726089	1.581512	3.563660	-2.541020
2	2021-02-03	1.727094	0.004455	-0.781930	2.039700
3	2021-02-04	1.728088	0.000006	-1.836714	1.226246
4	2021-02-05	1.729092	0.000006	-1.827159	1.178977
...	...	...	...	...	...
69	2021-04-11	1.930440	1.614441	-2.829541	2.599476
70	2021-04-12	1.933740	6.214283	2.196791	-2.096936
71	2021-04-13	1.937054	0.000000	4.891191	-3.295530
72	2021-04-14	1.940352	0.043861	0.547026	-0.059496
73	2021-04-15	1.940711	0.046124	-0.063789	-0.383259

74 rows × 5 columns

4 - Plot time series of LAI#

[4]:

df.plot(x="date", y=["lailow"])

[4]:

<AxesSubplot:xlabel='date'>

../_images/notebooks_4_weather_insights_8_1.png

5 - Search S2#

[5]:

# Retrieves all S2 images corresponding to the aoi, start date, and end date
res_S2 = client.s2_search(aoi=aoi, start_date=start_date, end_date=end_date, query_filters = {"valid_pixel_percentage" : {">=": 95}})
res_S2.dataframe

[5]:

	id	date	tile	valid_pixel_percentage	platform	relative_orbit_number	product_id	datetime	swath_coverage_percentage	no_data	cloud_shadows	vegetation	not_vegetated	water	cloud_medium_probability	cloud_high_probability	thin_cirrus	snow
11	S2A_32TMK_20210205_0_L2A	2021-02-05	32TMK	100.00	sentinel-2a	065	S2A_MSIL2A_20210205T102221_N0214_R065_T32TMK_2...	2021-02-05T10:29:43Z	100.0	0.0	0.00	97.40	0.16	0.0	0.00	0.00	0.0	0.0
10	S2A_32TMK_20210215_0_L2A	2021-02-15	32TMK	100.00	sentinel-2a	065	S2A_MSIL2A_20210215T102121_N0214_R065_T32TMK_2...	2021-02-15T10:29:42Z	100.0	0.0	0.00	97.86	0.00	0.0	0.00	0.00	0.0	0.0
9	S2B_32TMK_20210217_0_L2A	2021-02-17	32TMK	100.00	sentinel-2b	022	S2B_MSIL2A_20210217T101029_N0214_R022_T32TMK_2...	2021-02-17T10:19:45Z	100.0	0.0	0.00	97.97	0.00	0.0	0.00	0.00	0.0	0.0
8	S2B_32TMK_20210220_0_L2A	2021-02-20	32TMK	100.00	sentinel-2b	065	S2B_MSIL2A_20210220T101939_N0214_R065_T32TMK_2...	2021-02-20T10:29:41Z	100.0	0.0	0.00	97.86	0.00	0.0	0.00	0.00	0.0	0.0
7	S2A_32TMK_20210225_0_L2A	2021-02-25	32TMK	100.00	sentinel-2a	065	S2A_MSIL2A_20210225T102021_N0214_R065_T32TMK_2...	2021-02-25T10:29:43Z	100.0	0.0	0.00	97.80	0.00	0.0	0.00	0.00	0.0	0.0
6	S2A_32TMK_20210307_0_L2A	2021-03-07	32TMK	99.00	sentinel-2a	065	S2A_MSIL2A_20210307T102021_N0214_R065_T32TMK_2...	2021-03-07T10:29:43Z	100.0	0.0	0.00	68.59	0.00	0.0	1.00	0.00	0.0	0.0
5	S2B_32TMK_20210322_0_L2A	2021-03-22	32TMK	97.78	sentinel-2b	065	S2B_MSIL2A_20210322T101649_N0214_R065_T32TMK_2...	2021-03-22T10:29:43Z	100.0	0.0	0.00	92.87	0.98	0.0	2.22	0.00	0.0	0.0
4	S2B_32TMK_20210329_0_L2A	2021-03-29	32TMK	100.00	sentinel-2b	022	S2B_MSIL2A_20210329T100609_N0214_R022_T32TMK_2...	2021-03-29T10:19:46Z	100.0	0.0	0.00	97.86	0.16	0.0	0.00	0.00	0.0	0.0
3	S2B_32TMK_20210401_0_L2A	2021-04-01	32TMK	100.00	sentinel-2b	065	S2B_MSIL2A_20210401T101559_N0300_R065_T32TMK_2...	2021-04-01T10:29:42Z	100.0	0.0	0.00	97.97	0.00	0.0	0.00	0.00	0.0	0.0
2	S2A_32TMK_20210406_0_L2A	2021-04-06	32TMK	100.00	sentinel-2a	065	S2A_MSIL2A_20210406T102021_N0300_R065_T32TMK_2...	2021-04-06T10:29:40Z	100.0	0.0	0.00	98.56	0.00	0.0	0.00	0.00	0.0	0.0
1	S2B_32TMK_20210408_0_L2A	2021-04-08	32TMK	100.00	sentinel-2b	022	S2B_MSIL2A_20210408T100549_N0300_R022_T32TMK_2...	2021-04-08T10:19:45Z	100.0	0.0	0.00	98.13	0.00	0.0	0.00	0.00	0.0	0.0
0	S2A_32TMK_20210413_0_L2A	2021-04-13	32TMK	96.34	sentinel-2a	022	S2A_MSIL2A_20210413T101021_N0300_R022_T32TMK_2...	2021-04-13T10:19:42Z	100.0	0.0	0.49	89.35	3.12	0.0	3.01	0.16	0.0	0.0

6 - Filter weather dates matching S2 dates#

[6]:

res_weather.dataframe = res_weather.dataframe[[date in list(res_S2.dataframe["date"]) for date in list(res_weather.dataframe["date"])]]
res_weather.dataframe

[6]:

	date	lailow	prec	uwind	vwind
4	2021-02-05	1.729092	0.000006	-1.827159	1.178977
14	2021-02-15	1.740311	0.000006	-0.302282	-1.600299
16	2021-02-17	1.747522	0.000006	-0.037975	-0.273452
19	2021-02-20	1.758355	0.004262	-2.808861	0.954008
24	2021-02-25	1.776382	0.028380	0.015086	-0.116508
34	2021-03-07	1.812463	0.682086	-2.230686	1.315975
49	2021-03-22	1.864313	0.125496	-0.026054	-4.197672
56	2021-03-29	1.887462	0.009414	-0.267097	-0.117241
59	2021-04-01	1.897375	0.000000	-0.250064	-0.198988
64	2021-04-06	1.913904	0.299103	5.917795	-2.332012
66	2021-04-08	1.920513	0.000000	-0.086882	-0.620076
71	2021-04-13	1.937054	0.000000	4.891191	-3.295530

7 - Filter S2 dates with U-wind<1#

[7]:

res_S2_df_no_wind = res_S2.dataframe[list(res_weather.dataframe["uwind"]<1)]
res_S2_df_no_wind

[7]:

	id	date	tile	valid_pixel_percentage	platform	relative_orbit_number	product_id	datetime	swath_coverage_percentage	no_data	cloud_shadows	vegetation	not_vegetated	water	cloud_medium_probability	cloud_high_probability	thin_cirrus	snow
11	S2A_32TMK_20210205_0_L2A	2021-02-05	32TMK	100.00	sentinel-2a	065	S2A_MSIL2A_20210205T102221_N0214_R065_T32TMK_2...	2021-02-05T10:29:43Z	100.0	0.0	0.0	97.40	0.16	0.0	0.00	0.0	0.0	0.0
10	S2A_32TMK_20210215_0_L2A	2021-02-15	32TMK	100.00	sentinel-2a	065	S2A_MSIL2A_20210215T102121_N0214_R065_T32TMK_2...	2021-02-15T10:29:42Z	100.0	0.0	0.0	97.86	0.00	0.0	0.00	0.0	0.0	0.0
9	S2B_32TMK_20210217_0_L2A	2021-02-17	32TMK	100.00	sentinel-2b	022	S2B_MSIL2A_20210217T101029_N0214_R022_T32TMK_2...	2021-02-17T10:19:45Z	100.0	0.0	0.0	97.97	0.00	0.0	0.00	0.0	0.0	0.0
8	S2B_32TMK_20210220_0_L2A	2021-02-20	32TMK	100.00	sentinel-2b	065	S2B_MSIL2A_20210220T101939_N0214_R065_T32TMK_2...	2021-02-20T10:29:41Z	100.0	0.0	0.0	97.86	0.00	0.0	0.00	0.0	0.0	0.0
7	S2A_32TMK_20210225_0_L2A	2021-02-25	32TMK	100.00	sentinel-2a	065	S2A_MSIL2A_20210225T102021_N0214_R065_T32TMK_2...	2021-02-25T10:29:43Z	100.0	0.0	0.0	97.80	0.00	0.0	0.00	0.0	0.0	0.0
6	S2A_32TMK_20210307_0_L2A	2021-03-07	32TMK	99.00	sentinel-2a	065	S2A_MSIL2A_20210307T102021_N0214_R065_T32TMK_2...	2021-03-07T10:29:43Z	100.0	0.0	0.0	68.59	0.00	0.0	1.00	0.0	0.0	0.0
5	S2B_32TMK_20210322_0_L2A	2021-03-22	32TMK	97.78	sentinel-2b	065	S2B_MSIL2A_20210322T101649_N0214_R065_T32TMK_2...	2021-03-22T10:29:43Z	100.0	0.0	0.0	92.87	0.98	0.0	2.22	0.0	0.0	0.0
4	S2B_32TMK_20210329_0_L2A	2021-03-29	32TMK	100.00	sentinel-2b	022	S2B_MSIL2A_20210329T100609_N0214_R022_T32TMK_2...	2021-03-29T10:19:46Z	100.0	0.0	0.0	97.86	0.16	0.0	0.00	0.0	0.0	0.0
3	S2B_32TMK_20210401_0_L2A	2021-04-01	32TMK	100.00	sentinel-2b	065	S2B_MSIL2A_20210401T101559_N0300_R065_T32TMK_2...	2021-04-01T10:29:42Z	100.0	0.0	0.0	97.97	0.00	0.0	0.00	0.0	0.0	0.0
1	S2B_32TMK_20210408_0_L2A	2021-04-08	32TMK	100.00	sentinel-2b	022	S2B_MSIL2A_20210408T100549_N0300_R022_T32TMK_2...	2021-04-08T10:19:45Z	100.0	0.0	0.0	98.13	0.00	0.0	0.00	0.0	0.0	0.0

8 - Merge dataframes#

[8]:

global_df = pd.merge(res_weather.dataframe, res_S2.dataframe, on="date", how="outer")
global_df = global_df[["date", "lailow", "prec", "product_id"]]
global_df

[8]:

	date	lailow	prec	product_id
0	2021-02-05	1.729092	0.000006	S2A_MSIL2A_20210205T102221_N0214_R065_T32TMK_2...
1	2021-02-15	1.740311	0.000006	S2A_MSIL2A_20210215T102121_N0214_R065_T32TMK_2...
2	2021-02-17	1.747522	0.000006	S2B_MSIL2A_20210217T101029_N0214_R022_T32TMK_2...
3	2021-02-20	1.758355	0.004262	S2B_MSIL2A_20210220T101939_N0214_R065_T32TMK_2...
4	2021-02-25	1.776382	0.028380	S2A_MSIL2A_20210225T102021_N0214_R065_T32TMK_2...
5	2021-03-07	1.812463	0.682086	S2A_MSIL2A_20210307T102021_N0214_R065_T32TMK_2...
6	2021-03-22	1.864313	0.125496	S2B_MSIL2A_20210322T101649_N0214_R065_T32TMK_2...
7	2021-03-29	1.887462	0.009414	S2B_MSIL2A_20210329T100609_N0214_R022_T32TMK_2...
8	2021-04-01	1.897375	0.000000	S2B_MSIL2A_20210401T101559_N0300_R065_T32TMK_2...
9	2021-04-06	1.913904	0.299103	S2A_MSIL2A_20210406T102021_N0300_R065_T32TMK_2...
10	2021-04-08	1.920513	0.000000	S2B_MSIL2A_20210408T100549_N0300_R022_T32TMK_2...
11	2021-04-13	1.937054	0.000000	S2A_MSIL2A_20210413T101021_N0300_R022_T32TMK_2...