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Contents
  • 1 - Import spacesense object(s) and other dependencies
  • 2 - Define AOI and TOI
  • 3 - Search Weather
  • 4 - Plot time series of LAI
  • 5 - Search S2
  • 6 - Filter weather dates matching S2 dates
  • 7 - Filter S2 dates with U-wind<1
  • 8 - Merge dataframes

Weather Data Access in Search

Contents

  • 1 - Import spacesense object(s) and other dependencies
  • 2 - Define AOI and TOI
  • 3 - Search Weather
  • 4 - Plot time series of LAI
  • 5 - Search S2
  • 6 - Filter weather dates matching S2 dates
  • 7 - Filter S2 dates with U-wind<1
  • 8 - Merge dataframes

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 specified weather variables 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...

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