---

Monthly optical images

---

Short description

This notebook demonstrates a general usecase for the SpaceSense library.

In this notebook, you will search for, select, and obtain Sentinel-2 data. The selected data will be cloud-free and the expected result is to have a single image per month of the year.

---

1 - Import spacesense object(s) and other dependencies

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

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 output options

The GeoJSON of this AOI, containing the Cirque de Garvanie in the Pyrenees Mountains, can be downloaded here

# Define the AOI
with open("../resources/aois/cirque_de_garvanie.geojson", mode="r") as file:
    aoi = json.load(file)

# Get an instance of the SpaceSense Client object
client = Client(id="s2_optical_monthly")

# Enable to save data in local files
client.enable_local_output()

3 - Search S2

start_date = "2020-01-01"
end_date = "2021-01-01"
# Create a query filter, limiting cloud coverage to 10% or less, and only taking scenes with 100% swath coverage
query_filter = {"cloud_medium_probability" : {"<=": 10},
                "cloud_high_probability" : {"<=": 10},
                "cloud_shadows" : {"<=": 10},
                "swath_coverage_percentage": {"==": 100}}
res_S2 = client.s2_search(aoi=aoi, start_date=start_date, end_date=end_date, query_filters = query_filter)
res_S2.filter_duplicate_dates()
res_S2.dataframe

	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
0	S2A_30TYN_20200101_0_L2A	2020-01-01	30TYN	57.44	sentinel-2a	051	S2A_MSIL2A_20200101T105441_N0213_R051_T30TYN_2...	2020-01-01T10:59:11.266000Z	100.0	0.0	0.81	0.85	1.96	7.56	0.14	0.63	0.01	40.97
1	S2B_30TYN_20200106_0_L2A	2020-01-06	30TYN	57.35	sentinel-2b	051	S2B_MSIL2A_20200106T105339_N0213_R051_T30TYN_2...	2020-01-06T10:59:11.633000Z	100.0	0.0	1.06	0.93	2.48	7.28	0.18	0.74	0.00	40.67
2	S2B_30TYN_20200116_0_L2A	2020-01-16	30TYN	56.64	sentinel-2b	051	S2B_MSIL2A_20200116T105309_N0213_R051_T30TYN_2...	2020-01-16T10:59:10.849000Z	100.0	0.0	1.39	1.02	5.01	6.09	0.17	0.98	0.00	40.82
3	S2A_30TYN_20200131_0_L2A	2020-01-31	30TYN	44.26	sentinel-2a	051	S2A_MSIL2A_20200131T105251_N0213_R051_T30TYN_2...	2020-01-31T10:59:09.147000Z	100.0	0.0	0.14	0.35	0.33	4.15	0.09	0.12	0.00	55.39
4	S2B_30TYN_20200205_0_L2A	2020-02-05	30TYN	46.62	sentinel-2b	051	S2B_MSIL2A_20200205T105129_N0214_R051_T30TYN_2...	2020-02-05T10:59:10.605000Z	100.0	0.0	1.02	1.26	2.69	3.76	0.28	0.64	0.00	51.44
5	S2A_30TYN_20200210_0_L2A	2020-02-10	30TYN	46.31	sentinel-2a	051	S2A_MSIL2A_20200210T105201_N0214_R051_T30TYN_2...	2020-02-10T10:59:09.328000Z	100.0	0.0	1.30	1.54	4.71	3.05	0.05	0.53	0.00	51.81
6	S2B_30TYN_20200215_0_L2A	2020-02-15	30TYN	44.75	sentinel-2b	051	S2B_MSIL2A_20200215T105029_N0214_R051_T30TYN_2...	2020-02-15T10:59:12.451000Z	100.0	0.0	1.48	1.72	5.96	2.40	0.28	0.93	0.00	52.56
7	S2A_30TYN_20200220_0_L2A	2020-02-20	30TYN	43.54	sentinel-2a	051	S2A_MSIL2A_20200220T105051_N0214_R051_T30TYN_2...	2020-02-20T10:59:11.040000Z	100.0	0.0	1.58	2.65	7.58	1.83	0.27	1.01	0.00	53.60
8	S2A_30TYN_20200301_0_L2A	2020-03-01	30TYN	0.15	sentinel-2a	051	S2A_MSIL2A_20200301T105021_N0214_R051_T30TYN_2...	2020-03-01T10:59:12.233000Z	100.0	0.0	0.00	0.00	0.14	0.00	4.44	6.32	0.00	89.09
9	S2A_30TYN_20200311_0_L2A	2020-03-11	30TYN	20.12	sentinel-2a	051	S2A_MSIL2A_20200311T105021_N0214_R051_T30TYN_2...	2020-03-11T10:59:12.935000Z	100.0	0.0	0.48	0.91	0.48	0.65	0.16	0.09	0.00	79.15
10	S2A_30TYN_20200321_0_L2A	2020-03-21	30TYN	32.54	sentinel-2a	051	S2A_MSIL2A_20200321T105021_N0214_R051_T30TYN_2...	2020-03-21T10:59:13.094000Z	100.0	0.0	0.68	2.09	13.14	0.35	0.96	0.57	0.00	65.25
11	S2A_30TYN_20200410_0_L2A	2020-04-10	30TYN	24.51	sentinel-2a	051	S2A_MSIL2A_20200410T105031_N0214_R051_T30TYN_2...	2020-04-10T10:59:15.012000Z	100.0	0.0	1.52	6.31	8.57	0.50	1.63	9.43	0.00	62.91
12	S2A_30TYN_20200520_0_L2A	2020-05-20	30TYN	53.59	sentinel-2a	051	S2A_MSIL2A_20200520T105031_N0214_R051_T30TYN_2...	2020-05-20T10:59:22.363000Z	100.0	0.0	0.04	32.70	16.82	0.16	0.82	1.63	0.00	43.92
13	S2A_30TYN_20200530_0_L2A	2020-05-30	30TYN	58.84	sentinel-2a	051	S2A_MSIL2A_20200530T105031_N0214_R051_T30TYN_2...	2020-05-30T10:59:22.967000Z	100.0	0.0	1.30	37.21	16.90	0.43	1.23	4.62	0.00	34.01
14	S2B_30TYN_20200624_0_L2A	2020-06-24	30TYN	68.59	sentinel-2b	051	S2B_MSIL2A_20200624T104629_N0214_R051_T30TYN_2...	2020-06-24T10:59:19.327000Z	100.0	0.0	0.75	46.88	17.58	0.13	1.64	4.30	0.00	24.72
15	S2B_30TYN_20200704_0_L2A	2020-07-04	30TYN	78.01	sentinel-2b	051	S2B_MSIL2A_20200704T104619_N0214_R051_T30TYN_2...	2020-07-04T10:59:18.977000Z	100.0	0.0	0.28	51.04	22.23	0.04	2.51	3.77	0.00	15.43
16	S2A_30TYN_20200709_0_L2A	2020-07-09	30TYN	71.72	sentinel-2a	051	S2A_MSIL2A_20200709T105031_N0214_R051_T30TYN_2...	2020-07-09T10:59:20.829000Z	100.0	0.0	2.88	46.20	19.82	0.20	4.15	9.84	0.00	11.41
17	S2B_30TYN_20200714_0_L2A	2020-07-14	30TYN	80.75	sentinel-2b	051	S2B_MSIL2A_20200714T104619_N0214_R051_T30TYN_2...	2020-07-14T10:59:18.200000Z	100.0	0.0	0.47	51.60	23.56	0.02	4.07	5.65	0.00	9.06
18	S2A_30TYN_20200719_0_L2A	2020-07-19	30TYN	83.64	sentinel-2a	051	S2A_MSIL2A_20200719T105031_N0214_R051_T30TYN_2...	2020-07-19T10:59:21.847000Z	100.0	0.0	0.61	51.86	25.36	0.14	3.41	7.26	0.00	5.08
19	S2A_30TYN_20200729_0_L2A	2020-07-29	30TYN	86.56	sentinel-2a	051	S2A_MSIL2A_20200729T105031_N0214_R051_T30TYN_2...	2020-07-29T10:59:22.508000Z	100.0	0.0	0.80	50.94	28.15	0.02	4.25	5.34	0.00	3.05
20	S2B_30TYN_20200803_0_L2A	2020-08-03	30TYN	88.60	sentinel-2b	051	S2B_MSIL2A_20200803T104629_N0214_R051_T30TYN_2...	2020-08-03T10:59:19.827000Z	100.0	0.0	0.69	49.25	30.00	0.02	4.82	4.16	0.00	1.73
21	S2A_30TYN_20200808_0_L2A	2020-08-08	30TYN	82.34	sentinel-2a	051	S2A_MSIL2A_20200808T105031_N0214_R051_T30TYN_2...	2020-08-08T10:59:22.783000Z	100.0	0.0	1.42	42.16	27.86	0.02	7.62	7.12	0.00	1.50
22	S2B_30TYN_20200823_0_L2A	2020-08-23	30TYN	94.13	sentinel-2b	051	S2B_MSIL2A_20200823T104629_N0214_R051_T30TYN_2...	2020-08-23T10:59:20.275000Z	100.0	0.0	1.24	49.92	33.75	0.05	2.34	1.56	0.00	0.73
23	S2B_30TYN_20200902_0_L2A	2020-09-02	30TYN	91.56	sentinel-2b	051	S2B_MSIL2A_20200902T104629_N0214_R051_T30TYN_2...	2020-09-02T10:59:23.832000Z	100.0	0.0	2.27	48.39	29.37	0.08	2.18	3.04	0.00	0.95
24	S2B_30TYN_20200912_0_L2A	2020-09-12	30TYN	81.66	sentinel-2b	051	S2B_MSIL2A_20200912T104629_N0214_R051_T30TYN_2...	2020-09-12T10:59:18.972000Z	100.0	0.0	7.71	35.06	28.57	0.06	4.27	6.24	0.00	0.12
25	S2A_30TYN_20200927_0_L2A	2020-09-27	30TYN	19.93	sentinel-2a	051	S2A_MSIL2A_20200927T105031_N0214_R051_T30TYN_2...	2020-09-27T10:59:22.256000Z	100.0	0.0	0.43	6.84	0.18	0.39	0.52	0.05	0.00	79.07
26	S2B_30TYN_20201012_0_L2A	2020-10-12	30TYN	26.37	sentinel-2b	051	S2B_MSIL2A_20201012T104909_N0214_R051_T30TYN_2...	2020-10-12T10:59:20.517000Z	100.0	0.0	1.59	6.85	0.70	1.14	0.77	9.61	0.00	61.66
27	S2A_30TYN_20201017_0_L2A	2020-10-17	30TYN	40.44	sentinel-2a	051	S2A_MSIL2A_20201017T105041_N0214_R051_T30TYN_2...	2020-10-17T10:59:22.444000Z	100.0	0.0	2.79	9.11	6.41	0.83	2.38	2.35	0.06	51.98
28	S2B_30TYN_20201101_0_L2A	2020-11-01	30TYN	69.02	sentinel-2b	051	S2B_MSIL2A_20201101T105209_N0214_R051_T30TYN_2...	2020-11-01T10:59:19.525000Z	100.0	0.0	6.70	12.90	22.01	0.84	1.59	2.62	0.86	19.21
29	S2B_30TYN_20201111_0_L2A	2020-11-11	30TYN	75.63	sentinel-2b	051	S2B_MSIL2A_20201111T105259_N0214_R051_T30TYN_2...	2020-11-11T10:59:17.920000Z	100.0	0.0	4.41	14.45	17.83	2.60	0.42	1.38	0.00	18.16
30	S2B_30TYN_20201121_0_L2A	2020-11-21	30TYN	78.80	sentinel-2b	051	S2B_MSIL2A_20201121T105349_N0214_R051_T30TYN_2...	2020-11-21T10:59:17.294000Z	100.0	0.0	4.08	11.19	19.89	3.74	4.11	2.82	0.00	10.19
31	S2A_30TYN_20201216_0_L2A	2020-12-16	30TYN	46.75	sentinel-2a	051	S2A_MSIL2A_20201216T105441_N0214_R051_T30TYN_2...	2020-12-16T10:59:14.504000Z	100.0	0.0	0.40	0.16	0.24	5.98	0.33	0.14	0.51	51.87
32	S2A_30TYN_20201226_0_L2A	2020-12-26	30TYN	52.46	sentinel-2a	051	S2A_MSIL2A_20201226T105451_N0214_R051_T30TYN_2...	2020-12-26T10:59:16.273000Z	100.0	0.0	0.09	0.11	0.47	7.33	2.82	0.57	0.03	44.03

4 - Specify bands

Only selecting blue, green, and red bands from S2

res_S2.output_bands = ["B02","B03","B04"]

5 - Filter search results

While we initially filtered the search results by 10% or less on cloud coverage, we may want to be more selective. We can then set a higher threshold for our monthly images, such as limiting some clouds to 5% or less as shown below.

res_S2.dataframe = res_S2.dataframe[(res_S2.dataframe["cloud_medium_probability"] < 5) & (res_S2.dataframe["cloud_shadows"] < 5)]
res_S2.dataframe

	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
0	S2A_30TYN_20200101_0_L2A	2020-01-01	30TYN	57.44	sentinel-2a	051	S2A_MSIL2A_20200101T105441_N0213_R051_T30TYN_2...	2020-01-01T10:59:11.266000Z	100.0	0.0	0.81	0.85	1.96	7.56	0.14	0.63	0.01	40.97
1	S2B_30TYN_20200106_0_L2A	2020-01-06	30TYN	57.35	sentinel-2b	051	S2B_MSIL2A_20200106T105339_N0213_R051_T30TYN_2...	2020-01-06T10:59:11.633000Z	100.0	0.0	1.06	0.93	2.48	7.28	0.18	0.74	0.00	40.67
2	S2B_30TYN_20200116_0_L2A	2020-01-16	30TYN	56.64	sentinel-2b	051	S2B_MSIL2A_20200116T105309_N0213_R051_T30TYN_2...	2020-01-16T10:59:10.849000Z	100.0	0.0	1.39	1.02	5.01	6.09	0.17	0.98	0.00	40.82
3	S2A_30TYN_20200131_0_L2A	2020-01-31	30TYN	44.26	sentinel-2a	051	S2A_MSIL2A_20200131T105251_N0213_R051_T30TYN_2...	2020-01-31T10:59:09.147000Z	100.0	0.0	0.14	0.35	0.33	4.15	0.09	0.12	0.00	55.39
4	S2B_30TYN_20200205_0_L2A	2020-02-05	30TYN	46.62	sentinel-2b	051	S2B_MSIL2A_20200205T105129_N0214_R051_T30TYN_2...	2020-02-05T10:59:10.605000Z	100.0	0.0	1.02	1.26	2.69	3.76	0.28	0.64	0.00	51.44
5	S2A_30TYN_20200210_0_L2A	2020-02-10	30TYN	46.31	sentinel-2a	051	S2A_MSIL2A_20200210T105201_N0214_R051_T30TYN_2...	2020-02-10T10:59:09.328000Z	100.0	0.0	1.30	1.54	4.71	3.05	0.05	0.53	0.00	51.81
6	S2B_30TYN_20200215_0_L2A	2020-02-15	30TYN	44.75	sentinel-2b	051	S2B_MSIL2A_20200215T105029_N0214_R051_T30TYN_2...	2020-02-15T10:59:12.451000Z	100.0	0.0	1.48	1.72	5.96	2.40	0.28	0.93	0.00	52.56
7	S2A_30TYN_20200220_0_L2A	2020-02-20	30TYN	43.54	sentinel-2a	051	S2A_MSIL2A_20200220T105051_N0214_R051_T30TYN_2...	2020-02-20T10:59:11.040000Z	100.0	0.0	1.58	2.65	7.58	1.83	0.27	1.01	0.00	53.60
8	S2A_30TYN_20200301_0_L2A	2020-03-01	30TYN	0.15	sentinel-2a	051	S2A_MSIL2A_20200301T105021_N0214_R051_T30TYN_2...	2020-03-01T10:59:12.233000Z	100.0	0.0	0.00	0.00	0.14	0.00	4.44	6.32	0.00	89.09
9	S2A_30TYN_20200311_0_L2A	2020-03-11	30TYN	20.12	sentinel-2a	051	S2A_MSIL2A_20200311T105021_N0214_R051_T30TYN_2...	2020-03-11T10:59:12.935000Z	100.0	0.0	0.48	0.91	0.48	0.65	0.16	0.09	0.00	79.15
10	S2A_30TYN_20200321_0_L2A	2020-03-21	30TYN	32.54	sentinel-2a	051	S2A_MSIL2A_20200321T105021_N0214_R051_T30TYN_2...	2020-03-21T10:59:13.094000Z	100.0	0.0	0.68	2.09	13.14	0.35	0.96	0.57	0.00	65.25
11	S2A_30TYN_20200410_0_L2A	2020-04-10	30TYN	24.51	sentinel-2a	051	S2A_MSIL2A_20200410T105031_N0214_R051_T30TYN_2...	2020-04-10T10:59:15.012000Z	100.0	0.0	1.52	6.31	8.57	0.50	1.63	9.43	0.00	62.91
12	S2A_30TYN_20200520_0_L2A	2020-05-20	30TYN	53.59	sentinel-2a	051	S2A_MSIL2A_20200520T105031_N0214_R051_T30TYN_2...	2020-05-20T10:59:22.363000Z	100.0	0.0	0.04	32.70	16.82	0.16	0.82	1.63	0.00	43.92
13	S2A_30TYN_20200530_0_L2A	2020-05-30	30TYN	58.84	sentinel-2a	051	S2A_MSIL2A_20200530T105031_N0214_R051_T30TYN_2...	2020-05-30T10:59:22.967000Z	100.0	0.0	1.30	37.21	16.90	0.43	1.23	4.62	0.00	34.01
14	S2B_30TYN_20200624_0_L2A	2020-06-24	30TYN	68.59	sentinel-2b	051	S2B_MSIL2A_20200624T104629_N0214_R051_T30TYN_2...	2020-06-24T10:59:19.327000Z	100.0	0.0	0.75	46.88	17.58	0.13	1.64	4.30	0.00	24.72
15	S2B_30TYN_20200704_0_L2A	2020-07-04	30TYN	78.01	sentinel-2b	051	S2B_MSIL2A_20200704T104619_N0214_R051_T30TYN_2...	2020-07-04T10:59:18.977000Z	100.0	0.0	0.28	51.04	22.23	0.04	2.51	3.77	0.00	15.43
16	S2A_30TYN_20200709_0_L2A	2020-07-09	30TYN	71.72	sentinel-2a	051	S2A_MSIL2A_20200709T105031_N0214_R051_T30TYN_2...	2020-07-09T10:59:20.829000Z	100.0	0.0	2.88	46.20	19.82	0.20	4.15	9.84	0.00	11.41
17	S2B_30TYN_20200714_0_L2A	2020-07-14	30TYN	80.75	sentinel-2b	051	S2B_MSIL2A_20200714T104619_N0214_R051_T30TYN_2...	2020-07-14T10:59:18.200000Z	100.0	0.0	0.47	51.60	23.56	0.02	4.07	5.65	0.00	9.06
18	S2A_30TYN_20200719_0_L2A	2020-07-19	30TYN	83.64	sentinel-2a	051	S2A_MSIL2A_20200719T105031_N0214_R051_T30TYN_2...	2020-07-19T10:59:21.847000Z	100.0	0.0	0.61	51.86	25.36	0.14	3.41	7.26	0.00	5.08
19	S2A_30TYN_20200729_0_L2A	2020-07-29	30TYN	86.56	sentinel-2a	051	S2A_MSIL2A_20200729T105031_N0214_R051_T30TYN_2...	2020-07-29T10:59:22.508000Z	100.0	0.0	0.80	50.94	28.15	0.02	4.25	5.34	0.00	3.05
20	S2B_30TYN_20200803_0_L2A	2020-08-03	30TYN	88.60	sentinel-2b	051	S2B_MSIL2A_20200803T104629_N0214_R051_T30TYN_2...	2020-08-03T10:59:19.827000Z	100.0	0.0	0.69	49.25	30.00	0.02	4.82	4.16	0.00	1.73
22	S2B_30TYN_20200823_0_L2A	2020-08-23	30TYN	94.13	sentinel-2b	051	S2B_MSIL2A_20200823T104629_N0214_R051_T30TYN_2...	2020-08-23T10:59:20.275000Z	100.0	0.0	1.24	49.92	33.75	0.05	2.34	1.56	0.00	0.73
23	S2B_30TYN_20200902_0_L2A	2020-09-02	30TYN	91.56	sentinel-2b	051	S2B_MSIL2A_20200902T104629_N0214_R051_T30TYN_2...	2020-09-02T10:59:23.832000Z	100.0	0.0	2.27	48.39	29.37	0.08	2.18	3.04	0.00	0.95
25	S2A_30TYN_20200927_0_L2A	2020-09-27	30TYN	19.93	sentinel-2a	051	S2A_MSIL2A_20200927T105031_N0214_R051_T30TYN_2...	2020-09-27T10:59:22.256000Z	100.0	0.0	0.43	6.84	0.18	0.39	0.52	0.05	0.00	79.07
26	S2B_30TYN_20201012_0_L2A	2020-10-12	30TYN	26.37	sentinel-2b	051	S2B_MSIL2A_20201012T104909_N0214_R051_T30TYN_2...	2020-10-12T10:59:20.517000Z	100.0	0.0	1.59	6.85	0.70	1.14	0.77	9.61	0.00	61.66
27	S2A_30TYN_20201017_0_L2A	2020-10-17	30TYN	40.44	sentinel-2a	051	S2A_MSIL2A_20201017T105041_N0214_R051_T30TYN_2...	2020-10-17T10:59:22.444000Z	100.0	0.0	2.79	9.11	6.41	0.83	2.38	2.35	0.06	51.98
29	S2B_30TYN_20201111_0_L2A	2020-11-11	30TYN	75.63	sentinel-2b	051	S2B_MSIL2A_20201111T105259_N0214_R051_T30TYN_2...	2020-11-11T10:59:17.920000Z	100.0	0.0	4.41	14.45	17.83	2.60	0.42	1.38	0.00	18.16
30	S2B_30TYN_20201121_0_L2A	2020-11-21	30TYN	78.80	sentinel-2b	051	S2B_MSIL2A_20201121T105349_N0214_R051_T30TYN_2...	2020-11-21T10:59:17.294000Z	100.0	0.0	4.08	11.19	19.89	3.74	4.11	2.82	0.00	10.19
31	S2A_30TYN_20201216_0_L2A	2020-12-16	30TYN	46.75	sentinel-2a	051	S2A_MSIL2A_20201216T105441_N0214_R051_T30TYN_2...	2020-12-16T10:59:14.504000Z	100.0	0.0	0.40	0.16	0.24	5.98	0.33	0.14	0.51	51.87
32	S2A_30TYN_20201226_0_L2A	2020-12-26	30TYN	52.46	sentinel-2a	051	S2A_MSIL2A_20201226T105451_N0214_R051_T30TYN_2...	2020-12-26T10:59:16.273000Z	100.0	0.0	0.09	0.11	0.47	7.33	2.82	0.57	0.03	44.03

6 - Select one image per month

Here we use Pandas and its “groupby” function to select the first available image for each month. The frequency, “freq”, can be changed to a large number of time frequencies, called offset aliases. Among them are, daily (D), weekly (W), monthly (M), quarterly (Q), twice monthly (SM) and yearly (A or Y). A full list can be found here.

Custom frequencies can be also made and applied using the general pd.Grouper function and/or pd.DateOffset and pd.cut.

# Clone the search result so as to not need to re-run the search if our manipulation doesn't work as intended
new_S2 = res_S2
# Set the Dataframe index as the "date" column, in order to apply the grouper function
new_S2.dataframe.set_index('date', inplace=True)
# Group search result by taking only the first image of the month
new_S2.dataframe = new_S2.dataframe.groupby(pd.Grouper(freq='M')).first()
new_S2.dataframe

	id	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
date
2020-01-31	S2A_30TYN_20200101_0_L2A	30TYN	57.44	sentinel-2a	051	S2A_MSIL2A_20200101T105441_N0213_R051_T30TYN_2...	2020-01-01T10:59:11.266000Z	100.0	0.0	0.81	0.85	1.96	7.56	0.14	0.63	0.01	40.97
2020-02-29	S2B_30TYN_20200205_0_L2A	30TYN	46.62	sentinel-2b	051	S2B_MSIL2A_20200205T105129_N0214_R051_T30TYN_2...	2020-02-05T10:59:10.605000Z	100.0	0.0	1.02	1.26	2.69	3.76	0.28	0.64	0.00	51.44
2020-03-31	S2A_30TYN_20200301_0_L2A	30TYN	0.15	sentinel-2a	051	S2A_MSIL2A_20200301T105021_N0214_R051_T30TYN_2...	2020-03-01T10:59:12.233000Z	100.0	0.0	0.00	0.00	0.14	0.00	4.44	6.32	0.00	89.09
2020-04-30	S2A_30TYN_20200410_0_L2A	30TYN	24.51	sentinel-2a	051	S2A_MSIL2A_20200410T105031_N0214_R051_T30TYN_2...	2020-04-10T10:59:15.012000Z	100.0	0.0	1.52	6.31	8.57	0.50	1.63	9.43	0.00	62.91
2020-05-31	S2A_30TYN_20200520_0_L2A	30TYN	53.59	sentinel-2a	051	S2A_MSIL2A_20200520T105031_N0214_R051_T30TYN_2...	2020-05-20T10:59:22.363000Z	100.0	0.0	0.04	32.70	16.82	0.16	0.82	1.63	0.00	43.92
2020-06-30	S2B_30TYN_20200624_0_L2A	30TYN	68.59	sentinel-2b	051	S2B_MSIL2A_20200624T104629_N0214_R051_T30TYN_2...	2020-06-24T10:59:19.327000Z	100.0	0.0	0.75	46.88	17.58	0.13	1.64	4.30	0.00	24.72
2020-07-31	S2B_30TYN_20200704_0_L2A	30TYN	78.01	sentinel-2b	051	S2B_MSIL2A_20200704T104619_N0214_R051_T30TYN_2...	2020-07-04T10:59:18.977000Z	100.0	0.0	0.28	51.04	22.23	0.04	2.51	3.77	0.00	15.43
2020-08-31	S2B_30TYN_20200803_0_L2A	30TYN	88.60	sentinel-2b	051	S2B_MSIL2A_20200803T104629_N0214_R051_T30TYN_2...	2020-08-03T10:59:19.827000Z	100.0	0.0	0.69	49.25	30.00	0.02	4.82	4.16	0.00	1.73
2020-09-30	S2B_30TYN_20200902_0_L2A	30TYN	91.56	sentinel-2b	051	S2B_MSIL2A_20200902T104629_N0214_R051_T30TYN_2...	2020-09-02T10:59:23.832000Z	100.0	0.0	2.27	48.39	29.37	0.08	2.18	3.04	0.00	0.95
2020-10-31	S2B_30TYN_20201012_0_L2A	30TYN	26.37	sentinel-2b	051	S2B_MSIL2A_20201012T104909_N0214_R051_T30TYN_2...	2020-10-12T10:59:20.517000Z	100.0	0.0	1.59	6.85	0.70	1.14	0.77	9.61	0.00	61.66
2020-11-30	S2B_30TYN_20201111_0_L2A	30TYN	75.63	sentinel-2b	051	S2B_MSIL2A_20201111T105259_N0214_R051_T30TYN_2...	2020-11-11T10:59:17.920000Z	100.0	0.0	4.41	14.45	17.83	2.60	0.42	1.38	0.00	18.16
2020-12-31	S2A_30TYN_20201216_0_L2A	30TYN	46.75	sentinel-2a	051	S2A_MSIL2A_20201216T105441_N0214_R051_T30TYN_2...	2020-12-16T10:59:14.504000Z	100.0	0.0	0.40	0.16	0.24	5.98	0.33	0.14	0.51	51.87

Please note, the value of the “date” column changes to account for the grouper function assigning only one value per month. However, the true value of the Sentinel-2 images are kept, and are viewable and accessible in the final result.

7 - Obtain S2 data through Fuse function

We pass the filtered S2 search result object to the “fuse” function in order to download the organize the S2 result

fusion = client.fuse(
    catalogs_list=[new_S2],
    additional_info={"Info": "You can put any additional information, such as custom non-raster or non-vector data, in this parameter"}
    )
fusion.dataset

2023-06-22 12:50:43,988 INFO spacesense.core : created everything

<xarray.Dataset>
Dimensions:  (time: 12, y: 678, x: 722)
Coordinates:
  * time     (time) datetime64[ns] 2020-01-01 2020-02-05 ... 2020-12-16
  * y        (y) float32 42.74 42.74 42.74 42.74 ... 42.68 42.68 42.68 42.68
  * x        (x) float32 -0.07177 -0.07165 -0.07152 ... 0.019 0.01913 0.01925
Data variables:
    S2_B02   (time, y, x) float32 0.9697 0.9752 0.9456 ... 0.666 0.6728 nan
    S2_B03   (time, y, x) float32 0.9881 1.017 0.9744 1.058 ... 0.6882 0.696 nan
    S2_B04   (time, y, x) float32 0.9375 0.9807 0.9945 ... 0.6836 0.6948 nan
Attributes:
    transform:        [ 1.26249832e-04  0.00000000e+00 -7.18361548e-02  0.000...
    crs:              +init=epsg:4326
    res:              [1.26249832e-04 8.68139450e-05]
    descriptions:     ['B02', 'B03', 'B04']
    AREA_OR_POINT:    Area
    _FillValue:       nan
    s2_data_lineage:  {"Data origin": "S3 bucket (ARN=arn:aws:s3:::sentinel-c...
    ulx, uly:         [-0.07183615 42.73798515]
    additional_info:  {'Info': 'You can put any additional information, such ...

xarray.Dataset

• Dimensions:
  • time: 12
  • y: 678
  • x: 722
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2020-01-01 ... 2020-12-16

    array(['2020-01-01T00:00:00.000000000', '2020-02-05T00:00:00.000000000',
           '2020-03-01T00:00:00.000000000', '2020-04-10T00:00:00.000000000',
           '2020-05-20T00:00:00.000000000', '2020-06-24T00:00:00.000000000',
           '2020-07-04T00:00:00.000000000', '2020-08-03T00:00:00.000000000',
           '2020-09-02T00:00:00.000000000', '2020-10-12T00:00:00.000000000',
           '2020-11-11T00:00:00.000000000', '2020-12-16T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • y
    (y)
    float32
    42.74 42.74 42.74 ... 42.68 42.68

    _FillValue :

    nan

    axis :

    Y

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([42.73794 , 42.737854, 42.737766, ..., 42.67934 , 42.679253, 42.67917 ],
          dtype=float32)

  • x
    (x)
    float32
    -0.07177 -0.07165 ... 0.01925

    _FillValue :

    nan

    axis :

    X

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([-0.071773, -0.071647, -0.071521, ...,  0.019001,  0.019127,  0.019253],
          dtype=float32)

• Data variables: (3)
  • S2_B02
    (time, y, x)
    float32
    0.9697 0.9752 0.9456 ... 0.6728 nan

    _FillValue :

    nan

    transform :

    [ 1.26249832e-04 0.00000000e+00 -7.18361548e-02 0.00000000e+00 -8.68139450e-05 4.27379851e+01]

    crs :

    +init=epsg:4326

    res :

    [1.26249829e-04 8.68148269e-05]

    descriptions :

    AREA_OR_POINT :

    Area

    color_description :

    BLUE

    band_wavelength :

    490 nm

    scene_id :

    ['S2A_30TYN_20200101_0_L2A', 'S2A_30TYN_20200301_0_L2A', 'S2A_30TYN_20200410_0_L2A', 'S2A_30TYN_20200520_0_L2A', 'S2A_30TYN_20201216_0_L2A', 'S2B_30TYN_20200205_0_L2A', 'S2B_30TYN_20200624_0_L2A', 'S2B_30TYN_20200704_0_L2A', 'S2B_30TYN_20200803_0_L2A', 'S2B_30TYN_20200902_0_L2A', 'S2B_30TYN_20201012_0_L2A', 'S2B_30TYN_20201111_0_L2A']

    array([[[0.9697, 0.9752, 0.9456, ..., 0.582 , 0.5596, 0.5508],
            [0.82  , 0.9408, 0.9785, ..., 0.5635, 0.5464, 0.544 ],
            [0.7956, 0.9289, 1.0696, ..., 0.5572, 0.5448, 0.5492],
            ...,
            [1.1184, 0.9967, 0.9304, ..., 0.65  , 0.674 , 0.6784],
            [1.1648, 1.0048, 0.8952, ..., 0.6637, 0.682 , 0.684 ],
            [1.0792, 0.9681, 0.8944, ..., 0.6432, 0.6864,    nan]],
    
           [[0.8817, 0.9232, 0.9592, ..., 0.5732, 0.5692, 0.5445],
            [0.9024, 0.9663, 0.9832, ..., 0.5616, 0.5555, 0.548 ],
            [0.742 , 0.8656, 0.9664, ..., 0.5524, 0.5561, 0.5556],
            ...,
            [0.9039, 0.8248, 0.7624, ..., 0.4871, 0.6343, 0.6555],
            [0.9608, 0.8768, 0.7956, ..., 0.5595, 0.6683, 0.6739],
            [0.9352, 0.8544, 0.7783, ..., 0.6571, 0.6727,    nan]],
    
           [[1.1784, 1.1976, 1.2048, ..., 1.4128, 1.4144, 1.4152],
            [1.1552, 1.1752, 1.1872, ..., 1.4152, 1.4152, 1.4152],
            [1.1376, 1.1536, 1.1776, ..., 1.4152, 1.416 , 1.416 ],
            ...,
    ...
            [0.4308, 0.4544, 0.4296, ..., 0.188 , 0.2008, 0.2202],
            [0.4688, 0.4728, 0.4244, ..., 0.3924, 0.3754, 0.218 ],
            [0.6676, 0.4992, 0.4664, ..., 0.1792, 0.1034,    nan]],
    
           [[0.0561, 0.0556, 0.058 , ..., 0.1548, 0.1312, 0.1604],
            [0.0679, 0.0637, 0.0623, ..., 0.1466, 0.1874, 0.2998],
            [0.0698, 0.0706, 0.0698, ..., 0.2358, 0.2862, 0.3494],
            ...,
            [0.538 , 0.4672, 0.3996, ..., 0.7104, 0.728 , 0.7496],
            [0.4772, 0.4208, 0.3858, ..., 0.7184, 0.7364, 0.7464],
            [0.6624, 0.5292, 0.4732, ..., 0.726 , 0.7436,    nan]],
    
           [[0.9776, 1.0336, 1.068 , ..., 0.7301, 0.6841, 0.66  ],
            [1.0441, 1.0792, 1.0415, ..., 0.7128, 0.6812, 0.6732],
            [0.8624, 1.0256, 1.096 , ..., 0.7051, 0.6827, 0.6764],
            ...,
            [1.0919, 1.0472, 0.9752, ..., 0.6465, 0.6596, 0.6775],
            [1.1008, 0.9504, 0.853 , ..., 0.6549, 0.668 , 0.6718],
            [1.036 , 0.8862, 0.79  , ..., 0.666 , 0.6728,    nan]]],
          dtype=float32)

  • S2_B03
    (time, y, x)
    float32
    0.9881 1.017 0.9744 ... 0.696 nan

    _FillValue :

    nan

    transform :

    [ 1.26249832e-04 0.00000000e+00 -7.18361548e-02 0.00000000e+00 -8.68139450e-05 4.27379851e+01]

    crs :

    +init=epsg:4326

    res :

    [1.26249829e-04 8.68148269e-05]

    descriptions :

    AREA_OR_POINT :

    Area

    color_description :

    GREEN

    band_wavelength :

    560 nm

    scene_id :

    ['S2A_30TYN_20200101_0_L2A', 'S2A_30TYN_20200301_0_L2A', 'S2A_30TYN_20200410_0_L2A', 'S2A_30TYN_20200520_0_L2A', 'S2A_30TYN_20201216_0_L2A', 'S2B_30TYN_20200205_0_L2A', 'S2B_30TYN_20200624_0_L2A', 'S2B_30TYN_20200704_0_L2A', 'S2B_30TYN_20200803_0_L2A', 'S2B_30TYN_20200902_0_L2A', 'S2B_30TYN_20201012_0_L2A', 'S2B_30TYN_20201111_0_L2A']

    array([[[0.9881, 1.0168, 0.9744, ..., 0.4812, 0.4705, 0.4532],
            [0.8304, 0.9728, 1.0129, ..., 0.4656, 0.4584, 0.4559],
            [0.8013, 0.9505, 1.0945, ..., 0.4634, 0.4598, 0.4621],
            ...,
            [1.0751, 0.9606, 0.8993, ..., 0.673 , 0.6944, 0.7016],
            [1.1416, 0.9767, 0.8897, ..., 0.6904, 0.7036, 0.7051],
            [1.0761, 0.944 , 0.8714, ..., 0.68  , 0.7108,    nan]],
    
           [[0.924 , 0.9632, 0.9985, ..., 0.4476, 0.4372, 0.4296],
            [0.9047, 0.9775, 1.0033, ..., 0.4432, 0.4311, 0.4336],
            [0.7659, 0.8982, 0.9952, ..., 0.447 , 0.4396, 0.4405],
            ...,
            [0.8855, 0.8032, 0.7321, ..., 0.4963, 0.6586, 0.6817],
            [0.9432, 0.8545, 0.7813, ..., 0.6047, 0.6835, 0.7016],
            [0.932 , 0.8472, 0.7813, ..., 0.6786, 0.6992,    nan]],
    
           [[1.2064, 1.2152, 1.2272, ..., 1.4504, 1.4504, 1.4504],
            [1.1936, 1.1976, 1.2176, ..., 1.4504, 1.4504, 1.4512],
            [1.1776, 1.1832, 1.2032, ..., 1.4504, 1.4512, 1.4512],
            ...,
    ...
            [0.4355, 0.45  , 0.4303, ..., 0.2222, 0.2728, 0.2364],
            [0.484 , 0.4752, 0.4356, ..., 0.3964, 0.3484, 0.2852],
            [0.6952, 0.5331, 0.4684, ..., 0.2416, 0.1528,    nan]],
    
           [[0.0973, 0.0993, 0.1042, ..., 0.107 , 0.1052, 0.1234],
            [0.1042, 0.104 , 0.1042, ..., 0.1324, 0.1498, 0.221 ],
            [0.106 , 0.1062, 0.1082, ..., 0.1758, 0.2066, 0.2684],
            ...,
            [0.498 , 0.4284, 0.3606, ..., 0.7364, 0.7508, 0.772 ],
            [0.4648, 0.3918, 0.3572, ..., 0.7428, 0.7628, 0.7764],
            [0.6392, 0.5048, 0.4492, ..., 0.7524, 0.7676,    nan]],
    
           [[1.0096, 1.0472, 1.1144, ..., 0.6713, 0.6572, 0.6444],
            [1.0561, 1.0936, 1.1047, ..., 0.6572, 0.6464, 0.6375],
            [0.8856, 1.0449, 1.1336, ..., 0.6557, 0.6408, 0.6369],
            ...,
            [1.0976, 1.0392, 0.9615, ..., 0.6676, 0.6808, 0.6997],
            [1.0784, 0.9192, 0.8391, ..., 0.6822, 0.692 , 0.6976],
            [1.0496, 0.8848, 0.7963, ..., 0.6882, 0.696 ,    nan]]],
          dtype=float32)

  • S2_B04
    (time, y, x)
    float32
    0.9375 0.9807 0.9945 ... 0.6948 nan

    _FillValue :

    nan

    transform :

    [ 1.26249832e-04 0.00000000e+00 -7.18361548e-02 0.00000000e+00 -8.68139450e-05 4.27379851e+01]

    crs :

    +init=epsg:4326

    res :

    [1.26249829e-04 8.68148269e-05]

    descriptions :

    AREA_OR_POINT :

    Area

    color_description :

    RED

    band_wavelength :

    665 nm

    scene_id :

    ['S2A_30TYN_20200101_0_L2A', 'S2A_30TYN_20200301_0_L2A', 'S2A_30TYN_20200410_0_L2A', 'S2A_30TYN_20200520_0_L2A', 'S2A_30TYN_20201216_0_L2A', 'S2B_30TYN_20200205_0_L2A', 'S2B_30TYN_20200624_0_L2A', 'S2B_30TYN_20200704_0_L2A', 'S2B_30TYN_20200803_0_L2A', 'S2B_30TYN_20200902_0_L2A', 'S2B_30TYN_20201012_0_L2A', 'S2B_30TYN_20201111_0_L2A']

    array([[[0.9375, 0.9807, 0.9945, ..., 0.3709, 0.3614, 0.3493],
            [0.8471, 0.984 , 0.9944, ..., 0.3601, 0.3522, 0.348 ],
            [0.784 , 0.9552, 1.1114, ..., 0.358 , 0.3528, 0.3507],
            ...,
            [1.0208, 0.8887, 0.8224, ..., 0.6728, 0.692 , 0.7056],
            [1.0793, 0.9136, 0.8135, ..., 0.6865, 0.7132, 0.7072],
            [1.0216, 0.8839, 0.8232, ..., 0.6851, 0.7148,    nan]],
    
           [[0.9184, 0.9544, 1.0039, ..., 0.3464, 0.3364, 0.3189],
            [0.9144, 0.9455, 1.0056, ..., 0.3392, 0.3273, 0.3168],
            [0.7624, 0.8871, 1.0031, ..., 0.337 , 0.3251, 0.3147],
            ...,
            [0.8456, 0.7595, 0.7028, ..., 0.526 , 0.6475, 0.6704],
            [0.9152, 0.8248, 0.7457, ..., 0.608 , 0.6801, 0.7013],
            [0.9151, 0.8286, 0.7544, ..., 0.6793, 0.6952,    nan]],
    
           [[1.2576, 1.2552, 1.2824, ..., 1.4704, 1.4704, 1.472 ],
            [1.2448, 1.2472, 1.2728, ..., 1.472 , 1.472 , 1.472 ],
            [1.2328, 1.2376, 1.2624, ..., 1.472 , 1.472 , 1.472 ],
            ...,
    ...
            [0.4328, 0.4517, 0.4256, ..., 0.231 , 0.2536, 0.2636],
            [0.4884, 0.4692, 0.4384, ..., 0.394 , 0.373 , 0.2612],
            [0.6752, 0.5352, 0.4812, ..., 0.227 , 0.1676,    nan]],
    
           [[0.104 , 0.1072, 0.109 , ..., 0.0811, 0.0674, 0.0866],
            [0.1078, 0.11  , 0.107 , ..., 0.0832, 0.0867, 0.131 ],
            [0.1082, 0.11  , 0.1118, ..., 0.1238, 0.141 , 0.1776],
            ...,
            [0.4708, 0.3786, 0.333 , ..., 0.7264, 0.7464, 0.7656],
            [0.4252, 0.352 , 0.3408, ..., 0.7368, 0.7572, 0.7724],
            [0.6056, 0.4752, 0.4228, ..., 0.7456, 0.764 ,    nan]],
    
           [[1.0128, 1.0264, 1.0872, ..., 0.6188, 0.5972, 0.5688],
            [1.0592, 1.0912, 1.0896, ..., 0.596 , 0.582 , 0.57  ],
            [0.824 , 1.0096, 1.1136, ..., 0.586 , 0.5812, 0.5756],
            ...,
            [1.0496, 0.9912, 0.9008, ..., 0.6684, 0.686 , 0.7028],
            [1.0744, 0.8928, 0.7872, ..., 0.6808, 0.6932, 0.6968],
            [0.9944, 0.844 , 0.756 , ..., 0.6836, 0.6948,    nan]]],
          dtype=float32)

• Attributes: (9)

  transform :

  [ 1.26249832e-04 0.00000000e+00 -7.18361548e-02 0.00000000e+00 -8.68139450e-05 4.27379851e+01]

  crs :

  +init=epsg:4326

  res :

  [1.26249832e-04 8.68139450e-05]

  descriptions :

  ['B02', 'B03', 'B04']

  AREA_OR_POINT :

  Area

  _FillValue :

  nan

  s2_data_lineage :

  {"Data origin": "S3 bucket (ARN=arn:aws:s3:::sentinel-cogs-inventory)"}

  ulx, uly :

  [-0.07183615 42.73798515]

  additional_info :

  {'Info': 'You can put any additional information, such as custom non-raster or non-vector data, in this parameter'}

8 - Plot using matplotlib

This code sets up and plots each time in the dataset. Because we had high cloud and swath requirements, only 9 of 12 months have valid images

# Define a function to normalize reflectance from S2 bands
rgb = fusion.plot_rgb(all_dates = True, brightness_factor = 3)

2023-06-22 12:50:44,238 WARNING matplotlib.image : Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
2023-06-22 12:50:44,274 WARNING matplotlib.image : Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
2023-06-22 12:50:44,309 WARNING matplotlib.image : Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
2023-06-22 12:50:44,344 WARNING matplotlib.image : Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
2023-06-22 12:50:44,380 WARNING matplotlib.image : Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
2023-06-22 12:50:44,415 WARNING matplotlib.image : Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
2023-06-22 12:50:44,451 WARNING matplotlib.image : Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
2023-06-22 12:50:44,488 WARNING matplotlib.image : Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
2023-06-22 12:50:44,526 WARNING matplotlib.image : Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
2023-06-22 12:50:44,563 WARNING matplotlib.image : Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
2023-06-22 12:50:44,606 WARNING matplotlib.image : Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
2023-06-22 12:50:44,652 WARNING matplotlib.image : Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).