Search Results
Contents
Search Results#
Sentinel-1 Search Result#
- class models.Sentinel1SearchResult[source]#
Create an instance of the Sentinel-1 Search Result class
models.Sentinel1SearchResult
- aoi#
A GeoJSON polygon passed from the
client.Client.s1_search
- data_coverage#
0 to 100 measure of the tile level data coverage for the result
- scenes#
S1 scenes found matching the
client.Client.s1_search
- ok#
Status output of the search, True indicates a valid return of the seach result
- reason#
Description of the ‘ok’ status
- dataframe#
The search result returned as a dataframe containing various properties (described below)
- output_bands#
List of strings specifying the names of output bands from the following list of Sentinel-1 bands.
By default, all bands are selected and fused. If overwriting the default bands, the fused result will keep the order specified in the “output_bands” parameter.
VV
VH
LIA
MASK
To learn more about SAR polarization (VV and VH), LIA, and the processing steps we apply, please review our SAR basics page.
The following Sentinel-1 properties are available in the S1 search result, and are identical to those found from the Copernicus Open Access Hub Search:
title
date
relativeorbitnumber
lastrelativeorbitnumber
producttype
sensoroperationalmode
acquisitiontype
polarisationmode
beginposition
platformname
missiondatatakeid
orbitdirection
orbitnumber
instrumentname
lastorbitnumber
endposition
ingestiondate
slicenumber
platformidentifier
- static concat(search_results)[source]#
Method to combine/concatenate multiple search results into a single
models.Sentinel1SearchResult
.Example
>>> from spacesense import Client, Sentinel1SearchResult >>> client = Client() >>> res_S1_1 = client.s1_search(aoi, start_date, end_date) >>> res_S1_2 = client.s1_search(aoi, start_date_2, end_date_2) >>> res_S1 = Sentinel1SearchResult.concat([res_S1_1, res_S1_2])
Sentinel-2 Search Result#
- class models.Sentinel2SearchResult[source]#
Create an instance of the Sentinel-2 Search Result class
models.Sentinel2SearchResult
.- aoi#
A GeoJSON polygon passed from the
client.Client.s2_search
- item_collection#
S2 scenes found matching the
client.Client.s2_search
- ok#
Status output of the search, True indicates a valid return of the seach result
- reason#
Description of the ‘ok’ status
- dataframe#
The search result returned as a dataframe containing various properties (described below)
- output_bands#
List of strings specifying the names of output bands from the following list of Sentinel-2 bands.
By default, only B02, B03, B04, B08, and the SCL bands are selected and fused. If overwriting the default bands, the fused result will keep the order specified in the “output_bands” parameter.
B01
B02
B03
B04
B05
B06
B07
B08
B8A
B09
B11
B12
SCL
- B02, B03, B04 and B08 are also available with their color names:
BLUE
GREEN
RED
NIR
Please note, Band 10 is not available for level 2A Sentinel-2 data, as this band is used for atmospheric corrections only.
We also provide several pre-computed vegetation indices (VIs) from Sentinel-2 data. For details about these VIs, please see this page. The following VIs can be selected and fused along with the previously enumerated bands:
NDVI
LAI
NDRE
CHI
NDWI
EVI
The Sentinel-2 data retrieved is L2A, meaning it represents the bottom of the atmosphere (BOA) reflectance values. This link describes Sentinel-2 data levels and products.
To learn about the Sentinel-2 data levels, bands, and scene classifications, to help you select the right data for your use case, please visit our Optial Basics page
The following Sentinel-2 properties are available in the S2 search result:
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*
swath_coverage_percentage is simply the percentage of data covered by the Sentinel-2 swath at the AOI level.
valid_pixel_percentage is defined as a percentage of the combination of pixels, at the AOI level, NOT classified as no_data, cloud_shadows, cloud_medium_probability, cloud_high_probability, and snow. This is a very useful property to use when determining if a Sentinel-2 scene clear of clouds and snow for vegetation and infrastructure monitoring.
“*” denotes that the property is a direct calculation of the percent coverage of the associated SCL bands over the AOI.
- static concat(search_results)[source]#
Method to combine/concatenate multiple search results into a single
models.Sentinel2SearchResult
.Example
>>> from spacesense import Client, Sentinel2SearchResult >>> client = Client() >>> res_S2_1 = client.s1_search(aoi, start_date, end_date) >>> res_S2_2 = client.s1_search(aoi, start_date_2, end_date_2) >>> res_S2 = Sentinel2SearchResult.concat([res_S2_1, res_S2_2])
Landsat Search Result#
- class models.LandsatSearchResult[source]#
Create an instance of the Landsat Search Result class
models.LandsatSearchResult
.- aoi#
A GeoJSON polygon passed from the
client.Client.landsat_search
- item_collection#
Landsat scenes found matching the
client.Client.landsat_search
- ok#
Status output of the search, True indicates a valid return of the seach result
- reason#
Description of the ‘ok’ status
- dataframe#
The search result returned as a dataframe containing various properties (described below)
- output_bands#
List of strings specifying the names of output bands from the following list of Landsat bands.
By default, only RED GREEN BLUE and NIR08 bands are selected and fused. If overwriting the default bands, the fused result will keep the order specified in the “output_bands” parameter.
coastal
blue
green
red
nir08
swir16
swir22
qa_aerosol
qa_pixel
qa_radsat
The Landsat data retrieved is c2l2-sr
The following Sentinel-2 properties are available in the S2 search result:
id
platform
tile
date
datetime
wrs_path
wrs_row
wrs_type
scene_id
sun_azimuth
sun_elevation
instruments
off_nadir
collection_category
collection_number
correction
card4l:specification
card4l:specification_version
fill*
dilated*
cirrus*
cloud*
shadow*
snow*
clear*
water*
swath_coverage_percentage*
valid_pixel_percentage*
swath_coverage_percentage is simply the percentage of data covered by the Landsat swath at the AOI level.
valid_pixel_percentage is defined as a percentage of the combination of pixels, at the AOI level, NOT classified as no_data, cloud_shadows, cloud_medium_probability, cloud_high_probability, and snow. This is a very useful property to use when determining if a Landsat scene clear of clouds and snow for vegetation and infrastructure monitoring.
“*” denotes that the property is a direct calculation of the percent coverage of the associated QA_PIXEL bands over the AOI.
- static concat(search_results)[source]#
Method to combine/concatenate multiple search results into a single
models.LandsatSearchResult
.Example
>>> from spacesense import Client, LandsatSearchResult >>> client = Client() >>> res_LS_1 = client.landsat_search(aoi, start_date, end_date) >>> res_LS_2 = client.landsat_search(aoi, start_date_2, end_date_2) >>> res_LS = LandsatSearchResult.concat([res_LS_1, res_LS_2])
- filter_duplicate_dates()[source]#
Finds any duplicate dates with the Landsat search result and drops (filters) the duplicate item with the lowest valid_pixel_percentage (if different). If valid_pixel_percentage is the same in both results, the item that comes second in the dataframe index is dropped.
ERA5 Weather Search Results#
- class models.WeatherSearchResult[source]#
Create an instance of the Weather Search Result class
models.WeatherSearchResult
- aoi#
A GeoJSON polygon passed from the
client.Client.weather_search
- ok#
Status output of the search, True indicates a valid return of the seach result
- reason#
Description of the ‘ok’ status
- dataframe#
The search result returned as a dataframe containing various properties (described below)
By default, all bands are selected and fused. If overwriting the default bands, the fused result will keep the order specified in the “variables” parameter.
MAXTEMP
MINTEMP
AVGTEMP
PREC
VWIND
UWIND
LAILOW
LAIHIGH
DEWTEMP
To learn more the weather variables please visit ERA5 Copernicus Climate Data Store.