Hi,
I’ve just started trialling sentinel hub, with the objective of being able to download a time-series of imager for a specific AOI, less than 10km2.
Ideally, it would be best to store the extracted data locally, with an appropriate filename, with the sensor name and acquisition date in the GoeTiff filename.
However, when I use the SentinelHubCatalog API and the standard SentinelHubRequest API together, I am only able to pull imagery down with the request format (folder with random request ID, and then the standard ‘request.json’ and ‘response.tif’ files).
Is there anyway to rename the file so that it can be identified, otherwise you have to use the request.json each time?
Alternatively, am I using the wrong tool? Is there a way of capturing a time-series for a small AOI?
Below is my current script, in which I search using Catalog, and then use Request to collect 3 images (I have added a download folder path extracting the date from the timestamp):
import python libraries
import os
import datetime as dt
import numpy as np
import matplotlib.pyplot as plt
#import Sentinel Hub specific libraries
from sentinelhub import SHConfig
from sentinelhub import SHConfig, BBox, CRS, DataCollection
from sentinelhub import SentinelHubCatalog
from sentinelhub import SentinelHubRequest, filter_times, bbox_to_dimensions, MimeType, SentinelHubDownloadClient
config = SHConfig()
Print the config if you wish to see the login authentification configuration (“instance_id”, “sh_client_id”, and “sh_client_secret”)
#print(config)
the below flags if the client details (login details) are missing
if not config.sh_client_id or not config.sh_client_secret:
print(“Warning! To use Process API, please provide the credentials (OAuth client ID and client secret).”)
catalog = SentinelHubCatalog(config=config)
#print(catalog.get_info())
collections = catalog.get_collections()
collections = [collection for collection in collections if not collection[“id”].startswith((“byoc”, “batch”))]
#print('collections = ', collections)
caspian_sea_bbox = BBox([49.9604, 44.7176, 51.0481, 45.2324], crs=CRS.WGS84)
time_interval = “2021-12-10”, “2022-02-01”
search_iterator = catalog.search(
DataCollection.SENTINEL2_L2A,
bbox=caspian_sea_bbox,
time=time_interval,
query={“eo:cloud_cover”: {“lt”: 5}},
fields={“include”: [“id”, “properties.datetime”, “properties.eo:cloud_cover”], “exclude”: []},
)
results = list(search_iterator)
print(“Total number of results:”, len(results))
print(results)
time_difference = dt.timedelta(hours=1)
all_timestamps = search_iterator.get_timestamps()
unique_acquisitions = filter_times(all_timestamps, time_difference)
print(unique_acquisitions)
false_color_evalscript = “”"
//VERSION=3
function setup() {
return {
input: [{
bands: [“B01”,“B02”,“B03”,“B04”,“B05”,“B06”,“B07”,“B08”,“B8A”,“B09”,“B11”,“B12”],
units: “DN”
}],
output: {
bands: 13,
sampleType: “INT16”
}
};
}
function evaluatePixel(sample) {
return [sample.B01,
sample.B02,
sample.B03,
sample.B04,
sample.B05,
sample.B06,
sample.B07,
sample.B08,
sample.B8A,
sample.B09,
sample.B11,
sample.B12];
}
“”"
process_requests = []
for timestamp in unique_acquisitions:
print(‘timestamp being pulled = ‘, timestamp)
str_timestamp = timestamp.strftime(’%m%d%Y’)
print(‘str_timestamp = ‘, str_timestamp)
request_time_interval = SentinelHubRequest(
data_folder=“C:\Users\(------)\Desktop\Test_sentinelhub\”+timestamp.strftime(’%m%d%Y’)+’//’,
evalscript=false_color_evalscript,
input_data=[
SentinelHubRequest.input_data(
data_collection=DataCollection.SENTINEL2_L2A,
time_interval=(timestamp - time_difference, timestamp + time_difference),
)
],
responses=[SentinelHubRequest.output_response(“default”, MimeType.TIFF)],
bbox=caspian_sea_bbox,
size=bbox_to_dimensions(caspian_sea_bbox, 100),
config=config,
)
process_requests.append(request_time_interval)
for request_time_interval in process_requests:
print(request_time_interval)
request_time_interval.get_data(save_data=True)[0]