# Assistance Needed for Year-long Sentinel-2 L1C Imagery Downloads for China Region

Hello everyone,

I am currently working on a project that requires a year’s worth of Sentinel-2 L1C multi-band imagery, along with cloud masks and cloud properties, specifically for the China region. However, I am encountering some challenges with my download code. At times, it results in downloading large amounts of blank areas, wasting valuable resources. Additionally, I am unsure if the areas I am downloading are correct.

I have attached my data download and processing code below. I would greatly appreciate any guidance or suggestions to improve the accuracy and efficiency of my downloads.

Thank you very much for your help!

# Define your large GeoJSON area
geojson_coords = [[[69.7803771496, 9.8819587269], [134.5557677746, 9.8819587269], [134.5557677746, 54.673645043], [69.7803771496, 54.673645043], [69.7803771496, 9.8819587269]]]
# Create a Polygon object
polygon = shapely.geometry.Polygon(geojson_coords[0])
# Set the WGS84 coordinate reference system
wgs84 = pyproj.CRS("EPSG:4326")
# Get the UTM zone for the first coordinate
utm_coords = utm.from_latlon(geojson_coords[0][0][1], geojson_coords[0][0][0])
utm_zone = utm_coords[2]
utm_crs = pyproj.CRS(f"EPSG:326{utm_zone}")
# Define the transformer
project = pyproj.Transformer.from_crs(wgs84, utm_crs, always_xy=True).transform
# Transform the coordinates
utm_polygon = shapely.ops.transform(project, polygon)
# Get the bounding box
minx, miny, maxx, maxy = utm_polygon.bounds
# Define the grid size for splitting the large area, e.g., 10km x 10km
grid_size = 10000
# Create an empty list to store all BBox objects
bbox_list = []
# Split the large area into smaller areas based on the grid
x_coords = list(range(int(minx), int(maxx), grid_size))
y_coords = list(range(int(miny), int(maxy), grid_size))

for x in x_coords:
for y in y_coords:
# Create a bounding box for each sub-area
sub_bbox = BBox(((x, y), (x + grid_size, y + grid_size)), crs=CRS(f"326{utm_zone}"))
bbox_list.append(sub_bbox)

# Now you can iterate over bbox_list and perform the same processing for each BBox object
for idx, bbox in enumerate(bbox_list):
print(f"Processing BBox {idx + 1}/{len(bbox_list)}: {bbox}")

data_collection = DataCollection.SENTINEL2_L1C
evalscript = generate_evalscript(
data_collection=data_collection,
meta_bands=["dataMask"],
merged_bands_output="bands",
prioritize_dn=False,
)

request = SentinelHubRequest(
evalscript=evalscript,
input_data=[SentinelHubRequest.input_data(data_collection=data_collection, time_interval="2020-01-01")],
responses=[
SentinelHubRequest.output_response("bands", MimeType.TIFF),
SentinelHubRequest.output_response("dataMask", MimeType.TIFF),
],
bbox=bbox,
resolution=(10, 10),
config=config,
)

try:
data = request.get_data()[0]

bands = data["bands.tif"]
mask = data["dataMask.tif"]

print("Bands shape:", bands.shape)
print("Mask shape:", mask.shape)

# Initialize the cloud detector and perform cloud detection
cloud_detector = S2PixelCloudDetector(threshold=0.4, average_over=4, dilation_size=2, all_bands=True)
cloud_prob = cloud_detector.get_cloud_probability_maps(bands[np.newaxis, ...])[0]
cloud_mask = cloud_detector.get_cloud_masks(bands[np.newaxis, ...])[0]

# Check if cloud_prob is all zero
if np.all(cloud_prob == 0):
print(f"Skipping BBox {idx + 1} due to no clouds detected.")
continue

# Get the timestamp for file naming
timestamp = dt.datetime.strptime("2020-01-01", "%Y-%m-%d").strftime("%Y%m%d")
bands_file_base = f"E:/s2cloud/data/sentinel2_bands_{timestamp}_bbox_{idx + 1}"
cloud_file_base = f"E:/s2cloud/data/sentinel2_cloud_{timestamp}_bbox_{idx + 1}"

# Save bands data as npy file
np.save(f"{bands_file_base}.npy", bands)

# Save cloud_mask and cloud_prob data as npy file
cloud_data = np.stack((cloud_mask, cloud_prob), axis=-1)
np.save(f"{cloud_file_base}.npy", cloud_data)

# Extract geospatial information
crs = bbox.crs.value
transform = from_bounds(*bbox, width=bands.shape[1], height=bands.shape[0]).to_gdal()

# Save metadata to JSON file
metadata = {
"bbox": list(bbox),
"crs": str(crs),  # Save EPSG code as string
"transform": transform  # Save transform information as list
}

with open(f"{bands_file_base}.json", "w") as f:
json.dump(metadata, f)

except requests.exceptions.RequestException as e:
print(f"Skipping BBox {idx + 1} due to request error: {e}")
continue

except Exception as e:
print(f"Skipping BBox {idx + 1} due to an unexpected error: {e}")
continue

Hi,

I am not entirely sure what all of your code is doing, however, I can point you to some resources that will help you to optimise your workflow:

• You can filter out the most cloud acquisitions from your requests: Sentinel-2 L1C. This could be done in the evalscript or could be part of the request body itself.
• If you are trying to access large amounts of data over long time periods, I also recommend looking into Batch Processing API, designed for this purpose.

Thank you so much for your response!
I appreciate the tips on filtering out the most cloud acquisitions using Sentinel-2 L1C and the suggestion to check out the Batch Processing API for large data requests. This is really helpful, and I’ll definitely look into both options.
Thanks again for your assistance!