Hello,
I am investigating the Statistical API to pull data for specific bands. The only values I am looking for are mean reflectance values for each of the bands. I have been able to successfully pull data from the API, but the processing units are higher than I expected. Can someone look at our API request and help me optimize it to lower our processing units? See below code for my API request:
from sentinelhub import SentinelHubStatistical, DataCollection, CRS, BBox, bbox_to_dimensions, \
Geometry, SHConfig, parse_time, parse_time_interval, SentinelHubStatisticalDownloadClient
'''
Example Polygon in fields_gpd:
POLYGON ((-98.3142984128739 46.7284272171651, -98.313747 46.727692, -98.3121418637797 46.727692, -98.312192 46.728987, -98.3142984128739 46.7284272171651))
'''
#Sentinelhub API call
evalscript = """
//VERSION=3
function setup() {
return {
input: [{
bands: ["B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B09", "B11", "B12", "CLM", "CLP", "dataMask"],
units: "DN"
}],
output: [
{
id: "bands",
bands: ["B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B09", "B11", "B12"],
sampleType: "UINT16"
},
{
id: "masks",
bands: ["CLM"],
sampleType: "UINT16"
},
{
id: "indices",
bands: ["CLP"],
sampleType: "UINT16"
},
{
id: "dataMask",
bands: 1
}]
}
}
function evaluatePixel(samples) {
// cloud probability normalized to interval [0, 1]
let CLP = samples.CLP / 255.0;
// masking cloudy pixels
let combinedMask = samples.dataMask
if (samples.CLM > 0) {
combinedMask = 0;
}
const f = 5000;
return {
bands: [samples.B01, samples.B02, samples.B03, samples.B04, samples.B05,samples.B06,
samples.B07, samples.B08, samples.B8A, samples.B09, samples.B1972, samples.B12],
masks: [samples.CLM],
indices: [toUINT(CLP, f)],
dataMask: [combinedMask]
};
}
function toUINT(product, constant){
// Clamp the output to [-1, 10] and convert it to a UNIT16
// value that can be converted back to float later.
if (product < -1) {
product = -1;
} else if (product > 10) {
product = 10;
}
return Math.round(product * constant) + constant;
}
"""
ndvi_requests = []
#see example polygon for the geometry values in fields_gpd
for field in fields_gpd.geometry.values:
request = SentinelHubStatistical(
aggregation=SentinelHubStatistical.aggregation(
evalscript=evalscript,
time_interval=('2022-09-01', '2022-09-30'),
aggregation_interval='P1D',
),
input_data = [SentinelHubStatistical.input_data(
DataCollection.SENTINEL2_L2A,
)],
geometry=Geometry(field, crs=CRS.WGS84),
config=config,
)
ndvi_requests.append(request)
download_requests = [band_request.download_list[0] for band_request in ndvi_requests]
client = SentinelHubStatisticalDownloadClient(config=config)
band_stats = client.download(download_requests)
