Is it a limit problem

Apps / Services
1

I tried many times to run the attached code in the Statistic Requests Builder Requests Builder but I always got an error for a time longer than a week. It works for a short time interval. Is it a limit problem? How could I solve this issue? I contacted the company by email and they said I should post my request here. The issue is that I need all the time series from the beginning of Sentinel2 for my research about water quality. I can’t imagine doing this task in 20 steps instead of one step. I still have to repeat this task in the other three study locations which mean >90 steps. help!

Converting one .json file to .csv file format will take a long time to figure it out correctly, I don’t want to deal with 20 files instead of one series. Is there a possibility to have the same .csv output format as the Feature Info Service (FIS).

Could you please help me to run the attached code and obtain the .json file?

Next is the code in sh-py + attached error screenshot
Greetings

error
error1863×915 189 KB 

from sentinelhub import SHConfig, SentinelHubStatistical, BBox, Geometry, DataCollection, CRS

# Credentials
config = SHConfig()
config.sh_client_id = '<your client id here>'
config.sh_client_secret = '<your client secret here>'

evalscript = """
//VERSION=3
function setup() {
  return {
    input: [{
      bands: ["B01","B02","B03","B04","B05","B06","B07","B08","B8A","B09","B11","B12","CLP","CLM","SCL","dataMask"]
    }],
    output: [
      {
        id: "bands",
        bands: ["B01","B02","B03","B04","B05","B06","B07","B08","B8A","B09","B11","B12","CLP","CLM"]
      },
      {
        id: "scl",
        sampleType: "UINT8",
        bands: ["scl"]
      },
      {
        id: "dataMask",
        bands: 1
      }]
  };
}
 
function evaluatePixel(sample) {
    return {
        bands: [sample.B01, sample.B02, sample.B03, sample.B04, sample.B05,sample.B06,sample.B07, sample.B08, sample.B8A, sample.B09, sample.B11, sample.B12, sample.CLP, sample.CLM],
        dataMask: [sample.dataMask],
        scl: [sample.SCL]
    };
}
 

"""

calculations = {
    "default": {
        "histograms": {
            "scl": {
                "bins": [
                    0,
                    1,
                    2,
                    3,
                    4,
                    5,
                    6,
                    7,
                    8,
                    9,
                    10,
                    11,
                    12
                ]
            }
        },
        "statistics": {
            "default": {
                "percentiles": {
                    "k": [
                        10,
                        50,
                        90
                    ],
                    "interpolation": "higher"
                }
            }
        }
    }
}

bbox = BBox(bbox=[6.089312, 51.855536, 6.0909, 51.856888], crs=CRS.WGS84)
geometry = Geometry(geometry={"type":"Polygon","coordinates":[[[6.090825,51.855536],[6.0909,51.856039],[6.090814,51.856477],[6.090385,51.856888],[6.089677,51.856782],[6.089312,51.856245],[6.090825,51.855536]]]}, crs=CRS.WGS84)

request = SentinelHubStatistical(
    aggregation=SentinelHubStatistical.aggregation(
        evalscript=evalscript,
        time_interval=('2016-11-04T00:00:00Z', '2022-05-06T23:59:59Z'),
        aggregation_interval='P1D',
        size=[512, 705.759],        
    ),
    input_data=[
        SentinelHubStatistical.input_data(
            DataCollection.SENTINEL2_L2A,                        
      ),
    ],
    bbox=bbox,
    geometry=geometry,
    calculations=calculations,
    config=config
)

response = request.get_data()
2

Hi Max,

For such a long time interval, it is most likely that you are hitting the processing units per minute limit for your subscription plan. In this case, subsequent requests are sent before sufficient PUs are available for the next request.

Please check your dashboard for the rate limits.

Trial plans have a lower PU limit per minute, so one alternative is to request shorter time interval, try one year

if you are familiar with python, even better because you can run a request for all the three geometries in one loop, see this example notebook, there is a helper function to convert the statistics to a dataframe which you can then write to .csv

The other alternative is,

  • In your python script , you can try adding a wait time of a few second before making the next request , thus reducing chances of hitting the limit

I hope that helps