Comparing NDVI mean with sentinel-hub request builder and the value from statistical API

I used the following modified evalscript to download statistics for NDVI and NDMI for an AOI. here is the evalscript:

ndvi_new_evalscript = """



//VERSION=3



function setup() {

  return {

    input: [{

      bands: ["B04","B05","B08", "B8A","B11", "B12", "CLM", "CLP", "dataMask"],

      units: "DN"

    }],

    output: [

      {

        id: "bands",

        bands: ["B04"],

        sampleType: "UINT16"

      },

      {

        id: "masks",

        bands: ["CLM"],

        sampleType: "UINT16"

      },

      {

        id: "indices",

        bands: ["NDVI", "NDVI_RE1", "NDMI","CLP"],

        sampleType: "UINT16"

      },

      {

        id: "dataMask",

        bands: 1

      }]

  }

}



function evaluatePixel(samples) {

    // Normalised Difference Vegetation Index and variation

    let NDVI = (samples.B08 - samples.B04) / (samples.B08 + samples.B04);

    let NDVI_RE1 = index(samples.B08, samples.B05);

    let NDMI = (samples.B08 - samples.B11) / (samples.B08 + samples.B11);



    // Bare Soil Index

   // let NBSI = index((samples.B11 + samples.B04), (samples.B08 + samples.B02));



    // 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;

    }



    // masking water pixels 

    let waterMask = samples.dataMask

    if (samples.SCL == 6) {

        waterMask = 0;

    }







    const f = 5000;

    return {

        bands: [samples.B04],

        masks: [samples.CLM],

        indices: [toUINT(NDVI, f), toUINT(NDVI_RE1, f), toUINT(CLP, f), toUINT(NDMI, f) ],

        dataMask: [combinedMask*waterMask]

    };

}



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;

}

"""

The python call was like this:

aggregation = SentinelHubStatistical.aggregation(

    evalscript=ndvi_new_evalscript, time_interval=yearly_time_interval, aggregation_interval="P1D", resolution=(10, 10)

)



#calculations = {"default": {"statistics": {"default": {"percentiles": {"k": [5, 50, 95]}}}}}

histogram_calculations = {"ndvi": {"histograms": {"default": {"nBins": 20, "lowEdge": -1.0, "highEdge": 1.0}}}}



features_requests = []

for geo_shape in AOI_polygon.geometry.values:

    request = SentinelHubStatistical(

        aggregation=aggregation,

        input_data=[SentinelHubStatistical.input_data(DataCollection.SENTINEL2_L2A)],

        geometry=Geometry(geo_shape, crs=CRS(AOI_polygon.crs)),

        calculations=histogram_calculations,

        config=config

    )



    features_requests.append(request)

I got an negative NDVI value and I wanted to check if it is the same Sentinel-2 image that is returned with just getting the least cloud cover image from that particular date or interval (2018-6-29 to 2018-6-30). Looks like the image from 2018-6-29 to 2018-6-30 returns a median 0.3 NDMI value as opposed to -1 that was returned by the statistical API.

It can also be that I am not using the exact evalscript and the masks that were used in the statistical API. Do you know if I can do a one-to-one comparison as further checks.

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Hi,

To use the image acquisition with the least cloud cover in your time interval you will need to explicitly define this as an argument in your Statistical API request; this will be part of the input_data section of your request payload. By default the API will obtain the most recent image, so this could be the reason why you are returning different NDMI values.

input_data=[
        SentinelHubStatistical.input_data(
            DataCollection.SENTINEL2_L2A,            
            other_args={"dataFilter": {"mosaickingOrder": "leastCC"}},            
      ),

If I have misinterpreted your question, then let us know

Thanks for the answers. Is there a way that I can also return RGB images for which the mean NDVI was calculated from the statistical API

Hi, you can do this using a separate Process API request. Statistical API doesn’t return any pixels, only the underlying statistics.

Yes, I am wondering how I can adapt the same script to produce images since it has data masks and cloud masks.

As I said, you need to use Process API to produce images; your current script uses Statistical API so you cannot produce images.

If you are looking for a True Color evalscript that uses a cloud mask, then I can point you to this example.

//VERSION=3

function setup() {

  return {

    input: ["B02", "B03", "B04", "CLM"],

    output: { bands: 3 }

  }

}



function evaluatePixel(sample) {

  if (sample.CLM == 1) {

    return [0.75 + sample.B04, sample.B03, sample.B02]

  }

  return [3.5*sample.B04, 3.5*sample.B03, 3.5*sample.B02];

}