Hi @sabatabesh ,

I’d suggest using Statistical API and create a histogram with arbitrary bins.

An example would be the following:

curl -X POST https://services.sentinel-hub.com/api/v1/statistics \

 -H 'Authorization: Bearer ' \

 -H 'Accept: application/json' \

 -H 'Content-Type: application/json' \

 -d '{

  "input": {

    "bounds": {

      "bbox": :

        4526313.815668,

        2089673.283266,

        4530446.599833,

        2092523.566946

      ],

      "properties": {

        "crs": "http://www.opengis.net/def/crs/EPSG/0/3035"

      }

    },

    "data": :

      {

        "dataFilter": {},

        "type": "sentinel-2-l2a"

      }

    ]

  },

  "aggregation": {

    "timeRange": {

      "from": "2023-05-01T00:00:00Z",

      "to": "2023-05-30T23:59:59Z"

    },

    "aggregationInterval": {

      "of": "P1D"

    },

    "resx": "10",

    "resy": "10",

    "evalscript": "//VERSION=3\nfunction setup() {\n  return {\n    input: :{\n      bands: :\n        \"B04\",\n        \"B08\",\n        \"dataMask\"\n      ]\n    }],\n    output: :\n      {\n        id: \"ndvi\",\n        bands: 1\n      },\n      {\n        id: \"dataMask\",\n        bands: 1\n      }]\n  };\n}\n\nfunction evaluatePixel(samples) {\n    let index = (samples.B08 - samples.B04) / (samples.B08+samples.B04);\n    return {\n        ndvi: :index],\n        dataMask: :samples.dataMask],\n    };\n}\n"

  },

  "calculations": {

    "ndvi": {

      "histograms": {

        "default": {

          "bins": :

            "-1.0",

            "0.3",

            "1.0"

          ]

        }

      }

    }

  }

}'

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