Examples of Batch Processing Workflow

The requests below are written in Python. To execute them you need to create an OAuth client as is explained here. It is named oauth in these examples.

A Postman collection with examples can be downloaded here.

Create a batch processing request

Option 1: GeoTiff format output

This request defines which data is requested and how it will be processed. In this particular example we will calculate maximum NDVI over two months period for an area in Corsica and visualize the results using a built-in visualizer. The resulting image will in a Geotiff format. To create a batch processing request replace <MyBucket> with the name of your S3 bucket and run:

url = "https://services.sentinel-hub.com/api/v1/batch/process"

evalscript = """
    //VERSION=3
    function setup() {
        return {
            input: [{
                bands: ["B04", "B08"]
            }],
            output: [{
                id: "default",
                bands: 3
            }],
            mosaicking: Mosaicking.ORBIT
        }
    }

    function calcNDVI(sample) {
        var denom = sample.B04 + sample.B08
        return ((denom != 0) ? (sample.B08 - sample.B04) / denom : 0.0)
    }

    const maxNDVIcolors = [
        [-0.2, 0xbfbfbf],
        [0, 0xebebeb],
        [0.1, 0xc8c682],
        [0.2, 0x91bf52],
        [0.4, 0x4f8a2e],
        [0.6, 0x0f540c]
    ]

    const visualizer = new ColorRampVisualizer(maxNDVIcolors);

    function evaluatePixel(samples) {
        var max = 0
        for (var i = 0; i < samples.length; i++) {
            var ndvi = calcNDVI(samples[i])
            max = ndvi > max ? ndvi : max
        }
        ndvi = max
        return visualizer.process(ndvi)
    }
"""

payload = {
    "processRequest": {
        "input": {
            "bounds": {
                "bbox": [
                    8.44,
                    41.31,
                    9.66,
                    43.1
                ],
                "properties": {
                    "crs": "http://www.opengis.net/def/crs/OGC/1.3/CRS84"
                }
            },
            "data": [{
                "dataFilter": {
                    "timeRange": {
                        "from": "2019-04-01T00:00:00Z",
                        "to": "2019-06-30T00:00:00Z"
                    },
                "maxCloudCoverage": 70.0
                },
                "type": "sentinel-2-l2a"
            }]
        },
        "output": {
            "responses": [{
                "identifier": "default",
                "format": {
                    "type": "image/tiff"
                }
            }]
        },
        "evalscript": evalscript
    },
    "tilingGrid": {
        "id": 0,
        "resolution": 60.0
    },
    "bucketName": "<MyBucket>",

    "description": "Max NDVI over Corsica"
}

headers = {
  'Content-Type': 'application/json'
}

response = oauth.request("POST", url, headers=headers, json = payload)

response.json()

Extracting the batch request id from the response:

batch_request_id = response.json()['id']

Option 2: Zarr format output

In this example we will calculate maximum NDVI over two months period for an area in Corsica. Besides maximum NDVI, we will also return values of bands B04 and B08, which were used to calculate maximum NDVI. All three results will be stored as arrays of an output Zarr file. To create a batch processing request replace <MyBucket> with the name of your S3 bucket and run:

url = "https://services.sentinel-hub.com/api/v1/batch/process"

evalscript = """
    //VERSION=3
    function setup() {
        return {
            input: [{
                bands: ["B04", "B08"]
            }],
            output: [{
                id: "maxNDVI",
                sampleType: "FLOAT32",
                bands: 1
            },
            {
                id: "band04",
                sampleType: "UINT16",
                bands: 1
            },
            {
                id: "band08",
                sampleType: "UINT16",
                bands: 1
            }],
            mosaicking: Mosaicking.ORBIT
        }
    }

    function calcNDVI(sample) {
        var denom = sample.B04 + sample.B08
        return ((denom != 0) ? (sample.B08 - sample.B04) / denom : 0.0)
    }

    function evaluatePixel(samples) {
        var maxNDVI = 0
        var band04 = 0
        var band08 = 0
        for (var i = 0; i < samples.length; i++) {
            var ndvi = calcNDVI(samples[i])
            if (ndvi > maxNDVI){
                maxNDVI = ndvi
                band04 = samples[i].B04
                band08 = samples[i].B08
            }
        }

        return {
            maxNDVI: [maxNDVI],
            band04: [band04],
            band08: [band08]
        }
    }
"""

payload = {
    "processRequest": {
        "input": {
            "bounds": {
                "bbox": [
                    8.44,
                    41.31,
                    9.66,
                    43.1
                ],
                "properties": {
                    "crs": "http://www.opengis.net/def/crs/OGC/1.3/CRS84"
                }
            },
            "data": [{
                "dataFilter": {
                    "timeRange": {
                        "from": "2019-04-01T00:00:00Z",
                        "to": "2019-06-30T00:00:00Z"
                    },
                "maxCloudCoverage": 70.0
                },
                "type": "sentinel-2-l2a"
            }]
        },
        "output": {
            "responses": [{
                "identifier": "band08",
                "format": {
                    "type": "zarr/array"
                }
            },
                {
                "identifier": "band04",
                "format": {
                    "type": "zarr/array"
                }
            },
                {
                "identifier": "maxNDVI",
                "format": {
                    "type": "zarr/array"
                }
            }]
        },
        "evalscript": evalscript
    },
    "tilingGrid": {
        "id": 6,
        "resolution": 100.0
    },
    "zarrOutput": {
        "path": "<MyBucket>/<requestId>",
        "group": {
            "zarr_format": 2
        },
        "arrayParameters": {
            "dtype": "<u2",
            "order": "C",
            "chunks": [1, 1000, 1000],
            "fill_value": 0
        },
        "arrayOverrides": {
            "maxNDVI": {
                "dtype": "<f4",
                "fill_value": "NaN"
            },
        }
    },
    "description": "Max NDVI over Corsica with Zarr format output"
}

headers = {
  'Content-Type': 'application/json'
}

response = oauth.request("POST", url, headers=headers, json = payload)

response.json()

Extracting the batch request id from the response:

batch_request_id = response.json()['id']

Get information about all your batch processing requests

url = f"https://services.sentinel-hub.com/api/v1/batch/process"

response = oauth.request("GET", url)

response.json()

Get information about a batch processing request

url = f"https://services.sentinel-hub.com/api/v1/batch/process/{batch_request_id}"

response = oauth.request("GET", url)

response.json()

Get current status of a batch processing request

url = f"https://services.sentinel-hub.com/api/v1/batch/process/{batch_request_id}"

response = oauth.request("GET", url)

response.json()['status']

Request detailed analysis (ANALYSE)

url = f"https://services.sentinel-hub.com/api/v1/batch/process/{batch_request_id}/analyse"

response = oauth.request("POST", url)

response.status_code

Get tiles for a batch processing request (optional)

url = f"https://services.sentinel-hub.com/api/v1/batch/process/{batch_request_id}/tiles"

response = oauth.request("GET", url)

response.json()

Request the start of processing (START)

url = f"https://services.sentinel-hub.com/api/v1/batch/process/{batch_request_id}/start"

response = oauth.request("POST", url)

response.status_code

Get the latest user's action for a batch processing request

url = f"https://services.sentinel-hub.com/api/v1/batch/process/{batch_request_id}"

response = oauth.request("GET", url)

response.json()['userAction']

Cancel a batch processing request (CANCEL)

url = f"https://services.sentinel-hub.com/api/v1/batch/process/{batch_request_id}/cancel"

response = oauth.request("POST", url)

response.status_code

Create a new batch collection

Add the parameters cogOutput and createCollection as true to your request output. Add also description": "<Name>" to the request, to name your collection.

"description": "<Name>",
"output": {
  "defaultTilePath": "s3://<MyBucket>/<MyFolder>",
  "cogOutput": true,
  "createCollection": true
}

Note that custom collections can only contain single-band TIFFs. To create a multi-band collection, return separate bands as multiple outputs in the evalscript and connect them to multiple identifiers in the request.

The output format of batch requests determines the data format of the collection. By default, the output format of batch requests will be in sampleType.AUTO, which means that batch results 0..1 will be scaled to 0..255 and stored as UINT8. Process API request on the resulting collection will thus get values 0..255 as input. We recommend you instead use FLOAT32 as the sampleType for the batch request, so the batch request output is exactly the same as what you get with a process requests on the resulting collection.