Examples

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.

Create a BatchV2 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/v2/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
    },
    "input": {
        "type" : "tiling-grid",
        "id": 0,
        "resolution": 60.0
    },
    "output": {
        "type": "raster",
        "delivery": {
            "s3": {
                "url": "s3://<your-bucket>",
                "iamRoleARN": "<your-IAM-role-ARN>"
            }
        }
    },
    "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/v2/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
                    },
                    "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
    },
    "input": {
        "type": "tiling-grid",
        "id": 6,
        "resolution": 100.0
    },
    "output": {
        "type": "zarr",
        "delivery": {
            "s3": {
                "url": "s3://<your-bucket>/<requestId>",
                "iamRoleARN": "<your-IAM-role-ARN>"
            }
        },
        "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()

Option 3: GeoPackage input and GeoTiff 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/v2/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
    },
    "input": {
        "type" : "geopackage",
        "features": {
            "s3": {
                "url": "s3://<my-bucket>/<path-to-geopackage>",
                "iamRoleARN": "<your-IAM-role-ARN>",
            }
        }
    },
    "output": {
        "type": "raster",
        "delivery": {
            "s3": {
                "url": "s3://<your-bucket>",
                "iamRoleARN": "<your-IAM-role-ARN>"
            }
        }
    },
    "description": "Max NDVI over Corsica"
}

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

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

response.json()

Get information about all your batch processing requests

url = "https://services.sentinel-hub.com/api/v2/batch/process"

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

response.json()

Get information about a batch processing request

url = f"https://services.sentinel-hub.com/api/v2/batch/process/{batch_request_id}"

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

response.json()

Request detailed analysis (ANALYSE)

url = f"https://services.sentinel-hub.com/api/v2/batch/process/{batch_request_id}/analyse"

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

response.status_code

Request the start of processing (START)

url = f"https://services.sentinel-hub.com/api/v2/batch/process/{batch_request_id}/start"

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

response.status_code

Cancel a batch processing request (STOP)

url = f"https://services.sentinel-hub.com/api/v2/batch/process/{batch_request_id}/stop"

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

response.status_code