Examples of Data Fusion

To request data using any of the request below, you will need to replace the string <your access token> with your Sentinel Hub access token. Sentinel Hub access token will look something like this:

ayJhbGciOiJSUzI1NiJ9.ayJzdWIiOiI0MmYwODZjCy1kMzI3LTRlOTMtYWMxNS00ODAwOGFiZjI0YjIiLCJhdWQiOiJlY2I1MGM1Zi1i
MWM1LTQ3ZTgtYWE4NC0zZTU4NzJlM2I2MTEiLCJqdGkiOiI5MzYxMWE4ODEyNTM4Y2M0MmU0NDJjYjUyMTY0YmJlNyIsImV4cCI6MTU1N
TQyMzk3MiwibmFtZSI6ImFuamEudnJlY2tvQHNpbmVyZ2lzZS5jb20iLCJlbWFpbCI6ImFuamEudnJlY2tvQHNpbmVyZ2lzZS5jb20iLC
JzaWQiOiIzZjVjZDVkNS04MjRiLTQ3ZjYtODgwNy0wNDMyNWY4ODQxZmQifQ.U7FPOy_2jlEOFxXSjyN5KEdBROna3-Dyec0feShIbUOY
1p9lEXdNaMmR5euiINi2RXDayX9Kr47CuSTsvq1zHFvZs1YgkFr1iH6kDuX-t_-wfWpqu5oPjoPVKZ4Rj0Ms_dxAUTQFTXR0rlbLuO-KS
gnaeLVb5iiv_qY3Ctq2XKdIRcFRQLFziFcP4yZJl-NZMlwzsiiwjakcpYpI5jSYAdU2hpZLHRzceseeZt5YfZOe5Px1kZXro9Nd0L2GPC
-qzOXw_V1saMGFa2ov8qV6Dvk92iv2SDDdGhOdII_JOf8XkK4E3g2z0EEFdWhG9F4Iky4ukNsqBPgE8LRb31s0hg

and can be obtained as described in the Authentication chapter.

A Postman collection with examples can be downloaded here. Our Postman collections are deprecated and are not being updated since July 2022.

Pan-sharpen Landsat-8 with Sentinel-2

curl -X POST \
  https://services.sentinel-hub.com/api/v1/process \
  -H 'Authorization: Bearer <your access token>' \
  -F 'request={
  "input": {
    "bounds": {
      "bbox": [
        2.1421623229980464,
        41.37796785471600,
        2.2084236145019527,
        41.40784461738553
      ],
      "properties": {
        "crs": "http://www.opengis.net/def/crs/EPSG/0/4326"
      }
    },
    "data": [{
        "id": "ls8",
        "type": "landsat-8-l1c",
        "dataFilter": {
          "timeRange": {
            "from": "2020-05-21T00:00:00Z",
            "to": "2020-05-22T00:00:00Z"
          }
        }
      },
      {
        "id": "l2a",
        "type": "sentinel-2-l2a",
        "dataFilter": {
          "timeRange": {
            "from": "2020-05-22T00:00:00Z",
            "to": "2020-05-23T00:00:00Z"
          }
        }
      }
    ]
  },
  "output": {
    "width": 1024,
    "height": 462
  }
}' \
  -F 'evalscript=//VERSION=3
function setup() {
  return {
    input: [{
        datasource: "ls8",
        bands: ["B02", "B03", "B04"]
      }, {
        datasource: "l2a",
        bands: ["B02", "B03", "B04"]
      }
    ],
    output: [{
      bands: 3
    }]
  }
}
let minVal = 0.0
let maxVal = 0.4
let viz = new DefaultVisualizer(minVal, maxVal)

function evaluatePixel(samples, inputData, inputMetadata, customData, outputMetadata) {
  var ls8 = samples.ls8[0]
  var s2 = samples.l2a[0]
  // Use weighted arithmetic average of S2.B02 to S2.B04 for pan-sharpening
  let sudoPanW3 = (ls8.B04 + ls8.B03 + ls8.B02) / 3
  let s2PanR3 = (s2.B04 + s2.B03 + s2.B02) / 3
  let s2ratioWR3 = s2PanR3 / sudoPanW3
  let val = [ls8.B04 * s2ratioWR3, ls8.B03 * s2ratioWR3, ls8.B02 * s2ratioWR3]
  return viz.processList(val)
}'

Replace clouds in Sentinel-2 images with Sentinel-1 data

curl -X POST \
  https://services.sentinel-hub.com/api/v1/process \
  -H 'Authorization: Bearer <your access token>' \
  -F 'request={
  "input": {
    "bounds": {
      "bbox": [
        33.3219,
        44.7014,
        33.8073,
        44.4791
      ],
      "properties": {
        "crs": "http://www.opengis.net/def/crs/EPSG/0/4326"
      }
    },
    "data": [{
        "id": "s2l1c",
        "type": "sentinel-2-l1c",
        "dataFilter": {
          "timeRange": {
            "from": "2020-05-11T00:00:00Z",
            "to": "2020-05-11T23:59:00Z"
          }
        }
      },
      {
        "id": "s1grd",
        "type": "sentinel-1-grd",
        "dataFilter": {
          "timeRange": {
            "from": "2020-05-11T00:00:00Z",
            "to": "2020-05-11T23:59:00Z"
          }
        },
        "processing": {
          "orthorectify": "true",
          "backCoeff": "SIGMA0_ELLIPSOID"
        }
      }
    ]
  },
  "output": {
    "width": 1024,
    "height": 663
  }
}' \
  -F 'evalscript=// VERSION=3
function setup() {
  return {
    input: [{
        datasource: "s2l1c",
        bands: ["B02", "B03", "B04", "CLP"]
      },
      {
        datasource: "s1grd",
        bands: ["VV", "VH"]
      }
    ],
    output: [{
      bands: 3
    }]
  }
}

function evaluatePixel(samples, inputData, inputMetadata, customData, outputMetadata) {
  var S2L1C = samples.s2l1c[0]
  var S1 = samples.s1grd[0]
  let WAT = 25 // Water Threshold for SAR
  let CLP = S2L1C.CLP / 2.55 // cloud probability in percent
  let CLPT = 70 // cloud probability threshold in percent
  if (CLP > CLPT) {
    if (S1.VV / S1.VH <= WAT) {
      return [S1.VV * 3.0, S1.VV * 1.1 + S1.VH * 8.75, S1.VH * 1.75]
    } else { // S1.VV / S1.VH > WAT
      return [S1.VV * 1, S1.VV * 8, 0.5 + S1.VV * 3 + S1.VH * 2000]
    }
  }
  return [3 * S2L1C.B04, 3 * S2L1C.B03, 3 * S2L1C.B02]
}'

NDVI with Sentinel-1 and Sentinel-2

curl -X POST \
  https://services.sentinel-hub.com/api/v1/process \
  -H 'Authorization: Bearer <your access token>' \
  -F 'request={
  "input": {
    "bounds": {
      "bbox": [
        -100.9204,
        37.5718,
        -100.4865,
        37.8640
      ],
      "properties": {
        "crs": "http://www.opengis.net/def/crs/EPSG/0/4326"
      }
    },
    "data": [{
        "id": "s1",
        "type": "sentinel-1-grd",
        "dataFilter": {
          "timeRange": {
            "from": "2019-04-26T00:00:00Z",
            "to": "2019-04-26T23:59:00Z"
          }
        },
        "processing": {
          "orthorectify": "true",
          "backCoeff": "SIGMA0_ELLIPSOID"
        }
      },
      {
        "id": "l2a",
        "type": "sentinel-2-l2a",
        "dataFilter": {
          "timeRange": {
            "from": "2019-04-26T00:00:00Z",
            "to": "2019-04-26T23:59:00Z"
          }
        }
      }
    ]
  },
  "output": {
    "width": 1024,
    "height": 1024
  }
}' \
  -F 'evalscript=//VERSION=3
function setup() {
  return {
    input: [{
        datasource: "s1",
        bands: ["VV", "VH"]
      }, {
        datasource: "l2a",
        bands: ["B08", "B04", "SCL"]
      }
    ],
    output: [{
      bands: 3
    }]
  }
}

function toDb(linear) {
  // Convert the linear backscatter to DB (Filgueiras et al. (2019), eq. 3)
  return 10 * Math.LN10 * linear
}

function calc_s1_ndvi(sigmaVV, sigmaVH) {
  // Convert sigma0 to Decibels
  let vh_Db = toDb(sigmaVH)
  let vv_Db = toDb(sigmaVV)
  // Calculate NRPB (Filgueiras et al. (2019), eq. 4)
  let NRPB = (vh_Db - vv_Db) / (vh_Db + vv_Db)
  // Calculate NDVI_nc with approach A3 (Filgueiras et al. (2019), eq. 14)
  let NDVInc = 2.572 - 0.05047 * vh_Db + 0.176 * vv_Db + 3.422 * NRPB
  return NDVInc
}

// Create an NDVI visualiser
var viz = new ColorMapVisualizer([
  [0.0, 0xa50026],
  [0.0, 0xd73027],
  [0.2, 0xf46d43],
  [0.3, 0xfdae61],
  [0.4, 0xfee08b],
  [0.5, 0xffffbf],
  [0.6, 0xd9ef8b],
  [0.7, 0xa6d96a],
  [0.8, 0x66bd63],
  [0.9, 0x1a9850],
  [1.0, 0x006837]
])

function evaluatePixel(samples) {
  var s1 = samples.s1[0]
  var s2 = samples.l2a[0]

  // Use the S2-L2A classification to identify clouds
  if ([7, 8, 9, 10].includes(s2.SCL)) {
    // If clouds are present use S1 NDVI
    let s1_ndvi = calc_s1_ndvi(s1.VV, s1.VH) // Calculate S1 NDVI
    return viz.process(s1_ndvi)
  } else {
    // Otherwise use s2 NDVI
    let ndvi = index(s2.B08, s2.B04) // Calculate S2 NDVI
    return viz.process(ndvi)
  }
}'

Ship detection with Sentinel-1 and Sentinel-2

curl -X POST \
  https://services.sentinel-hub.com/api/v1/process \
  -H 'Authorization: Bearer <your access token>' \
  -F 'request={
  "input": {
    "bounds": {
      "bbox": [
        119.60987091064452,
        32.176774851931214,
        119.91474151611328,
        32.3640132852233
      ],
      "properties": {
        "crs": "http://www.opengis.net/def/crs/EPSG/0/4326"
      }
    },
    "data": [{
        "id": "s1grd",
        "type": "sentinel-1-grd",
        "dataFilter": {
          "timeRange": {
            "from": "2020-05-23T00:00:00Z",
            "to": "2020-05-23T23:59:00Z"
          }
        },
        "processing": {
          "orthorectify": "true",
          "backCoeff": "SIGMA0_ELLIPSOID"
        }
      },
      {
        "id": "s2l2a",
        "type": "sentinel-2-l2a",
        "dataFilter": {
          "timeRange": {
            "from": "2020-05-23T00:00:00Z",
            "to": "2020-05-23T23:59:00Z"
          }
        }
      }
    ]
  },
  "output": {
    "width": 1024,
    "height": 742
  }
}' \
  -F 'evalscript=//VERSION=3
function setup() {
  return {
    input: [{
        datasource: "s2l2a",
        bands: ["B02", "B03", "B04", "B08"]
      }, {
        datasource: "s1grd",
        bands: ["VV", "VH"]
      }
    ],
    output: [{
      bands: 3
    }]
  }
}

function evaluatePixel(samples, inputData, inputMetadata, customData, outputMetadata) {
  var S2L2A = samples.s2l2a[0]
  var S1 = samples.s1grd[0]

  let ndwi = (S2L2A.B03 - S2L2A.B08) / (S2L2A.B03 + S2L2A.B08)
  if (ndwi > 0.1) {
    if (S1.VV > 0.3 || S1.VH > 0.3) {
      return [1, 1, 1]
    }
    return [4 * S2L2A.B04 - 0.2, 4 * S2L2A.B03 - 0.2, 5 * S2L2A.B02 - 0.2]
  }
  return [4 * S2L2A.B04 - 0.2, 4 * S2L2A.B03 - 0.2, 4 * S2L2A.B02 - 0.2]
}'

Built up areas detection with Sentinel-1 and Sentinel-2

curl -X POST \
  https://services.sentinel-hub.com/api/v1/process \
  -H 'Authorization: Bearer <your access token>' \
  -F 'request={
  "input": {
    "bounds": {
      "bbox": [
        12.280998229980469,
        45.40206593659076,
        12.43274688720703,
        45.47361429775641
      ],
      "properties": {
        "crs": "http://www.opengis.net/def/crs/EPSG/0/4326"
      }
    },
    "data": [{
        "id": "s2l1c",
        "type": "sentinel-2-l1c",
        "dataFilter": {
          "timeRange": {
            "from": "2019-12-10T00:00:00Z",
            "to": "2019-12-10T23:59:00Z"
          }
        }
      }, {
        "id": "s1grd",
        "type": "sentinel-1-grd",
        "dataFilter": {
          "timeRange": {
            "from": "2019-12-10T00:00:00Z",
            "to": "2019-12-10T23:59:00Z"
          }
        },
        "processing": {
          "orthorectify": "true",
          "backCoeff": "SIGMA0_ELLIPSOID"
        }
      },
      {
        "id": "s2l2a",
        "type": "sentinel-2-l2a",
        "dataFilter": {
          "timeRange": {
            "from": "2019-12-10T00:00:00Z",
            "to": "2019-12-10T23:59:00Z"
          }
        }
      }
    ]
  },
  "output": {
    "width": 1024,
    "height": 1024
  }
}' \
  -F 'evalscript=//VERSION=3
function setup() {
  return {
    input: [{
        datasource: "s2l1c",
        bands: ["B02", "B03", "B04", "B08", "B11"]
      }, {
        datasource: "s1grd",
        bands: ["VV", "VH"]
      }, {
        datasource: "s2l2a",
        bands: ["B02", "B03", "B04"]
      }
    ],
    output: [{
      bands: 3
    }]
  }
}

function evaluatePixel(samples) {
  var S2L1C = samples.s2l1c[0]
  var S2L2A = samples.s2l2a[0]
  var S1 = samples.s1grd[0]
  let ndvi = (S2L1C.B08 - S2L1C.B04) / (S2L1C.B08 + S2L1C.B04)
  if (ndvi > 0.5) {
    return [3 * S2L2A.B04, 3 * S2L2A.B03, 3 * S2L2A.B02]
  }
  let ndmi = (S2L1C.B08 - S2L1C.B11) / (S2L1C.B08 + S2L1C.B11)
  if (ndmi > 0) {
    return [3 * S2L2A.B04, 3 * S2L2A.B03, 4 * S2L2A.B02]
  }
  if (S1.VH > 0.2 || S1.VV > 0.2) {
    return [S1.VH * 5.5, S1.VV, S1.VH * 8]
  }
  return [3 * S2L1C.B04 - 0.2, 3 * S2L1C.B03 - 0.2, 3 * S2L1C.B02 - 0.2]
}'

Fire monitoring with Sentinel-1 and Sentinel-2

curl -X POST \
  https://services.sentinel-hub.com/api/v1/process \
  -H 'Authorization: Bearer <your access token>' \
  -F 'request={
  "input": {
    "bounds": {
      "bbox": [
        -59.75738525390625,
        -19.919130502461016,
        -58.7274169921875,
        -19.062117883514652
      ],
      "properties": {
        "crs": "http://www.opengis.net/def/crs/EPSG/0/4326"
      }
    },
    "data": [{
        "type": "sentinel-2-l2a",
        "id": "l2a_t1",
        "dataFilter": {
          "timeRange": {
            "from": "2019-09-06T00:00:00Z",
            "to": "2019-09-08T23:59:59Z"
          }
        }
      },
      {
        "type": "sentinel-1-grd",
        "id": "s1_t1",
        "dataFilter": {
          "timeRange": {
            "from": "2019-09-06T00:00:00Z",
            "to": "2019-09-08T23:59:59Z"
          }
        }
      },
      {
        "type": "sentinel-1-grd",
        "id": "s1_t2",
        "dataFilter": {
          "timeRange": {
            "from": "2019-09-11T00:00:00Z",
            "to": "2019-09-13T23:59:59Z"
          }
        }
      }
    ]
  },
  "output": {
    "width": 1024,
    "height": 1024
  }
}' \
  -F 'evalscript=//VERSION=3
// Multitemporal forest fire progression monitoring script utilizing a) Sentinel-2 data from 7 September 2019 for the visualization of burned areas
// and b) Sentinel-1 SAR data to monitor forest fire progression in overcast conditions on 12 September 2019.
function setup() {
  return {
    input: [{
        datasource: "s1_t1",
        bands: ["VH"]
      }, // S1 data from 7 September 2019 (t1)
      {
        datasource: "s1_t2",
        bands: ["VV", "VH"]
      }, // S1 data from 12 September 2019 (t2)
      {
        datasource: "l2a_t1",
        bands: ["B03", "B04", "B08", "B11", "B12"]
      }
    ], // S2 data from 7 September 2019 (t1)
    output: [{
      bands: 3
    }]
  }
}

function evaluatePixel(samples, inputData, inputMetadata, customData, outputMetadata) {
  var s1_1 = samples.s1_t1[0] //Assigns S1 data from t1
  var s1_2 = samples.s1_t2[0] //Assigns S1 data from t2
  var s2_1 = samples.l2a_t1[0] //Assigns S2 data from t1

  // Calculate indices with S2 data from t1 for Burned Area visualization by Monja Sebela
  var NDWI = index(s2_1.B03, s2_1.B08)
  var NDVI = index(s2_1.B08, s2_1.B04)
  var INDEX = ((s2_1.B11 - s2_1.B12) / (s2_1.B11 + s2_1.B12)) + (s2_1.B08)

  // Calculate difference in S1 VH backscatter between second (t2) and first scene (t1) (Belenguer-Plomer et al. 2019)
  var VH_diff = (s1_2.VH - s1_1.VH)

  // Set classification threshholds
  var thr_VH = 0.03
  var thr_VH_diff = -0.015
  var thr_VV = 0.2

  if (NDWI > 0.15 || NDVI > 0.35 || INDEX > 0.2) { // If non-burned areas in S2 image from t1
    if (s1_2.VH < thr_VH && VH_diff < thr_VH_diff) { // are classified as burned in S1 image from t2 via thresholds for VH backscatter and the calculated difference layer
      return [1, 0, 0] // Return red color
    } else {
      return [2.5 * s2_1.B12, 2.5 * s2_1.B08, 2.5 * s2_1.B04] // Else return SWIR composite
    }
  } else {
    if (s1_2.VV < thr_VV) { // Else, if already burnt area is also burned in S1 image from t2
      return [0.9, 0.9, 0.7] // Return beige color
    } else {
      return [0, 0, 1] // Else return blue for areas that are no longer burned in S1 image from t2
    }
  }
}'