Sentinel 1 Stats UDP ported to APEX

algorithm_catalog/sentinel1_stats.json

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+{
+    "id": "sentinel1_stats",
+    "type": "Feature",
+    "conformsTo": [
+        "http://www.opengis.net/spec/ogcapi-records-1/1.0/req/record-core"
+    ],
+    "properties": {
+        "created": "2024-10-03T00:00:00Z",
+        "updated": "2024-10-03T00:00:00Z",
+        "type": "apex_algorithm",
+        "title": "Sentinel-1 statistics",
+        "description": "A statistics algorithm for Sentinel-1 data, calculating the mean, median, minimum, and maximum values of the data.",
+        "cost_estimate": 0.1,
+        "cost_unit": "platform credits per km²",
+        "keywords": [
+            "sentinel-1",
+            "statistics"
+        ],
+        "language": {
+            "code": "en-US",
+            "name": "English (United States)"
+        },
+        "languages": [
+            {
+                "code": "en-US",
+                "name": "English (United States)"
+            }
+        ],
+        "contacts": [
+            {
+                "name": "Pratichhya Sharma",
+                "position": "Researcher",
+                "organization": "VITO",
+                "links": [
+                    {
+                        "href": "https://www.vito.be/",
+                        "rel": "about",
+                        "type": "text/html"
+                    },
+                    {
+                        "href": "https://github.com/pratichhya",
+                        "rel": "about",
+                        "type": "text/html"
+                    }
+                ],
+                "contactInstructions": "Contact via VITO",
+                "roles": [
+                    "principal investigator"
+                ]
+            },
+            {
+                "name": "VITO",
+                "links": [
+                    {
+                        "href": "https://www.vito.be/",
+                        "rel": "about",
+                        "type": "text/html"
+                    }
+                ],
+                "contactInstructions": "SEE WEBSITE",
+                "roles": [
+                    "processor"
+                ]
+            }
+        ],
+        "themes": [
+            {
+                "concepts": [
+                    {
+                        "id": "Active Remote Sensing"
+                    },
+                    {
+                        "id": "Sentinel-1 GRD"
+                    }
+                ],
+                "scheme": "https://gcmd.earthdata.nasa.gov/kms/concept/c8fe757b-b530-4d67-a553-e4903f4430a5"
+            }
+        ],
+        "formats": [
+            "GeoTiff",
+            "netCDF"
+        ],
+        "license": "CC-BY-4.0"
+    },
+    "linkTemplates": [],
+    "links": [
+        {
+            "rel": "openeo-process",
+            "type": "application/json",
+            "title": "openEO Process Definition",
+            "href": "https://raw.githubusercontent.com/ESA-APEx/apex_algorithms/openeo_udp/sentinel1_stats/sentinel1_stats.json"
+        },
+        {
+            "rel": "service",
+            "type": "application/json",
+            "title": "CDSE openEO federation",
+            "href": "https://openeofed.dataspace.copernicus.eu"
+        },
+        {
+            "rel": "example",
+            "type": "application/json",
+            "title": "Example output",
+            "href": "https://s3.waw3-1.cloudferro.com/swift/v1/APEx-examples/sentinel_1/s1_stats.nc"
+        }
+    ]
+}

algorithm_catalog/sentinel1_stats/sentinel1_stats_description.md

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+# Description
+
+This algorithm derives temporal statistics from the Sentinel-1 GRD collection for the selected spatial- and temporal extent of interest. In order to callibrate the SAR backscatter signal, `sigma0-ellipsoid` has been applied within this workflow. 
+
+
+# Performance characteristics
+The algorithm was evaluated for a spatial extent of 20x20km, for a temporal period of 3 months. The total cost for the performed evaluation was equal to 4 credits, thereby highlighting the efficiency of the underlying algorithm.
+
+
+
+# Examples
+
+Below we show the derived minimum and maximum values for both VV and VH bands, calculated for the 20x20 km2 area of interest. 
+
+![s1_stats](sentinel1.png)

benchmark_scenarios/sentinel1_stats.json

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+[
+    {
+        "id": "sentinel1_stats",
+        "type": "openeo",
+        "description": "Sentinel 1 statistics example",
+        "backend": "openeofed.dataspace.copernicus.eu",
+        "process_graph": {
+            "s1stats1": {
+                "process_id": "sentinel1_stats",
+                "namespace":  "https://raw.githubusercontent.com/ESA-APEx/apex_algorithms/refs/heads/S1_stats/openeo_udp/sentinel1_stats/sentinel1_stats.json",
+                "arguments": {
+                    "spatial_extent": {
+                        "east": 8.92,
+                        "north": 44.45,
+                        "south": 44.4,
+                        "west": 8.82
+                    },
+                    "temporal_extent": [
+                        "2023-05-01",
+                        "2023-07-30"
+                    ]
+                },
+                "result": true  
+            }
+        }
+    }
+]

openeo_udp/sentinel1_stats/README.md

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+Calculate S1 SAR stats (min, max, mean, sd, q10, q50, q90). 
+
+This service can cost an approximate of 3-5 credits per sq km. 
+This cost is based on resource consumption only and added-value cost has not been included.

openeo_udp/sentinel1_stats/generate.py

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+
+import json
+from pathlib import Path
+
+import openeo
+from openeo.api.process import Parameter
+from openeo.rest.udp import build_process_dict
+from openeo.processes import array_create, array_concat
+
+
+def generate() -> dict:
+    # TODO: use/inject dummy connection instead of concrete one? (See cdse_marketplace_services)
+    connection = openeo.connect("openeofed.dataspace.copernicus.eu")
+
+    # define parameters
+    spatial_extent = Parameter.bounding_box(
+        name="spatial_extent",
+        default={"west": 8.82, "south": 44.40, "east": 8.92, "north": 44.45},
+    )
+    temporal_extent = Parameter.temporal_interval(
+        name="temporal_extent", default=["2023-05-01", "2023-07-30"]
+    )
+
+    # load collection
+    s1_raw = connection.load_collection(
+        collection_id="SENTINEL1_GRD",
+        temporal_extent=temporal_extent,
+        spatial_extent=spatial_extent,
+        bands=["VH", "VV"],
+    )
+
+    # apply back scatter filtering
+    s1_raw = s1_raw.sar_backscatter(coefficient="sigma0-ellipsoid")
+
+    # calculate stats
+    s1_stats = s1_raw.apply_dimension(
+        process=lambda data: array_concat(
+            array1=array_create([data.min(), data.max(), data.mean(), data.sd()]),
+            array2=data.quantiles([0.1, 0.5, 0.9]),
+        ),
+        dimension="t",
+        target_dimension="bands",
+    )
+
+    # rename the generated bands
+    s1_stats = s1_stats.rename_labels(
+        "bands",
+        [
+            f"{b}_{s}"
+            for b in s1_stats.metadata.band_names
+            for s in ["min", "max", "mean", "sd", "q10", "q50", "q90"]
+        ],
+    )
+
+    return build_process_dict(
+        process_graph=s1_stats,
+        process_id="sentinel1_stats",
+        summary="Calculate Sentinel-1 SAR stats",
+        description=(Path(__file__).parent / "README.md").read_text(),
+        parameters=[
+            spatial_extent,
+            temporal_extent
+        ],
+        returns=None,  # TODO
+        categories=None,  # TODO
+    )
+
+
+if __name__ == "__main__":
+    # TODO: how to enforce a useful order of top-level keys?
+    # save the generated process to a file
+    with open(Path(__file__).parent / "sentinel1_stats.json", "w") as f:
+        json.dump(generate(), f, indent=2)