Add modis-annual-lai-2003-2020 and oco2-geos-l3-daily

ingestion-data/production/collections/modis-annual-lai-2003-2020.json

@@ -0,0 +1,77 @@
+{
+    "id": "modis-annual-lai-2003-2020",
+    "type": "Collection",
+    "title": "Annual LAI maps for 2003 and 2021 (Bangladesh)",
+    "links": [],
+    "description": "The annual median Leaf Area Index (LAI) maps of 2003 and 2021 were captured using combined Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4 dataset (MCD15A3H Version 6.1, dataset link: https://modis.gsfc.nasa.gov/data/dataprod/mod15.php). The actual dataset represents one-sided green leaf area per unit ground area at 500 meters spatial resolution and provides information at every 4 days. Annual median of the LAI datasets were calculated for both the years of 2003 and 2021 to illustrate the difference in vegetation cover.",
+    "extent": {
+        "spatial": {
+            "bbox": [
+                [
+                    88.02591469087191,
+                    20.742099910319755,
+                    92.68367943903164,
+                    26.63504817414382
+                ]
+            ]
+        },
+        "temporal": {
+            "interval": [
+                [
+                    "2003-01-01T00:00:00Z",
+                    "2020-12-31T23:59:00Z"
+                ]
+            ]
+        }
+    },
+    "license": "CC0-1.0",
+    "stac_extensions": [
+        "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"
+    ],
+    "item_assets": {
+        "cog_default": {
+            "type": "image/tiff; application=geotiff; profile=cloud-optimized",
+            "roles": [
+                "data",
+                "layer"
+            ],
+            "title": "Default COG Layer",
+            "description": "Cloud optimized default layer to display on map"
+        }
+    },
+    "dashboard:is_periodic": false,
+    "dashboard:time_density": null,
+    "stac_version": "1.0.0",
+    "providers": [
+        {
+            "name": "USGS",
+            "url": "https://lpdaac.usgs.gov/product_search/",
+            "roles": [
+                "producer"
+            ]
+        },
+        {
+            "name": "NASA VEDA",
+            "url": "https://www.earthdata.nasa.gov/dashboard/",
+            "roles": [
+                "host"
+            ]
+        },
+        {
+            "name": "NASA VEDA",
+            "url": "https://www.earthdata.nasa.gov/dashboard/",
+            "roles": [
+                "processor"
+            ]
+        }
+    ],
+    "assets": {
+        "thumbnail": {
+            "title": "Thumbnail",
+            "description": "Photo by [USGS](https://unsplash.com/photos/d59NHNtT_Ss) (Annual land cover maps for 2001 and 2020 (Bangladesh))",
+            "href": "https://thumbnails.openveda.cloud/bangladesh-landcover-2001-2020--dataset-cover.jpg",
+            "type": "image/jpeg",
+            "roles": ["thumbnail"]
+        }
+    }
+}

ingestion-data/production/collections/oco2-geos-l3-daily.json

@@ -0,0 +1,142 @@
+{
+    "id": "oco2-geos-l3-daily",
+    "type": "Collection",
+    "title": "Gridded Daily OCO-2 Carbon Dioxide assimilated dataset",
+    "links": [
+        {
+            "rel": "external",
+            "href": "https://catalog.data.gov/dataset/oco-2-geos-level-3-daily-0-5x0-625-assimilated-co2-v10r-oco2-geos-l3co2-day-at-ges-disc-72b15",
+            "type": "text/html",
+            "title": "OCO-2 GEOS Level 3 daily, 0.5x0.625 assimilated CO2 V10r (OCO2_GEOS_L3CO2_DAY) at GES DISC",
+            "label:assets": null
+        }
+    ],
+    "description": "The OCO-2 mission provides the highest quality space-based XCO2 retrievals to date. However, the instrument data are characterized by large gaps in coverage due to OCO-2’s narrow 10-km ground track and an inability to see through clouds and thick aerosols. This global gridded dataset is produced using a data assimilation technique commonly referred to as state estimation within the geophysical literature. Data assimilation synthesizes simulations and observations, adjusting the state of atmospheric constituents like CO2 to reflect observed values, thus gap-filling observations when and where they are unavailable based on previous observations and short transport simulations by GEOS. Compared to other methods, data assimilation has the advantage that it makes estimates based on our collective scientific understanding, notably of the Earth's carbon cycle and atmospheric transport. OCO-2 GEOS (Goddard Earth Observing System) Level 3 data are produced by ingesting OCO-2 L2 retrievals every 6 hours with GEOS CoDAS, a modeling and data assimilation system maintained by NASA's Global Modeling and Assimilation Office (GMAO). GEOS CoDAS uses a high-performance computing implementation of the Gridpoint Statistical Interpolation approach for solving the state estimation problem. GSI finds the analyzed state that minimizes the three-dimensional variational (3D-Var) cost function formulation of the state estimation problem.",
+    "assets": {
+        "zarr": {
+          "href": "s3://veda-data-store/oco2-geos-l3-daily/OCO2_GEOS_L3CO2_day.zarr",
+          "type": "application/vnd+zarr",
+          "roles": [
+            "data"
+          ],
+          "title": "zarr"
+        }
+    },    
+    "extent": {
+        "spatial": {
+            "bbox": [
+                [
+                    -180.0,
+                    -90.0,
+                    180.0,
+                    90.0
+                ]
+            ]
+        },
+        "temporal": {
+            "interval": [
+                [
+                    null,
+                    null
+                ]
+            ]
+        }
+    },
+    "license": "CC0-1.0",
+    "stac_extensions": [
+        "https://stac-extensions.github.io/datacube/v2.0.0/schema.json"
+    ],
+    "cube:variables": {
+        "XCO2": {
+            "type": "data",
+            "unit": "mol CO2/mol dry",
+            "attrs": {
+                "units": "mol CO2/mol dry",
+                "long_name": "Assimilated dry-air column average CO2 daily mean"
+            },
+            "shape": [
+                2500,
+                361,
+                576
+            ],
+            "chunks": [
+                100,
+                100,
+                100
+            ],
+            "dimensions": [
+                "time",
+                "lat",
+                "lon"
+            ],
+            "description": "Assimilated dry-air column average CO2 daily mean"
+        },
+        "XCO2PREC": {
+            "type": "data",
+            "unit": "mol CO2/mol dry",
+            "attrs": {
+                "units": "mol CO2/mol dry",
+                "long_name": "Precision of dry-air column average CO2 daily mean from Desroziers et al. (2005) diagnostic"
+            },
+            "shape": [
+                2500,
+                361,
+                576
+            ],
+            "chunks": [
+                100,
+                100,
+                100
+            ],
+            "dimensions": [
+                "time",
+                "lat",
+                "lon"
+            ],
+            "description": "Precision of dry-air column average CO2 daily mean from Desroziers et al. (2005) diagnostic"
+        }
+    },
+    "cube:dimensions": {
+        "lat": {
+            "axis": "y",
+            "type": "spatial",
+            "extent": [
+                -90.0,
+                90.0
+            ],
+            "description": "latitude",
+            "reference_system": 4326
+        },
+        "lon": {
+            "axis": "x",
+            "type": "spatial",
+            "extent": [
+                -180.0,
+                179.375
+            ],
+            "description": "longitude",
+            "reference_system": 4326
+        },
+        "time": {
+            "step": "P1DT0H0M0S",
+            "type": "temporal",
+            "extent": [
+                "2015-01-01T12:00:00Z",
+                "2021-11-04T12:00:00Z"
+            ],
+            "description": "time"
+        }
+    },
+    "dashboard:is_periodic": true,
+    "dashboard:time_density": "day",
+    "stac_version": "1.0.0",
+    "providers": [
+        {
+            "name": "NASA VEDA",
+            "url": "https://www.earthdata.nasa.gov/dashboard/",
+            "roles": [
+                "host"
+            ]
+        }
+    ]
+}

ingestion-data/production/discovery-items/modis-annual-lai-2003-2020.json

@@ -0,0 +1,8 @@
+{
+    "collection": "modis-annual-lai-2003-2020",
+    "discovery": "s3",
+    "prefix": "modis-annual-lai-2003-2020/",
+    "bucket": "veda-data-store",
+    "filename_regex": "^(.*)MODIS_LAI_(.*)BD.cog.tif$",
+    "datetime_range": "year"
+}

ingestion-data/production/transfer-config/modis-annual-lai-2003-2020.json

@@ -0,0 +1,9 @@
+[
+    {
+        "origin_bucket": "veda-data-store-staging",
+        "origin_prefix": "EIS/COG/coastal-flooding-and-slr/",
+        "target_bucket": "veda-data-store",
+        "collection": "modis-annual-lai-2003-2020",
+        "filename_regex": "^(.*)MODIS_LAI_(.*)BD.cog.tif$"
+    }
+]