{ "cells": [ { "cell_type": "markdown", "id": "eecd1d2c-6b93-428e-8a60-5800274da618", "metadata": {}, "source": [ "## ESA CCI Toolbox Data Tree Access\n", "\n", "Some of the data from the Open Data Portal is provided through the Toolbox in the form of DataTrees. DataTrees are basically a collection of datasets, where each dataset may be accessed through an identifier. This structure is applied to all gridded data that is subdivided into regions. As each of these region-specific datasets is a standard dataset, all toolbox operations can be applied to them.\n", "\n", "This notebook shall serve to show what CCI datasets are concerned and how they can be opened.\n", "\n", "To run this Notebook, make sure the ESA CCI Toolbox is setup correctly." ] }, { "cell_type": "markdown", "id": "cb14d9ac-8491-410d-a836-c554dae785c7", "metadata": {}, "source": [ "We start, as usual, by opening the standard `esa-cci` data store." ] }, { "cell_type": "code", "execution_count": 1, "id": "c8835695-30f4-49aa-9a83-bd83f66db59e", "metadata": { "tags": [] }, "outputs": [], "source": [ "from xcube.core.store import new_data_store\n", "\n", "cci_store = new_data_store('esa-cci')" ] }, { "cell_type": "markdown", "id": "50f9dd64-758d-4c2e-8793-d34b7e2e3bb2", "metadata": {}, "source": [ "We list the available data types of the store to make sure 'datatree' is included in the list." ] }, { "cell_type": "code", "execution_count": 2, "id": "802746cd-8ad9-4a2f-a297-3c3f1b43c236", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('dataset', 'geodataframe', 'vectordatacube', 'datatree')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cci_store.get_data_types()" ] }, { "cell_type": "markdown", "id": "7911ed98-1c7e-44e7-a0e8-3bf46f86ef4c", "metadata": {}, "source": [ "As datatrees are provided, we can ask which datasets are actually provided in this form." ] }, { "cell_type": "code", "execution_count": 3, "id": "89450222-a4b9-48f0-bf98-5e2b8dfb4ee6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['esacci.FIRE.mon.L3S.BA.MODIS.Terra.MODIS_TERRA.v5-1.pixel',\n", " 'esacci.FIRE.mon.L3S.BA.multi-sensor.multi-platform.SYN.v1-1.pixel',\n", " 'esacci.FIRE.mon.L3S.BA.MSI-(Sentinel-2).Sentinel-2A.MSI.2-0.pixel',\n", " 'esacci.FIRE.mon.L3S.BA.MSI-(Sentinel-2).Sentinel-2A.MSI.v1-1.pixel',\n", " 'esacci.LC.yr.L4.Map.multi-sensor.multi-platform.HRLC10-A03.v1-2.Siberia',\n", " 'esacci.LC.yr.L4.Map.multi-sensor.multi-platform.HRLC10-A02.v1-2.Amazonia',\n", " 'esacci.LC.yr.L4.Map.multi-sensor.multi-platform.HRLC10-A01.v1-2.Africa',\n", " 'esacci.LC.5-yrs.L4.Map.multi-sensor.multi-platform.HRLC30-A03.v1-2.Siberia',\n", " 'esacci.LC.5-yrs.L4.Map.multi-sensor.multi-platform.HRLC30-A02.v1-2.Amazonia',\n", " 'esacci.LC.5-yrs.L4.Map.multi-sensor.multi-platform.HRLC30-A01.v1-2.Africa',\n", " 'esacci.LC.5-yrs.L4.CHANGE.multi-sensor.multi-platform.HRLCC30-A03.v1-2.Siberia',\n", " 'esacci.LC.5-yrs.L4.CHANGE.multi-sensor.multi-platform.HRLCC30-A02.v1-2.Amazonia',\n", " 'esacci.LC.5-yrs.L4.CHANGE.multi-sensor.multi-platform.HRLCC30-A01.v1-2.Africa',\n", " 'esacci.VEGETATION.5-days.L3S.VP_PRODUCTS.VEGETATION.SPOT-5.MERGED.v1-0.r1',\n", " 'esacci.VEGETATION.5-days.L3S.VP_PRODUCTS.VEGETATION.multi-platform.MERGED.v1-0.r1',\n", " 'esacci.VEGETATION.5-days.L3S.VP_PRODUCTS.Végétation-P.PROBA-V.MERGED.v1-0.r1',\n", " 'esacci.VEGETATION.5-days.L3S.VP_PRODUCTS.multi-sensor.multi-platform.MERGED.v1-0.r1']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(cci_store.get_data_ids(data_type=\"datatree\"))" ] }, { "cell_type": "markdown", "id": "8af3f605-81e9-4f08-8960-ed10121f5433", "metadata": {}, "source": [ "So, datatrees are provided for three ECVs: FIRE, LC, and VEGETATION. To fully show the use of datatrees, we will open FIRE and VEGETATION data." ] }, { "cell_type": "markdown", "id": "29873b2e-1c3a-4edd-a6d5-d43a0a5ba286", "metadata": {}, "source": [ "### Opening FIRE Data" ] }, { "cell_type": "markdown", "id": "5970efd7-3e66-4aa9-bcc4-e5bd56bdbf55", "metadata": {}, "source": [ "We start with opening one of the FIRE datasets." ] }, { "cell_type": "code", "execution_count": 4, "id": "26adb950-0022-44ef-8d57-e173e9bc7da1", "metadata": {}, "outputs": [], "source": [ "fire_dataset = \"esacci.FIRE.mon.L3S.BA.MODIS.Terra.MODIS_TERRA.v5-1.pixel\"" ] }, { "cell_type": "markdown", "id": "53527851-2e80-49f1-80e5-1bc273e212ef", "metadata": {}, "source": [ "As first step, we have a look at the potential opener parameters." ] }, { "cell_type": "code", "execution_count": 5, "id": "8d963156-ab57-4621-bb76-37d460b655d5", "metadata": {}, "outputs": [ { "data": { "application/json": { "additionalProperties": false, "properties": { "normalize_data": { "default": true, "type": "boolean" }, "place_names": { "items": { "enum": [ "AREA_1", "AREA_2", "AREA_3", "AREA_4", "AREA_5", "AREA_6" ], "type": "string" }, "type": "array" }, "time_range": { "items": [ { "format": "date", "maxDate": "2022-12-31", "minDate": "2001-01-01", "type": "string" }, { "format": "date", "maxDate": "2022-12-31", "minDate": "2001-01-01", "type": "string" } ], "type": "array" }, "variable_names": { "items": { "enum": [ "JD", "CL", "LC" ], "type": "string" }, "type": "array" } }, "type": "object" }, "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cci_store.get_open_data_params_schema(fire_dataset)" ] }, { "cell_type": "markdown", "id": "ecf2c33d-9ac9-4154-95b1-753a2966d0c4", "metadata": {}, "source": [ "One entry that we don't see when opening datasets of other type is the property place_names. If we extend this, we see a listing of Areas 1 to 6. Each of these identifiers stands for a different area. We can either open the data tree with all areas or pass a subset of this list as a parameter to only retrieve the area we are interested in. This will also increase performance." ] }, { "cell_type": "code", "execution_count": 6, "id": "38ccf195-94b9-475d-bff9-8d4f1c4205c2", "metadata": {}, "outputs": [], "source": [ "places = [\"AREA_1\"]" ] }, { "cell_type": "code", "execution_count": 7, "id": "268d8bf3-584d-4ace-9bb9-abed9195bcc0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "Group: /\n", "└── Group: /\n", " Dimensions: (time: 264, y: 28499, x: 57888, bnds: 2)\n", " Coordinates:\n", " * time (time) datetime64[ns] 2kB 2001-01-16T12:00:00 ... 2022-12-16T1...\n", " time_bnds (time, bnds) datetime64[ns] 4kB dask.array\n", " * x (x) float64 463kB -180.0 -180.0 -180.0 ... -50.0 -50.0 -50.0\n", " * y (y) float64 228kB 83.0 83.0 82.99 82.99 ... 19.01 19.0 19.0 19.0\n", " Dimensions without coordinates: bnds\n", " Data variables:\n", " CL (time, y, x) uint8 436GB dask.array\n", " JD (time, y, x) int16 871GB dask.array\n", " LC (time, y, x) uint8 436GB dask.array\n", " Attributes:\n", " Conventions: CF-1.7\n", " title: esacci.FIRE.mon.L3S.BA.MODIS.Terra.MODIS_TERRA.v...\n", " date_created: 2025-12-08T12:04:28.461120\n", " processing_level: L3S\n", " time_coverage_start: 2001-01-01T00:00:00\n", " time_coverage_end: 2023-01-01T00:00:00\n", " time_coverage_duration: P8035DT0H0M0S\n", " history: [{'program': 'xcube_cci.chunkstore.CciChunkStore..." ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fire_dt = cci_store.open_data(\n", " fire_dataset,\n", " place_names=places\n", ")\n", "fire_dt" ] }, { "cell_type": "markdown", "id": "124603f6-fb93-46db-8ffa-d38f822b6b85", "metadata": {}, "source": [ "We can see which datasets are available by asking for its keys." ] }, { "cell_type": "code", "execution_count": 8, "id": "c5e35caf-9d5c-4ed5-9ac4-7f6812d4c461", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['AREA_1']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(fire_dt.keys())" ] }, { "cell_type": "markdown", "id": "6cc0a662-2a99-421e-860d-d7ec229e9ef2", "metadata": {}, "source": [ "As expected, it is the place(s) we had requested. We can now retrieve a dataset from any of the keys." ] }, { "cell_type": "code", "execution_count": 9, "id": "1176d532-9408-453b-b6e1-f251d5516a6c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 2TB\n",
       "Dimensions:    (time: 264, y: 28499, x: 57888, bnds: 2)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2kB 2001-01-16T12:00:00 ... 2022-12-16T1...\n",
       "    time_bnds  (time, bnds) datetime64[ns] 4kB dask.array<chunksize=(264, 2), meta=np.ndarray>\n",
       "  * x          (x) float64 463kB -180.0 -180.0 -180.0 ... -50.0 -50.0 -50.0\n",
       "  * y          (y) float64 228kB 83.0 83.0 82.99 82.99 ... 19.01 19.0 19.0 19.0\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    CL         (time, y, x) uint8 436GB dask.array<chunksize=(1, 28499, 144), meta=np.ndarray>\n",
       "    JD         (time, y, x) int16 871GB dask.array<chunksize=(1, 28499, 144), meta=np.ndarray>\n",
       "    LC         (time, y, x) uint8 436GB dask.array<chunksize=(1, 28499, 144), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:             CF-1.7\n",
       "    title:                   esacci.FIRE.mon.L3S.BA.MODIS.Terra.MODIS_TERRA.v...\n",
       "    date_created:            2025-12-08T12:04:28.461120\n",
       "    processing_level:        L3S\n",
       "    time_coverage_start:     2001-01-01T00:00:00\n",
       "    time_coverage_end:       2023-01-01T00:00:00\n",
       "    time_coverage_duration:  P8035DT0H0M0S\n",
       "    history:                 [{'program': 'xcube_cci.chunkstore.CciChunkStore...
" ], "text/plain": [ " Size: 2TB\n", "Dimensions: (time: 264, y: 28499, x: 57888, bnds: 2)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2kB 2001-01-16T12:00:00 ... 2022-12-16T1...\n", " time_bnds (time, bnds) datetime64[ns] 4kB dask.array\n", " * x (x) float64 463kB -180.0 -180.0 -180.0 ... -50.0 -50.0 -50.0\n", " * y (y) float64 228kB 83.0 83.0 82.99 82.99 ... 19.01 19.0 19.0 19.0\n", "Dimensions without coordinates: bnds\n", "Data variables:\n", " CL (time, y, x) uint8 436GB dask.array\n", " JD (time, y, x) int16 871GB dask.array\n", " LC (time, y, x) uint8 436GB dask.array\n", "Attributes:\n", " Conventions: CF-1.7\n", " title: esacci.FIRE.mon.L3S.BA.MODIS.Terra.MODIS_TERRA.v...\n", " date_created: 2025-12-08T12:04:28.461120\n", " processing_level: L3S\n", " time_coverage_start: 2001-01-01T00:00:00\n", " time_coverage_end: 2023-01-01T00:00:00\n", " time_coverage_duration: P8035DT0H0M0S\n", " history: [{'program': 'xcube_cci.chunkstore.CciChunkStore..." ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = fire_dt.get(places[0]).to_dataset()\n", "ds" ] }, { "cell_type": "markdown", "id": "70bfb407-324a-49f9-a560-32db935cee0a", "metadata": {}, "source": [ "And, of course, we can open and plot any of the data variables:" ] }, { "cell_type": "code", "execution_count": 10, "id": "b6ccf7a1-97a3-4e5d-a1f4-d8e9fe435563", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds.CL.isel({\"time\": -6, \"x\": slice(25500, 26500), \"y\": slice(13500, 14500)}).plot()" ] }, { "cell_type": "markdown", "id": "6c3f5b59-1f31-4ea7-8ac5-1290b51fbcb6", "metadata": {}, "source": [ "### Opening VEGETATION Data" ] }, { "cell_type": "markdown", "id": "2d13d1f9-54b7-4166-ae03-f53722a3d14f", "metadata": {}, "source": [ "We continue by opening a VEGETATION data set. What's particular about this dataset is that it offers a lot of different sites, as becomes apparent from looking at the places in the schema properties" ] }, { "cell_type": "code", "execution_count": 11, "id": "24ba6730-9e02-4cd1-871d-dac38f8c241f", "metadata": {}, "outputs": [], "source": [ "vegetation_ds = \"esacci.VEGETATION.5-days.L3S.VP_PRODUCTS.VEGETATION.SPOT-5.MERGED.v1-0.r1\"" ] }, { "cell_type": "code", "execution_count": 12, "id": "79df3ecd-6bdf-4746-8fc0-e51d7ea0478b", "metadata": {}, "outputs": [ { "data": { "application/json": { "additionalProperties": false, "properties": { "normalize_data": { "default": true, "type": "boolean" }, "place_names": { "items": { "enum": [ "site_00001_ABRACOS_HILL", "site_00002_ADAMOWKA", "site_00003_AGUASCALIENTES", "site_00004_AIRE_ADOUR", "site_00005_AL_KHAZNAH", "site_00006_AMES", "site_00007_AOE_BAOTOU", "site_00008_ARM_CART_PONCA", "site_00009_ARM_CART_SGP", "site_00010_ARM_CART_SHIDLER", "site_00011_ASP", "site_00012_AU_FOG", "site_00013_AU_HOW", "site_00014_AU_TUM", "site_00015_AUTILLA", "site_00016_AZ_BORDER_STATION", "site_00017_BAC_LIEU", "site_00018_BAMBEY_ISRA", "site_00019_BANIZOUMBOU", "site_00020_BARTON_BENDISH", "site_00021_BASKIN", "site_00022_BE_LON", "site_00023_BELMANIP_00001", "site_00024_BELMANIP_00003", "site_00025_BELMANIP_00004", "site_00026_BELMANIP_00006", "site_00027_BELMANIP_00007", "site_00028_BELMANIP_00009", "site_00029_BELMANIP_00010", "site_00030_BELMANIP_00013", "site_00031_BELMANIP_00014", "site_00032_BELMANIP_00017", "site_00033_BELMANIP_00019", "site_00034_BELMANIP_00020", "site_00035_BELMANIP_00024", "site_00036_BELMANIP_00025", "site_00037_BELMANIP_00026", "site_00038_BELMANIP_00028", "site_00039_BELMANIP_00029", "site_00040_BELMANIP_00030", "site_00041_BELMANIP_00031", "site_00042_BELMANIP_00032", "site_00043_BELMANIP_00033", "site_00044_BELMANIP_00034", "site_00045_BELMANIP_00035", "site_00046_BELMANIP_00036", "site_00047_BELMANIP_00038", "site_00048_BELMANIP_00040", 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"site_00615_Canada_North6", "site_00616_Canada_North7", "site_00617_Piura", "site_00618_Cienaga", "site_00619_SalinerasLasPiletas", "site_00620_Missao", "site_00621_West_Three_", "site_00622_Namibe", "site_00623_Elba_NP", "site_00624_Hame", "site_00625_Darfur", "site_00626_Alto_Mbomou", "site_00627_Sodralekvattnet", "site_00628_Jamtland", "site_00629_Tangen", "site_00632_Vitebsk", "site_00633_Zakaznik_Kremennoye", "site_00634_Riazan", "site_00635_Oblast_de_Smolensk", "site_00636_Rahim_Yar_Khan", "site_00637_Khargai", "site_00638_Aksai_Chin", "site_00639_Khizaw", "site_00640_Surjandain", "site_00641_China_Desert1", "site_00642_China_Desert2", "site_00643_Yamalia_Memetsia1", "site_00644_Krai_de_Krasnoyarsk2", "site_00647_Oblast_de_Irkutsk", "site_00648_Republica_Saja_1", "site_00649_Republica_Saja_2", "site_00650_Republica_Saja_3", "site_00652_Republica_Saja_5", "site_00654_Yamalia_Nenetsia_1", "site_00658_Janty_Mansi_1", "site_00659_Janty_Mansi_2", "site_00661_Janty_Mansi_3", "site_00662_Aksu", "site_00663_China_Desert3", "site_00664_Nagqu", "site_00665_Wuxizuo", "site_00666_Shanjiao", "site_00667_Chita_1", "site_00668_Chita_2", "site_00684_Daxing_angling_1", "site_00685_Jilin_1", "site_00686_Jilin_2", "site_00687_Yichun_1", "site_00688_Yichun_2", "site_00689_Daxing_angling_2", "site_00690_Santa_Cruz", "site_00691_Magallanes", "site_00692_Goonoo_State_Forest", "site_00693_Barakuyla", "site_00694_Nowley", "site_00695_Boatmat", "site_00696_Omnogobi_1", "site_00697_Omnogobi_2", "site_00698_Omnogobi_3", "site_00699_Sinkiang_1", "site_00700_Sinkiang_2", "site_00701_Zhambyl_1", "site_00702_Zhambyl_2", "site_00703_Kyzylorda_1", "site_00704_Kyzylorda_2", "site_00705_Jilin_3", "site_00706_Pakistan_1", "site_00707_Pakistan_2", "site_00708_Chad_1", "site_00709_Chad_2", "site_00710_Mbomou", "site_00711_Bouba_Ndjida_NP", "site_00712_Bie", "site_00713_Katanga_1", "site_00714_Katanga_2", "site_00715_Hlane_NP", "site_00716_Republica_Saja_16", "site_00717_Republica_Saja_13", "site_00718_Yakutsk", "site_00719_Republica_Saja_14", "site_00720_Republica_Saja_15", "site_00721_AGRO_", "site_00722_CHEQ_", "site_00723__HARV_", "site_00724_KONZ_", "site_00725__METL_", "site_00726_NOBS_", "site_00727_SEVI_", "site_00728_TAPA_", "site_00730_Alpilles1_BU_", "site_00731_Flakaliden_", "site_00732_Ruokolahti_", "site_00733_Chateauguay1_", "site_00734__Chateauguay2_", "site_00735_Chateauguay3_", "site_00736_Chateauguay4_", "site_00737_Larose2_", "site_00738_SW_Ontario1_", "site_00739__SW_Ontario10_", "site_00740_SW_Ontario11_", "site_00741_SW_Ontario12_", "site_00743_SW_Ontario14_", "site_00744_SW_Ontario15_", "site_00745_SW_Ontario2_", "site_00746_SW_Ontario3_", "site_00747_SW_Ontario4_", "site_00748_SW_Ontario5_", "site_00749__SW_Ontario6_", "site_00750_SW_Ontario7_", "site_00751_SW_Ontario8_", "site_00752__SW_Ontario9_", "site_00753_Thompson1_", "site_00754_Thompson2_", "site_00755_Thompson3_", "site_00756_Thompson4_", "site_00757_Kejimikujik1_", "site_00758_Kejimikujik2_", "site_00759_WatsonLake1_", "site_00760__WatsonLake2_", "site_00761__WatsonLake3_", "site_00762_Appomattox_", "site_00763__Barrax2_", "site_00764__Walnut_Creek_", "site_00765_Chamela_", "site_00766_Chamela2_", "site_00767__LosInocentes_", "site_00768__AekLoba_", "site_00769_Alpilles1_", "site_00770_Alpilles2_", "site_00771_Barrax_", "site_00772__Camerons_", "site_00773_Concepcion_", "site_00774_Counami_", "site_00775__Counami2", "site_00776__Demmin_", "site_00777__Fundulea_", "site_00778_Gilching_", "site_00779__Gnangara_", "site_00780_Gourma_", "site_00781__Haouz_", "site_00782_Hirsikangas_", "site_00783_Hombori_", "site_00784_Jarvselja_", "site_00785__Laprida_", "site_00786__Larose_", "site_00787_Larzac_", "site_00788_Nezer_", "site_00789__Plan_De_Dieu_", "site_00790_Puechabon_", "site_00791_Romilly_", "site_00793_Sonian_", "site_00794__SudOuest_", "site_00795__Turco_", "site_00796_Turco2", "site_00797_Wankama_", "site_00798__Zhang_Bei_", "site_00799_Chimbolton", "site_00800_Donga", "site_00801_Hyyti_l_", "site_00802_Pandamatenga", "site_00803_Maun", "site_00804_Wiscousin", "site_00805_Guyaflux", "site_00806_Dahra_South", "site_00807_Tessekre_South", "site_00808_Tessekre_North", "site_00809_Kkmega_North", "site_00810_kkmega_South", "site_00811_Budongo_1", "site_00812_Budongo_2", "site_00813_Budongo_3", "site_00814_Budongo_4", "site_00815_Budongo_5", "site_00816_Budongo_6", "site_00817_Budongo_7", "site_00818_Budongo_8", "site_00819_Utiel", "site_00820_Okwa", "site_00821_Tshane", "site_00822_Mongu", "site_00823_Harth_Forest", "site_00824_Marmande", "site_00825_Pshenichne", "site_00826_Merguellil", "site_00827_SouthWest_1", "site_00828_SouthWest_2", "site_00829_Guangdong_Xuwen", "site_00830_25de_Mayo_Alfalfa", "site_00831_25de_Mayo_Shurb", "site_00832_Rosasco", "site_00833_LaReina_Cordoba_1", "site_00834_LaReina_Cordoba_2", "site_00835_Barrax_LasTiesas", "site_00836_Albufera", "site_00837_Ottawa", "site_00838_SanFernando", "site_00839_AHSPECT_MTO", "site_00840_AHSPECT_PEY", "site_00841_AHSPECT_URG", "site_00842_AHSPECT_CRE", "site_00843_AHSPECT_CON", "site_00844_AHSPECT_SAV", "site_00845_Collelongo", "site_00846_Capitanata", "site_00847_Muragua_Upper_Tana", "site_00848_Wielkopolska", "site_00849_Liria", "site_00850_Moncada", "site_00851_Wyhtam", "site_00852_Honghe_A", "site_00853_Honghe_B", "site_00854_Honghe_C", "site_00855_Honghe_D", "site_00856_Honghe_E", "site_00857_Hailun_A", "site_00858_Hailun_B", "site_00859_Hailun_C", "site_00860_Hailun_D", "site_00861_Hailun_E", "site_00862_BJ_wheat_1", "site_00863_BJ_wheat_2", "site_00864_BJ_wheat_3", "site_00865_BJ_wheat_8", "site_00866_HN_wheat_2", "site_00867_HN_wheat_5", "site_00868_HN_wheat_6", "site_00869_HN_wheat_8", "site_00870_HN_wheat_14", "site_00871_HN_wheat_16", "site_00872_HLJ_barley_1", "site_00873_HLJ_barley_2", "site_00874_HLJ_barley_3", "site_00875_HLJ_barley_4", "site_00876_HLJ_barley_8", "site_00877_HLJ_barley_13", "site_00878_HLJ_barley_14", "site_00879_HLJ_barley_16", "site_00880_HLJ_barley_19", "site_00881_HLJ_wheat_1", "site_00882_HLJ_wheat_2", "site_00883_HLJ_wheat_3", "site_00884_HLJ_wheat_6", "site_00885_HLJ_wheat_8", "site_00886_HLJ_wheat_14", "site_00887_HLJ_wheat_15", "site_00888_HLJ_wheat_16", "site_00889_AH_wheat_1", "site_00890_AH_wheat_3", "site_00891_AH_wheat_5", "site_00892_AH_wheat_12", "site_00893_BART", "site_00894_BLAN", "site_00895_CPER", "site_00896_DELA", "site_00897_DSNY", "site_00898_GUAN", "site_00899_HAIN", "site_00900_HARV", "site_00901_JERC", "site_00902_JORN", "site_00903_KONA", "site_00904_LAJA", "site_00905_MOAB", "site_00906_NIWO", "site_00907_ONAQ", "site_00908_ORNL", "site_00909_OSBS", "site_00910_SCBI", "site_00911_SERC", "site_00912_SRER", "site_00913_STEI", "site_00914_STER", "site_00915_TALL", "site_00916_TUMB", "site_00917_UNDE", "site_00918_VASN", "site_00919_WOOD", "site_00920_AGOUFOU_E_W", "site_00921_AGOUFOU_N_S", "site_00922_TIMBADIOR_E_W", "site_00923_TIMBADIOR_N_S", "site_00924_HOMBORI_HONDO_E_W", "site_00925_HOMBORI_HONDO_N_S", "site_00926_KELMA_FOREST_E_W", "site_00927_KELMA_HERBS_E_W", "site_00928_KELMA_PLAIN_E_W", "site_00929_TARA_NE_SW", "site_00930_TARA_NW_SE", "site_00931_EGUERIT_E_W", "site_00932_BILANTAO_NE_SW" ], "type": "string" }, "type": "array" }, "time_range": { "items": [ { "format": "date", "maxDate": "2020-06-30", "minDate": "2000-01-01", "type": "string" }, { "format": "date", "maxDate": "2020-06-30", "minDate": "2000-01-01", "type": "string" } ], "type": "array" }, "variable_names": { "items": { "enum": [ "crs", "LAI", "LAI_ERR", "fAPAR", "fAPAR_ERR", "LAI_fAPAR_correl", "n_bands_used", "p_chisquare", "invcode", "fAPAR_Cab", "fAPAR_Cab_ERR", "Cab", "Cab_ERR", "BHR_VIS", "BHR_VIS_ERR", "BHR_NIR", "BHR_NIR_ERR", "BHR_SW", "BHR_SW_ERR", "DHR_VIS", "DHR_VIS_ERR", "DHR_NIR", "DHR_NIR_ERR", "DHR_SW", "DHR_SW_ERR", "Cab_LAI_correl", "Cab_fAPAR_correl", "Cab_fAPAR_Cab_correl", "Cab_BHR_VIS_correl", "Cab_BHR_NIR_correl", "Cab_BHR_SW_correl", "Cab_DHR_VIS_correl", "Cab_DHR_NIR_correl", "Cab_DHR_SW_correl", "LAI_fAPAR_Cab_correl", "LAI_BHR_VIS_correl", "LAI_BHR_NIR_correl", "LAI_BHR_SW_correl", "LAI_DHR_VIS_correl", "LAI_DHR_NIR_correl", "LAI_DHR_SW_correl", "fAPAR_fAPAR_Cab_correl", "fAPAR_BHR_VIS_correl", "fAPAR_BHR_NIR_correl", "fAPAR_BHR_SW_correl", "fAPAR_DHR_VIS_correl", "fAPAR_DHR_NIR_correl", "fAPAR_DHR_SW_correl", "fAPAR_Cab_BHR_VIS_correl", "fAPAR_Cab_BHR_NIR_correl", "fAPAR_Cab_BHR_SW_correl", "fAPAR_Cab_DHR_VIS_correl", "fAPAR_Cab_DHR_NIR_correl", "fAPAR_Cab_DHR_SW_correl", "BHR_VIS_BHR_NIR_correl", "BHR_VIS_BHR_SW_correl", "BHR_VIS_DHR_VIS_correl", "BHR_VIS_DHR_NIR_correl", "BHR_VIS_DHR_SW_correl", "BHR_NIR_BHR_SW_correl", "BHR_NIR_DHR_VIS_correl", "BHR_NIR_DHR_NIR_correl", "BHR_NIR_DHR_SW_correl", "BHR_SW_DHR_VIS_correl", "BHR_SW_DHR_NIR_correl", "BHR_SW_DHR_SW_correl", "DHR_VIS_DHR_NIR_correl", "DHR_VIS_DHR_SW_correl", "DHR_NIR_DHR_SW_correl" ], "type": "string" }, "type": "array" } }, "type": "object" }, "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cci_store.get_open_data_params_schema(vegetation_ds)" ] }, { "cell_type": "markdown", "id": "8924c9a4-8683-46fe-a3c1-5e0633239052", "metadata": {}, "source": [ "We can pick any of those sites and use them to open the data tree." ] }, { "cell_type": "code", "execution_count": 13, "id": "e8ab0072-c383-498f-a0d4-f131977e58c4", "metadata": {}, "outputs": [], "source": [ "places = [\"site_00001_ABRACOS_HILL\", \"site_00021_BASKIN\"]" ] }, { "cell_type": "code", "execution_count": 14, "id": "cfa565ba-448a-4813-b796-5ac8bb9e3ff4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "Group: /\n", "├── Group: /\n", "│ Dimensions: (time: 730, lat: 3, lon: 3, nbnds: 2, bnds: 2)\n", "│ Coordinates:\n", "│ * lat (lat) float32 12B -10.75 -10.76 -10.77\n", "│ lat_bnds (lat, nbnds) float32 24B dask.array\n", "│ * lon (lon) float32 12B -62.37 -62.36 -62.35\n", "│ lon_bnds (lon, nbnds) float32 24B dask.array\n", "│ * time (time) datetime64[ns] 6kB 2004-01-01T11:59:59 ....\n", "│ time_bnds (time, bnds) datetime64[ns] 12kB dask.array\n", "│ Dimensions without coordinates: nbnds, bnds\n", "│ Data variables: (12/69)\n", "│ BHR_NIR (time, lat, lon) float64 53kB dask.array\n", "│ BHR_NIR_BHR_SW_correl (time, lat, lon) float64 53kB dask.array\n", "│ BHR_NIR_DHR_NIR_correl (time, lat, lon) float64 53kB dask.array\n", "│ BHR_NIR_DHR_SW_correl (time, lat, lon) float64 53kB dask.array\n", "│ BHR_NIR_DHR_VIS_correl (time, lat, lon) float64 53kB dask.array\n", "│ BHR_NIR_ERR (time, lat, lon) float64 53kB dask.array\n", "│ ... ...\n", "│ fAPAR_DHR_VIS_correl (time, lat, lon) float64 53kB dask.array\n", "│ fAPAR_ERR (time, lat, lon) float64 53kB dask.array\n", "│ fAPAR_fAPAR_Cab_correl (time, lat, lon) float64 53kB dask.array\n", "│ invcode (time, lat, lon) float64 53kB dask.array\n", "│ n_bands_used (time, lat, lon) float64 53kB dask.array\n", "│ p_chisquare (time, lat, lon) float64 53kB dask.array\n", "│ Attributes:\n", "│ Conventions: CF-1.7\n", "│ title: esacci.VEGETATION.5-days.L3S.VP_PRODUCTS.VEGETAT...\n", "│ date_created: 2025-12-08T12:10:15.529611\n", "│ processing_level: L3S\n", "│ time_coverage_start: 2004-01-01T00:00:00\n", "│ time_coverage_end: 2013-12-27T23:59:59\n", "│ time_coverage_duration: P3648DT23H59M59S\n", "│ history: [{'program': 'xcube_cci.chunkstore.CciChunkStore...\n", "└── Group: /\n", " Dimensions: (time: 730, lat: 3, lon: 3, nbnds: 2, bnds: 2)\n", " Coordinates:\n", " * lat (lat) float32 12B 32.29 32.29 32.28\n", " lat_bnds (lat, nbnds) float32 24B dask.array\n", " * lon (lon) float32 12B -91.75 -91.74 -91.73\n", " lon_bnds (lon, nbnds) float32 24B dask.array\n", " * time (time) datetime64[ns] 6kB 2004-01-01T11:59:59 ....\n", " time_bnds (time, bnds) datetime64[ns] 12kB dask.array\n", " Dimensions without coordinates: nbnds, bnds\n", " Data variables: (12/69)\n", " BHR_NIR (time, lat, lon) float64 53kB dask.array\n", " BHR_NIR_BHR_SW_correl (time, lat, lon) float64 53kB dask.array\n", " BHR_NIR_DHR_NIR_correl (time, lat, lon) float64 53kB dask.array\n", " BHR_NIR_DHR_SW_correl (time, lat, lon) float64 53kB dask.array\n", " BHR_NIR_DHR_VIS_correl (time, lat, lon) float64 53kB dask.array\n", " BHR_NIR_ERR (time, lat, lon) float64 53kB dask.array\n", " ... ...\n", " fAPAR_DHR_VIS_correl (time, lat, lon) float64 53kB dask.array\n", " fAPAR_ERR (time, lat, lon) float64 53kB dask.array\n", " fAPAR_fAPAR_Cab_correl (time, lat, lon) float64 53kB dask.array\n", " invcode (time, lat, lon) float64 53kB dask.array\n", " n_bands_used (time, lat, lon) float64 53kB dask.array\n", " p_chisquare (time, lat, lon) float64 53kB dask.array\n", " Attributes:\n", " Conventions: CF-1.7\n", " title: esacci.VEGETATION.5-days.L3S.VP_PRODUCTS.VEGETAT...\n", " date_created: 2025-12-08T12:10:36.067594\n", " processing_level: L3S\n", " time_coverage_start: 2004-01-01T00:00:00\n", " time_coverage_end: 2013-12-27T23:59:59\n", " time_coverage_duration: P3648DT23H59M59S\n", " history: [{'program': 'xcube_cci.chunkstore.CciChunkStore..." ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "abracos = cci_store.open_data(\n", " vegetation_ds,\n", " place_names=places\n", ")\n", "abracos" ] }, { "cell_type": "markdown", "id": "d9e813f3-09da-4644-8bf5-6a2a2d159b79", "metadata": {}, "source": [ "Again, we see check that the correct places are provided." ] }, { "cell_type": "code", "execution_count": 15, "id": "335e6013-a77e-48b1-8780-b8e5cb90f297", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['site_00001_ABRACOS_HILL', 'site_00021_BASKIN']" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(abracos.keys())" ] }, { "cell_type": "markdown", "id": "6cd70f0f-6449-444c-a93b-3da7ef4ca5ef", "metadata": {}, "source": [ "We now open one of these datasets." ] }, { "cell_type": "code", "execution_count": 16, "id": "f7a05c40-fcb9-46bf-bca4-ef63b4a29ca0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 4MB\n",
       "Dimensions:                   (time: 730, lat: 3, lon: 3, nbnds: 2, bnds: 2)\n",
       "Coordinates:\n",
       "  * lat                       (lat) float32 12B -10.75 -10.76 -10.77\n",
       "    lat_bnds                  (lat, nbnds) float32 24B dask.array<chunksize=(3, 2), meta=np.ndarray>\n",
       "  * lon                       (lon) float32 12B -62.37 -62.36 -62.35\n",
       "    lon_bnds                  (lon, nbnds) float32 24B dask.array<chunksize=(3, 2), meta=np.ndarray>\n",
       "  * time                      (time) datetime64[ns] 6kB 2004-01-01T11:59:59 ....\n",
       "    time_bnds                 (time, bnds) datetime64[ns] 12kB dask.array<chunksize=(730, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: nbnds, bnds\n",
       "Data variables: (12/69)\n",
       "    BHR_NIR                   (time, lat, lon) float64 53kB dask.array<chunksize=(73, 3, 3), meta=np.ndarray>\n",
       "    BHR_NIR_BHR_SW_correl     (time, lat, lon) float64 53kB dask.array<chunksize=(73, 3, 3), meta=np.ndarray>\n",
       "    BHR_NIR_DHR_NIR_correl    (time, lat, lon) float64 53kB dask.array<chunksize=(73, 3, 3), meta=np.ndarray>\n",
       "    BHR_NIR_DHR_SW_correl     (time, lat, lon) float64 53kB dask.array<chunksize=(73, 3, 3), meta=np.ndarray>\n",
       "    BHR_NIR_DHR_VIS_correl    (time, lat, lon) float64 53kB dask.array<chunksize=(73, 3, 3), meta=np.ndarray>\n",
       "    BHR_NIR_ERR               (time, lat, lon) float64 53kB dask.array<chunksize=(73, 3, 3), meta=np.ndarray>\n",
       "    ...                        ...\n",
       "    fAPAR_DHR_VIS_correl      (time, lat, lon) float64 53kB dask.array<chunksize=(73, 3, 3), meta=np.ndarray>\n",
       "    fAPAR_ERR                 (time, lat, lon) float64 53kB dask.array<chunksize=(73, 3, 3), meta=np.ndarray>\n",
       "    fAPAR_fAPAR_Cab_correl    (time, lat, lon) float64 53kB dask.array<chunksize=(73, 3, 3), meta=np.ndarray>\n",
       "    invcode                   (time, lat, lon) float64 53kB dask.array<chunksize=(73, 3, 3), meta=np.ndarray>\n",
       "    n_bands_used              (time, lat, lon) float64 53kB dask.array<chunksize=(73, 3, 3), meta=np.ndarray>\n",
       "    p_chisquare               (time, lat, lon) float64 53kB dask.array<chunksize=(73, 3, 3), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:             CF-1.7\n",
       "    title:                   esacci.VEGETATION.5-days.L3S.VP_PRODUCTS.VEGETAT...\n",
       "    date_created:            2025-12-08T12:10:15.529611\n",
       "    processing_level:        L3S\n",
       "    time_coverage_start:     2004-01-01T00:00:00\n",
       "    time_coverage_end:       2013-12-27T23:59:59\n",
       "    time_coverage_duration:  P3648DT23H59M59S\n",
       "    history:                 [{'program': 'xcube_cci.chunkstore.CciChunkStore...
" ], "text/plain": [ " Size: 4MB\n", "Dimensions: (time: 730, lat: 3, lon: 3, nbnds: 2, bnds: 2)\n", "Coordinates:\n", " * lat (lat) float32 12B -10.75 -10.76 -10.77\n", " lat_bnds (lat, nbnds) float32 24B dask.array\n", " * lon (lon) float32 12B -62.37 -62.36 -62.35\n", " lon_bnds (lon, nbnds) float32 24B dask.array\n", " * time (time) datetime64[ns] 6kB 2004-01-01T11:59:59 ....\n", " time_bnds (time, bnds) datetime64[ns] 12kB dask.array\n", "Dimensions without coordinates: nbnds, bnds\n", "Data variables: (12/69)\n", " BHR_NIR (time, lat, lon) float64 53kB dask.array\n", " BHR_NIR_BHR_SW_correl (time, lat, lon) float64 53kB dask.array\n", " BHR_NIR_DHR_NIR_correl (time, lat, lon) float64 53kB dask.array\n", " BHR_NIR_DHR_SW_correl (time, lat, lon) float64 53kB dask.array\n", " BHR_NIR_DHR_VIS_correl (time, lat, lon) float64 53kB dask.array\n", " BHR_NIR_ERR (time, lat, lon) float64 53kB dask.array\n", " ... ...\n", " fAPAR_DHR_VIS_correl (time, lat, lon) float64 53kB dask.array\n", " fAPAR_ERR (time, lat, lon) float64 53kB dask.array\n", " fAPAR_fAPAR_Cab_correl (time, lat, lon) float64 53kB dask.array\n", " invcode (time, lat, lon) float64 53kB dask.array\n", " n_bands_used (time, lat, lon) float64 53kB dask.array\n", " p_chisquare (time, lat, lon) float64 53kB dask.array\n", "Attributes:\n", " Conventions: CF-1.7\n", " title: esacci.VEGETATION.5-days.L3S.VP_PRODUCTS.VEGETAT...\n", " date_created: 2025-12-08T12:10:15.529611\n", " processing_level: L3S\n", " time_coverage_start: 2004-01-01T00:00:00\n", " time_coverage_end: 2013-12-27T23:59:59\n", " time_coverage_duration: P3648DT23H59M59S\n", " history: [{'program': 'xcube_cci.chunkstore.CciChunkStore..." ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = abracos.get(places[0]).to_dataset()\n", "ds" ] }, { "cell_type": "markdown", "id": "546ef35b-16b5-4e1c-91b4-d1a07ae076eb", "metadata": {}, "source": [ "We see that this dataset has only a small extent spatially, but provides a long time series, so we do not plot an area, but pick the time series for one of the grid cells." ] }, { "cell_type": "code", "execution_count": 17, "id": "7be47e79-3a1d-4a1b-97f0-af2e7c0fc9e2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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