ESA CCI Surface Soil Moisture COMBINED active+passive Product
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/2018_ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED.yaml")
ds=cat.netcdf.read()Metadata
| title | ESA CCI Surface Soil Moisture COMBINED active+passive Product |
| location | /shared/land/CCI/daily_files/COMBINED/2018 |
| tags | global,satellite,daily |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/2018_ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED.yaml |
| last updated | 2021-09-02 |
Dataset Contents
<xarray.Dataset>
Dimensions: (lat: 720, lon: 1440, time: 365)
Coordinates:
* lat (lat) float64 89.88 89.62 89.38 ... -89.38 -89.62 -89.88
* time (time) int64 17532 17533 17534 17535 ... 17894 17895 17896
* lon (lon) float64 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9
Data variables:
sm (time, lat, lon) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
sm_uncertainty (time, lat, lon) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
flag (time, lat, lon) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
freqbandID (time, lat, lon) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
dnflag (time, lat, lon) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
mode (time, lat, lon) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
sensor (time, lat, lon) float32 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
t0 (time, lat, lon) float64 dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
Attributes:
title: ESA CCI Surface Soil Moisture COMBINED acti...
institution: Technical University of Vienna (AUT); Vande...
contact: cci_sm_contact@eodc.eu
source: WARP 5.5R1.1/AMI-WS/ERS12 Level 2 Soil Mois...
platform: Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-...
sensor: SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, S...
references: http://www.esa-soilmoisture-cci.org; Dorigo...
product_version: v06.1
id: ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-20180...
tracking_id: 2e7ea2db-10a5-4cf3-ae7d-96991c0a3355
Conventions: CF-1.7
standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadata C...
summary: This dataset was produced with funding of t...
keywords: Soil Moisture/Water Content
naming_authority: TU Wien
keywords_vocabulary: NASA Global Change Master Directory (GCMD) ...
cdm_data_type: Grid
comment: This dataset was produced with funding of t...
history: 2021-02-16 05:21:34 - product produced
date_created: 20210216T052134Z
creator_name: Department of Geodesy and Geoinformation, T...
creator_url: https://climers.geo.tuwien.ac.at/
creator_email: cci_sm_contact@eodc.eu
project: Climate Change Initiative - European Space ...
license: Data use is free and open for all registere...
time_coverage_start: 20180101T000000Z
time_coverage_end: 20180101T235959Z
time_coverage_duration: P42Y
time_coverage_resolution: P1D
geospatial_lat_min: -90.0
geospatial_lat_max: 90.0
geospatial_lon_min: -180.0
geospatial_lon_max: 180.0
geospatial_vertical_min: 0.0
geospatial_vertical_max: 0.0
geospatial_lat_units: degrees_north
geospatial_lon_units: degrees_east
geospatial_lat_resolution: 0.25 degree
geospatial_lon_resolution: 0.25 degree
spatial_resolution: 25km
time_coverage_start_product: 19781101T000000Z
time_coverage_end_product: 20201231T235959Zxarray.Dataset
- lat: 720
- lon: 1440
- time: 365
- lat(lat)float6489.88 89.62 89.38 ... -89.62 -89.88
- standard_name :
- latitude
- units :
- degrees_north
- valid_range :
- [-90. 90.]
- _CoordinateAxisType :
- Lat
array([ 89.875, 89.625, 89.375, ..., -89.375, -89.625, -89.875])
- time(time)int6417532 17533 17534 ... 17895 17896
- standard_name :
- time
- units :
- days since 1970-01-01T00:00:00+00:00
- calendar :
- standard
array([17532, 17533, 17534, ..., 17894, 17895, 17896])
- lon(lon)float64-179.9 -179.6 ... 179.6 179.9
- standard_name :
- longitude
- units :
- degrees_east
- valid_range :
- [-180. 180.]
- _CoordinateAxisType :
- Lon
array([-179.875, -179.625, -179.375, ..., 179.375, 179.625, 179.875])
- sm(time, lat, lon)float32dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
- dtype :
- float32
- units :
- m3 m-3
- valid_range :
- [0. 1.]
- long_name :
- Volumetric Soil Moisture
- _CoordinateAxes :
- time lat lon
Array Chunk Bytes 1.51 GB 4.15 MB Shape (365, 720, 1440) (1, 720, 1440) Count 1095 Tasks 365 Chunks Type float32 numpy.ndarray - sm_uncertainty(time, lat, lon)float32dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
- dtype :
- float32
- units :
- m3 m-3
- valid_range :
- [0. 1.]
- long_name :
- Volumetric Soil Moisture Uncertainty
- _CoordinateAxes :
- time lat lon
Array Chunk Bytes 1.51 GB 4.15 MB Shape (365, 720, 1440) (1, 720, 1440) Count 1095 Tasks 365 Chunks Type float32 numpy.ndarray - flag(time, lat, lon)float32dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
- dtype :
- int8
- long_name :
- Flag
- _CoordinateAxes :
- time lat lon
- flag_values :
- [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 127]
- flag_meanings :
- ['no_data_inconsistency_detected', 'snow_coverage_or_temperature_below_zero', 'dense_vegetation', 'combination_of_flag_values_1_and_2', 'others_no_convergence_in_the_model_thus_no_valid_sm_estimates', 'combination_of_flag_values_1_and_4', 'combination_of_flag_values_2_and_4', 'combination_of_flag_values_1_and_2_and_4', 'soil_moisture_value_exceeds_physical_boundary', 'combination_of_flag_values_1_and_8', 'combination_of_flag_values_2_and_8', 'combination_of_flag_values_1_and_2_and_8', 'combination_of_flag_values_4_and_8', 'combination_of_flag_values_1_and_4_and_8', 'combination_of_flag_values_2_and_4_and_8', 'combination_of_flag_values_1_and_2_and_4_and_8', 'weight_of_measurement_below_threshold', 'combination_of_flag_values_1_and_16', 'combination_of_flag_values_2_and_16', 'combination_of_flag_values_1_and_2_and_16', 'combination_of_flag_values_4_and_16', 'combination_of_flag_values_1_and_4_and_16', 'combination_of_flag_values_2_and_4_and_16', 'combination_of_flag_values_1_and_2_and_4_and_16', 'combination_of_flag_values_8_and_16', 'combination_of_flag_values_1_and_8_and_16', 'combination_of_flag_values_2_and_8_and_16', 'combination_of_flag_values_1_and_2_and_8_and_16', 'combination_of_flag_values_4_and_8_and_16', 'combination_of_flag_values_1_and_4_and_8_and_16', 'combination_of_flag_values_2_and_4_and_8_and_16', 'combination_of_flag_values_1_and_2_and_4_and_8_and_16', 'combination_of_flag_values_1_and_2_and_4_and_8_and_16_and_32_and_64']
- bits :
- [0 1 2 3 4 5 6 7]
- bit_meanings :
- ['no_data_inconsistency_detected', 'snow_coverage_or_temperature_below_zero', 'dense_vegetation', 'others_no_convergence_in_the_model_thus_no_valid_sm_estimates', 'soil_moisture_value_exceeds_physical_boundary', 'weight_of_measurement_below_threshold', 'all_datasets_deemed_unreliable', 'NaN']
- valid_range :
- [ 0 127]
Array Chunk Bytes 1.51 GB 4.15 MB Shape (365, 720, 1440) (1, 720, 1440) Count 1095 Tasks 365 Chunks Type float32 numpy.ndarray - freqbandID(time, lat, lon)float32dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
- dtype :
- int16
- long_name :
- Frequency Band Identification
- _CoordinateAxes :
- time lat lon
- flag_values :
- [ 0 1 2 3 16 17 18 19 32 33 34 35 64 65 66 67 80 81 82 83 96 97 98 99]
- flag_meanings :
- ['NaN', 'L14', 'C53', 'L14+C53', 'C69', 'L14+C69', 'C53+C69', 'L14+C53+C69', 'C73', 'L14+C73', 'C53+C73', 'L14+C53+C73', 'X107', 'L14+X107', 'C53+X107', 'L14+C53+X107', 'C69+X107', 'L14+C69+X107', 'C53+C69+X107', 'L14+C53+C69+X107', 'C73+X107', 'L14+C73+X107', 'C53+C73+X107', 'L14+C53+C73+X107']
- valid_range :
- [ 0 511]
- bits :
- [0 1 2 3 4 5 6 7 8 9]
- bit_meanings :
- ['NaN', 'L14', 'C53', 'C66', 'C68', 'C69', 'C73', 'X107', 'K194', 'MODEL']
Array Chunk Bytes 1.51 GB 4.15 MB Shape (365, 720, 1440) (1, 720, 1440) Count 1095 Tasks 365 Chunks Type float32 numpy.ndarray - dnflag(time, lat, lon)float32dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
- dtype :
- int8
- long_name :
- Day / Night Flag
- _CoordinateAxes :
- time lat lon
- flag_values :
- [0 1 2 3]
- flag_meanings :
- ['NaN', 'day', 'night', 'day_night_combination']
- bits :
- [0 1 2]
- bit_meanings :
- ['NaN', 'day', 'night']
- valid_range :
- [0 3]
Array Chunk Bytes 1.51 GB 4.15 MB Shape (365, 720, 1440) (1, 720, 1440) Count 1095 Tasks 365 Chunks Type float32 numpy.ndarray - mode(time, lat, lon)float32dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
- dtype :
- int8
- long_name :
- Satellite Mode
- _CoordinateAxes :
- time lat lon
- flag_values :
- [0 1 2 3]
- flag_meanings :
- ['NaN', 'ascending', 'descending', 'ascending_descending_combination']
- bits :
- [0 1 2]
- bit_meanings :
- ['NaN', 'ascending', 'descending']
- valid_range :
- [0 3]
Array Chunk Bytes 1.51 GB 4.15 MB Shape (365, 720, 1440) (1, 720, 1440) Count 1095 Tasks 365 Chunks Type float32 numpy.ndarray - sensor(time, lat, lon)float32dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
- dtype :
- int16
- long_name :
- Sensor
- _CoordinateAxes :
- time lat lon
- flag_values :
- [ 0 32 64 96 256 288 320 352 512 544 576 608 768 800 832 864 1024 1056 1088 1120 1280 1312 1344 1376 1536 1568 1600 1632 1792 1824 1856 1888 4096 4128 4160 4192 4352 4384 4416 4448 4608 4640 4672 4704 4864 4896 4928 4960 5120 5152 5184 5216 5376 5408 5440 5472 5632 5664 5696 5728 5888 5920 5952 5984 8192 8224 8256 8288 8448 8480 8512 8544 8704 8736 8768 8800 8960 8992 9024 9056 9216 9248 9280 9312 9472 9504 9536 9568 9728 9760 9792 9824 9984 10016 10048 10080 12288 12320 12352 12384 12544 12576 12608 12640 12800 12832 12864 12896 13056 13088 13120 13152 13312 13344 13376 13408 13568 13600 13632 13664 13824 13856 13888 13920 14080 14112 14144 14176]
- flag_meanings :
- ['NaN', 'AMSR2', 'SMOS', 'AMSR2+SMOS', 'ASCATA', 'AMSR2+ASCATA', 'SMOS+ASCATA', 'AMSR2+SMOS+ASCATA', 'ASCATB', 'AMSR2+ASCATB', 'SMOS+ASCATB', 'AMSR2+SMOS+ASCATB', 'ASCATA+ASCATB', 'AMSR2+ASCATA+ASCATB', 'SMOS+ASCATA+ASCATB', 'AMSR2+SMOS+ASCATA+ASCATB', 'SMAP', 'AMSR2+SMAP', 'SMOS+SMAP', 'AMSR2+SMOS+SMAP', 'ASCATA+SMAP', 'AMSR2+ASCATA+SMAP', 'SMOS+ASCATA+SMAP', 'AMSR2+SMOS+ASCATA+SMAP', 'ASCATB+SMAP', 'AMSR2+ASCATB+SMAP', 'SMOS+ASCATB+SMAP', 'AMSR2+SMOS+ASCATB+SMAP', 'ASCATA+ASCATB+SMAP', 'AMSR2+ASCATA+ASCATB+SMAP', 'SMOS+ASCATA+ASCATB+SMAP', 'AMSR2+SMOS+ASCATA+ASCATB+SMAP', 'GPM', 'AMSR2+GPM', 'SMOS+GPM', 'AMSR2+SMOS+GPM', 'ASCATA+GPM', 'AMSR2+ASCATA+GPM', 'SMOS+ASCATA+GPM', 'AMSR2+SMOS+ASCATA+GPM', 'ASCATB+GPM', 'AMSR2+ASCATB+GPM', 'SMOS+ASCATB+GPM', 'AMSR2+SMOS+ASCATB+GPM', 'ASCATA+ASCATB+GPM', 'AMSR2+ASCATA+ASCATB+GPM', 'SMOS+ASCATA+ASCATB+GPM', 'AMSR2+SMOS+ASCATA+ASCATB+GPM', 'SMAP+GPM', 'AMSR2+SMAP+GPM', 'SMOS+SMAP+GPM', 'AMSR2+SMOS+SMAP+GPM', 'ASCATA+SMAP+GPM', 'AMSR2+ASCATA+SMAP+GPM', 'SMOS+ASCATA+SMAP+GPM', 'AMSR2+SMOS+ASCATA+SMAP+GPM', 'ASCATB+SMAP+GPM', 'AMSR2+ASCATB+SMAP+GPM', 'SMOS+ASCATB+SMAP+GPM', 'AMSR2+SMOS+ASCATB+SMAP+GPM', 'ASCATA+ASCATB+SMAP+GPM', 'AMSR2+ASCATA+ASCATB+SMAP+GPM', 'SMOS+ASCATA+ASCATB+SMAP+GPM', 'AMSR2+SMOS+ASCATA+ASCATB+SMAP+GPM', 'FY3B', 'AMSR2+FY3B', 'SMOS+FY3B', 'AMSR2+SMOS+FY3B', 'ASCATA+FY3B', 'AMSR2+ASCATA+FY3B', 'SMOS+ASCATA+FY3B', 'AMSR2+SMOS+ASCATA+FY3B', 'ASCATB+FY3B', 'AMSR2+ASCATB+FY3B', 'SMOS+ASCATB+FY3B', 'AMSR2+SMOS+ASCATB+FY3B', 'ASCATA+ASCATB+FY3B', 'AMSR2+ASCATA+ASCATB+FY3B', 'SMOS+ASCATA+ASCATB+FY3B', 'AMSR2+SMOS+ASCATA+ASCATB+FY3B', 'SMAP+FY3B', 'AMSR2+SMAP+FY3B', 'SMOS+SMAP+FY3B', 'AMSR2+SMOS+SMAP+FY3B', 'ASCATA+SMAP+FY3B', 'AMSR2+ASCATA+SMAP+FY3B', 'SMOS+ASCATA+SMAP+FY3B', 'AMSR2+SMOS+ASCATA+SMAP+FY3B', 'ASCATB+SMAP+FY3B', 'AMSR2+ASCATB+SMAP+FY3B', 'SMOS+ASCATB+SMAP+FY3B', 'AMSR2+SMOS+ASCATB+SMAP+FY3B', 'ASCATA+ASCATB+SMAP+FY3B', 'AMSR2+ASCATA+ASCATB+SMAP+FY3B', 'SMOS+ASCATA+ASCATB+SMAP+FY3B', 'AMSR2+SMOS+ASCATA+ASCATB+SMAP+FY3B', 'GPM+FY3B', 'AMSR2+GPM+FY3B', 'SMOS+GPM+FY3B', 'AMSR2+SMOS+GPM+FY3B', 'ASCATA+GPM+FY3B', 'AMSR2+ASCATA+GPM+FY3B', 'SMOS+ASCATA+GPM+FY3B', 'AMSR2+SMOS+ASCATA+GPM+FY3B', 'ASCATB+GPM+FY3B', 'AMSR2+ASCATB+GPM+FY3B', 'SMOS+ASCATB+GPM+FY3B', 'AMSR2+SMOS+ASCATB+GPM+FY3B', 'ASCATA+ASCATB+GPM+FY3B', 'AMSR2+ASCATA+ASCATB+GPM+FY3B', 'SMOS+ASCATA+ASCATB+GPM+FY3B', 'AMSR2+SMOS+ASCATA+ASCATB+GPM+FY3B', 'SMAP+GPM+FY3B', 'AMSR2+SMAP+GPM+FY3B', 'SMOS+SMAP+GPM+FY3B', 'AMSR2+SMOS+SMAP+GPM+FY3B', 'ASCATA+SMAP+GPM+FY3B', 'AMSR2+ASCATA+SMAP+GPM+FY3B', 'SMOS+ASCATA+SMAP+GPM+FY3B', 'AMSR2+SMOS+ASCATA+SMAP+GPM+FY3B', 'ASCATB+SMAP+GPM+FY3B', 'AMSR2+ASCATB+SMAP+GPM+FY3B', 'SMOS+ASCATB+SMAP+GPM+FY3B', 'AMSR2+SMOS+ASCATB+SMAP+GPM+FY3B', 'ASCATA+ASCATB+SMAP+GPM+FY3B', 'AMSR2+ASCATA+ASCATB+SMAP+GPM+FY3B', 'SMOS+ASCATA+ASCATB+SMAP+GPM+FY3B', 'AMSR2+SMOS+ASCATA+ASCATB+SMAP+GPM+FY3B']
- valid_range :
- [ 0 16383]
- bits :
- [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]
- bit_meanings :
- ['NaN', 'SMMR', 'SSMI', 'TMI', 'AMSRE', 'WindSat', 'AMSR2', 'SMOS', 'AMIWS', 'ASCATA', 'ASCATB', 'SMAP', 'MODEL', 'GPM', 'FY3B']
Array Chunk Bytes 1.51 GB 4.15 MB Shape (365, 720, 1440) (1, 720, 1440) Count 1095 Tasks 365 Chunks Type float32 numpy.ndarray - t0(time, lat, lon)float64dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
- dtype :
- float64
- long_name :
- Observation Timestamp
- _CoordinateAxes :
- time lat lon
- valid_range :
- [17531. 17533.]
- units :
- days since 1970-01-01T00:00:00+00:00
- calendar :
- proleptic_gregorian
Array Chunk Bytes 3.03 GB 8.29 MB Shape (365, 720, 1440) (1, 720, 1440) Count 1095 Tasks 365 Chunks Type float64 numpy.ndarray
- title :
- ESA CCI Surface Soil Moisture COMBINED active+passive Product
- institution :
- Technical University of Vienna (AUT); VanderSat B.V. Noordwijk (NL)
- contact :
- cci_sm_contact@eodc.eu
- source :
- WARP 5.5R1.1/AMI-WS/ERS12 Level 2 Soil Moisture; WARP 5.4R1.0/AMI-WS/ERS2 Level 2 Soil Moisture; H119: Metop ASCAT Surface Soil Moisture Climate Data Record 12.5 km sampling; H119: Metop ASCAT Surface Soil Moisture Climate Data Record 12.5 km sampling;; LPRMv061/SMMR/Nimbus 7 L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv061/SSMI/F08, F11, F13 DMSP L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv061/TMI/TRMM L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv061/AMSR-E/Aqua L2B Surface Soil Moisture, Ancillary Params, and QC; LPRMv061/WINDSAT/CORIOLIS L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv061/AMSR2/GCOM-W1 L3 Surface Soil Moisture, Ancillary Params; LPRMv061/SMOS/MIRAS L3 Surface Soil Moisture, CATDS Level 3 Brightness Temperatures (L3TB) version 300 RE03 & RE04; LPRMv061/SMAP_radiometer/SMAP L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv061/GMI/GPM L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv061/VIRR/FengYun-3B L3 Surface Soil Moisture, Ancillary Params, and quality flags;;
- platform :
- Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1, MIRAS, SMAP, GPM, FengYun-3B; ERS-1, ERS-2, METOP-A, METOP-B
- sensor :
- SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP_radiometer, GMI, VIRR; AMI-WS, ASCAT-A, ASCAT-B
- references :
- http://www.esa-soilmoisture-cci.org; Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017) ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001; Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dorigo, W. (2019) Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth System Science Data 11, 717-739, https://doi.org/10.5194/essd-11-717-2019; Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. https://doi.org/10.1109/TGRS.2017.2734070
- product_version :
- v06.1
- id :
- ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-20180130000000-fv06.1.nc
- tracking_id :
- 2e7ea2db-10a5-4cf3-ae7d-96991c0a3355
- Conventions :
- CF-1.7
- standard_name_vocabulary :
- NetCDF Climate and Forecast (CF) Metadata Convention
- summary :
- This dataset was produced with funding of the ESA CCI+ Soil Moisture project; ESRIN Contract No: 4000126684/19/I-NB
- keywords :
- Soil Moisture/Water Content
- naming_authority :
- TU Wien
- keywords_vocabulary :
- NASA Global Change Master Directory (GCMD) Science Keywords
- cdm_data_type :
- Grid
- comment :
- This dataset was produced with funding of the ESA CCI+ Soil Moisture project; ESRIN Contract No: 4000126684/19/I-NB
- history :
- 2021-02-16 05:21:34 - product produced
- date_created :
- 20210216T052134Z
- creator_name :
- Department of Geodesy and Geoinformation, Technical University of Vienna
- creator_url :
- https://climers.geo.tuwien.ac.at/
- creator_email :
- cci_sm_contact@eodc.eu
- project :
- Climate Change Initiative - European Space Agency
- license :
- Data use is free and open for all registered users.
- time_coverage_start :
- 20180101T000000Z
- time_coverage_end :
- 20180101T235959Z
- time_coverage_duration :
- P42Y
- time_coverage_resolution :
- P1D
- geospatial_lat_min :
- -90.0
- geospatial_lat_max :
- 90.0
- geospatial_lon_min :
- -180.0
- geospatial_lon_max :
- 180.0
- geospatial_vertical_min :
- 0.0
- geospatial_vertical_max :
- 0.0
- geospatial_lat_units :
- degrees_north
- geospatial_lon_units :
- degrees_east
- geospatial_lat_resolution :
- 0.25 degree
- geospatial_lon_resolution :
- 0.25 degree
- spatial_resolution :
- 25km
- time_coverage_start_product :
- 19781101T000000Z
- time_coverage_end_product :
- 20201231T235959Z