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/2013_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/2013 |
| tags | global,satellite,daily |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/2013_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 15706 15707 15708 15709 ... 16068 16069 16070
* 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-20130...
tracking_id: 050e05eb-3d47-48f6-b573-18f94f5f8996
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 05:11:40 - product produced
date_created: 20210216T051140Z
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: 20130101T000000Z
time_coverage_end: 20130101T235959Z
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)int6415706 15707 15708 ... 16069 16070
- standard_name :
- time
- units :
- days since 1970-01-01T00:00:00+00:00
- calendar :
- standard
array([15706, 15707, 15708, ..., 16068, 16069, 16070])
- 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 4 32 36 64 68 96 100 256 260 288 292 320 324 352 356 512 516 544 548 576 580 608 612 768 772 800 804 832 836 864 868 8192 8196 8224 8228 8256 8260 8288 8292 8448 8452 8480 8484 8512 8516 8544 8548 8704 8708 8736 8740 8768 8772 8800 8804 8960 8964 8992 8996 9024 9028 9056 9060]
- flag_meanings :
- ['NaN', 'TMI', 'AMSR2', 'TMI+AMSR2', 'SMOS', 'TMI+SMOS', 'AMSR2+SMOS', 'TMI+AMSR2+SMOS', 'ASCATA', 'TMI+ASCATA', 'AMSR2+ASCATA', 'TMI+AMSR2+ASCATA', 'SMOS+ASCATA', 'TMI+SMOS+ASCATA', 'AMSR2+SMOS+ASCATA', 'TMI+AMSR2+SMOS+ASCATA', 'ASCATB', 'TMI+ASCATB', 'AMSR2+ASCATB', 'TMI+AMSR2+ASCATB', 'SMOS+ASCATB', 'TMI+SMOS+ASCATB', 'AMSR2+SMOS+ASCATB', 'TMI+AMSR2+SMOS+ASCATB', 'ASCATA+ASCATB', 'TMI+ASCATA+ASCATB', 'AMSR2+ASCATA+ASCATB', 'TMI+AMSR2+ASCATA+ASCATB', 'SMOS+ASCATA+ASCATB', 'TMI+SMOS+ASCATA+ASCATB', 'AMSR2+SMOS+ASCATA+ASCATB', 'TMI+AMSR2+SMOS+ASCATA+ASCATB', 'FY3B', 'TMI+FY3B', 'AMSR2+FY3B', 'TMI+AMSR2+FY3B', 'SMOS+FY3B', 'TMI+SMOS+FY3B', 'AMSR2+SMOS+FY3B', 'TMI+AMSR2+SMOS+FY3B', 'ASCATA+FY3B', 'TMI+ASCATA+FY3B', 'AMSR2+ASCATA+FY3B', 'TMI+AMSR2+ASCATA+FY3B', 'SMOS+ASCATA+FY3B', 'TMI+SMOS+ASCATA+FY3B', 'AMSR2+SMOS+ASCATA+FY3B', 'TMI+AMSR2+SMOS+ASCATA+FY3B', 'ASCATB+FY3B', 'TMI+ASCATB+FY3B', 'AMSR2+ASCATB+FY3B', 'TMI+AMSR2+ASCATB+FY3B', 'SMOS+ASCATB+FY3B', 'TMI+SMOS+ASCATB+FY3B', 'AMSR2+SMOS+ASCATB+FY3B', 'TMI+AMSR2+SMOS+ASCATB+FY3B', 'ASCATA+ASCATB+FY3B', 'TMI+ASCATA+ASCATB+FY3B', 'AMSR2+ASCATA+ASCATB+FY3B', 'TMI+AMSR2+ASCATA+ASCATB+FY3B', 'SMOS+ASCATA+ASCATB+FY3B', 'TMI+SMOS+ASCATA+ASCATB+FY3B', 'AMSR2+SMOS+ASCATA+ASCATB+FY3B', 'TMI+AMSR2+SMOS+ASCATA+ASCATB+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 :
- [15705. 15707.]
- 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-20130305000000-fv06.1.nc
- tracking_id :
- 050e05eb-3d47-48f6-b573-18f94f5f8996
- 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 05:11:40 - product produced
- date_created :
- 20210216T051140Z
- 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 :
- 20130101T000000Z
- time_coverage_end :
- 20130101T235959Z
- 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