<xarray.Dataset>
Dimensions: (time: 15767, lat: 720, lon: 1440)
Coordinates:
* lat (lat) float64 89.88 89.62 89.38 ... -89.38 -89.62 -89.88
* lon (lon) float64 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9
* time (time) datetime64[ns] 1978-11-01 1978-11-02 ... 2021-12-31
Data variables:
dnflag (time, lat, lon) float32 dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
flag (time, lat, lon) float32 dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
freqbandID (time, lat, lon) float32 dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
mode (time, lat, lon) float32 dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
sensor (time, lat, lon) float64 dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
sm (time, lat, lon) float32 dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
sm_uncertainty (time, lat, lon) float32 dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
t0 (time, lat, lon) float64 dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
Attributes: (12/44)
Conventions: CF-1.9
cdm_data_type: Grid
comment: This dataset was produced with funding of t...
contact: cci_sm_contact@eodc.eu
creator_email: cci_sm_developer@eodc.eu
creator_name: Department of Geodesy and Geoinformation, V...
... ...
time_coverage_end_product: 20211231T235959Z
time_coverage_resolution: P1D
time_coverage_start: 1978-11-01 00:00:00
time_coverage_start_product: 19781101T000000Z
title: ESA CCI Surface Soil Moisture COMBINED acti...
tracking_id: ad35798e-58e0-488f-b5b9-593874a47700 Dimensions: time : 15767lat : 720lon : 1440
Coordinates: (3)
lat
(lat)
float64
89.88 89.62 89.38 ... -89.62 -89.88
_CoordinateAxisType : Lat standard_name : latitude units : degrees_north valid_range : [-90.0, 90.0] array([ 89.875, 89.625, 89.375, ..., -89.375, -89.625, -89.875]) lon
(lon)
float64
-179.9 -179.6 ... 179.6 179.9
_CoordinateAxisType : Lon standard_name : longitude units : degrees_east valid_range : [-180.0, 180.0] array([-179.875, -179.625, -179.375, ..., 179.375, 179.625, 179.875]) time
(time)
datetime64[ns]
1978-11-01 ... 2021-12-31
array(['1978-11-01T00:00:00.000000000', '1978-11-02T00:00:00.000000000',
'1978-11-03T00:00:00.000000000', ..., '2021-12-29T00:00:00.000000000',
'2021-12-30T00:00:00.000000000', '2021-12-31T00:00:00.000000000'],
dtype='datetime64[ns]') Data variables: (8)
dnflag
(time, lat, lon)
float32
dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
_CoordinateAxes : time lat lon bit_meanings : ['NaN', 'day', 'night'] bits : ['0b0', '0b1', '0b10'] dtype : int8 flag_meanings : ['NaN', 'day', 'night', 'day_night_combination'] flag_values : [0, 1, 2, 3] long_name : Day / Night Flag valid_range : [0, 3]
Array
Chunk
Bytes
60.90 GiB
31.64 MiB
Shape
(15767, 720, 1440)
(16, 720, 720)
Dask graph
1972 chunks in 2 graph layers
Data type
float32 numpy.ndarray
1440
720
15767
flag
(time, lat, lon)
float32
dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
_CoordinateAxes : time lat lon 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', 'barren_ground_advisory_flag', 'NaN'] bits : ['0b0', '0b1', '0b10', '0b100', '0b1000', '0b10000', '0b100000', '0b1000000', '0b10000000'] dtype : int16 flag_meanings : ['no_data_inconsistency_detected', 'snow_coverage_or_temperature_below_zero', 'dense_vegetation', 'combination_of_flag_values_1_and_2', 'soil_moisture_value_exceeds_physical_boundary', 'combination_of_flag_values_1_and_8', 'combination_of_flag_values_2_and_8', 'barren_ground_advisory_flag', 'combination_of_flag_values_1_and_64', 'combination_of_flag_values_2_and_64', 'combination_of_flag_values_1_and_2_and_64', 'combination_of_flag_values_8_and_64', 'combination_of_flag_values_1_and_8_and_64', 'combination_of_flag_values_2_and_8_and_64', 'combination_of_flag_values_1_and_2_and_4_and_8_and_16_and_32_and_64'] flag_values : [0, 1, 2, 3, 8, 9, 10, 64, 65, 66, 67, 72, 73, 74, 127] long_name : Flag standard_name : soil_moisture_content status_flag valid_range : [0, 255]
Array
Chunk
Bytes
60.90 GiB
31.64 MiB
Shape
(15767, 720, 1440)
(16, 720, 720)
Dask graph
1972 chunks in 2 graph layers
Data type
float32 numpy.ndarray
1440
720
15767
freqbandID
(time, lat, lon)
float32
dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
_CoordinateAxes : time lat lon bit_meanings : ['NaN', 'L14', 'C53', 'C66', 'C68', 'C69', 'C73', 'X107', 'K194', 'MODEL'] bits : ['0b0', '0b1', '0b10', '0b100', '0b1000', '0b10000', '0b100000', '0b1000000', '0b10000000', '0b100000000'] dtype : int16 flag_meanings : ['NaN', 'C66', 'X107', 'C66+X107'] flag_values : [0, 4, 64, 68] long_name : Frequency Band Identification valid_range : [0, 511]
Array
Chunk
Bytes
60.90 GiB
31.64 MiB
Shape
(15767, 720, 1440)
(16, 720, 720)
Dask graph
1972 chunks in 2 graph layers
Data type
float32 numpy.ndarray
1440
720
15767
mode
(time, lat, lon)
float32
dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
_CoordinateAxes : time lat lon bit_meanings : ['NaN', 'ascending', 'descending'] bits : ['0b0', '0b1', '0b10'] dtype : int8 flag_meanings : ['NaN', 'ascending', 'descending', 'ascending_descending_combination'] flag_values : [0, 1, 2, 3] long_name : Satellite Mode valid_range : [0, 3]
Array
Chunk
Bytes
60.90 GiB
31.64 MiB
Shape
(15767, 720, 1440)
(16, 720, 720)
Dask graph
1972 chunks in 2 graph layers
Data type
float32 numpy.ndarray
1440
720
15767
sensor
(time, lat, lon)
float64
dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
_CoordinateAxes : time lat lon bit_meanings : ['NaN', 'SMMR', 'SSMI', 'TMI', 'AMSRE', 'WindSat', 'AMSR2', 'SMOS', 'AMIWS', 'ASCATA', 'ASCATB', 'SMAP', 'MODEL', 'GPM', 'FY3B', 'FY3D', 'ASCATC', 'FY3C'] bits : ['0b0', '0b1', '0b10', '0b100', '0b1000', '0b10000', '0b100000', '0b1000000', '0b10000000', '0b100000000', '0b1000000000', '0b10000000000', '0b100000000000', '0b1000000000000', '0b10000000000000', '0b100000000000000', '0b1000000000000000', '0b10000000000000000'] dtype : int32 flag_meanings : ['NaN', 'SMMR'] flag_values : [0, 1] long_name : Sensor valid_range : [0, 131071]
Array
Chunk
Bytes
121.80 GiB
63.28 MiB
Shape
(15767, 720, 1440)
(16, 720, 720)
Dask graph
1972 chunks in 2 graph layers
Data type
float64 numpy.ndarray
1440
720
15767
sm
(time, lat, lon)
float32
dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
_CoordinateAxes : time lat lon ancillary_variables : sm_uncertainty flag t0 dtype : float32 long_name : Volumetric Soil Moisture standard_name : soil_moisture_content units : m3 m-3 valid_range : [0.0, 1.0]
Array
Chunk
Bytes
60.90 GiB
31.64 MiB
Shape
(15767, 720, 1440)
(16, 720, 720)
Dask graph
1972 chunks in 2 graph layers
Data type
float32 numpy.ndarray
1440
720
15767
sm_uncertainty
(time, lat, lon)
float32
dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
_CoordinateAxes : time lat lon dtype : float32 long_name : Volumetric Soil Moisture Uncertainty standard_name : soil_moisture_content standard_error units : m3 m-3 valid_range : [0.0, 1.0]
Array
Chunk
Bytes
60.90 GiB
31.64 MiB
Shape
(15767, 720, 1440)
(16, 720, 720)
Dask graph
1972 chunks in 2 graph layers
Data type
float32 numpy.ndarray
1440
720
15767
t0
(time, lat, lon)
float64
dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
_CoordinateAxes : time lat lon dtype : float64 long_name : Observation Timestamp units : days after 1970-01-01 00:00:00 UTC valid_range : [3225.0, 3227.0]
Array
Chunk
Bytes
121.80 GiB
63.28 MiB
Shape
(15767, 720, 1440)
(16, 720, 720)
Dask graph
1972 chunks in 2 graph layers
Data type
float64 numpy.ndarray
1440
720
15767
Indexes: (3)
PandasIndex
PandasIndex(Float64Index([ 89.875, 89.625, 89.375, 89.125, 88.875, 88.625, 88.375,
88.125, 87.875, 87.625,
...
-87.625, -87.875, -88.125, -88.375, -88.625, -88.875, -89.125,
-89.375, -89.625, -89.875],
dtype='float64', name='lat', length=720)) PandasIndex
PandasIndex(Float64Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625,
-178.375, -178.125, -177.875, -177.625,
...
177.625, 177.875, 178.125, 178.375, 178.625, 178.875,
179.125, 179.375, 179.625, 179.875],
dtype='float64', name='lon', length=1440)) PandasIndex
PandasIndex(DatetimeIndex(['1978-11-01', '1978-11-02', '1978-11-03', '1978-11-04',
'1978-11-05', '1978-11-06', '1978-11-07', '1978-11-08',
'1978-11-09', '1978-11-10',
...
'2021-12-22', '2021-12-23', '2021-12-24', '2021-12-25',
'2021-12-26', '2021-12-27', '2021-12-28', '2021-12-29',
'2021-12-30', '2021-12-31'],
dtype='datetime64[ns]', name='time', length=15767, freq=None)) Attributes: (44)
Conventions : CF-1.9 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 contact : cci_sm_contact@eodc.eu creator_email : cci_sm_developer@eodc.eu creator_name : Department of Geodesy and Geoinformation, Vienna University of Technology creator_url : https://climers.geo.tuwien.ac.at/ date_created : File created: 2022-04-06 12:31:39.526359 geospatial_lat_max : 90.0 geospatial_lat_min : -90.0 geospatial_lat_resolution : 0.25 degree geospatial_lat_units : degrees_north geospatial_lon_max : 180.0 geospatial_lon_min : -180.0 geospatial_lon_resolution : 0.25 degree geospatial_lon_units : degrees_east geospatial_vertical_max : 0.0 geospatial_vertical_min : 0.0 history : 2022-04-06 12:29:35 - product produced
2023-08-09 17:33:51 - converted by nc2zarr, version 1.1.2.dev0 id : ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-20211231000000-fv07.1.nc institution : TU Wien (AUT); VanderSat B.V. (NL) key_variables : sm keywords : soil moisture, soil water content, microwave remote sensing, active, passive, combined, climate data record keywords_vocabulary : NASA Global Change Master Directory (GCMD) Science Keywords license : Data use is free and open for all registered users. naming_authority : TU Wien platform : Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1, MIRAS, SMAP, GPM, FengYun-3B, FengYun-3C, FengYun-3D; ERS-1, ERS-2, METOP-A, METOP-B processing_level : Quality-controlled, super-collocated (L3S) Satellite Soil Moisture (SM) data from multiple sensors product_version : v07.1 project : Climate Change Initiative - European Space Agency 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 sensor : SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP_radiometer, GMI, VIRR-3B, VIRR-3C, VIRR-3D; AMI-WS, ASCAT-A, ASCAT-B, ASCAT-C source : WARP 5.5R1.1/AMI-WS/ERS12 Level 2 Soil Moisture; WARP 5.4R1.0/AMI-WS/ERS2 Level 2 Soil Moisture; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling;; LPRMv7/SMMR/Nimbus 7 L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/SSMI/F08, F11, F13 DMSP L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/TMI/TRMM L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/AMSR-E/Aqua L2B Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/WINDSAT/CORIOLIS L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/AMSR2/GCOM-W1 L3 Surface Soil Moisture, Ancillary Params; LPRMv7/SMOS/MIRAS L3 Surface Soil Moisture, CATDS Level 3 Brightness Temperatures (L3TB) version 300 RE03 & RE04; LPRMv7/SMAP_radiometer/SMAP L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/GMI/GPM L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3B L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3C L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3D L3 Surface Soil Moisture, Ancillary Params, and quality flags;; spatial_resolution : 25km standard_name_vocabulary : CF Standard Name Table v77 summary : This dataset was produced with funding of the ESA CCI+ Soil Moisture project; ESRIN Contract No: 4000126684/19/I-NB time_coverage_duration : P43Y time_coverage_end : 2021-12-31 00:00:00 time_coverage_end_product : 20211231T235959Z time_coverage_resolution : P1D time_coverage_start : 1978-11-01 00:00:00 time_coverage_start_product : 19781101T000000Z title : ESA CCI Surface Soil Moisture COMBINED active+passive Product tracking_id : ad35798e-58e0-488f-b5b9-593874a47700