Dataset: ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020), version 2.00#
Dataset identifier: esacci.LST.day.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.NIGHT
Data store: cciodp
How to open this dataset in AVL JupyterLab
cciodp_store = new_data_store('cciodp')
ds = cciodp_store.open_data('esacci.LST.day.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.NIGHT')
Bounding box map#
cciodp_store = new_data_store('cciodp')
ds = cciodp_store.open_data('esacci.LST.day.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.NIGHT')
Map tiles and data from OpenStreetMap, under the ODbL.
Basic information#
Parameter | Value |
---|---|
Bounding box longitude (°) | -180.0 to 180.0 |
Bounding box latitude (°) | -90.0 to 90.0 |
Time range | 1995-08-01 to 2020-12-31 |
Time period | 1D |
Click here for full dataset metadata.
Variable list#
Click on a variable name to jump to the variable’s full metadata.
Variable | Long name | Units |
---|---|---|
dtime | time difference from reference time | seconds |
satze | satellite zenith angle | degrees |
sataz | satellite azimuth angle | degrees |
solze | solar zenith angle | degrees |
solaz | solar azimuth angle | degrees |
qual_flag | Quality Flags | [none] |
lst | land surface temperature | kelvin |
lst_uncertainty | land surface temperature total uncertainty | kelvin |
lst_unc_ran | uncertainty from uncorrelated errors | kelvin |
lst_unc_loc_atm | uncertainty from locally correlated errors on atmospheric scales | kelvin |
lst_unc_loc_sfc | uncertainty from locally correlated errors on surface scales | kelvin |
lst_unc_sys | uncertainty from large-scale systematic errors | kelvin |
lcc | land cover class | 1 |
lst_unc_loc_cor | uncertainty from locally correlated errors on LST corrections | kelvin |
Full variable metadata#
dtime#
Field | Value |
---|---|
long_name | time difference from reference time |
units | seconds |
valid_min | 0.0 |
valid_max | 86400.0 |
coordinates | lon lat |
orig_data_type | float32 |
fill_value | -32768.0 |
size | 5638248000000 |
shape | 8701, 18000, 36000 |
chunk_sizes | 1, 1000, 1000 |
file_chunk_sizes | 1, 1000, 1000 |
data_type | float32 |
dimensions | time, lat, lon |
file_dimensions | time, lat, lon |
satze#
Field | Value |
---|---|
long_name | satellite zenith angle |
units | degrees |
add_offset | 0.0 |
scale_factor | 0.01 |
valid_min | 0 |
valid_max | 18000 |
coordinates | lon lat |
orig_data_type | int16 |
fill_value | -32768 |
size | 5638248000000 |
shape | 8701, 18000, 36000 |
chunk_sizes | 1, 1000, 1000 |
file_chunk_sizes | 1, 1000, 1000 |
data_type | int16 |
dimensions | time, lat, lon |
file_dimensions | time, lat, lon |
sataz#
Field | Value |
---|---|
long_name | satellite azimuth angle |
units | degrees |
add_offset | 0.0 |
scale_factor | 0.01 |
valid_min | -18000 |
valid_max | 18000 |
coordinates | lon lat |
orig_data_type | int16 |
fill_value | -32768 |
size | 5638248000000 |
shape | 8701, 18000, 36000 |
chunk_sizes | 1, 1000, 1000 |
file_chunk_sizes | 1, 1000, 1000 |
data_type | int16 |
dimensions | time, lat, lon |
file_dimensions | time, lat, lon |
solze#
Field | Value |
---|---|
long_name | solar zenith angle |
units | degrees |
add_offset | 0.0 |
scale_factor | 0.01 |
valid_min | 0 |
valid_max | 18000 |
coordinates | lon lat |
orig_data_type | int16 |
fill_value | -32768 |
size | 5638248000000 |
shape | 8701, 18000, 36000 |
chunk_sizes | 1, 1000, 1000 |
file_chunk_sizes | 1, 1000, 1000 |
data_type | int16 |
dimensions | time, lat, lon |
file_dimensions | time, lat, lon |
solaz#
Field | Value |
---|---|
long_name | solar azimuth angle |
units | degrees |
add_offset | 0.0 |
scale_factor | 0.01 |
valid_min | -18000 |
valid_max | 18000 |
coordinates | lon lat |
orig_data_type | int16 |
fill_value | -32768 |
size | 5638248000000 |
shape | 8701, 18000, 36000 |
chunk_sizes | 1, 1000, 1000 |
file_chunk_sizes | 1, 1000, 1000 |
data_type | int16 |
dimensions | time, lat, lon |
file_dimensions | time, lat, lon |
qual_flag#
Field | Value |
---|---|
long_name | Quality Flags |
valid_min | 0 |
coordinates | lon lat |
flag_meanings | observation_proximity_to_local_time-1_is_closest_observation |
flag_masks | 1 |
valid_max | 1 |
orig_data_type | int16 |
fill_value | -32768 |
size | 5638248000000 |
shape | 8701, 18000, 36000 |
chunk_sizes | 1, 1000, 1000 |
file_chunk_sizes | 1, 1000, 1000 |
data_type | int16 |
dimensions | time, lat, lon |
file_dimensions | time, lat, lon |
lst#
Field | Value |
---|---|
long_name | land surface temperature |
units | kelvin |
add_offset | 273.15 |
scale_factor | 0.01 |
valid_min | -8315 |
valid_max | 7685 |
coordinates | lon lat |
orig_data_type | int16 |
fill_value | -32768 |
size | 5638248000000 |
shape | 8701, 18000, 36000 |
chunk_sizes | 1, 1000, 1000 |
file_chunk_sizes | 1, 1000, 1000 |
data_type | int16 |
dimensions | time, lat, lon |
file_dimensions | time, lat, lon |
lst_uncertainty#
Field | Value |
---|---|
long_name | land surface temperature total uncertainty |
units | kelvin |
add_offset | 0.0 |
scale_factor | 0.001 |
valid_min | 0 |
valid_max | 10000 |
coordinates | lon lat |
orig_data_type | int16 |
fill_value | -32768 |
size | 5638248000000 |
shape | 8701, 18000, 36000 |
chunk_sizes | 1, 1000, 1000 |
file_chunk_sizes | 1, 1000, 1000 |
data_type | int16 |
dimensions | time, lat, lon |
file_dimensions | time, lat, lon |
lst_unc_ran#
Field | Value |
---|---|
long_name | uncertainty from uncorrelated errors |
units | kelvin |
add_offset | 0.0 |
scale_factor | 0.001 |
valid_min | 0 |
valid_max | 10000 |
coordinates | lon lat |
orig_data_type | int16 |
fill_value | -32768 |
size | 5638248000000 |
shape | 8701, 18000, 36000 |
chunk_sizes | 1, 1000, 1000 |
file_chunk_sizes | 1, 1000, 1000 |
data_type | int16 |
dimensions | time, lat, lon |
file_dimensions | time, lat, lon |
lst_unc_loc_atm#
Field | Value |
---|---|
long_name | uncertainty from locally correlated errors on atmospheric scales |
units | kelvin |
add_offset | 0.0 |
scale_factor | 0.001 |
valid_min | 0 |
valid_max | 10000 |
coordinates | lon lat |
orig_data_type | int16 |
fill_value | -32768 |
size | 5638248000000 |
shape | 8701, 18000, 36000 |
chunk_sizes | 1, 1000, 1000 |
file_chunk_sizes | 1, 1000, 1000 |
data_type | int16 |
dimensions | time, lat, lon |
file_dimensions | time, lat, lon |
lst_unc_loc_sfc#
Field | Value |
---|---|
long_name | uncertainty from locally correlated errors on surface scales |
units | kelvin |
add_offset | 0.0 |
scale_factor | 0.001 |
valid_min | 0 |
valid_max | 10000 |
coordinates | lon lat |
orig_data_type | int16 |
fill_value | -32768 |
size | 5638248000000 |
shape | 8701, 18000, 36000 |
chunk_sizes | 1, 1000, 1000 |
file_chunk_sizes | 1, 1000, 1000 |
data_type | int16 |
dimensions | time, lat, lon |
file_dimensions | time, lat, lon |
lst_unc_sys#
Field | Value |
---|---|
long_name | uncertainty from large-scale systematic errors |
units | kelvin |
add_offset | 0.0 |
scale_factor | 0.001 |
valid_min | 0 |
valid_max | 10000 |
orig_data_type | int16 |
fill_value | -32768 |
size | 5638248000000 |
shape | 8701, 18000, 36000 |
chunk_sizes | 1, 1000, 1000 |
file_chunk_sizes | 1, 1000, 1000 |
data_type | int16 |
dimensions | time, lat, lon |
file_dimensions | time, lat, lon |
lcc#
Field | Value |
---|---|
long_name | land cover class |
units | 1 |
flag_meanings | cropland_rainfed cropland_rainfed_herbaceous_cover cropland_rainfed_tree_or_shrub_cover cropland_irrigated mosaic_cropland mosaic_natural_vegetation tree_broadleaved_evergreen_closed_to_open tree_broadleaved_deciduous_closed_to_open tree_broadleaved_deciduous_closed tree_broadleaved_deciduous_open tree_needleleaved_evergreen_closed_to_open tree_needleleaved_evergreen_closed tree_needleleaved_evergreen_open tree_needleleaved_deciduous_closed_to_open tree_needleleaved_deciduous_closed tree_needleleaved_deciduous_open tree_mixed mosaic_tree_and_shrub mosaic_herbaceous shrubland shrubland_evergreen shrubland_deciduous grassland lichens_and_mosses sparse_vegetation sparse_tree sparse_shrub sparse_herbaceous tree_cover_flooded_fresh_or_brakish_water tree_cover_flooded_saline_water shrub_or_herbaceous_cover_flooded urban Bare_areas_of_soil_types_not_contained_in_biomes_21_to_25 Unconsolidated_bare_areas_of_soil_types_not_contained_in_biomes_21_to_25 Consolidated_bare_areas_of_soil_types_not_contained_in_biomes_21_to_25 Bare_areas_of_soil_type_Entisols_Orthents Bare_areas_of_soil_type_Shifting_sand Bare_areas_of_soil_type_Aridisols_Calcids Bare_areas_of_soil_type_Aridisols_Cambids Bare_areas_of_soil_type_Gelisols_Orthels water snow_and_ice Sea_ice |
flag_values | 10, 11, 12, 20, 30, 40, 50, 60, 61, 62, 70, 71, 72, 80, 81, 82, 90, 100, 110, 120, 121, 122, 130, 140, 150, 151, 152, 153, 160, 170, 180, 190, 200, 201, 202, 203, 204, 205, 206, 207, 210, 220, 230 |
valid_min | 10 |
valid_max | 230 |
coordinates | lon lat |
orig_data_type | int16 |
fill_value | -32768 |
size | 5638248000000 |
shape | 8701, 18000, 36000 |
chunk_sizes | 1, 1000, 1000 |
file_chunk_sizes | 1, 1000, 1000 |
data_type | int16 |
dimensions | time, lat, lon |
file_dimensions | time, lat, lon |
lst_unc_loc_cor#
Field | Value |
---|---|
long_name | uncertainty from locally correlated errors on LST corrections |
units | kelvin |
add_offset | 0.0 |
scale_factor | 0.001 |
valid_min | 0 |
valid_max | 10000 |
coordinates | lon lat |
orig_data_type | int16 |
fill_value | -32768 |
size | 5638248000000 |
shape | 8701, 18000, 36000 |
chunk_sizes | 1, 1000, 1000 |
file_chunk_sizes | 1, 1000, 1000 |
data_type | int16 |
dimensions | time, lat, lon |
file_dimensions | time, lat, lon |
Full dataset metadata#
Field | Value |
---|---|
title | ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020), version 2.00 |
source | ESA LST CCI ATSR_2 L3U V3.00 |
history | Created using software developed at University of Leicester |
references | https://climate.esa.int/en/projects/land-surface-temperature |
product_version | 2-00 |
comment | These data were produced as part of the ESA LST CCI project. |
project | Climate Change Initiative - European Space Agency |
ecv | LST |
institute | University of Leicester |
processing_level | L3S |
product_string | IRCDR |
data_type | LST |
abstract | This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water. Daytime and night-time temperatures are provided in separate files corresponding to 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class. The dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3A (SLSTRA); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra) to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from August 1995 to July 2002; AATSR from August 2002 to March 2012; MODIS Terra from April 2012 to July 2016; and SLSTRA from August 2016 to December 2020. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width. The dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml). LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. Full Earth coverage is achieved in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface. Dataset coverage starts on 1st August 1995 and ends on 31st December 2020. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies. The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain. The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards. |
publication_date | 2022-02-25T09:30:26 |
uuid | ef8ce37b6af24469a2a4bdc31d3db27d |
catalog_url | https://catalogue.ceda.ac.uk/uuid/ef8ce37b6af24469a2a4bdc31d3db27d |
sensor_id | multi-sensor |
platform_id | multi-platform |
cci_project | LST |