Datasources

['MODIS', 'MOD13C1_VIM_NATIVE']

MODIS TERRA 16-day composite NDVI (MOD13C1) at 5km CMG\n\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13C1) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. The Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the MOD13A2 products that overlap the month and employs a weighted temporal average. Global MOD13C2 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG.

['MODIS', 'MYD13C1_VIM_NATIVE']

MODIS AQUA 16-day composite NDVI (MYD13C1) at 5km CMG\n\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MYD13C1) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. The Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the MOD13A2 products that overlap the month and employs a weighted temporal average. Global MOD13C2 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG.

['MODIS', 'MOD13A2_VIM_NATIVE']

MODIS TERRA 16-day composite NDVI (MOD13A2) at 1km sinusoidal tiles\n\nThe Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value.

['MODIS', 'MYD13A2_VIM_NATIVE']

MODIS AQUA 16-day composite NDVI (MYD13A2) at 1km sinusoidal tiles\n\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MYD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value.

['MODIS', 'MXD13C1_VIM_DEKAD']

MODIS dekadal NDVI filtered and gapfilled at 5km CMG.\n\nThis product combines both MODIS TERRA (MOD13C1) and AQUA (MYD13C1) datasets to create an interleaved 8-day NDVI timeseries, which is subsequenty filtered and gapfilled using a Whittaker filter to remove atmospherical noise. The resulting filtered product is then downsampled to a daily timegrid and aggregated to dekad timesteps. The values are unitless (NDVI) and scaled by a factor of 0.0001.

['MODIS', 'NDVI_SMOOTHED_1KM']

MODIS dekadal NDVI filtered and gapfilled at 1km sinusoidal tiles.\n\nThis product combines both MODIS TERRA (MOD13A2) and AQUA (MYD13A2) datasets to create an interleaved 8-day NDVI timeseries, which is subsequenty filtered and gapfilled using a Whittaker filter to remove atmospherical noise. The resulting filtered product is then downsampled to a daily timegrid and aggregated to dekad timesteps. The values are unitless (NDVI) and scaled by a factor of 0.0001.

['MODIS', '250M_16_DAYS_NDVI']

The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.

['MODIS', 'MYD11A2_TDA_NATIVE']

MODIS Aqua 8-day composite Daytime Land Surface Temperature (MYD11A2) at 1km sinusoidal tiles\n\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MYD11A2) Version 6.1 product provides Land Surface Temperature and Emissivity (LST&E) values with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. This collection contains items with only the daytime land surface temperature. The values are in degree Kelvin and scaled by a factor of 0.02.

['MODIS', 'MYD11A2_TNA_NATIVE']

MODIS Aqua 8-day composite Nighttime Land Surface Temperature (MYD11A2) at 1km sinusoidal tiles\n\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MYD11A2) Version 6.1 product provides Land Surface Temperature and Emissivity (LST&E) values with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. This collection contains items with only the nighttime land surface temperature. The values are in degree Kelvin and scaled by a factor of 0.02.

['MODIS', 'MYD11C2_TDA_NATIVE']

MODIS Aqua 8-day composite Daytime Land Surface Temperature  (MYD11C2) at 5km CMG\n\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MYD11C2) Version 6.1 product provides Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule follows a geographic grid with 7,200 columns and 3,600 rows, representing the entire globe. The LST&E values in the MYD11C2 product are derived by compositing and averaging the values from the corresponding eight MYD11C1 daily files.

['MODIS', 'MYD11C2_TNA_NATIVE']

MODIS Aqua 8-day composite Nighttime Land Surface Temperature  (MYD11C2) at 5km CMG\n\nThe Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MYD11C2) Version 6.1 product provides Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule follows a geographic grid with 7,200 columns and 3,600 rows, representing the entire globe. The LST&E values in the MYD11C2 product are derived by compositing and averaging the values from the corresponding eight MYD11C1 daily files.

['MODIS', 'LST_SMOOTHED_1KM_TDA']

MODIS filtered and gapfilled dekadal Land Surface Temperature (LST) at 1km sinusoidal tiles\n\nThis product contains filtered and gapfilled MODIS daytime and nighttime dekad Land Surface Temperature (LST) at 1km resolution sinusoidal tiles. The raw LST timeseries are filtered and gapfilled independently using a Whittaker filter to remove atmospherical noise. The resulting filtered products are then downsampled to a daily timegrid and aggregated to dekad timesteps. The values are in degree Kelvin and scaled by a factor of 0.02.

['MODIS', 'LST_SMOOTHED_1KM_TNA']

MODIS filtered and gapfilled dekadal Land Surface Temperature (LST) at 1km sinusoidal tiles\n\nThis product contains filtered and gapfilled MODIS daytime and nighttime dekad Land Surface Temperature (LST) at 1km resolution sinusoidal tiles. The raw LST timeseries are filtered and gapfilled independently using a Whittaker filter to remove atmospherical noise. The resulting filtered products are then downsampled to a daily timegrid and aggregated to dekad timesteps. The values are in degree Kelvin and scaled by a factor of 0.02.

['MODIS', 'LST_SMOOTHED_5KM_TDA']

MODIS filtered and gapfilled dekadal Land Surface Temperature (LST) at 5km CMG\n\nThis product contains filtered and gapfilled MODIS daytime and nighttime Land Surface Temperature (LST) at 5km resolution CMG. The raw LST timeseries are filtered and gapfilled independently using a Whittaker filter to remove atmospherical noise. The resulting filtered products are then downsampled to a daily timegrid and aggregated to dekad timesteps. The values are in degree Kelvin and scaled by a factor of 0.02.

['MODIS', 'LST_SMOOTHED_5KM_TNA']

MODIS filtered and gapfilled dekadal Land Surface Temperature (LST) at 5km CMG\n\nThis product contains filtered and gapfilled MODIS daytime and nighttime Land Surface Temperature (LST) at 5km resolution CMG. The raw LST timeseries are filtered and gapfilled independently using a Whittaker filter to remove atmospherical noise. The resulting filtered products are then downsampled to a daily timegrid and aggregated to dekad timesteps. The values are in degree Kelvin and scaled by a factor of 0.02.

['MODIS', 'LST_SMOOTHED']

MODIS filtered and gapfilled dekadal Land Surface Temperature (LST) at 1km sinusoidal tiles\n\nThis product contains filtered and gapfilled MODIS daytime and nighttime dekad Land Surface Temperature (LST) at 1km resolution sinusoidal tiles. The raw LST timeseries are filtered and gapfilled independently using a Whittaker filter to remove atmospherical noise. The resulting filtered products are then downsampled to a daily timegrid and aggregated to dekad timesteps. The values are in degree Kelvin and scaled by a factor of 0.02.

['MODIS', 'NDVI_SMOOTHED']

MODIS dekadal NDVI filtered and gapfilled at 250m sinusoidal tiles.\n\nThis product combines both MODIS TERRA (MOD13Q1) and AQUA (MYD13Q1) datasets to create an interleaved 8-day NDVI timeseries, which is subsequenty filtered and gapfilled using a Whittaker filter to remove atmospherical noise. The resulting filtered product is then downsampled to a daily timegrid and aggregated to dekad timesteps. The values are unitless (NDVI) and scaled by a factor of 0.0001.

['MODIS', 'MYD11C2_TDA_DEKAD_LTA']

Long term average (mean) for the filtered and gapfilled MODIS dekad daytime and nighttime LSTs at 5km CMG, calculated over a reference period from 2002-07-01 to 2018-07-01. The values are in degree Kelvin and scaled by a factor of 0.02.

['MODIS', 'MYD11C2_TDA_DEKAD']

MODIS filtered and gapfilled dekadal Land Surface Temperature (LST) at 5km CMG\n\nThis product contains filtered and gapfilled MODIS daytime and nighttime Land Surface Temperature (LST) at 5km resolution CMG. The raw LST timeseries are filtered and gapfilled independently using a Whittaker filter to remove atmospherical noise. The resulting filtered products are then downsampled to a daily timegrid and aggregated to dekad timesteps. The values are in degree Kelvin and scaled by a factor of 0.02.

['MODIS', 'MYD11C2_TDD_DEKAD']

Anomalies of filtered and gapfilled dekad daytime and nighttime Land Surface Temperatures (LST) in relation to the long term average, expressed in degrees difference and with scale factor 0.02.

['MODIS', 'MYD11C2_TND_DEKAD']

Anomalies of filtered and gapfilled dekad daytime and nighttime Land Surface Temperatures (LST) in relation to the long term average, expressed in degrees difference and with scale factor 0.02.

['MODIS', 'MXD13A2_VIM_DEKAD']

MODIS dekadal NDVI filtered and gapfilled at 1km sinusoidal tiles.\n\nThis product combines both MODIS TERRA (MOD13A2) and AQUA (MYD13A2) datasets to create an interleaved 8-day NDVI timeseries, which is subsequenty filtered and gapfilled using a Whittaker filter to remove atmospherical noise. The resulting filtered product is then downsampled to a daily timegrid and aggregated to dekad timesteps. The values are unitless (NDVI) and scaled by a factor of 0.0001.

['MODIS', 'MXD13A2_VIM_DEKAD_LTA']

Long term average (mean) for the filtered and gapfilled MODIS NDVI at 1km sinusoidal tiles per dekad, calculated over a reference period from 2002-07-01 to 2018-07-01. The values are unitless (NDVI) and scaled by a factor of 0.0001.

['MODIS', 'MXD13C1_VIM_DEKAD_LTA']

Long term average (mean) for the filtered and gapfilled MODIS NDVI at 5km CMG per dekad, calculated over a reference period from 2002-07-01 to 2018-07-01. The values are unitless (NDVI) and scaled by a factor of 0.0001.

['MODIS', 'LST_SMOOTHED_5KM']

MODIS filtered and gapfilled dekadal Land Surface Temperature (LST) at 5km CMG\n\nThis product contains filtered and gapfilled MODIS daytime and nighttime Land Surface Temperature (LST) at 5km resolution CMG. The raw LST timeseries are filtered and gapfilled independently using a Whittaker filter to remove atmospherical noise. The resulting filtered products are then downsampled to a daily timegrid and aggregated to dekad timesteps. The values are in degree Kelvin and scaled by a factor of 0.02.

['MODIS', 'SUR_REFL_STATE_500M']

The Moderate Resolution Imaging Spectroradiometer (MODIS) 09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.

['MODIS', 'CLOUD_MASK']

['CHIRP', 'RFR_DAILY']

Satellite-only Climate Hazards group Infrared Precipitation (CHIRP) is a 30+ year quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes), starting in 1981 to near-present.

['CHIRP', 'RFR_DEKAD']

Satellite-only Climate Hazards group Infrared Precipitation (CHIRP) is a 30+ year quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes), starting in 1981 to near-present.

['CHIRPS', 'RFH_DAILY']

Daily Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS) version 2\n\nClimate Hazards center InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes), starting in 1981 to near-present, CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. This collection contains a moving update of CHIRPS final and prelim.

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Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p. ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CHIRPS-2.0/docs/USGS-DS832.CHIRPS.pdf

['CHIRPS', 'RFH_DAILY_PCT']

This collection contains the rainfall percentiles used to calculate the number of high, intense and extreme rainfall days: these are 75th, 90th and 95th percentiles.

['CHIRPS', 'RFH_DAILY_PCT_75']

This collection contains the rainfall percentiles used to calculate the number of high, intense and extreme rainfall days: these are 75th, 90th and 95th percentiles.

['CHIRPS', 'RFH_DAILY_PCT_90']

This collection contains the rainfall percentiles used to calculate the number of high, intense and extreme rainfall days: these are 75th, 90th and 95th percentiles.

['CHIRPS', 'RFH_DAILY_PCT_95']

This collection contains the rainfall percentiles used to calculate the number of high, intense and extreme rainfall days: these are 75th, 90th and 95th percentiles.

['CHIRPS', 'RFH_DEKAD']

Dekadal Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS) version 2

Climate Hazards center InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes), starting in 1981 to near-present, CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. This collection contains a moving update of CHIRPS final and prelim,

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Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p. ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CHIRPS-2.0/docs/USGS-DS832.CHIRPS.pdf

['CHIRPS', 'R1H_DEKAD']

Aggregation of dekadal CHIRPS data over 1 month. The aggregation is performed using a look-back, covering the specified duration and ending with the dekad inidcated in the filename.

['CHIRPS', 'R2H_DEKAD']

Aggregation of dekadal CHIRPS data over 2 months. The aggregation is performed using a look-back, covering the specified duration and ending with the dekad inidcated in the filename.

['CHIRPS', 'R3H_DEKAD']

Aggregation of dekadal CHIRPS data over 3 months. The aggregation is performed using a look-back, covering the specified duration and ending with the dekad inidcated in the filename.

['CHIRPS', 'R4H_DEKAD']

Aggregation of dekadal CHIRPS data over 4 months. The aggregation is performed using a look-back, covering the specified duration and ending with the dekad inidcated in the filename.

['CHIRPS', 'R5H_DEKAD']

Aggregation of dekadal CHIRPS data over 5 month. The aggregation is performed using a look-back, covering the specified duration and ending with the dekad inidcated in the filename.

['CHIRPS', 'R6H_DEKAD']

Aggregation of dekadal CHIRPS data over 6 months. The aggregation is performed using a look-back, covering the specified duration and ending with the dekad inidcated in the filename.

['CHIRPS', 'R7H_DEKAD']

Aggregation of dekadal CHIRPS data over 7 months. The aggregation is performed using a look-back, covering the specified duration and ending with the dekad inidcated in the filename.

['CHIRPS', 'R8H_DEKAD']

Aggregation of dekadal CHIRPS data over 8 months. The aggregation is performed using a look-back, covering the specified duration and ending with the dekad inidcated in the filename.

['CHIRPS', 'R9H_DEKAD']

Aggregation of dekadal CHIRPS data over 9 months. The aggregation is performed using a look-back, covering the specified duration and ending with the dekad inidcated in the filename.

['CHIRPS', 'RYH_DEKAD']

Aggregation of dekadal CHIRPS data over 12 months. The aggregation is performed using a look-back, covering the specified duration and ending with the dekad inidcated in the filename.

['CHIRPS', 'RFH_DEKAD_LTA']

Long term average (mean) for Climate Hazards center InfraRed Precipitation with Station data (CHIRPS) v2 per dekad and for all processed aggregations, calculated over reference period from 1989-01-01 to 2018-12-31.

['CHIRPS', 'R1H_DEKAD_LTA']

Long term average (mean) for Climate Hazards center InfraRed Precipitation with Station data (CHIRPS) v2 per dekad and for all processed aggregations, calculated over reference period from 1989-01-01 to 2018-12-31.

['CHIRPS', 'R2H_DEKAD_LTA']

Long term average (mean) for Climate Hazards center InfraRed Precipitation with Station data (CHIRPS) v2 per dekad and for all processed aggregations, calculated over reference period from 1989-01-01 to 2018-12-31.

['CHIRPS', 'R3H_DEKAD_LTA']

Long term average (mean) for Climate Hazards center InfraRed Precipitation with Station data (CHIRPS) v2 per dekad and for all processed aggregations, calculated over reference period from 1989-01-01 to 2018-12-31.

['CHIRPS', 'R4H_DEKAD_LTA']

Long term average (mean) for Climate Hazards center InfraRed Precipitation with Station data (CHIRPS) v2 per dekad and for all processed aggregations, calculated over reference period from 1989-01-01 to 2018-12-31.

['CHIRPS', 'R5H_DEKAD_LTA']

Long term average (mean) for Climate Hazards center InfraRed Precipitation with Station data (CHIRPS) v2 per dekad and for all processed aggregations, calculated over reference period from 1989-01-01 to 2018-12-31.

['CHIRPS', 'R6H_DEKAD_LTA']

Long term average (mean) for Climate Hazards center InfraRed Precipitation with Station data (CHIRPS) v2 per dekad and for all processed aggregations, calculated over reference period from 1989-01-01 to 2018-12-31.

['CHIRPS', 'R7H_DEKAD_LTA']

Long term average (mean) for Climate Hazards center InfraRed Precipitation with Station data (CHIRPS) v2 per dekad and for all processed aggregations, calculated over reference period from 1989-01-01 to 2018-12-31.

['CHIRPS', 'R8H_DEKAD_LTA']

Long term average (mean) for Climate Hazards center InfraRed Precipitation with Station data (CHIRPS) v2 per dekad and for all processed aggregations, calculated over reference period from 1989-01-01 to 2018-12-31.

['CHIRPS', 'R9H_DEKAD_LTA']

Long term average (mean) for Climate Hazards center InfraRed Precipitation with Station data (CHIRPS) v2 per dekad and for all processed aggregations, calculated over reference period from 1989-01-01 to 2018-12-31.

['CHIRPS', 'RYH_DEKAD_LTA']

Long term average (mean) for Climate Hazards center InfraRed Precipitation with Station data (CHIRPS) v2 per dekad and for all processed aggregations, calculated over reference period from 1989-01-01 to 2018-12-31.

['CHIRPS', 'RFQ_DEKAD']

Anomaly of dekadal CHIRPS data compared to long term average (LTA). The anomaly is specified on percent relatvive to the LTA (i.e. > 100 indicating above LTA and < 100 below)

['CHIRPS', 'R1Q_DEKAD']

Anomaly of dekadal CHIRPS data over 1 month aggregation, compared to long term average (LTA). The anomaly is specified on percent relatvive to the LTA (i.e. > 100 indicating above LTA and < 100 below)

['CHIRPS', 'R2Q_DEKAD']

Anomaly of dekadal CHIRPS data over 2 months aggregation, compared to long term average (LTA). The anomaly is specified on percent relatvive to the LTA (i.e. > 100 indicating above LTA and < 100 below)

['CHIRPS', 'R3Q_DEKAD']

Anomaly of dekadal CHIRPS data over 3 months aggregation, compared to long term average (LTA). The anomaly is specified on percent relatvive to the LTA (i.e. > 100 indicating above LTA and < 100 below)

['CHIRPS', 'R4Q_DEKAD']

Anomaly of dekadal CHIRPS data over 4 months aggregation, compared to long term average (LTA). The anomaly is specified on percent relatvive to the LTA (i.e. > 100 indicating above LTA and < 100 below)

['CHIRPS', 'R5Q_DEKAD']

Anomaly of dekadal CHIRPS data over 5 months aggregation, compared to long term average (LTA). The anomaly is specified on percent relatvive to the LTA (i.e. > 100 indicating above LTA and < 100 below)

['CHIRPS', 'R6Q_DEKAD']

Anomaly of dekadal CHIRPS data over 6 months aggregation, compared to long term average (LTA). The anomaly is specified on percent relatvive to the LTA (i.e. > 100 indicating above LTA and < 100 below)

['CHIRPS', 'R7Q_DEKAD']

Anomaly of dekadal CHIRPS data over 7 months aggregation, compared to long term average (LTA). The anomaly is specified on percent relatvive to the LTA (i.e. > 100 indicating above LTA and < 100 below)

['CHIRPS', 'R8Q_DEKAD']

Anomaly of dekadal CHIRPS data over 8 months aggregation, compared to long term average (LTA). The anomaly is specified on percent relatvive to the LTA (i.e. > 100 indicating above LTA and < 100 below)

['CHIRPS', 'R9Q_DEKAD']

Anomaly of dekadal CHIRPS data over 9 months aggregation, compared to long term average (LTA). The anomaly is specified on percent relatvive to the LTA (i.e. > 100 indicating above LTA and < 100 below)

['CHIRPS', 'RYQ_DEKAD']

Anomaly of dekadal CHIRPS data over 12 months aggregation, compared to long term average (LTA). The anomaly is specified on percent relatvive to the LTA (i.e. > 100 indicating above LTA and < 100 below)

['CHIRPS', 'R1S_DEKAD']

Standardized Precipitation Index (SPI) of dekadal CHIRPS data over 1 month aggregation period. The units are "SPI values" scaled by a factor of 1000.

['CHIRPS', 'R2S_DEKAD']

Standardized Precipitation Index (SPI) of dekadal CHIRPS data over 2 months aggregation period. The units are "SPI values" scaled by a factor of 1000.

['CHIRPS', 'R3S_DEKAD']

Standardized Precipitation Index (SPI) of dekadal CHIRPS data over 3 months aggregation period. The units are "SPI values" scaled by a factor of 1000.

['CHIRPS', 'R6S_DEKAD']

Standardized Precipitation Index (SPI) of dekadal CHIRPS data over 6 months aggregation period. The units are "SPI values" scaled by a factor of 1000.

['CHIRPS', 'R9S_DEKAD']

Standardized Precipitation Index (SPI) of dekadal CHIRPS data over 9 months aggregation period. The units are "SPI values" scaled by a factor of 1000.

['CHIRPS', 'RYS_DEKAD']

Standardized Precipitation Index (SPI) of dekadal CHIRPS data over 12 months aggregation period. The units are "SPI values" scaled by a factor of 1000.

['CHIRPS', 'RDS_DEKAD']

Standardized Precipitation Index (SPI) of dekadal CHIRPS data over 24 months aggregation period. The units are "SPI values" scaled by a factor of 1000.

['CHIRPS', 'RHS_DEKAD']

Standardized Precipitation Index (SPI) of dekadal CHIRPS data over 36 months aggregation period. The units are "SPI values" scaled by a factor of 1000.

['CHIRPS', 'RPS_DEKAD']

Standardized Precipitation Index (SPI) of dekadal CHIRPS data over 60 months aggregation period. The units are "SPI values" scaled by a factor of 1000.

['CHIRPS', 'DLX_DEKAD']

The longest consecutive dry sequence (also called longest dryspell) is calculated from CHIRPS daily dataset, with a lookback period of 30 days, ending in last day of the dekad indicated by the timestamp. A dry sequence is considered as one or more consecutive days with rainfall below 2mm. The longest run is the maximum number of consecutive dry days in the lookback period.

['CHIRPS', 'R1H_AVG_SMOOTH_1996_2015']

None

['AGERA5', 'AIR_TEMPERATURE_24H_MEAN']

This dataset contains daily air temperature data (2m) for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5.

Data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.

['AGERA5', 'AIR_TEMPERATURE_DAY_MEAN']

This dataset contains daily air temperature data (2m) for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5.

Data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.

['AGERA5', 'AIR_TEMPERATURE_24H_MAX']

This dataset contains daily air temperature data (2m) for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5.

Data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.

['AGERA5', 'SOIL_MOIST']

This dataset contains dekadal average root zone soil moisture, interpolated from monthly, over Africa from 1981-February 2024. This is derived from layers 1-3 of ERA5 Land, with each layer weighted by depth (0.07 for layer 1, 0.21 for layer 2, and 0.72 for layer 3).

['WFP_RAM', 'BIMODAL_MASK_40YR']

1 for bimodal, 0 for unimodal, and nan for ocean, indicating the rainfall pattern of a given pixel.

['WFP_RAM', 'WAPOR_M1I_LTA']

The long-term average of P/ETP ratio, derived from the ratio of r1h LTA to e1p LTA.

['WFP_RAM', 'M1I_RELATIVE_THRESHOLD_WAPOR']

The relative threshold (0.1 to 0.35) of m1i, derived from the ratio of sum(rfh LTA) to sum(ET0 LTA) and constrained within the specified range.

['WFP_RAM', 'SOC_EOC']

The climatological start and end of season (as dekadal index) using relative threshold (0.1 to 0.35) on m1i LTA.

['WFP_RAM', 'SOC_EOC_ANOMALY']

The climatological start and end of season (as dekadal index) using anomaly based method on 40-year r1h (1982 to 2022).

['NOAA_PSL', 'ET0_DAILY']

Hobbins Evapotranspiration daily

['LANDSAT', 'NIR']

Landsat Collection 2 Level-2 Science Products, consisting of atmospherically corrected surface reflectance and surface temperature image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.

This dataset represents the global archive of Level-2 data from Landsat Collection 2 acquired by the Thematic Mapper onboard Landsat 4 and 5, the Enhanced Thematic Mapper onboard Landsat 7, and the Operatational Land Imager and Thermal Infrared Sensor onboard Landsat 8 and 9. Images are stored in cloud-optimized GeoTIFF format.

['LANDSAT', 'RED']

Landsat Collection 2 Level-2 Science Products, consisting of atmospherically corrected surface reflectance and surface temperature image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.

This dataset represents the global archive of Level-2 data from Landsat Collection 2 acquired by the Thematic Mapper onboard Landsat 4 and 5, the Enhanced Thematic Mapper onboard Landsat 7, and the Operatational Land Imager and Thermal Infrared Sensor onboard Landsat 8 and 9. Images are stored in cloud-optimized GeoTIFF format.

['LANDSAT', 'QA_PIXEL']

Landsat Collection 2 Level-2 Science Products, consisting of atmospherically corrected surface reflectance and surface temperature image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.

This dataset represents the global archive of Level-2 data from Landsat Collection 2 acquired by the Thematic Mapper onboard Landsat 4 and 5, the Enhanced Thematic Mapper onboard Landsat 7, and the Operatational Land Imager and Thermal Infrared Sensor onboard Landsat 8 and 9. Images are stored in cloud-optimized GeoTIFF format.

['LANDSAT', 'BLUE']

Landsat Collection 2 Level-2 Science Products, consisting of atmospherically corrected surface reflectance and surface temperature image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.

This dataset represents the global archive of Level-2 data from Landsat Collection 2 acquired by the Thematic Mapper onboard Landsat 4 and 5, the Enhanced Thematic Mapper onboard Landsat 7, and the Operatational Land Imager and Thermal Infrared Sensor onboard Landsat 8 and 9. Images are stored in cloud-optimized GeoTIFF format.

['LANDSAT', 'GREEN']

Landsat Collection 2 Level-2 Science Products, consisting of atmospherically corrected surface reflectance and surface temperature image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.

This dataset represents the global archive of Level-2 data from Landsat Collection 2 acquired by the Thematic Mapper onboard Landsat 4 and 5, the Enhanced Thematic Mapper onboard Landsat 7, and the Operatational Land Imager and Thermal Infrared Sensor onboard Landsat 8 and 9. Images are stored in cloud-optimized GeoTIFF format.

['LANDSAT', 'SWIR16']

Landsat Collection 2 Level-2 Science Products, consisting of atmospherically corrected surface reflectance and surface temperature image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.

This dataset represents the global archive of Level-2 data from Landsat Collection 2 acquired by the Thematic Mapper onboard Landsat 4 and 5, the Enhanced Thematic Mapper onboard Landsat 7, and the Operatational Land Imager and Thermal Infrared Sensor onboard Landsat 8 and 9. Images are stored in cloud-optimized GeoTIFF format.

['LANDSAT', 'SWIR22']

Landsat Collection 2 Level-2 Science Products, consisting of atmospherically corrected surface reflectance and surface temperature image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.

This dataset represents the global archive of Level-2 data from Landsat Collection 2 acquired by the Thematic Mapper onboard Landsat 4 and 5, the Enhanced Thematic Mapper onboard Landsat 7, and the Operatational Land Imager and Thermal Infrared Sensor onboard Landsat 8 and 9. Images are stored in cloud-optimized GeoTIFF format.

['LANDSAT', 'LWIR']

Landsat Collection 2 Level-2 Science Products, consisting of atmospherically corrected surface reflectance and surface temperature image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.

This dataset represents the global archive of Level-2 data from Landsat Collection 2 acquired by the Thematic Mapper onboard Landsat 4 and 5, the Enhanced Thematic Mapper onboard Landsat 7, and the Operatational Land Imager and Thermal Infrared Sensor onboard Landsat 8 and 9. Images are stored in cloud-optimized GeoTIFF format.

['LANDSAT', 'LWIR11']

Landsat Collection 2 Level-2 Science Products, consisting of atmospherically corrected surface reflectance and surface temperature image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.

This dataset represents the global archive of Level-2 data from Landsat Collection 2 acquired by the Thematic Mapper onboard Landsat 4 and 5, the Enhanced Thematic Mapper onboard Landsat 7, and the Operatational Land Imager and Thermal Infrared Sensor onboard Landsat 8 and 9. Images are stored in cloud-optimized GeoTIFF format.

['LANDSAT', 'NDVI_SMOOTHED']

NormalizedDifferenceIndex(key=['LANDSAT', 'NDVI_SMOOTHED'], description=None, scale=1, offset=0, min_val=0.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='LANDSAT', method=None, name='LANDSAT-NDVI-smoothed', smooth=True, mask_clouds=True, band_1='nir', band_2='red', upscale=True)

['LANDSAT', 'NDVI']

NormalizedDifferenceIndex(key=['LANDSAT', 'NDVI'], description=None, scale=1, offset=0, min_val=0.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='LANDSAT', method=None, name='LANDSAT-NDVI', smooth=False, mask_clouds=True, band_1='nir', band_2='red', upscale=True)

['LANDSAT', 'NDVI_RAW']

NormalizedDifferenceIndex(key=['LANDSAT', 'NDVI_RAW'], description=None, scale=1, offset=0, min_val=0.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='LANDSAT', method=None, name='LANDSAT-raw-NDVI', smooth=False, mask_clouds=False, band_1='nir', band_2='red', upscale=True)

['LANDSAT', 'NDWI']

NormalizedDifferenceIndex(key=['LANDSAT', 'NDWI'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='LANDSAT', method=None, name='LANDSAT-NDWI', smooth=False, mask_clouds=True, band_1='green', band_2='nir', upscale=True)

['LANDSAT', 'NDWI_SMOOTHED']

NormalizedDifferenceIndex(key=['LANDSAT', 'NDWI_SMOOTHED'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='LANDSAT', method=None, name='LANDSAT-NDWI-smoothed', smooth=True, mask_clouds=True, band_1='green', band_2='nir', upscale=True)

['LANDSAT', 'MNDWI']

NormalizedDifferenceIndex(key=['LANDSAT', 'MNDWI'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='LANDSAT', method=None, name='LANDSAT-MNDWI', smooth=False, mask_clouds=True, band_1='green', band_2='swir16', upscale=True)

['LANDSAT', 'MNDWI_SMOOTHED']

NormalizedDifferenceIndex(key=['LANDSAT', 'MNDWI_SMOOTHED'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='LANDSAT', method=None, name='LANDSAT-MNDWI-smoothed', smooth=True, mask_clouds=True, band_1='green', band_2='swir16', upscale=True)

['LANDSAT', 'NDMI']

NormalizedDifferenceIndex(key=['LANDSAT', 'NDMI'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='LANDSAT', method=None, name='LANDSAT-NDMI', smooth=False, mask_clouds=True, band_1='nir', band_2='swir16', upscale=True)

['LANDSAT', 'NDMI_SMOOTHED']

NormalizedDifferenceIndex(key=['LANDSAT', 'NDMI_SMOOTHED'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='LANDSAT', method=None, name='LANDSAT-NDMI-smoothed', smooth=True, mask_clouds=True, band_1='nir', band_2='swir16', upscale=True)

['LANDSAT', 'GRVI']

NormalizedDifferenceIndex(key=['LANDSAT', 'GRVI'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='LANDSAT', method=None, name='LANDSAT-GRVI', smooth=False, mask_clouds=True, band_1='green', band_2='red', upscale=True)

['LANDSAT', 'GRVI_SMOOTHED']

NormalizedDifferenceIndex(key=['LANDSAT', 'GRVI_SMOOTHED'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='LANDSAT', method=None, name='LANDSAT-GRVI-smoothed', smooth=True, mask_clouds=True, band_1='green', band_2='red', upscale=True)

['LANDSAT', 'NBR']

NormalizedDifferenceIndex(key=['LANDSAT', 'NBR'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='LANDSAT', method=None, name='LANDSAT-NBR', smooth=False, mask_clouds=True, band_1='nir', band_2='swir22', upscale=True)

['LANDSAT', 'NBR_SMOOTHED']

NormalizedDifferenceIndex(key=['LANDSAT', 'NBR_SMOOTHED'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='LANDSAT', method=None, name='LANDSAT-NBR-smoothed', smooth=True, mask_clouds=True, band_1='nir', band_2='swir22', upscale=True)

['LANDSAT', 'NBR2']

NormalizedDifferenceIndex(key=['LANDSAT', 'NBR2'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='LANDSAT', method=None, name='LANDSAT-NBR2', smooth=False, mask_clouds=True, band_1='swir16', band_2='swir22', upscale=True)

['LANDSAT', 'NBR2_SMOOTHED']

NormalizedDifferenceIndex(key=['LANDSAT', 'NBR2_SMOOTHED'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='LANDSAT', method=None, name='LANDSAT-NBR2-smoothed', smooth=True, mask_clouds=True, band_1='swir16', band_2='swir22', upscale=True)

['LANDSAT', 'LST_SMOOTHED']

Load the Landsat Land Surface Temperature, smoothed if requested.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
lst	DataArray	Loaded and pre-processed (cloud masking, data quality, smoothing if requested) LST.

['LANDSAT', 'LST']

Load the Landsat Land Surface Temperature, smoothed if requested.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
lst	DataArray	Loaded and pre-processed (cloud masking, data quality, smoothing if requested) LST.

['LANDSAT', 'BRIGHTNESS']

Load the Brightness Index defined by its product object.

Parameters:

Name	Type	Description	Default
product	SpectralIndex	SpectralIndex object defining the index	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	None
load_config	dict	Settings for how to load data, ex. chunking or resampling method	None
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	None
modified	bool	bool whether to use the classic brightness (red, green, blue) or the modified one (red, green, nir, swir)	False

Returns:

Name	Type	Description
brightness	DataArray	Computed and pre-processed (cloud masking, data quality) brightness index.

Additional information

Formulas:

sqrt(sum(bands**2)) / sqrt(len(bands)) with

    - bands = [red, green, blue] if modified=False

    - bands = [red, green, nir, swir] if modified=True

Application:

Soil characteristics (moisture, coverage, etc.)

Reference:

classic: https://www.sciencedirect.com/science/article/abs/pii/S0034425798000303

modified: https://www.sciencedirect.com/science/article/pii/S0034425718305145

['LANDSAT', 'MODIFIED_BRIGHTNESS']

Load the modified Brightness Index defined by its product object.

Parameters:

Name	Type	Description	Default
product	SpectralIndex	SpectralIndex object defining the index	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea ojbect, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
brightness	DataArray	Computed and pre-processed (cloud masking, data quality) modified brightness index.

Additional information

Formulas:

sqrt(sum(bands**2)) / sqrt(len(bands)) with bands = [red, green, nir, swir]

Application:

Soil characteristics (moisture, coverage, etc.)

Reference:

https://www.sciencedirect.com/science/article/pii/S0034425718305145

['LANDSAT', 'VNSIR']

Load VNSIR defined by its product object.

Visible-to-Shortwave-InfraRed Tendency Index (VNSIR)

Parameters:

Name	Type	Description	Default
product	SpectralIndex	SpectralIndex object defining the index	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
vnsir	DataArray	Computed and pre-processed (cloud masking, data quality) VNSIR index.

Additional information

Formulas:

1 - (2*red - green - blue + 3*(swir22 - nir))

Application:

Soil, bare soil detection

Reference:

https://www.nature.com/articles/s41598-020-61408-1

['LANDSAT', 'CLOUD_MASK']

['SENTINEL_2', 'NIR_RAW']

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

['SENTINEL_2', 'RED_RAW']

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

['SENTINEL_2', 'BLUE_RAW']

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

['SENTINEL_2', 'GREEN_RAW']

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

['SENTINEL_2', 'REDEDGE1_RAW']

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

['SENTINEL_2', 'REDEDGE2_RAW']

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

['SENTINEL_2', 'REDEDGE3_RAW']

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

['SENTINEL_2', 'REDEDGE4_RAW']

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

['SENTINEL_2', 'SWIR16_RAW']

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

['SENTINEL_2', 'SWIR22_RAW']

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

['SENTINEL_2', 'COASTAL_AEROSOL_RAW']

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

['SENTINEL_2', 'WATER_VAPOR_RAW']

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

['SENTINEL_2', 'SCL']

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

['SENTINEL_2', 'QA60']

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

['SENTINEL_2', 'TRUE_COLOR']

The Sentinel-2 program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using Sen2Cor and converted to cloud-optimized GeoTIFF format.

['SENTINEL_2', 'NDVI_SMOOTHED']

NormalizedDifferenceIndex(key=['SENTINEL_2', 'NDVI_SMOOTHED'], description=None, scale=1, offset=0, min_val=0.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='SENTINEL_2', method=None, name='SENTINEL-2-NDVI-smoothed', smooth=True, mask_clouds=True, band_1='nir', band_2='red', upscale=True)

['SENTINEL_2', 'NDVI']

NormalizedDifferenceIndex(key=['SENTINEL_2', 'NDVI'], description=None, scale=1, offset=0, min_val=0.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='SENTINEL_2', method=None, name='SENTINEL-2-NDVI', smooth=False, mask_clouds=True, band_1='nir', band_2='red', upscale=True)

['SENTINEL_2', 'NDVI_RAW']

NormalizedDifferenceIndex(key=['SENTINEL_2', 'NDVI_RAW'], description=None, scale=1, offset=0, min_val=0.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='SENTINEL_2', method=None, name='SENTINEL-2-NDVI', smooth=False, mask_clouds=False, band_1='nir', band_2='red', upscale=True)

['SENTINEL_2', 'NDWI']

NormalizedDifferenceIndex(key=['SENTINEL_2', 'NDWI'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='SENTINEL_2', method=None, name='SENTINEL-2-NDWI', smooth=False, mask_clouds=True, band_1='green', band_2='nir', upscale=True)

['SENTINEL_2', 'NDWI_SMOOTHED']

NormalizedDifferenceIndex(key=['SENTINEL_2', 'NDWI_SMOOTHED'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='SENTINEL_2', method=None, name='SENTINEL-2-NDWI-smoothed', smooth=True, mask_clouds=True, band_1='green', band_2='nir', upscale=True)

['SENTINEL_2', 'MNDWI']

NormalizedDifferenceIndex(key=['SENTINEL_2', 'MNDWI'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='SENTINEL_2', method=None, name='SENTINEL-2-MNDWI', smooth=False, mask_clouds=True, band_1='green', band_2='swir16', upscale=True)

['SENTINEL_2', 'MNDWI_SMOOTHED']

NormalizedDifferenceIndex(key=['SENTINEL_2', 'MNDWI_SMOOTHED'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='SENTINEL_2', method=None, name='SENTINEL-2-MNDWI-smoothed', smooth=True, mask_clouds=True, band_1='green', band_2='swir16', upscale=True)

['SENTINEL_2', 'NDMI']

NormalizedDifferenceIndex(key=['SENTINEL_2', 'NDMI'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='SENTINEL_2', method=None, name='SENTINEL-2-NDMI', smooth=False, mask_clouds=True, band_1='nir', band_2='swir16', upscale=True)

['SENTINEL_2', 'NDMI_SMOOTHED']

NormalizedDifferenceIndex(key=['SENTINEL_2', 'NDMI_SMOOTHED'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='SENTINEL_2', method=None, name='SENTINEL-2-NDMI-smoothed', smooth=True, mask_clouds=True, band_1='nir', band_2='swir16', upscale=True)

['SENTINEL_2', 'GRVI']

NormalizedDifferenceIndex(key=['SENTINEL_2', 'GRVI'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='SENTINEL_2', method=None, name='SENTINEL-2-GRVI', smooth=False, mask_clouds=True, band_1='green', band_2='red', upscale=True)

['SENTINEL_2', 'GRVI_SMOOTHED']

NormalizedDifferenceIndex(key=['SENTINEL_2', 'GRVI_SMOOTHED'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='SENTINEL_2', method=None, name='SENTINEL-2-GRVI-smoothed', smooth=True, mask_clouds=True, band_1='green', band_2='red', upscale=True)

['SENTINEL_2', 'NBR']

NormalizedDifferenceIndex(key=['SENTINEL_2', 'NBR'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='SENTINEL_2', method=None, name='SENTINEL-2-NBR', smooth=False, mask_clouds=True, band_1='nir', band_2='swir22', upscale=True)

['SENTINEL_2', 'NBR_SMOOTHED']

NormalizedDifferenceIndex(key=['SENTINEL_2', 'NBR_SMOOTHED'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='SENTINEL_2', method=None, name='SENTINEL-2-NBR-smoothed', smooth=True, mask_clouds=True, band_1='nir', band_2='swir22', upscale=True)

['SENTINEL_2', 'NBR2']

NormalizedDifferenceIndex(key=['SENTINEL_2', 'NBR2'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='SENTINEL_2', method=None, name='SENTINEL-2-NBR2', smooth=False, mask_clouds=True, band_1='swir16', band_2='swir22', upscale=True)

['SENTINEL_2', 'NBR2_SMOOTHED']

NormalizedDifferenceIndex(key=['SENTINEL_2', 'NBR2_SMOOTHED'], description=None, scale=1, offset=0, min_val=-1.0, max_val=1.0, nodata=nan, persist=True, product_band=None, satellite='SENTINEL_2', method=None, name='SENTINEL-2-NBR2-smoothed', smooth=True, mask_clouds=True, band_1='swir16', band_2='swir22', upscale=True)

['SENTINEL_2', 'NIR_HARMONIZED']

Load a raw Sentinel-2 band and harmonize it.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
da_harmonized	DataArray	DataArray of the harmonized S2 band.

['SENTINEL_2', 'RED_HARMONIZED']

Load a raw Sentinel-2 band and harmonize it.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
da_harmonized	DataArray	DataArray of the harmonized S2 band.

['SENTINEL_2', 'BLUE_HARMONIZED']

Load a raw Sentinel-2 band and harmonize it.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
da_harmonized	DataArray	DataArray of the harmonized S2 band.

['SENTINEL_2', 'GREEN_HARMONIZED']

Load a raw Sentinel-2 band and harmonize it.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
da_harmonized	DataArray	DataArray of the harmonized S2 band.

['SENTINEL_2', 'REDEDGE1_HARMONIZED']

Load a raw Sentinel-2 band and harmonize it.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
da_harmonized	DataArray	DataArray of the harmonized S2 band.

['SENTINEL_2', 'REDEDGE2_HARMONIZED']

Load a raw Sentinel-2 band and harmonize it.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
da_harmonized	DataArray	DataArray of the harmonized S2 band.

['SENTINEL_2', 'REDEDGE3_HARMONIZED']

Load a raw Sentinel-2 band and harmonize it.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
da_harmonized	DataArray	DataArray of the harmonized S2 band.

['SENTINEL_2', 'REDEDGE4_HARMONIZED']

Load a raw Sentinel-2 band and harmonize it.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
da_harmonized	DataArray	DataArray of the harmonized S2 band.

['SENTINEL_2', 'SWIR16_HARMONIZED']

Load a raw Sentinel-2 band and harmonize it.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
da_harmonized	DataArray	DataArray of the harmonized S2 band.

['SENTINEL_2', 'SWIR22_HARMONIZED']

Load a raw Sentinel-2 band and harmonize it.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
da_harmonized	DataArray	DataArray of the harmonized S2 band.

['SENTINEL_2', 'COASTAL_AEROSOL_HARMONIZED']

Load a raw Sentinel-2 band and harmonize it.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
da_harmonized	DataArray	DataArray of the harmonized S2 band.

['SENTINEL_2', 'WATER_VAPOR_HARMONIZED']

Load a raw Sentinel-2 band and harmonize it.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
da_harmonized	DataArray	DataArray of the harmonized S2 band.

['SENTINEL_2', 'CLOUD_MASK']

['SENTINEL_2', 'BRIGHTNESS']

Load the Brightness Index defined by its product object.

Parameters:

Name	Type	Description	Default
product	SpectralIndex	SpectralIndex object defining the index	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	None
load_config	dict	Settings for how to load data, ex. chunking or resampling method	None
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	None
modified	bool	bool whether to use the classic brightness (red, green, blue) or the modified one (red, green, nir, swir)	False

Returns:

Name	Type	Description
brightness	DataArray	Computed and pre-processed (cloud masking, data quality) brightness index.

Additional information

Formulas:

sqrt(sum(bands**2)) / sqrt(len(bands)) with

    - bands = [red, green, blue] if modified=False

    - bands = [red, green, nir, swir] if modified=True

Application:

Soil characteristics (moisture, coverage, etc.)

Reference:

classic: https://www.sciencedirect.com/science/article/abs/pii/S0034425798000303

modified: https://www.sciencedirect.com/science/article/pii/S0034425718305145

['SENTINEL_2', 'MODIFIED_BRIGHTNESS']

Load the modified Brightness Index defined by its product object.

Parameters:

Name	Type	Description	Default
product	SpectralIndex	SpectralIndex object defining the index	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea ojbect, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
brightness	DataArray	Computed and pre-processed (cloud masking, data quality) modified brightness index.

Additional information

Formulas:

sqrt(sum(bands**2)) / sqrt(len(bands)) with bands = [red, green, nir, swir]

Application:

Soil characteristics (moisture, coverage, etc.)

Reference:

https://www.sciencedirect.com/science/article/pii/S0034425718305145

['SENTINEL_2', 'MCARI_OSAVI']

Load MCARI/OSAVI defined by its product object.

Modified Chlorophyll Absorption in Reflectance / Optimized Soil Adjusted Vegetation Index (MCARI/OSAVI)

Parameters:

Name	Type	Description	Default
product	SpectralIndex	SpectralIndex object defining the index	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
mcari_osavi	DataArray	Computed and pre-processed (cloud masking, data quality) MCARI/OSAVI index.

Additional information

Formulas:

(((RE1 - R) - 0.2 * (RE1 - G)) * (RE1 / R)) / (1.16 * (N - R) / (N + R + 0.16))

Application:

Vegetation, helps to characterize leaf chlorophyll concentration compared to classic vegetation indices.

Reference:

- https://www.sciencedirect.com/science/article/abs/pii/S0034425700001139

['SENTINEL_2', 'RECI']

Load the Red-Edge Carbon Index (RE-CI) defined by its product object.

Parameters:

Name	Type	Description	Default
product	SpectralIndex	SpectralIndex object defining the index	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
reci	DataArray	Computed and pre-processed (cloud masking, data quality) RE-CI index.

Additional information

Formulas:

mean(rededge1, rededge2)

Application:

Soil, soil organic carbon mapping

Reference:

https://www.mdpi.com/2072-4292/11/18/2121

['SENTINEL_2', 'RRECI']

Load the Red-Red-Edge Carbon Index (RRE-CI) defined by its product object.

Parameters:

Name	Type	Description	Default
product	SpectralIndex	SpectralIndex object defining the index	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
rreci	DataArray	Computed and pre-processed (cloud masking, data quality) RRE-CI index.

Additional information

Formulas:

mean(red, rededge1)

Application:

Soil, soil organic carbon mapping

Reference:

https://www.mdpi.com/2072-4292/11/18/2121

['SENTINEL_2', 'VNSIR']

Load VNSIR defined by its product object.

Visible-to-Shortwave-InfraRed Tendency Index (VNSIR)

Parameters:

Name	Type	Description	Default
product	SpectralIndex	SpectralIndex object defining the index	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
vnsir	DataArray	Computed and pre-processed (cloud masking, data quality) VNSIR index.

Additional information

Formulas:

1 - (2*red - green - blue + 3*(swir22 - nir))

Application:

Soil, bare soil detection

Reference:

https://www.nature.com/articles/s41598-020-61408-1

['SENTINEL_1', 'VV']

The Sentinel-1 mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Sentinel-1 Radiometrically Terrain Corrected (RTC) data in this collection is a radiometrically terrain corrected product derived from the Ground Range Detected (GRD) Level-1 products produced by the European Space Agency. The RTC processing is performed by Catalyst.

Radiometric Terrain Correction accounts for terrain variations that affect both the position of a given point on the Earth's surface and the brightness of the radar return, as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover-induced backscatter differences. Additionally, comparison of backscatter from multiple satellites, modes, or tracks loses meaning.

A Planetary Computer account is required to retrieve SAS tokens to read the RTC data. See the documentation for more information.

Methodology

The Sentinel-1 GRD product is converted to calibrated intensity using the conversion algorithm described in the ESA technical note ESA-EOPG-CSCOP-TN-0002, Radiometric Calibration of S-1 Level-1 Products Generated by the S-1 IPF. The flat earth calibration values for gamma correction (i.e. perpendicular to the radar line of sight) are extracted from the GRD metadata. The calibration coefficients are applied as a two-dimensional correction in range (by sample number) and azimuth (by time). All available polarizations are calibrated and written as separate layers of a single file. The calibrated SAR output is reprojected to nominal map orientation with north at the top and west to the left.

The data is then radiometrically terrain corrected using PlanetDEM as the elevation source. The correction algorithm is nominally based upon D. Small, “Flattening Gamma: Radiometric Terrain Correction for SAR Imagery”, IEEE Transactions on Geoscience and Remote Sensing, Vol 49, No 8., August 2011, pp 3081-3093. For each image scan line, the digital elevation model is interpolated to determine the elevation corresponding to the position associated with the known near slant range distance and arc length for each input pixel. The elevations at the four corners of each pixel are estimated using bilinear resampling. The four elevations are divided into two triangular facets and reprojected onto the plane perpendicular to the radar line of sight to provide an estimate of the area illuminated by the radar for each earth flattened pixel. The uncalibrated sum at each earth flattened pixel is normalized by dividing by the flat earth surface area. The adjustment for gamma intensity is given by dividing the normalized result by the cosine of the incident angle. Pixels which are not illuminated by the radar due to the viewing geometry are flagged as shadow.

Calibrated data is then orthorectified to the appropriate UTM projection. The orthorectified output maintains the original sample sizes (in range and azimuth) and was not shifted to any specific grid.

RTC data is processed only for the Interferometric Wide Swath (IW) mode, which is the main acquisition mode over land and satisfies the majority of service requirements.

['SENTINEL_1', 'VH']

The Sentinel-1 mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Sentinel-1 Radiometrically Terrain Corrected (RTC) data in this collection is a radiometrically terrain corrected product derived from the Ground Range Detected (GRD) Level-1 products produced by the European Space Agency. The RTC processing is performed by Catalyst.

Radiometric Terrain Correction accounts for terrain variations that affect both the position of a given point on the Earth's surface and the brightness of the radar return, as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover-induced backscatter differences. Additionally, comparison of backscatter from multiple satellites, modes, or tracks loses meaning.

A Planetary Computer account is required to retrieve SAS tokens to read the RTC data. See the documentation for more information.

Methodology

The Sentinel-1 GRD product is converted to calibrated intensity using the conversion algorithm described in the ESA technical note ESA-EOPG-CSCOP-TN-0002, Radiometric Calibration of S-1 Level-1 Products Generated by the S-1 IPF. The flat earth calibration values for gamma correction (i.e. perpendicular to the radar line of sight) are extracted from the GRD metadata. The calibration coefficients are applied as a two-dimensional correction in range (by sample number) and azimuth (by time). All available polarizations are calibrated and written as separate layers of a single file. The calibrated SAR output is reprojected to nominal map orientation with north at the top and west to the left.

The data is then radiometrically terrain corrected using PlanetDEM as the elevation source. The correction algorithm is nominally based upon D. Small, “Flattening Gamma: Radiometric Terrain Correction for SAR Imagery”, IEEE Transactions on Geoscience and Remote Sensing, Vol 49, No 8., August 2011, pp 3081-3093. For each image scan line, the digital elevation model is interpolated to determine the elevation corresponding to the position associated with the known near slant range distance and arc length for each input pixel. The elevations at the four corners of each pixel are estimated using bilinear resampling. The four elevations are divided into two triangular facets and reprojected onto the plane perpendicular to the radar line of sight to provide an estimate of the area illuminated by the radar for each earth flattened pixel. The uncalibrated sum at each earth flattened pixel is normalized by dividing by the flat earth surface area. The adjustment for gamma intensity is given by dividing the normalized result by the cosine of the incident angle. Pixels which are not illuminated by the radar due to the viewing geometry are flagged as shadow.

Calibrated data is then orthorectified to the appropriate UTM projection. The orthorectified output maintains the original sample sizes (in range and azimuth) and was not shifted to any specific grid.

RTC data is processed only for the Interferometric Wide Swath (IW) mode, which is the main acquisition mode over land and satisfies the majority of service requirements.

['FLDAS_NOAH_CENTRAL_ASIA', 'SNOW_SOIL_MOISTURE_DEKAD']

None

['CROP', 'GDHY']

None

['CROPMASK', 'JRC']

JRC ASAP (Anomaly hot Spots of Agricultural Production) global crop and rangeland masks version 03 at ~1km resolution. Each pixel represents the area fraction of the specific cover (i.e. percentage of the pixel with crops/rangelands). To convert DN to %, multiply by 0.5 scalefactor.

---

Pérez-Hoyos, Ana (2018): Global crop and rangeland masks. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-10112-10005

['RANGELANDMASK', 'JRC']

JRC ASAP (Anomaly hot Spots of Agricultural Production) global crop and rangeland masks version 03 at ~1km resolution. Each pixel represents the area fraction of the specific cover (i.e. percentage of the pixel with crops/rangelands). To convert DN to %, multiply by 0.5 scalefactor.

---

Pérez-Hoyos, Ana (2018): Global crop and rangeland masks. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-10112-10005

['FAO', 'WAPOR_ET0_DEKAD']

Reference evapotranspiration per dekad with a spatial resolution of 0.1 degree. Unit: mm dekad-1. The dataset contains dekadal values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56.

The input variables are part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S).

The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5.

---

References: https://doi.org/10.24381/cds.6c68c9bb

['FAO', 'WAPOR_ET0_DAILY']

Reference evapotranspiration per day with a spatial resolution of 0.1 degree. Unit: mm day-1. The dataset contains daily values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56.

The input variables are part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S).

The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5.

---

References: https://doi.org/10.24381/cds.6c68c9bb

['FAO', 'WAPOR_ET0_DEKAD_LTA']

The long-term average of WAPOR ET0 (dekadal sum)

['FAO', 'WAPOR_E1P_LTA']

The long-term average of monthly aggregated ET0 LTA, aligned with the aggregation period of r1h LTA.

['DEA', 'NDVI_CLIM']

Monthly NDVI Climatologies produced by Digital Earth Africa.

['DEM', 'DEM_COPERNICUS_30']

The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs and comes from the Copernicus DEM 2021 release.

['DEM', 'HEM_COPERNICUS_30']

The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs and comes from the Copernicus DEM 2021 release.

['DEM', 'EDM_COPERNICUS_30']

The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs and comes from the Copernicus DEM 2021 release.

['DEM', 'FLM_COPERNICUS_30']

The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs and comes from the Copernicus DEM 2021 release.

['DEM', 'WBM_COPERNICUS_30']

The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs and comes from the Copernicus DEM 2021 release.

['DEM', 'SLOPE_COPERNICUS_30']

Compute and load the slope of the Earth's surface in degrees.

Compute the slope from the elevation of the Earth's surface given by the Copernicus' DEM at 30m resolution.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
slope	DataArray	DataArray of slope in degrees.

Additional information

Reference:

https://xarray-spatial.org/reference/_autosummary/xrspatial.slope.slope.html

['DEM', 'SLOPE_ASPECT_COPERNICUS_30']

Compute and load the slope aspect of the Earth's surface.

Compute the slope apsect from the elevation of the Earth's surface given by the Copernicus' DEM at 30m resolution.

Parameters:

Name	Type	Description	Default
product	CallablePipeline	CallablePipeline object	required
geobox	GeoBox	odc.geo.Geobox object specifying desired CRS, extent and resolution.	required
dates	DatetimeLike	DatetimeLike range of the data to fetch. When None, it defaults to 'area.datetime_range'.	required
area_config	dict	Analysis settings from AnalysisArea object, ex. cloud masking parameters.	required
load_config	dict	Settings for how to load data, ex. chunking or resampling method.	required
extra_config	dict	Dictionary with additional configuration options that doesn't affect the output dataset.	required

Returns:

Name	Type	Description
slope	DataArray	DataArray of slope in degrees.

Additional information

Interpretation of the values Downward slope direction of each pixel based on the elevation of its neighbors in a 3x3 grid. The value is measured clockwise in degrees with 0 (due north), and 360 (again due north). Values along the edges are not calculated. Direction of the aspect can be determined by its value:

        From 0 to 22.5: North
        From 22.5 to 67.5: Northeast
        From 67.5 to 112.5: East
        From 112.5 to 157.5: Southeast
        From 157.5 to 202.5: South
        From 202.5 to 247.5: West
        From 247.5 to 292.5: Northwest
        From 337.5 to 360: North

Note that values of -1 denote flat areas.

Reference:

https://xarray-spatial.org/reference/_autosummary/xrspatial.aspect.aspect.html

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE1']

The seasonal forecasts issued in 01 since 1981 produced by the SEAS5 model of ECMWF

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE2']

The seasonal forecasts issued in 02 since 1981 produced by the SEAS5 model of ECMWF

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE3']

The seasonal forecasts issued in 03 since 1981 produced by the SEAS5 model of ECMWF

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE4']

The seasonal forecasts issued in 04 since 1981 produced by the SEAS5 model of ECMWF

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE5']

The seasonal forecasts issued in 05 since 1981 produced by the SEAS5 model of ECMWF

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE6']

The seasonal forecasts issued in 06 since 1981 produced by the SEAS5 model of ECMWF

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE7']

The seasonal forecasts issued in 07 since 1981 produced by the SEAS5 model of ECMWF

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE8']

The seasonal forecasts issued in 08 since 1981 produced by the SEAS5 model of ECMWF

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE9']

The seasonal forecasts issued in 09 since 1981 produced by the SEAS5 model of ECMWF

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE10']

The seasonal forecasts issued in 10 since 1981 produced by the SEAS5 model of ECMWF

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE11']

The seasonal forecasts issued in 11 since 1981 produced by the SEAS5 model of ECMWF

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE12']

The seasonal forecasts issued in 12 since 1981 produced by the SEAS5 model of ECMWF

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE1_DAILY']

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE2_DAILY']

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE3_DAILY']

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE4_DAILY']

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE5_DAILY']

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE6_DAILY']

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE7_DAILY']

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE8_DAILY']

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE9_DAILY']

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE10_DAILY']

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE11_DAILY']

['ECMWF', 'RFH_FORECASTS_SEAS5_ISSUE12_DAILY']

['TEST', 'TESTZARRSATELLITEPRODUCT']

None