Sensitivity of global terrestrial ecosystems to 14 years of climate variability
We derived monthly time-series of four key terrestrial ecosystem variables at 0.05 degree (~5km) resolution from observations by the MODIS sensor on Terra (AM) for the period February 2010-December 2013 inclusive. We used the MOD13C2 product (Huete et al 2002) which comprises monthly, global Enhanc...
| Main Authors: | , , , , |
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| Format: | Dataset |
| Published: |
University of Oxford
2016
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| Subjects: |
| _version_ | 1826294052538023936 |
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| author | Seddon, A Fauria, M Long, P Benz, D Willis, K |
| author2 | Seddon, A |
| author_facet | Seddon, A Seddon, A Fauria, M Long, P Benz, D Willis, K |
| author_sort | Seddon, A |
| collection | OXFORD |
| description | We derived monthly time-series of four key terrestrial ecosystem variables at 0.05 degree (~5km) resolution from observations by the MODIS sensor on Terra (AM) for the period February 2010-December 2013 inclusive.
We used the MOD13C2 product (Huete et al 2002) which comprises monthly, global Enhanced Vegetation Index (EVI) at 0.05 degree resolution.
We used the MOD07_L2 Atmospheric Profile product (Seeman et al 2006) as a measure of air temperature . Five-minue swaths of Retrieved Temperature Profile were projected to geographic co-ordinates. Pixels from the highest available pressure level, corresponding to the temperature closest to the Earth's surface, were selected from each swath. Swaths were then mean-mosaiced into global daily grids, and the daily global grids were mean-composited to monthly grids of air temperature.
We used the MOD35_L2 Cloud Mask product (Ackerman et al 2010). This product provides daily records on the presence of cloudy vs cloudless skies, and we used this to make an index of the proportion of of cloudy to clear days in a given pixel. After conversion to geographic co-ordinates, five-minute swaths at 1-km resolution were reclassed as clear sky or cloudy, and these daily swaths were mean-mosaiced to global coverages, mean composited from daily to monthly, and mean-aggregated from 1km to 0.05 degree.
We used the MOD16 Global Evapotranspiration product (Mu et al 2011) to calculate the monthly 0.05 degree ratio of Actual to Potential Evapotranspiration (AET/PET).
These data are provided in four zip files evi.zip, airtemp.zip, aetpet.zip, cloudiness.zip, each containing 167 text files, one per month of available data.
EVI
format = ascii text file
projection = geographic projection
spatial resolution = 0.05 degrees
min x = -180
max x = 180
min y = -60
max x = 90
rows = 3000
cols = 7200
bit depth = 16 bit signed integer
nodata (sea) = -9999
missing data (on land) = -999
units = dimensionless
scale factor = 10000 (divide the value by 10000 to get EVI)
filenames = yyyy_mm_evi_pt05deg.txt
Air temperature
format = ascii text file
projection = geographic projection
spatial resolution = 0.05 degrees
min x = -180
max x = 180
min y = -60
max x = 90
rows = 3000
cols = 7200
bit depth = 16 bit signed integer
nodata (sea) = -9999
missing data (on land) = -999
units = degrees C
scale factor = 1 (divide the value by 1 to get Air temperature)
filenames = yyyy_mm_airtemp_pt05deg.txt
AET/PET
format = ascii text file
projection = geographic projection
spatial resolution = 0.05 degrees
min x = -180
max x = 180
min y = -60
max x = 90
rows = 3000
cols = 7200
bit depth = 16 bit signed integer
nodata (sea) = -9999
missing data (on land) = -999
units = dimensionless
scale factor = 10000 (divide the value by 10000 to get AET/PET)
filenames = yyyy_mm_aetpet_pt05deg.txt
Cloudiness
format = ascii text file
projection = geographic projection
spatial resolution = 0.05 degrees
min x = -180
max x = 180
min y = -60
max x = 90
rows = 3000
cols = 7200
bit depth = 16 bit signed integer
nodata (sea) = -9999
missing data (on land) = -999
units = percentage of days in the month which were cloudy
scale factor = 100 (divide the value by 100 to get percentage cloudy days)
filenames = yyyy_mm_ins_pt05deg.txt
References
Ackerman, S. et al. (2010) Discriminating clear-sky from cloud with MODIS: Algorithm Theoretical Basis Document (MOD35), Version 6.1. . at
Huete, A. et al. (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83, 195–213.
Mu, Q., Zhao, M., Running, S.R. (2011) Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment 115, 1781-1800
Seeman, S. W., Borbas, E. E., Li, J., Menzel, W. P. & Gumley, L. E. (2006) MODIS Atmospheric Profile Retrieval Algorithm Theoretical Basis Document, Version 6 (URL: http://modis-atmos.gsfc.nasa.gov/_docs/MOD07_atbd_v7_April2011.pdf) |
| first_indexed | 2024-03-07T03:39:41Z |
| format | Dataset |
| id | oxford-uuid:bd744beb-dddb-4854-9326-daaeacecfe1b |
| institution | University of Oxford |
| last_indexed | 2024-03-07T03:39:41Z |
| publishDate | 2016 |
| publisher | University of Oxford |
| record_format | dspace |
| spelling | oxford-uuid:bd744beb-dddb-4854-9326-daaeacecfe1b2022-03-27T05:32:04ZSensitivity of global terrestrial ecosystems to 14 years of climate variabilityDatasethttp://purl.org/coar/resource_type/c_ddb1uuid:bd744beb-dddb-4854-9326-daaeacecfe1bEngORA DepositUniversity of Oxford2016Seddon, AFauria, MLong, PBenz, DWillis, KSeddon, AWillis, KFauria, MLong, PBenz, DWe derived monthly time-series of four key terrestrial ecosystem variables at 0.05 degree (~5km) resolution from observations by the MODIS sensor on Terra (AM) for the period February 2010-December 2013 inclusive. We used the MOD13C2 product (Huete et al 2002) which comprises monthly, global Enhanced Vegetation Index (EVI) at 0.05 degree resolution. We used the MOD07_L2 Atmospheric Profile product (Seeman et al 2006) as a measure of air temperature . Five-minue swaths of Retrieved Temperature Profile were projected to geographic co-ordinates. Pixels from the highest available pressure level, corresponding to the temperature closest to the Earth's surface, were selected from each swath. Swaths were then mean-mosaiced into global daily grids, and the daily global grids were mean-composited to monthly grids of air temperature. We used the MOD35_L2 Cloud Mask product (Ackerman et al 2010). This product provides daily records on the presence of cloudy vs cloudless skies, and we used this to make an index of the proportion of of cloudy to clear days in a given pixel. After conversion to geographic co-ordinates, five-minute swaths at 1-km resolution were reclassed as clear sky or cloudy, and these daily swaths were mean-mosaiced to global coverages, mean composited from daily to monthly, and mean-aggregated from 1km to 0.05 degree. We used the MOD16 Global Evapotranspiration product (Mu et al 2011) to calculate the monthly 0.05 degree ratio of Actual to Potential Evapotranspiration (AET/PET). These data are provided in four zip files evi.zip, airtemp.zip, aetpet.zip, cloudiness.zip, each containing 167 text files, one per month of available data. EVI format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = dimensionless scale factor = 10000 (divide the value by 10000 to get EVI) filenames = yyyy_mm_evi_pt05deg.txt Air temperature format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = degrees C scale factor = 1 (divide the value by 1 to get Air temperature) filenames = yyyy_mm_airtemp_pt05deg.txt AET/PET format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = dimensionless scale factor = 10000 (divide the value by 10000 to get AET/PET) filenames = yyyy_mm_aetpet_pt05deg.txt Cloudiness format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = percentage of days in the month which were cloudy scale factor = 100 (divide the value by 100 to get percentage cloudy days) filenames = yyyy_mm_ins_pt05deg.txt References Ackerman, S. et al. (2010) Discriminating clear-sky from cloud with MODIS: Algorithm Theoretical Basis Document (MOD35), Version 6.1. . at Huete, A. et al. (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83, 195–213. Mu, Q., Zhao, M., Running, S.R. (2011) Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment 115, 1781-1800 Seeman, S. W., Borbas, E. E., Li, J., Menzel, W. P. & Gumley, L. E. (2006) MODIS Atmospheric Profile Retrieval Algorithm Theoretical Basis Document, Version 6 (URL: http://modis-atmos.gsfc.nasa.gov/_docs/MOD07_atbd_v7_April2011.pdf) |
| spellingShingle | Eng Seddon, A Fauria, M Long, P Benz, D Willis, K Sensitivity of global terrestrial ecosystems to 14 years of climate variability |
| title | Sensitivity of global terrestrial ecosystems to 14 years of climate variability |
| title_full | Sensitivity of global terrestrial ecosystems to 14 years of climate variability |
| title_fullStr | Sensitivity of global terrestrial ecosystems to 14 years of climate variability |
| title_full_unstemmed | Sensitivity of global terrestrial ecosystems to 14 years of climate variability |
| title_short | Sensitivity of global terrestrial ecosystems to 14 years of climate variability |
| title_sort | sensitivity of global terrestrial ecosystems to 14 years of climate variability |
| topic | Eng |
| work_keys_str_mv | AT seddona sensitivityofglobalterrestrialecosystemsto14yearsofclimatevariability AT fauriam sensitivityofglobalterrestrialecosystemsto14yearsofclimatevariability AT longp sensitivityofglobalterrestrialecosystemsto14yearsofclimatevariability AT benzd sensitivityofglobalterrestrialecosystemsto14yearsofclimatevariability AT willisk sensitivityofglobalterrestrialecosystemsto14yearsofclimatevariability |