---TITLE: swi82-03

---AUTHOR(S): Martha Raynolds, Josefino Comiso, Donald Walker

-Name(s) of PI: Donald A. Walker

-Complete mailing address: 311 Irving, P.O. Box 757000, Fairbanks, AK 99775
-Telephone: 907-474-6720
-Web pages: www.arcticatlas.org
-PI E-mail address: DAWalker@alaska.edu

-E-mail address for data questions: MKRaynolds@alaska.edu

---FUNDING SOURCE AND GRANT NUMBER:OPP-ARCSS grant #0531180

---DATA SET OVERVIEW:
Identification_Information:
Citation:
Citation_Information:
Originator: Martha Raynolds
Publication_Date: 2008
Title: swi82-03
Geospatial_Data_Presentation_Form: raster digital data
Other_Citation_Details: Raynolds, M. K., J. C. Comiso, D. A. Walker, and D. Verbyla. 2008. Relationship between satellite-derived land surface temperatures, arctic vegetation types, and NDVI. Remote Sensing of Environment 112: 1884-1894.
Online_Linkage: http://www.arcticatlas.org
Description:
Abstract:
Summer Warmth Index average from 1982-2003
This data set was calculated from monthly AVHRR thermal infrared data (Comiso 2003). Summer Warmth Index (SWI) is the sum of monthly mean temperatures above 0 degrees Celsius. The months of May-September were evaluated for the years 1982 - 2003.

Comiso, J. C. 2003. Warming trends in the Arctic from clear sky satellite observations. Journal of Climate 16:3498-3510.
Purpose: A temperature dataset relevant to arctic vegetation, developed by Martha Raynolds as part of her Doctoral Dissertation work at the University of Alaska Fairbanks
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1982
Beginning_Time: May
Ending_Date: 2003
Ending_Time: September
Currentness_Reference: ground condition
Status:
Progress: Final
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -180.000000
East_Bounding_Coordinate: 180.000000
North_Bounding_Coordinate: 90.000000
South_Bounding_Coordinate: 30.980000
Keywords:
Theme:
Theme_Keyword_Thesaurus: none
Theme_Keyword: Summer Warmth Index
Theme_Keyword: SWI
Theme_Keyword: temperature
Theme_Keyword: land surface temperature
Theme_Keyword: LST
Place:
Place_Keyword: Arctic
Place_Keyword: circumpolar
Place_Keyword: Alaska
Place_Keyword: Canada
Place_Keyword: Greenland
Place_Keyword: Iceland
Place_Keyword: Norway
Place_Keyword: Russia
Place_Keyword: Svalbard
Place_Keyword: United States
Access_Constraints: none
Use_Constraints: Data provided by the Alaska Geobotany Center are copyright protected under the provisions of a Creative Commons Attribution-NonCommercial-ShareAlike License (http://creativecommons.org/licenses/by-nc-sa/1.0/). Materials may be freely downloaded, reprinted and redistributed for non-commercial use. Reproduction and redistribution of the materials on this web site, including derivative works, shall give credit to the Alaska Geobotany Center and remain subject to the above license. Any web pages that use this material shall contain a link pointing to the Alaska Geobotany Center home page (http://www.geobotany.uaf.edu/).
Point_of_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Martha Raynolds
Contact_Organization: University of Alaska Fairbanks
Contact_Position: Research Faculty
Contact_Address:
Address_Type: mailing address
Address: Institute of Arctic Biology
Address: 311 Irving, P.O. Box 757000
City: Fairbanks
State_or_Province: AK
Postal_Code: 99775-7000
Country: USA
Contact_Voice_Telephone: 907-474-6720
Contact_Voice_Telephone: 907-474-2459
Contact_Electronic_Mail_Address: mkraynolds@alaska.edu
Contact_Instructions: www.arcticatlas.org
Native_Data_Set_Environment: Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 3; ESRI ArcCatalog 9.3.1.1850
Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
Comiso, J. C. 2000. Variability and trends in Antarctic surface temperatures from in situ and satellite infrared measurements. Journal of Climate 13:1674-1696.

Comiso, J. C. 2003. Warming trends in the Arctic from clear sky satellite observations. Journal of Climate 16: 3498-3510.

Comiso, J. C. 2006. Arctic warming signals from satellite observations. Weather 61: 70-76.
Logical_Consistency_Report:
1 Temperature data set
1.1 Reasons for choosing the AVHRR temperature data
We chose the AVHRR temperature data because of the relatively detailed spatial resolution over the entire polar region, and the long time period spanned by the record. The AVHRR is a horizontally scanning radiometer with a swath width of 2900 km and a field-of-view of 1 mrad, thereby providing data at a spatial resolution of 1.1 km at nadir. Continuous global coverage, however, is available only in a sub-sampled format at about 5 by 3 km resolution. The AVHRR temperature data provide better spatial resolution than modeled data sets, which interpolate between climate stations. Arctic climate stations are few, unevenly distributed around the pole, and located mostly along coasts (Rawlins and Willmot, 2003). The station data have been found to have numerous problems that bring into question the reliability of their time-series data (Pielke et al., 2007). The interpolated data sets derived from the station data tend to have high temporal resolution, but relatively coarse spatial resolution (55-100 km pixels, (Rawlins and Willmot, 2003; Rigor et al., 2000), whereas the finer spatial resolution and coarser temporal resolution of the AVHRR temperature data are more appropriate for analyzing vegetation distribution.

The AVHRR data were compiled from 1982 to 2003, providing the longest satellite temperature record available. The length of this record, especially the inclusion of the earliest years, was important in producing a mean that characterized the conditions that created the present distribution of arctic vegetation. Arctic vegetation communities are only beginning to respond to recent climate changes, and our goal was to minimize this effect in the temperature data.

1.2 Calculating land surface temperature from AVHRR data
The satellite surface temperature record was calculated from data from several NOAA AVHRR sensors onboard a series of satellites (NOAA 7-14). Geolocation and orbital drift were corrected using standard NOAA procedures (Comiso, 2000; Comiso, 2003). Because of lack of overlapping data, data from each sensor was normalized separately using in-situ data. Daily orbital data of all 5 bands including those of thermal infra-red channels 3 (3.5 - 3.9 ?m), 4 (10.3 - 11.3 ?m) and 5 (11.5 - 12.5 ?m) were mapped separately for day (ascending orbits) and night (descending orbits). Day and night data were later combined and used to generate the monthly average data used in this study (Comiso, 2000).

While thermal infrared sensors currently provide the only means to obtain synoptic coverage of surface temperature of land surfaces from space, the data are only useful during clear sky conditions. Cloud masking in polar regions is difficult because it requires discriminating between clouds and snow (or ice) covered surfaces. Conventional techniques include the use of the difference between channel 3 and channel 4, but this was not effective for polar regions because channel 3 data deteriorates in quality at colder temperatures. An effective technique suitable for polar regions was developed, using a daily differencing technique in which orbital data were compared with data from orbits covering approximately the same area the day before and the day after. The daily differencing technique detected areas that changed temperature dramatically from one day to the next, removing the colder (cloud-covered) data (Comiso, 2000; Comiso, 2003). Visible channel data (bands 1 and 2) were used to validated this technique and the success rate was reasonably good (about 75%) (Comiso, 2000). Remaining cloud-covered pixels were removed using a standard deviation technique that assumed that cloud covered areas had higher spatial variability in radiance than cloud free areas. A running 3 by 3 matrix with the pixel under consideration at the center was evaluated and if the standard deviation of the pixels was greater than 3 o K, the data were considered cloudy and eliminated. Pixels were also eliminated if they were considerably colder (about 5 o K) than the average of surrounding pixels. Again, this technique was validated using visible channel data (Comiso, 2000; Comiso, 2003). The effectiveness of the cloud screening was also demonstrated by the results of this study (see Section 4.1: Validity of methodology).

Once cloudy pixels were removed, an algorithm was used to convert radiance values to temperature (Comiso, 2000; Comiso, 2003). The equation was based on emissivity (see Section 1.3 below), and included viewing angle. The algorithm did not include atmospheric correction because water vapor data with adequate spatial and temporal detail were not available for the Arctic, and split window techniques did not improve the data compared to ground data (Comiso 2000). Finally the data were geographically mapped to a polar stereographic grid with a resolution of 6.25 by 6.25 km pixels, and composited into longer time period and larger pixels for ease of analysis. The data used for this analysis were monthly means from 1982-2003, with 12.5 km pixels in North Pole Stereographic projection (Comiso, 2003; Comiso, 2006).

1.3Emissivity
Temperature was calculated from the radiance measured by the AVHRR sensor using the Stefan-Boltzmann Law relating the radiance of black bodies to temperature. Any object that is not a black body has an emissivity less than 1.0, the ratio of real emission to blackbody emission. Since the radiance is a function of both the temperature and the emissivity, it is difficult to accurately separate these two factors, so emissivity is usually estimated (Kerr et al., 2002). Three different emissivity values were used to calculate temperature for the AVHRR data set: ice and snow = 0.997, ocean = 0.97, land = 0.94. We only used summer land surface temperatures in this analysis, so all the temperature data were derived using an emissivity of 0.94. Despite variations in emissivity caused by land cover and viewing angle, spectral emissivity characteristics for terrestrial land covers are relatively stable in the wavelength range 10.5-12.5 ?m (AVHRR channels 4 and 5) (Wan and Dozier, 1996). The effect of variation in emissivity is further reduced by the fact that (emissivity)1⁄4 is divided by the radiance to get temperature. Moreover, spectral contrast in surface emissivities usually decreases with aggregation as spatial scale increases (Wan and Dozier, 1996).

Broad band thermal emissivity values have been measured from 0.90-0.96 for bare dry soil (Jin and Liang, 2006; Kant and Badarinath, 2002; Rees, 1993), 0.925 for sparsely vegetated areas (Jin and Liang, 2006), and 0.963-0.985 for vegetated areas (Jin and Liang, 2006; Kerr et al., 2002; Rees, 1993). The lower end of the range for emissivities in the arctic for AVHRR Channels 4 and 5 is closer to 0.92 than 0.90, as emissivities for 10-12 ?m are higher than broad-band emissivities (8-14 ?m) (Jin and Liang, 2006). The high end of the range of emissivities in the Arctic may get as high as 0.985 for heavily vegetated areas in the southern arctic, but would generally be closer to 0.96 as measured for grasslands (Jin and Liang, 2006). Temperatures calculated for areas with high emissivities (heavily vegetated) in the Low Arctic will be somewhat lower than the true values, and for areas with low emissivities (dry bare soils) in the High Arctic will be slightly higher than true values, reducing the total range of the temperature data. Emissivity of water is 0.99 (Kant and Badarinath, 2002), which is 0.005 higher than the highest land measurement. This would reduce the calculated temperatures compared to the actual temperatures for pixels with large amounts of water cover. The expected range of arctic land emissivities from 0.92-0.96 would result in temperatures with a range of +/- 1.5 oC from the temperature calculated using an emissivity of 0.94. Differences in calculated temperature due to emissivity would be greatest for pixels with water inclusions, however there was no evidence of this effect in the analysis.

1.4 Differences between surface temperature data and station data
The AVHRR temperature is the surface skin radiant temperature of approximately the first 50 ?m (Lillesand and Kiefer, 1989), the temperature of leaf surfaces and soil surfaces (in bare areas). These land surface temperatures characterize the environment of low growing tundra plants better than climate station temperature data, which are measured 2 m above the ground in shelters that protect against sun, wind and precipitation. In many situations, especially throughout the winter, there is little difference between ground and surface temperatures (Comiso, 2003). However, when snow melts and albedo of the surface drops, the soil surface warms from the sun's radiation. Differences start to appear for temperatures above 0 oC, and are largest for sunny days and warmest temperatures (Comiso, 2003; Karlsen and Elvebakk, 2003). On a monthly basis, arctic mid-summer land surface temperatures are warmer than air temperatures at 2 m by about 2 oC (AVHRR LST warmer than NOAA data from Umiat Alaska 1982-2000: 2.18 oC in June, 2.08 oC in July; AVHRR LST warmer than Toolik LTER data 1989-2003: 2.92 oC in June, 0.83 oC in July).

1.5 Cloud free bias
Surface temperatures can only be retrieved from satellite thermal infrared data during cloud-free conditions. In winter (Oct-March), the surface temperatures are generally cooler under clear sky conditions than under cloudy conditions; in summer the reverse is true (Wang and Key, 2005). In areas with frequent cloud cover this may result in a warmer satellite summer temperature value than the plants actually experience. However, this bias was not large enough to affect this analysis.

1.6 Summer warmth index
Summer warmth index (SWI) was calculated from the AVHRR temperature data (Comiso, 2006). This index characterizes the plant growing season by summing monthly mean temperatures, with a 0 oC threshold required for a month to be included. The months of May-September were evaluated for each year. This index combines the effect of both the length and the warmth of summer temperatures, and is the climate variable found to correlate best with variations in arctic vegetation distribution (Edlund, 1990; Young, 1971).

Comiso, J. C. (2000). Variability and trends in Antarctic surface temperatures from in situ and satellite infrared measurements. Journal of Climate 13, 1674-1696.
Comiso, J. C. (2003). Warming trends in the Arctic from clear sky satellite observations. Journal of Climate 16, 3498-3510.
Comiso, J. C. (2006). Arctic warming signals from satellite observations. Weather 61, 70-76.
Edlund, S. A. (1990). Bioclimatic zones in the Canadian Arctic Archipelago. In "Canada's missing dimension - science and history in the Canadian Arctic Islands." (C. R. Harrington, Ed.), pp. 421-441. Canadian Museum of Nature, Ottawa.
Jin, M., and Liang, S. (2006). An improved land surface emissivity parameter for land surface models using global remote sensing observations. Journal of Climate 19, 2867-2881.
Kant, Y., and Badarinath, K. V. S. (2002). Ground-based method for measuring thermal infrared effective emissivities: implications and perspectives on the measurement of landsurface temperature from satellite data. International Journal of Remote Sensing 23, 2179-2191.
Karlsen, S. R., and Elvebakk, A. (2003). A method using indicator plants to map local climatic variation in the Kagerlussuaq/Scoresby Sund area, East Greenland. Journal of Biogeography 30, 1469-1491.
Kerr, Y. H., Lagouarde, J. P., Nerry, F., and Ottle, C. (2002). Land surface temperature retrieval techniques and applications. In "Thermal remote sensing in land surface processes." (D. A. Quattrochi, and J. C. Luvall, Eds.), pp. 33-109. CRC Press, Boca Raton.
Lillesand, T. M., and Kiefer, R. W. (1989). "Remote sensing and image interpretation." John Wiley & Sons, New York.
Pielke, R. A., Davey, C. A., Niyogi, D., Steinweg-Woods, J., Hubbard, K., Lin, X., Cai, M., Li, H., Nielsen-Gammon, J., Gallo, K., Hale, R., Mahmood, R., Foster, S., McNider, R. T., and Blanken, P. (2007). Unresolved issues with the assessment of multi-decadal global land surface temperature trends. Journal of Geophysical Research 112:D24S08.
Rawlins, M. A., and Willmot, C. J. (2003). Winter Air Temperature Change over the Terrestrial Arctic, 1961-1990. Arctic, Antarctic and Alpine Research 35, 530-537.
Rees, W. G. (1993). Infrared emissivities of Arctic land cover types. International Journal of Remote Sensing 15, 1013-1017.
Rigor, I. G., Colony, R. L., and Martin, S. (2000). Variations in surface air temperature observations in the Arctic, 1979-1997. Journal of Climate 13, 896-914.
Wan, Z., and Dozier, J. (1996). A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing 34, 892-905.
Wang, X., and Key, J. R. (2005). Arctic surface, cloud, and radiation properties based on the AVHRR Polar Pathfinder Dataset. Part I: spatial and temporal characteristics. Journal of Climate 18, 2558-2574.
Young, S. B. (1971). The vascular flora of St. Lawrence Island with special reference to floristic zonation in the Arctic Regions. Contributions from the Gray Herbarium 201, 11-115.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Title: Comiso 2003, Comiso 2006
Other_Citation_Details:
Comiso, J. C. 2003. Warming trends in the Arctic from clear sky satellite observations. Journal of Climate 16: 3498-3510.

Comiso, J. C. 2006. Arctic warming signals from satellite observations. Weather 61: 70-76.
Type_of_Source_Media: electronic mail system
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1982
Beginning_Time: May
Ending_Date: 2003
Ending_Time: September
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Josefino Comiso and Larry Stock, NASA Goddard Space Flight Center
Process_Step:
Process_Description:
Calculated each year's SWI by summing the monthly temperature means for each month May-September with positive value ( > 0 C)
Averaged the SWI of years 1982-2003
Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Grid Cell
Row_Count: 896
Column_Count: 608
Vertical_Count: 1
Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Map_Projection:
Map_Projection_Name: Stereographic_North_Pole
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: row and column
Coordinate_Representation:
Abscissa_Resolution: 12500.000000
Ordinate_Resolution: 12500.000000
Planar_Distance_Units: meters
Geodetic_Model:
Horizontal_Datum_Name: D_User_Defined
Ellipsoid_Name: User_Defined_Spheroid
Semi-major_Axis: 6378273.000000
Denominator_of_Flattening_Ratio: 298.273148
Entity_and_Attribute_Information:
Detailed_Description:
Entity_Type:
Entity_Type_Label: swi
Entity_Type_Definition: average summer warmth index 1982-2003
Attribute:
Attribute_Value_Accuracy_Information:
Attribute_Value_Accuracy: +/- 0.5 degrees centigrade
Attribute_Value_Accuracy_Explanation: The land surface temperature is accurate to approximately 0.1 degrees centigrade. The Summer Warmth Index is a sum of up to five months, so the accuracy for SWI is 5*0.1, or 0.5 degrees centigrade
Distribution_Information:
Distributor:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Alaska Geobotany Center
Contact_Organization: University of Alaska Fairbanks
Contact_Address:
Address_Type: mailing address
Address: Institute of Arctic Biology
Address: 311 Irving, P.O Box 757000
City: Fairbanks
State_or_Province: AK
Postal_Code: 99775
Country: USA
Contact_Voice_Telephone: 907-474-6720
Contact_Voice_Telephone: 907-474-2459
Contact_Electronic_Mail_Address: mkraynolds@alaska.edu
Contact_Instructions: www.arcticatlas.org
Resource_Description: Downloadable Data
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Transfer_Size: 3.534
Fees: none
Metadata_Reference_Information:
Metadata_Date: 20100422
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: University of Alaska Fairbanks
Contact_Person: Martha Raynolds
Contact_Position: Research Faculty
Contact_Address:
Address_Type: mailing address
Address: Institute of Arctic Biology
Address: 311 Irving, P.O. Box 757000
City: Fairbanks
State_or_Province: AK
Postal_Code: 99775-7000
Country: USA
Contact_Voice_Telephone: 907-474-6720
Contact_Voice_Telephone: 907-474-2459
Contact_Electronic_Mail_Address: mkraynolds@alaska.edu
Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
Metadata_Time_Convention: local time
Metadata_Extensions:
Online_Linkage: http://www.esri.com/metadata/esriprof80.html
Profile_Name: ESRI Metadata Profile