Alisa L. Gallant
2017
Remotely sensed variables analyzed and reported in the paper titled "Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions"
Comma-delimited text file
Sioux Falls, SD
U.S. Geological Survey
https://doi.org/10.5066/F7HH6J1S
Walt Sadinski
Alisa L. Gallant
Mark Roth
Jesslyn Brown
Gabriel Senay
Wayne Brininger
Perry M. Jones
Jason Stoker
20180907
Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions
publication
PLOS ONE
vol. 13, issue 9
n/a
Public Library of Science (PLoS)
ppg. e0201951
https://doi.org/10.1371/journal.pone.0201951
The comma-delimited fields in this dataset provide values for the remotely sensed variables analyzed for landscape blocks described in the paper, "Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions," by Sadinski et al. (submitted). The field labeled “BlockSite” links the records in this file with a set of boundaries in a shapefile called “Study_Block_Boundaries.shp” The records represent weekly measurements of normalized difference vegetation index (BlockNDVI) values and total evapotranspiration (BlockETmm), as well as the annual snow-off date (BlockDOYsnowfree) for the study blocks from January through August from 2008 to 2012.
These data were compiled to study relations between climate dynamics and key ecological conditions and processes on wetland-upland landscapes in a set of sites distributed across four study areas in the midwestern United States. The variables studied included both ground- and satellite-based measures. The comma-delimited dataset described here pertains to the satellite-based measures, which cover January through August of 2008 to 2012 for 33 square landscape blocks measuring 2 km on a side. We used these data to characterize growing-season primary productivity (normalized difference vegetation index), evapotranspiration (estimated actual evapotranspiration), and timing of snow-free conditions.
2008
2012
ground condition
None planned
-95.680000000002
-89.54
47.1
42.95
ISO 19115 Topic Categories; USGS Thesaurus; NASA Global Change Master Directory; National Agricultural Library Thesaurus
Environment
Remote sensing
Normalized difference vegetation index (NDVI)
Evapotranspiration
Snow
NASA Global Change Master Directory
Climate
Hydrology
Vegetation
NASA Global Change Master Directory
Climate
USGS Metadata Identifier
USGS:5ffca734d34e52c3b3d9d927
Geographic Names Information System (GNIS)
Minnesota
Wisconsin
NASA Global Change Master Directory
Land surface
Terrestrial hydrosphere
USGS Thesaurus
Spring
Seasons
none
none
Alisa L. Gallant
U.S. Geological Survey, Earth Resources Observation and Science Center
mailing and physical
47914 252nd St.
Sioux Falls
South Dakota
57198-0001
USA
605.594.2696
gallant@usgs.gov
W. Sadinski
A.L. Gallant
M. Roth
J. Brown
G. Senay
W. Brininger
J. Stoker
2017
Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions
Online
PLOS ONE
Alisa L. Gallant
2017
Study_Block_Boundaries
Sioux Falls, SD, USA
U.S. Geologcial Survey, Earth Resources Observation and Science Center
No formal attribute accuracy tests were conducted
Values were checked to make sure they fell within the numerically possible range for each attribute.
Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record and particularly the paper to which these data apply.
No formal positional accuracy tests were conducted
No formal positional accuracy tests were conducted
We generated BlockNDVI values by spatially averaging NDVI values from source data layers of 250 m x 250 m pixels whose cell centers fell within each study landscape block. The source NDVI layers were derived from eMODIS data (Jenkerson et al. 2010; see Kautz 2002 for data access) that were processed as described in Brown et al. (2015), including temporal smoothing. For the current analysis, we re-projected these data from the Lambert azimuthal equal-area projection to an Albers equal-area projection using parameter settings commonly applied by the U.S. Geological Survey for conterminous U.S. products (e.g., see Multi-Resolution Land Cover Characteristics Consortium 2017) before calculating the average for the landscape block. The source NDVI data were derived from a seven-day composite period that rolled over from one year to the next. We assigned each seven-day interval to a specific week-of-year (based on where the majority of days fell), from week 1 to week 34, to standardize temporal units for analyses with the other remotely sensed variables. CITATION 1: Brown, J.F., Howard, D., Wylie, B., Frieze, A., Ji, L., and Gacke, C., 2015, Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition: Remote Sensing, v. 7, no. 12, p. 16226-16240. CITATION 2: Jenkerson, C., Maiersperger, T., and Schmidt, G., 2010, eMODIS: A user-friendly data source; U.S. Geological Survey, Open-File Report 2010-1055. CITATION 3: Kautz, S., 2002, eMODIS Data set: U.S. Geological Survey, at https://doi.org/10.5066/F7MW2FBS. CITATION 4: Multi-Resolution Land Cover Characteristics Consortium [internet], 2017, National Land Cover Database, Frequently Asked Questions: U.S. Geological Survey, at https://www.mrlc.gov/faq_dau.php.
2016
We acquired source data for BlockETmm from a dataset of estimated actual ET produced operationally at EROS as a seamless product for the conterminous United States. The data set consisted of eight-day totals of mm of ET generated with the Operational Simplified Surface Energy Balance model (SSEBop) in a geographic coordinate system at an approximate spatial resolution of 1 km2 (Senay et al. 2013; available from https://www.sciencebase.gov/catalog/item/52a87c34e4b027f847db1ac0). This model estimated actual (as opposed to potential) ET with an energy-balance approach that used the remotely sensed temperature of the land surface to solve for the latent heat-energy component as a residual of the surface-energy balance terms. Main model inputs were eight-day average land-surface temperatures from the Collection-5 MODIS MOD11A product (Wan 2007), air temperatures in the form of gridded surfaces interpolated and extrapolated from daily meteorological observations by the Daily Surface Weather and Climatological Summaries (“DAYMET”) model (Thornton et al. 1997, 2012), and potential ET (Senay et al. 2008) derived from weather fields produced by the Global Data Assimilation System (Kanamitsu 1989). To develop the BlockETmm data for the current analyses, we re-projected these source data from the geographic projection system to an Albers equal-area projection using parameter settings commonly applied by the U.S. Geological Survey for conterminous U.S. products (e.g., see Multi-Resolution Land Cover Characteristics Consortium 2017). The value provided in the record of this comma-delimited file is the spatial average of 1-km pixels whose cell centers intersected each study block. CITATION 1: Kanamitsu, M., 1989, Description of the NMC Global Data Assimilation and Forecast System: Weath Forecas, v. 4, p. 334−342. CITATION 2: Multi-Resolution Land Cover Characteristics Consortium [internet], 2017, National Land Cover Database, Frequently Asked Questions: U.S. Geological Survey, at https://www.mrlc.gov/faq_dau.php. CITATION 3: Senay, G.B., Bohms, S., Singh, R.K., Gowda, P.H., Velpuri, N.M., Alemu, H., and Verdin, J.P., 2013, Operational evapotranspiration mapping using remote sensing and weather datasets: A new parameterization for the SSEB approach: Journal of the American Water Resources Association, v. 49, no. 3, p. 577−591. CITATION 4: Senay, G.B., Verdin, J.P., Lietzow, R., and Melesse, A.M., 2008, Global reference evapotranspiration modeling and evaluation: Journal of the American Water Resources Association, v. 44, no. 4, p. 969−979. CITATION 5: Thornton, P.E., Running, S.W., and White, M.A., 1997, Generating surfaces of daily meteorology variables over large regions of complex terrain: Journal of Hydology, v. 190, p. 214−251. CITATION 6: Thornton, P.E., Thornton, M.M., Mayer, B.W., Wilhelmi, N., Wei, Y., and Cook, R.B., 2012, Daymet: Daily surface weather on a 1 km grid for North America, 1980−2008 data set, at http://daymet.ornl.gov. CITATION 7: Wan, Z., 2007, Collection-5 MODIS land surface temperature products user’s guide: University of California--Santa Barbara, Institute for Computational Earth System Science, at http://www.icess.ucsb.edu/modis/LstUsrGuide/MODIS_LST_products_Users_guide_C5.pdf.
2016
Timing of the snow-free day of year (DOY) for the BlockDOYsnowfree data field was determined using the Collection-5 MOD10A2 product derived with data from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor. This dataset was available through the National Snow & Ice Data Center (nsidc.org/data/MOD10A2; doi:10.5067/MODIS/MOD10A2.006) and represented the maximum snow extent for each successive eight-day composite period from January through December at a spatial resolution of 500 m in a sinusoidal map projection (Riggs et al. 2006), with pixels labeled as “snow” if snow was present for one or more days during a composite period. For the study blocks, we re-projected the data to an Albers equal-area projection using parameter settings commonly applied by the U.S. Geological Survey for conterminous U.S. products (e.g., see Multi-Resolution Land Cover Characteristics Consortium 2017). We defined the onset of continuous snow-free conditions as the first date of two contiguous, snow-free, eight-day composite intervals that occurred after the end of February. This was based on an assumption that the likelihood of vegetation growth prior to the end of February was zero across the study areas. Two contiguous snow-free intervals were considered to be more relevant ecologically, given the daily variation in snowfall and snow cover that can occur during late winter and early spring in this region. We averaged the onset dates for snow-free conditions for each year across the 500-m cells whose center points intersected the study blocks. CITATION 1: Multi-Resolution Land Cover Characteristics Consortium [internet], 2017, National Land Cover Database, Frequently Asked Questions: U.S. Geological Survey, at https://www.mrlc.gov/faq_dau.php. CITATION 2: Riggs G.A., Hall. D.K., and Salomonson, V.V., 2006, MODIS snow product user guide for collection 4 data products: National Aeronautics and Space Administration, at http://modis-snow-ice.gsfc.nasa.gov/?c=sug_main.
2016
Midwestern United States
Vector
Albers Conical Equal Area
29.5
45.5
-96.0
23.0
0.0
0.0
coordinate pair
2000.0
2000.0
METERS
North American Datum 1983
Geodetic Reference System 1980
6378137.0
298.257222101
Weekly_data_for_Study_Blocks.csv
Comma-delimited file of weekly measurements for study blocks from January through August of 2008 to 2012
Dataset author
StudyArea
Name for a study area.
Dataset author
A name used to represent each of four general study areas.
BlockSite
Unique identification code for a study block.
Dataset author
A unique alphanumeric identifier for a 2 km x 2 km study block.
Year
A four-digit integer for the year that the data represent
Dataset author
2008
2012
years
Week
An integer for the week-of-the-year that the data represent. Measurements run through the first 34 weeks of the year (from January to approximately the end of August).
Dataset author
1
34
weeks
BlockNDVI
Normalized Difference Vegetation Index (NDVI) value associated with a 2 km x 2 km landscape block for a specific week of the year. Values were linearly rescaled from their native floating-point range (-1.0 to 1.0) to an integer range of 0 to 200.
Dataset author
0
200
NDVI is a unitless ratio.
BlockETmm
Estimated 8-day accumulated mm of actual (as opposed to potential) evapotranspiration associated with a 2 km x 2 km study block for a particular week of the year. Cross-walking 46 8-day intervals to 52 weeks resulted in no ET data for weeks 5, 13, 21, and 29 during the January through August period of analysis. Weeks with missing ET data are indicated with a dash in the CSV file.
Dataset author
0
open-ended maximum
total millimeters of ET
BlockDOYsnowfree
BlockDOYsnowfree is the annual day-of-year during 2008-2012 when we considered a 2 km x 2 km study block to be snow-free for the remainder of growing season.
Dataset author
Number of the average day of year that the raster cells within a study block were deemed snow-free for the growing season. We defined the onset of continuous snow-free conditions for a 500-m cell as the first date of two contiguous, snow-free, eight-day composite intervals that occurred after the end of February. We populated the data field here with the average date across all cells within each study block.
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Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty.
No specific software is required to open this comma-delimited text file, but software that reads ESRI shapefiles is required to link these data records with the geospatial boundaries to which they apply (Study_Block_Boundaries.shp).
20220623
Alisa L. Gallant
U.S. Geological Survey, Earth Resources Observation and Science Center
Research Physical Scientist
mailing and physical
47914 252nd St.
Sioux Falls
SD
57198-0001
USA
605.594.2696
gallant@usgs.gov
Content Standard for Digital Geospatial Metadata
FGDC-STD-001-1998