Data Set ID:

SMAPVEX12 In Situ Vegetation Data for Agricultural Area, Version 1

This data set contains in situ vegetation data collected at several agricultural sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).

This is the most recent version of these data.

STANDARD Level of Service

Data: Data integrity and usability verified

Documentation: Key metadata and user guide available

User Support: Assistance with data access and usage; guidance on use of data in tools

See All Level of Service Details

Data Format(s):
  • ASCII Text
Spatial Coverage:
N: 49.96, 
S: 49.44, 
E: -97.85, 
W: -98.51
Spatial Resolution:
  • 0.5 m x 0.5 m
Temporal Coverage:
  • 7 June 2012 to 19 July 2012
Temporal Resolution13 dayMetadata XML:View Metadata Record
Data Contributor(s):McNairn, H., J. Powers, and G. Wiseman.

Geographic Coverage

Other Access Options

Other Access Options


As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our Use and Copyright Web page.

McNairn, H., J. Powers, and G. Wiseman. 2014. SMAPVEX12 In Situ Vegetation Data for Agricultural Area, Version 1. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: [Date Accessed].
9 March 2020
Last modified: 
10 March 2020

Data Description

This data set contains in situ vegetation data collected at several agricultural sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).


The parameters included in this data are described in Table 1.

Table 1. Parameter units and valid ranges
Parameter Unit Valid range
Vegetation Water Content (VWC) g/m2 0 - 40,000
Total net weight g 0 - 8000
Total dry biomass g 0 - 2000
Fraction of water content % 0 - 100
Height of the plants cm 0 - 300
Diameter of the plants cm 0 - 3

File Information

Format and File Contents

Data are provided in ASCII text files.
SV12VA_Crop_Biomass_ver4.txt contains biomass related parameters. 
SV12VA_Crop_Height_Diam_ver4.txt contains plant height and diameter measurements.
Field_Sites_ver4_coords.txt contains the locations of the sampling sites.

The following tables provide descriptions for each column in the data files.

Table 2. Data Column Descriptions for SV12VA_Crop_Biomass_ver4.txt (Biomass file)
Column Heading Description
OBJECTID ID of the sample
Sample_Date month/day/year
Site_ID ID of the field and the sample point within the field
Crop_Type Type of the crop
Crop_Part Part of the plant (total, stems, leaves, tassel, flower or heads, or pasture)
Net_W_Sample_Total_g Total net weight of the sample [g]
Total_Dry_Biomass_g Total dry biomass in the sample [g]
Plant_Water_Cont_PCT Fraction of water content in the sample (in percentage)
Area_Plant_Water_Cont_g_m2 Vegetation Water Content [g/m2]
Air_to_OD_Cor_Applied Indicates whether the air-dry to oven-dry correction has been applied
Table 3. Data Columns for SV12VA_Crop_Height_Diam_ver4.txt (Plant height and diameter file)
Column Description
OBJECTID ID of the sample
Date Date of the sample (month/day/year)
Crop Type of the crop
Site_ID ID of the field and the sample point within the field
Mean_Heigh Mean height computed from the 10 height measurements [cm]
Mean_Diame Mean diameter computed from the 10 diameter measurements [cm]
Height_1 Height sample 1 [cm]
Height_2 Height sample 2 [cm]
Height_3 Height sample 3 [cm]
Height_4 Height sample 4 [cm]
Height_5 Height sample 5 [cm]
Height_6 Height sample 6 [cm]
Height_7 Height sample 7 [cm]
Height_8 Height sample 8 [cm]
Height_9 Height sample 9 [cm]
Height_10 Height sample 10 [cm]
Diameter_1 Diameter sample 1 [cm]
Diameter_2 Diameter sample 2 [cm]
Diameter_3 Diameter sample 3 [cm]
Diameter_4 Diameter sample 4 [cm]
Diameter_5 Diameter sample 5 [cm]
Diameter_6 Diameter sample 6 [cm]
Diameter_7 Diameter sample 7 [cm]
Diameter_8 Diameter sample 8 [cm]
Diameter_9 Diameter sample 9 [cm]
Diameter_10 Diameter sample 10 [cm]
Table 4. Data Column Descriptions for Field_Sites_ver4_coords.txt
Column Heading Description
OBJECTID ID of the data record
Site_ID ID of the field and the sample point within the field
X UTM easting coordinate (meters)
Y UTM northing coordinate (meters)

Spatial Information


Southernmost Latitude: 49.44°N
Northernmost Latitude: 49.96°N
Westernmost Longitude: 98.51°W
Easternmost Longitude: 97.85°W


In situ measurements were sampled within .5 m by .5 m squares.


Data are provided in Universal Transverse Mercator (UTM), Zone 14 N, World Geodetic System 1984 (WGS84) coordinates.

Temporal Information

Coverage and Resolution

In situ samples and measurements were collected on thirteen days from 07 June through 19 July 2012.

Software and Tools

No special tools are required to view these data. A spreadsheet program such as Microsoft Excel is recommended.

Data Acquisition and Processing

During SMAPVEX12, non-flight days were dedicated to collecting vegetation data. The goal of the experimental plan was to measure vegetation conditions once per week for each field. This strategy was a compromise between capturing the changes in crop growth and available resources. By and large for the vast majority of fields this time table was achieved. However, field access due to impassible roads or aerial spraying did influence this schedule.

Vegetation measurements were collected at three of the 16 soil moisture sample points. Sample points 2, 11 and 14 were selected as this distribution was expected to capture variance in vegetation conditions across each field, and also provided ease of navigation within the field. At each of these three points, measurements of crop height and stem diameter were made. In addition biomass samples were collected and photos to measure Leaf Area Index (LAI) were taken. Due to different planting approaches of various crops, and the accumulation of crop biomass, the strategy to acquire vegetation measurements varied depending on crop type. For wheat, forage and pasture crops crews collected biomass, height and stem diameter measurements within a 0.5 m x 0.5 meter biomass square. All other crops have wide row and plant spacing and thus these measurements were taken along two adjacent plant rows.

Crop height and stem diameter can vary significantly along and between rows and thus multiple measurements are required. For all crops, 10 height and diameter measurements were taken using a tape measure (height) or caliper (stem diameters). For wheat, forage and pasture 10 plants were randomly selected within the biomass square for measurement. For corn, beans and canola five consecutive plants were measured in one row, with five more measured in the adjacent row. The height was measured to the top of the upper most part of the canopy, whether leaf or fruit. Leaves were left in their natural orientation, and not extended, for this measurement. The diameter was measured half way up the crop (at mid-level). Heights were recorded on field sheets.

Vegetation biomass and water content were determined via destructive sampling. One biomass sample was collected at each of the three measurement points (2, 11 and 14). For wheat, forage and pasture fields a 0.5 m x 0.5 meter square was placed over the canopy. All above ground biomass was collected by cutting all vegetation at the soil level. For beans, corn and canola crops, five plants along two rows (10 plants in total) were collected. Knowledge of the density of the crop permits scaling of these measurements to a unit area (m2). Dead plant matter (residue) was not included in the sampling. Following cutting, biomass samples were placed first in a paper bag, and then a plastic bag to minimize water loss prior to weighing the wet sample. Vegetation will degrade rapidly (within a few hours) and thus weighing of the wet sample must be completed quickly. During most vegetation sampling days, the lab crew set up a temporary weighing station located on site in Elm Creek. When convenient to do so, crews periodically brought their vegetation samples to this temporary station for weighing. More than 900 biomass samples were collected during SMAPVEX12.

The SMAPVEX experimental plan called for the determination of vegetation water content as a function of plant organ. Thus, for the sample collected from point 2, the lab crew segmented this sample by plant organs (heads, leaves, stalks, seeds/pods, tassels) prior to weighing the wet sample. Due to level of effort, the wheat samples were only segmented into heads and leaves/stems. The proportion of biomass in the wheat stalk is considered small relative to that of the leaves and heads. 

See more details in sections 1.1 and 2.1.3 of the SMAPVEX12 Database Report, released 18 December 2012.

Error Sources

Some additional error was introduced for the corn sampling due to the uncertainty in the air-dry to oven-dry conversion. See details of the sampling approach in section 2.1.3 of the SMAPVEX12 Database Report, released 18 December 2012.

Quality Assessment

In general, the quality of the data meets the typical quality expected from this type of field campaign with the given sampling strategy. See details of the sampling approach in section 2.1.3 of the SMAPVEX12 Database Report, released 18 December 2012.


Heather McNairn
Science and Technology Branch
Agriculture and Agri-Food Canada
960 Carling Avenue 
Ottawa, Ontario K1A 0C6 Canada 
phone: +1 613-759-1815

Jarrett Powers
Science and Technology Branch
Agriculture and Agri-Food Canada
200-303 Main Street
Winnipeg, Manitoba R3C 3G7 Canada
phone: +1 204.259.4006

Grant Wiseman
Science and Technology Branch
Agriculture and Agri-Food Canada
200-303 Main Street
Winnipeg, Manitoba R3C 3G7 Canada
phone: +1 204.259.4006


Agriculture and Agri-Food Canada, National Aeronautics and Space Administration, U.S. Department of Agriculture, Environment Canada, U. Manitoba, U. Guelph, Massachusetts Institute of Technology, U. South Carolina, U. Colorado, U. Sherbrooke, Ohio State, U. Montana, Florida International U., U. Southern California, Texas A&M, Georgia Institute of Technology, U. Washington are acknowledged for their support for the campaign.

Field Sampling Team

Aaron Berg, University of Guelph
Alan Rich, University of Manitoba
Alicia Joseph, NASA GSFC
Alexandra Konings, MIT
Amine Merzouki, Agriculture and Agri-Food Canada
Bin Fang, U.S. Carolina
Brandon Wyryha, Agriculture and Agri-Food Canada
Brian Miller, University of Manitoba
Catherine Champagne, Agriculture and Agri-Food Canada
Craig Smith, Environment Canada
Christina Neva Rivera, Agriculture and Agri-Food Canada
Dominik Schneider, University of Colorado
Erika Podest, JPL
Erle Einarsson, Agriculture and Agri-Food Canada
Evan Rodgers, Agriculture and Agri-Food Canada
Grant Wiseman, Agriculture and Agri-Food Canada
Greg Gibbons, Agriculture and Agri-Food Canada
Heather McNairn, Agriculture and Agri-Food Canada
Hida Manns, University of Guelph
Hoda Jafarian, University of Sherbrooke
Jacqueline Freeman, Agriculture and Agri-Food Canada
Jeff Ouellette, Ohio State
Jennifer Watts, University of Montana
Jiali Shang, Agriculture and Agri-Food Canada
John Fitzmaurice, Agriculture and Agri-Food Canada
Jon Belanger, University of Guelph
Justin Adams, University of Guelph
Kalifa Goïta, University of Sherbrooke
Karel Janik, University of Sherbrooke
Kaighin McColl, MIT
Kurt Gottfried, Agriculture and Agri-Food Canada
Luis Perez, FIU - Florida International University
Marco Carrera, Environment Canada, Meteorological Research Division
Maria Abrahamowicz, Environment Canada
Mariko Burgin, University of Southern California
Maheshwari Neelman, Texas A&M
Matt Jones, University of Montana
Mehdi Hosseini, University of Sherbrooke
Mike Cosh, USDA, ARS Hydrology and Remote Sensing Laboratory
Mustafa Aksoy, Ohio State
Najib Djamai, University of Sherbrooke
Nandita Gaur, Texas A&M
Narendra Das, JPL
Parag Narvekar, MIT
Parinaz Rahimzadeh, University of Guelph
Patrick Rollin, Agriculture and Agri-Food Canada
Paul Bullock, University of Manitoba
Peggy O'Neill, NASA GSFC
Rachel Molloy, Agriculture and Agri-Food Canada
Rebecca Warren, University of Guelph
Rebecca Scriver, University of Guelph
Ramata Magagi, University of Sherbrooke
Robert Terwilleger, University of Florida
Rotimi Ojo, University of Manitoba
Ruzbeh Akbar, University of Southern California
Sab Kim, JPL
Sarah Banks, Agriculture and Agri-Food Canada
Sarah Dyck, Environment Canada
Saeid Homayouni, Agriculture and Agri-Food Canada
Shawna McKnight, Georgia Institute of Technology
Sonia Becenko, Agriculture and Agri-Food Canada
Stacie Westervelt, University of Manitoba
Steven Chan, JPL
Syed Anwar, Agriculture and Agri-Food Canada
Tien-Hoa Liao, University of Washington
Tracy Rowlandson, University of Guelph
Vanessa Escobar, NASA GSFC

Document Information


October 2013


March 2020

How To

Programmatic Data Access Guide
Data from the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) can be accessed directly from our HTTPS file system or through our Application Programming Interface (API). Our API offers you the ability to order data using specific temporal and spatial filters... read more
Filter and order from a data set web page
Many NSIDC data set web pages provide the ability to search and filter data with spatial and temporal contstraints using a map-based interface. This article outlines how to order NSIDC DAAC data using advanced searching and filtering.  Step 1: Go to a data set web page This article will use the... read more