Data Set ID:

SMAPVEX08 Vegetation Water Content Map, Version 1

The Vegetation Water Content (VWC) map for the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) was derived by calculating Normalized Difference Water Index (NDWI) from Satellite Pour l'Observation de la Terre-4 (SPOT-4) overpasses on 11 October 2008. In addition, samples from a range of vegetation types were used to compare VWC and NDWI to the satellite imagery.

This is the most recent version of these data.

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Data Format(s):
  • Binary
Spatial Coverage:
N: 39.09, 
S: 38.93, 
E: -75.55, 
W: -76.25
Spatial Resolution:
  • 10 m x 10 m
Temporal Coverage:
  • 11 October 2008
Temporal ResolutionVariesMetadata XML:View Metadata Record
Data Contributor(s):Cosh, M.

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.

Cosh, M. 2015. SMAPVEX08 Vegetation Water Content Map, Version 1. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: [Date Accessed].

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Detailed Data Description

The Vegetation Water Content (VWC) Map for SMAPVEX08, the Soil Moisture Active Passive Validation Experiment 2008, was derived by calculating Normalized Difference Water Index (NDWI) from Satellite Pour l'Observation de la Terre-4 (SPOT-4) overpasses. In addition, samples from a range of vegetation types were used to compare VWC and NDWI to the satellite imagery.


Data are provided in a binary file and a header file called ENVI FST, which indicates it is an Environment for Visualizing Images dbFast data file. An associated Extensible Markup Language (XML) metadata file is also provided.

ENVI FST (dbFast binary data file)
BSQ (Band Sequential Format) with one band
Samples = 8885
Lines = 7956
Upper left easting: 375325.0
Upper left northing: 4361460.0
Pixel in east-west direction: 10 m
Pixel in north-south direction 10 m
Projection: UTM 18 N (WGS84)

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File and Directory Structure

Data are available at:

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File Naming Convention

The binary data file is named SV08VWC_vwc.fst, and the header file is SV08VWC_vwc.hdr.

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File Size

The data file is approximately 270 MB.

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Spatial Coverage

Southernmost Latitude: 38.93°N
Northernmost Latitude: 39.09°N
Westernmost Longitude: 76.25°W
Easternmost Longitude: 75.55°W

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Spatial Resolution

10 m

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Data are projected to Universal Transverse Mercator (UTM) Zone 18 N, World Geodetic System 1984 (WGS84).

Grid Description

Data are on a rectangular grid with a cell size of 10 m by 10 m.

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Temporal Coverage and Resolution

Data were collected on 11 October 2008 at various times.

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Parameter or Variable

Vegetation Water Content (VWC) [kg/m2]

Parameter Range

Valid parameter values are as follows:

Vegetation Water Content: 0-10 kg/m2
Missing data: 0

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Software and Tools

Various software packages can be used to read the data, such as the Environment for Visualizing Images (ENVI) and Interactive Data Language (IDL).

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Data Acquisition and Processing

Vegetation Water Content (VWC) was derived from the Normalized Difference Water Index (NDWI), which is a ratio of bands available from the Landsat (5&7) Thematic Mapper. The ratio is defined as:


One day of SPOT satellite coverage was available for the calculation of NDWI during SMAPVEX08. The overpasses occurred on 11 October 2008. During the study period, local farmers/operators indicated that corn remained relatively stable with respect to moisture, so only one sample of corn was taken pre-harvest and post-harvest. Soybean fields ranged widely depending on the degree of senescence and weed development; therefore, the final soybean value is held as a constant. Extensive forest measurements were made and are described herein.

The first step was to determine the trend of VWC versus NDWI for various vegetation types. All vegetation data is compared to the 7 October 2008 scene. The following equations were used to convert NDWI to VWC:

Table 1. Equations Used to Convert NDWI to VWC
Land Cover Equation
Grassland VWC = 1.1922 * NDWI + 0.2347 (From SMEX05 estimate)
Corn VWC = 9.1269 * NDWI – 4.25
Soybeans VWC = 0.5328 (a constant)
Forest VWC = 32.509 * NDWI - 18.364

These equations were applied to the scene when available.

The Corn equation is a result of two data points, one standing dry corn (the common condition at this time of year) and harvested corn with an assumed VWC of 0.0 kg/m2.

Soybeans were transitioning from green to dry brown with the majority of moisture contained in the beans themselves. This dynamic has not been captured in previous soil moisture experiments. Sampling did not reveal a significant pattern therefore a constant was used to estimate soybean VWC.

Soybean Vegetation Water Content
Figure 1. Relationship of Soybean VWC

Forest measurements were the most intensive portion of the VWC study. Diameter at Breast Height (DBH), tree species, and Basal area data were collected at multiple locations throughout the Delaware portion of the study region. The Basal Area was determined by a prism with a Basal Area Factor (BAF) of 2.0 (metric). The tree count from the prism is multiplied by 2 to produce an estimate of m2 of dry wood per hectare. To get volume of dry wood, the Basal Area is multiplied by the average height of the trees and divided by 2. This is an approximation from the Forest Service.

(Prism #)*BAF*(avg. hgt)/2 ~ volume of dry wood per hectare

Then the volume of wood is converted to kg/m2 by the density of hardwood (which is determined from the tree species).

(Vdrywood/hectare)*(1 hectare/10000 m2)*755 kg/m3= Masswood/m2

To convert to volume of water per m2, the ratio of wood weight to water weight in a forest is used (2:1) (Yilmaz et al., 2008)

Masswood/Area * 1 Masswater/2 Masswood ~ Masswater/Area

Relationship between NDWI and Forest VWC
Figure 2. Relationship between NDWI and Forest VWC

The resulting relationship between NDWI and Forest VWC is shown in Figure 2 and used to compute VWC throughout the SMAPVEX08 study region.

Error Sources

The lack of regression model and the application of a constant value to soybean fields cause error for the soybeans in particular.

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Quality Assessment

The quality of the data is good with the exception of the soybean fields as pointed out in the Error Sources section.

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References and Related Publications

Contacts and Acknowledgments

Michael H. Cosh
United States Department of Agriculture - Agricultural Research Service (USDA ARS) 
Hydrology and Remote Sensing Laboratory
10300 Baltimore Avenue
Bldg. 007, Rm. 104
Beltsville, MD 20705

Document Information


June 2015



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