Data Set ID:

CLASIC07 Vegetation Water Content Map, Version 1

The Vegetation Water Content (VWC) map for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) was derived by calculating Normalized Difference Water Index (NDWI) from ResourceSat-1 satellite imagery.

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

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Data Format(s):
  • Binary
Spatial Coverage:
N: 37.23, 
S: 34.35, 
E: -95.47, 
W: -99.53
Spatial Resolution:
  • 56 m x 56 m
Temporal Coverage:
  • 15 July 2007
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. CLASIC07 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 the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) was derived by calculating Normalized Difference Water Index (NDWI) from ResourceSat-1 satellite imagery.


Data are provided in a binary file and a header file. An associated Extensible Markup Language (XML) metadata file is also provided.

Samples: 6647
Lines: 5605
Number of bands: 1 
File layout: Band Sequential (BSQ) 
Upper left corner in east-west direction: 452625.440
Upper left corner in north-south direction: 4120433.791
Pixel size in east-west direction: 56 m
Pixel size in north-south direction: 56 m 
Projection: Universal Transverse Mercator (UTM) Zone 14 N, North American 1983 (NAD83) Datum

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File and Directory Structure

Data files are available at:

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File Naming Convention

The binary data file is named CL07VWC_vwc.bin, and the header file is CL07VWC_vwc.hdr.

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File Size

The data file is approximately 143 MB.

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Spatial Coverage

Southernmost Latitude: 34.35°N
Northernmost Latitude: 37.23°N
Westernmost Longitude: 99.53°W
Easternmost Longitude: 95.47°W

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Spatial Resolution

56 m

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Data are projected to Universal Transverse Mercator (UTM) Zone 14 N, North American 1983 (NAD83).

Grid Description

Data are on a rectangular grid with a cell size of 56 m by 56 m.

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Temporal Coverage

A satellite image obtained for 15 July 2007 is the basis for this data set.

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Parameter or Variable

The parameter for this data set is VWC [kg/m2].

Parameter Range

Valid parameter values are as follows:

VWC: 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

Traditional methods of vegetation water content estimation during the study period was significantly hampered by the presence of clouds in all available scenes during the CLASIC study period. After review of the data records for the AWiFS, Landsat-5 Thematic Mapper (TM), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensors, it was determined that there were no sufficient data to produce daily VWC estimates for the CLASIC study region during the experimental period. The two closest satellite images were a partly cloudy scene on 3 June from the Landsat-5 TM and a high-quality 15 July 2007 scene from AWiFS on ResourceSat-1. The 15 July scene, while outside of the study period, is the basis for this vegetation water content product. Due to the significant flooding and weather conditions in June 2007 in central Oklahoma, the harvest of much of the winter wheat crop was delayed until late in the month. Therefore much of the study domain has a low vegetation water content due to fallow fields or harvested winter wheat and summer crops were yet to be planted.

Atmospheric Correction

As the weather was always very cloudy during the CLASIC campaign, atmospheric corrections were needed to reflect the true ground. Table 1 shows important input parameters used in the MODerate resolution atmospheric TRANsmission (MODTRAN) computer program, which is designed to model atmospheric propagation of electromagnetic radiation for the 100-50,000 cm-1 (0.2 to 100 um) spectral range. These input parameters were used to generate the images.

Table 1. Important Input Parameters for MODTRAN for all Images
0415 a0.375 74.5724 54 105 35.276 97.037 17.492
0429 a0.325 41.1477 48 119 36.857 98.666 17.624
0519 a0.350 19.9010 44 139 36.857 94.445 17.341
0603 a0.325 40.52940 56 154 35.328 -98.048 17.034
0715 a0.325 30.6211 55 196 35.276 98.099 17.554
0806 a0.270 28.44432 49 218 35.319 -97.994 17.021
0808 a0.283 40.6304 56 220 35.276 98.100 17.551
0813 a0.294 28.35889 56 225 35.277 97.045 17.480
0814 a0.295 24.3875 48 226 37.654 95.312 17.121
0828 a0.300 28.69239 43 240 37.218 95.485 17.260
0901 a0.295 46.82300 47 244 35.862 97.928 17.541

The reflectance of AWiFS cannot be validated because there was no observed reflectance on the same day. The validation results of TM are shown in Figure 1.

Figure 1. Validation of reflectance in TM images.
Figure 1. Validation of reflectance in TM images.

The big error appears on lake pixels, which is reasonable. The root mean square error (RMSE) is 0.022462 if calculated data without lake pixels. This is an indication of the accuracy of the process of atmospheric correction using the available data. No RMSE is available for 15 June 2007, on which the VWC is based.

Regression between Observed Normalized Difference Water Index (NDWI) and VWC

Due to the insufficient observed data, the regression equation cannot be drawn for all of the crops. Regressions can be calculated for Pasture and Wheat, but the number of data points is small. See Figures 2 and 3. 

Figure 2. Regression between NDWI and VWC for Pasture.
Figure 2. Regression between NDWI and VWC for Pasture (together with cut winter wheat).
Figure 3. Regression between NDWI and VWC for Pasture.
Figure 3. Regression between NDWI and VWC of winter wheat.

The regression equations for winter wheat and pasture are:

Winter Wheat:
VWC=5.60680*NDWI+1.69831 (kg/m2)

Pasture and Harvested Winter Wheat:
VWC=0.96567*NDWI+0.30753 (kg/m2)

Since data were insufficient to generate an equation for the other crops, we referenced equations from other experiments and extrapolated/theorized an algorithm.

Soil Moisture Experiment 2002 (SMEX02):
Alfalfa, Cotton, and Soybean: VWC= 1.468 * NDWI2 + 1.3615 * NDWI + 0.3394 (kg/m2)

Soil Moisture Experiment 2003 (SMEX03):
Corn:VWC = 5.3347 * NDWI + 2.1957(kg/m2)
Unclassified, Water and Urban:0(kg/m2)
Forest: 10(kg/m2)

Vegetation Water Content Mapping

When combining land cover and NDWI for the 15 July scene, a VWC image can be generated. Almost all the data during CLASIC were contaminated by cloud. Users should take the cloud contamination into consideration to use these data reasonably.

Error Sources

Almost all the data during CLASIC were contaminated by cloud. Users should take the cloud contamination into consideration to use these data reasonably.

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Quality Assessment

The quality of the data is compromised due to errors caused by cloud cover (see 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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