Soil Moisture

Soil Properties

Soil Temperature

Vegetation and Land Cover

Aircraft Remote Sensing

Satellite Remote Sensing

Land Cover Classification of the SGP99 Region

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The Data Procedure Data File Specifications The Files File Names and Format Information Data Access and Contacts FTP site Points of Contact

The Data

1. Collected all available "cloud-free" Landsat-5 (March 9, May 12, and July 15) and Landsat-7 scenes (July 7 and July 23) from March 9 to July 23, 1999. No Landsat images for the months of April or June were available from Space Imaging or EOSAT.
2. Imported the 3 Landsat-5 (March 9, May 12, and July 15) and the two Landsat-7 scenes (July 7 and 23) into GIS software (PCIworks)
3. Mosaic the two Landsat scenes (Path 28, Rows # 35-36) together for each date
4. Screen digitized training sites from ground survey data collected during July 7-July 22, 1999. The ground-survey data identified 44 different land-cover categories

5. Re-grouped the 44 different land-use categories into the following 15 land-cover categories

5. The July 7th scenes had too many clouds and were discarded. Of the other scenes, March 9 had the most clouds, July 23 had the least clouds, and May 12 and July 15 have intermediate cloud cover relative to March 9 and July 23.

7. Created cloud and shadow masks for each image by screen digitizing cloud and shadow training sites and determining the mean digital numbers of these training sites from their signature statistics. The following values define the threshold digital numbers used to remove clouds and shadows from each image (where # designates the channel number):

1. March 9:

2. May12:

3. July 15:

4. July 23:

Shadows: removed pixels where #1 < 73 and #2 < 50 and #3 < 50 and #4 < 110.

7. Created two final cloud/shadow masks by combining masks from the March9/May12/July15/July23 images and combining masks from the July15/July23 images.

9. Performed two maximum likelihood classifications by grouping the images into two time series - March9/May12/July15/July23 and July15/July23.

10. Overall accuracy for the March/May/July classification was 72.60%, while overall accuracy for the July15/July23 classification was 70.15%.
11. The March/May/July classification was chosen as the primary classification because the accuracy was better than the July15/July23 classification and visual inspection revealed better classification results in the study areas.
12. The July15/July23 classification image was merged into the May/March/July classification image by substituting the unclassified pixels from the May/March/July cloud/shadow mask with pixels from the July15/July23 image.
13. The confusion and separability matrices indicated most classes had good separability (above 1.9 on a scale from 0.0 to 2.0). The classes with less than good separability included the following:

The Files

Data Access and Contacts

FTP Site

The Land Cover Classification data set from SGP99 resides on DAAC anonymous FTP. You may access it from this document,

Land Cover Classification Data

or directly via FTP at

ftp disc.gsfc.nasa.gov

login: anonymous

password: < your internet address >

cd /data/sgp99/LandCover/

Points of Contact

The Principal Investigator for the SGP99 Land Cover Classification data is

Thomas J. Jackson
USDA ARS Hydrology Lab
Bldg. 007, Rm. 104, BARC-West
Beltsville, MD 20705
tjackson@hydrolab.arsusda.gov
301-504-8511 (voice)

For information about or assistance in using SGP99 DAAC data, contact

Hydrology Data Support Team
EOS Distributed Active Archive Center (DAAC)
Code 610.2
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
hydrology-disc@listserv.gsfc.nasa.gov
301-614-5165 (voice)
301-614-5268 (fax)