Landsat Missions

Filling the Gaps to use in Scientific Analysis

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Many image-processing packages may have capabilities such as those described here.

(NOTE: References to non-USGS products do not constitute endorsement by the U.S. Government.)

Web-enabled Landsat Data (WELD)

The Web-enabled Landsat Data (WELD) project generates 30-meter composites of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) terrain corrected (Level-1T) mosaics at weekly, monthly, seasonal and annual periods for the conterminous United States (CONUS) and Alaska. These mosaics provide consistent data that can be used to derive land cover as well as geo-physical and biophysical products for regional assessment of surface dynamics and to study Earth system functioning.

Landsat 7 SLC-off Gap-filled data sources

A number of Satellite Business Partners may provide value-added and gap-filled products, and depending on your area of interest, a number of International Ground Stations may also provide gap-filled or value-added products. Businesses and ground stations may have charges associated with the products they provide.

Historic Techniques to Filling Gaps

Historically, the USGS produced gap-filled products using two methods: Phase One, which used a full Landsat 7 image (pre 2003) to fill the gaps of the SLC off scene, and Phase Two, which incorporated more than two SLC-off scenes together to create a final product. While we no longer offer the gap-filled data products, the methodology used for both methods is available:

ENVI/IDL - Mosaicking Method

Multiple SLC-off images are required to utilize this method. Individual bands of each image need to be gap-filled before creating a 3-band image. For instance, in order to gap-fill Image 1 with Image 2, a mosaic will need to be made of Band 1 from Image 1 and Image 2 together. The bands can then be stacked to create the RGB image.

  1. Open ENVI .
  2. Open the .tif band files to be used.
  3. Select Map -> Mosaicking -> Georeferenced
  4. Select Import -> Import Files and Edit Properties. Click Open to choose the files you want to gap-fill; they will populate the left-hand frame.
  5. Highlight one file and click OK, setting the Background See Through-Data Value to Ignore to 0. Color balancing can be done to adjust any brightness differences between the images, if needed.
  6. Repeat for all files
  7. Select File -> Apply, and assign an output file name and select other applicable options.
  8. Click OK.

ERDAS Imagine - Mosaicking Method

  1. Open ERDAS Imagine .
  2. Open the .tif band files to be used.
  3. Use model-maker to add each band in Image 1 to the corresponding band in Image 2. The following statement says:

    Where Image 1 > 0, use Image 1 data, otherwise, use Image 2. Image 2 data will fill the gaps in Image 1.

    Use the following syntax (this example is for Band 5) in the model: EITHER $n1_l71015033_03320070515_b50 IF ( $n1_l71015033_03320070515_b50 > 0) OR $n2_l71015033_03320070531_b50 OTHERWISE

  4. Then you can layer stack, or leave the files as individual bands. Note: these directions do not include radiometric matching.

Combining scenes in ERDAS Imagine ™

Figure 1. Combining scenes in ERDAS Imagine

While it still has a small residual gap, the image below is a combination of the two scenes (without histogram correction applied).

Combined scenes without Histogram Correction in ERDAS Imagine <sup>™</sup>

Figure 2. Combined scenes without Histogram Correction in ERDAS Imagine


Referenced articles on methods to fill gaps and a variety of science questions

These articles describe how scientists are using Landsat 7 gapped data. This is not a complete listing - many more examples can be found.

E.H. Helmer, Thomas S. Ruzycki, Jay Benner, Shannon M. Voggesser, Barbara P. Scobie, Courtenay Park, David W. Fanning, Seepersad Ramnarine. Detailed maps of tropical forest types are within reach: Forest tree communities for Trinidad and Tobago mapped with multiseason Landsat and multiseason fine-resolution imagery. Forest Ecology and Management

Jin Chen, Xiaolin Zhu, James E. Vogelmann,Feng Gao, Suming Jin A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of the Environment

B. Zheng, J.B. Campbell, and K.M. de Beurs. In press (as of Nov. 2011). Remote sensing of crop residue cover sing multi-temporal Landsat imagery. Remote Sensing of Environment.

V. Kovalskyy, D.P. Roy, X.Y. Zhang, and J. Ju. 2012 (online Aug 2011). The suitability of multi-temporal web-enabled Landsat data NDVI for phenological monitoring - a comparison with flux tower and MODIS NDVI. Remote Sensing Letters. Volume 3, Issue 4, Pages 325-334.

P. Potapov, S. Turubanova, and M. Hansen. 2011. Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia. Remote Sensing of Environment. Volume 115, Issue 2, Pages 548-561.

D.P. Roy, J. Ju, K. Kline, P.L. Scaramuzza, V. Kovalskyy, M. Hansen, T.R. Loveland, E. Vermote, and C. Zhang. 2010. Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States. Remote Sensing of Environment, Volume 114, Issue 1, Pages 35-49.

M.J. Pringle, M. Schmidt, and J.S. Muir. 2009. Geostatistical interpolation of SLC-off Landsat ETM+ images. ISPRS Journal of Photogrammetry and Remote Sensing. Volume 64, Issue 6, Pages 654-664.

S.K. Maxwell, G.L. Schmidt, and J.C. Storey. 2007. A multi-scale segmentation approach to filling gaps in Landsat ETM+ SLC-off images. International Journal of Remote Sensing. Volume 28, Issue 23, Pages 5339-5356.

C. Zhang, W. Li, and D. Travis. 2007. Gaps-fill of SLC-off Landsat ETM+ satellite image using a geostatistical approach. International Journal of Remote Sensing. Volume 28, Number 22, Pages 5103-5122.


Landsat represents the world's longest continuously acquired collection of space-based moderate-resolution land remote sensing data. Four decades of imagery provides a unique resource for those who work in agriculture, geology, forestry, regional planning, education, mapping, and global change research. Landsat images are also invaluable for emergency response and disaster relief.


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