Comparison of gap filling methods in Landsat 7 ETM+ images to estimate crop coefficient

Document Type : Research Paper


University of Guilan


Landsat 7 ETM+ data is widely used in studies of the spatial distribution Kc and vegetation cover parameters in regional and global scales but SLC failure has greatly reduces its usefulness. Additionally, the failure is permanent and has failed subsequent attempts to recover the SLC, so required and practical way to address this problem is filling the pixels of missed data in the SLC-off images. Although, there are several proposed methods to fill the gap, but still have filled images quality in heterogeneous area is not satisfactory for more applications. This study was conducted to compare the geostatistics and MODIS auxiliary data methods to fill the pixels of missed data in the SLC-off images. The results showed that the IDW method with NRMSE 6.09% was the best method. The fusion with auxiliary images (MODIS) and ordinary Kriging methods resulted in NRMSE 14.75 and 16.9, respectively. The method of fusion with classified auxiliary images (MODIS) presented the lowest accuracy in estimating missed data.


Main Subjects

Abdoul Jabar, A.S., Sulang, G. and George, L.E. (2014). Survey on gap filling algorithms in Landsat 7 ETM+ images. Journal of Theoretical and Applied Information Technology. 63, 136-146.
Ahadnezhad Rooshti, M. (2011). Provide an algorithm to reconstruct the defect images of not working Scan Line Corrector (SLC) Landsat 7 ETM+ and its use in the preparation of land use and land cover maps, a Case Study of Znjan. GeographyandDevelopment. 22, 23-38.
Alexandridis, T.K., Cherif, I., Kalogeropoulos, C., Monachou, S., Eskridge, K.  and Silleos, N. (2013). Rapid error assessment for quantitative estimations from Landsat 7 gap-filled images. Remote Sens. 9, 920–928.
Ali S. M and Mohammed M. J (2013) Gap-filling restoration methods for ETM+ sensor images. Iraqi Journal of Science. 54, 206–214.
Allen, R.G., Pereia, L.S., Raes, D. and Smith, M. (1998). Crop evapotranspiration. FAO Irrigation and Drainage Paper 56, Food and Agricultural Organization of the United Nations, Rome.
Bédard, F., Reichert, G., Dobbins, R. and Trépanier, I. (2008). Evaluation of segment-based gap-filled Landsat ETM+ SLC-off satellite data for land cover classification in southern Saskatchewan, Canada. International Journal of Remote Sensing. 29, 2041–2054.
Belmonte, A.C., Jochum, A.M., Garcia, A.C., Rodriguez, A.M. and Fuster, P.L. (2005). Irrigation management from space: Towards user-friendly products. Irrigation and Drainage Systems. 19, 337-353.
Boloorani, A.D., Erasmi, S. and Kappas, M. (2008). Multi-source remotely sensed data combination: projection transformation gap-fill procedure. Sensors. 8, 4429–4440.
Byrne, G.F., Crapper, P.F. and Mayo, K.K. (1980). Monitoring land-cover change by principal component analysis of multi-temporal Landsat data. Remote Sensing of Environment. 10, 175–184.
Chen, F., Tang, L.  and  Qiu, Q. (2010). Exploitation of CBERS-02B as auxiliary data in recovering the Landsat 7 ETM+ SLC-off image. 18TH international conference, Bijing. 1–6.
Chen, J., Zhu, X., Vogelmann, J.E., Gao, F. and Jin, S. (2011). A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of Environment. 115, 1053–1064.
Fisher, J.I., Mustard, J.F. and Vadeboncoeur, M.A. (2006). Green leaf phenology at Landsat resolution: Scaling from the field to the satellite. Remote Sensing of Environment. 100, 265–279.
Fuller, R.M., Groom, G.B. and Jones, A.R. (1994). The land-cover map of Great-Britain —An automated classification of Landsat Thematic Mapper data. Photogrammetric Engineering and Remote Sensing. 60, 553–562.
Gao, F., Masek, J., Hall, J. and Schwaller, S. (2006). On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote sensing. 44(8), 2207-2218.
Goward, S.N., Arvidson, T.J., Faundeen, F., Williams, D.L., Irons, D.L. and Franks, S. (2006). Historical record of Landsat global coverage: Mission operations, NSLRSDA, and international cooperator stations. Photogrammetric Engineering and Remote Sensing. 72(10), 1155–1169.
Hu, W., Li, M., Liu, Y., Huang, Q. and Mao, K. (2011). A new method of restoring ETM + SLC-off images based on multi-temporal images. 19TH international conference, Shanghai. 1-4.
Ju, J.C. and Roy, D.P. (2008). The availability of cloud-free Landsat ETM plus data over the conterminous United States and globally. Remote Sensing of Environment. 112, 1196–1211.
Masek, J.G., Huang, C.Q., Wolfe, R., Cohen, W., Hall F. and Kutler, J. (2008). North American forest disturbance mapped from a decadal Landsat record. Remote Sensing of Environment. 112: 2914–2926.
Maxwell, S. (2004). Filling landsat ETM+ SLC-off gaps using a segmentation model approach. Photogrammetric Engineering & Remote Eensing. 1109–111.
Maxwell, S. K., Schmidt, G.L. and Storey, J.C. (2007). A multi-scale segmentation approach to filling gaps in Landsat ETM+ SLC-off images. International Journal of  Remote Sensing. 28, 5339–5356.
Mohammady, M., Moradi, H.R., Zeinivand, H., Temme, A.J.A.M., Pourghasemi, H.R. and Alizadeh, H. (2013). Validating gap-filling of Landsat ETM+ satellite images in the Golestan Province, Iran. Arabian Journal Geosciences. 7, 3633-3638.
Liu, D. and Cai S. (2011). A spatial-temporal modeling approach to reconstructing land-cover change trajectories from multi-temporal satellite imagery. Annals of the Association of American Geographers. http:// dx. doi. org/ 10. 1080/ 00045608. 2011.596357.
PirmoradianN., Rezaei, M. Davatgar, N. Tajdari, K. and Abolpour, B. (2010).Comparing of interpolation methods in rice cultivation vulnerability mapping due to groundwater quality in Guilan, north of Iran. International Conference on Environmental Engineering and Applications (ICEEA), Singapore, 10-12 September.
Pringle, M.J., Schmidt, S. and Muir, J.S. (2009). Geostatistical interpolation of SLC-off Landsat ETM+ images. ISPRS Journal of Photogrammetry and Remote Sensing. 64, 654–664.
Reza, M.M. and Ali, S.N. (2008). Using IRS products to recover landsat 7 ETM+ Defective Images. American Journal of Applied Sciences. 5, 618-625.
Roy, D.P., Ju, J., Lewis, P., Schaaf, C., Gao, F., Hansen, M. and Lindquist, E. (2008). Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sensing of Environment. 112, 3112–3130.
Storey, J., Engineer, P.S. and Falls, S. (2005). Landsat 7 scan line corrector-off gap-filled product development. Global Priorities in Land Remote Sensing. 1-13.
USGS 2012 Landsat 5 suspension of operations extended. Available online at http:// (accessed on March 25, 2012).
USGS & NASA. (2013). SLC-off Gap-Filled Products Gap-fill Algorithm Methodology: Phase 2. October 2004. Gap-fill Algorithm, Available from www. ga. gov. au/ servlet/ Big Obj File Manager?  bigobjid=GA4861(accessed on 2013).
Xiaolin, Z., Desheng, L. and Chen, J. (2012). A new geostatistical approach for filling gaps in landsat ETM+ SLC-off images. Remote Sensing of Environment. 124, 49-60.
Zhang, C., Li, W. and Travis, D. (2007). Gaps-fill of SLC-off Landsat ETM plus satellite image using a geostatistical approach. International Journal of  Remote Sensing. 28, 5103–5122.
Zeng, C., Shen, H. and Zhang, L. (2013). Recovering missing pixels for Landsat ETM+ SLC-off
 imagery using multi-temporal regression analysis and a regularization method. Remote Sensing of Environment. 131, 182–194.
Zhu, X., Liu, D. and Chen, J. (2012). A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of Environment. 124, 49–60.