Addink, E.A., 1999. A comparison of conventional and geostatistical methods to replace clouded pixels in NOAA–AVHRR images. International Journal of Remote Sens, 20: 961– 977.
Ahmadian, J., Sheibani, D., Iraqi, H., Shirmohammadi, R., & Mojarad, M., 2002. agricultural Classification of climate for sustainable water resources management in developing countries. P. 593-605. Eleventh Meeting of the National Committee on Irrigation and Drainage, January 2002, Tehran, Iran.
Boegh, E., Soegaard, H., Christensen, J.H., Hasager, C.B., Jensen, N.O., Nielsen, N.W. and Rasmussen, M.S., 2004. Combining weather prediction and remote sensing data for the calculation of evapotranspiration rates: application to Denmark. International Journal of Remote Sensing, 25(13): 2553-2574.
Brooks, E.B., Thomas, V.A., Wynne, R.H. and Coulston, J.W., 2012. Fitting the multitemporal curve: A Fourier series approach to the missing data problem in remote sensing analysis. IEEE Transactions on Geoscience and Remote Sensing, 50(9): 3340-3353.
Chan, T., and Shen, J., 2001. Nontexture inpainting by curvature-driven diffusions. Journal of Visual Communication and Image Representation, 4: 436-449
Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B. and Eklundh, L., 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote sensing of Environment, 91(3): 332-344.
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(4): 1053-1064
Ferguson, C.R. and Wood, E.F., 2010. An evaluation of satellite remote sensing data products for land surface hydrology: Atmospheric infrared sounder. Journal of Hydrometeorology, 11(6): 1234-1262.
Gao, Y. and Mas, J.F., 2008. A comparison of the performance of pixel-based and object-based classifications over images with various spatial resolutions. Online journal of earth sciences, 2(1): 27-35.
Gerber, F., Furrer, R., Schaepman-Strub, G., de Jong, R. and Schaepman, M.E., 2016. Predicting missing values in spatio-temporal satellite data. arXiv preprint arXiv:1605.01038.
Grell G A, Dudhia J, Staurer F R. 1995. A description of the fifth-generation Penn State/NCAR mesoscale model (MM5). NCAR Tech Note NCAR/TN- 398 +STR. 122, Boulder, Colorado [EB/OL]. http://www.mmm.ucar.edu/mm5/doc1.html/2003-07-19
Hengl, T.G.B., Heuvelink, M. Perˇcec Tadi´c, and E.J. Pebesma. 2012. Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images. Journal of Theor. Appl. Climatol, 107: 265–277.
Jang, K., Kang, S., Kim, J., Lee, C.B., Kim, T., Kim, J., Hirata, R. and Saigusa, N., 2010. Mapping evapotranspiration using MODIS and MM5 four-dimensional data assimilation. Remote Sensing of Environment, 114(3): 657-673.
Kandasamy, S., Baret, F., Verger, A., Neveux, P. and Weiss, M., 2013. A comparison of methods for smoothing and gap filling time series of remote sensing observations–application to MODIS LAI products. Biogeosciences, 10(6): 4055-4071.
Kilibarda, M., Hengl, T., Heuvelink, G., Gräler, B., Pebesma, E., Perčec Tadić, M. and Bajat, B., 2014. Spatio‐temporal interpolation of daily temperatures for global land areas at 1 km resolution. Journal of Geophysical Research: Atmospheres, 119(5):2294-2313.
Li, Z.L., Tang, B.H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I.F. and Sobrino, J.A., 2013. Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 131: 14-37.
Lloyd, C.D. and Atkinson, P.M., 2002. Deriving DSMs from LiDAR data with kriging. International Journal of Remote Sensing, 23(12): 2519-2524.
Maxwell, S., Schmidt, G., and Storey, J., 2007. A multi-scale segmentation approach to filling gaps in Landsat ETM+SLC-off images. International Journal of Remote Sensing, 28:5339-5356.
McMillen, D.P., 2012. Quantile regression for spatial data. Springer Science & Business Media.
Meij, A.D., Gzella, A., Cuvelier, C., Thunis, P., Bessagnet, B., Vinuesa, J.F., Menut, L. and Kelder, H.M., 2009. The impact of MM5 and WRF meteorology over complex terrain on CHIMERE model calculations. Atmospheric Chemistry and Physics, 9(17): 6611-6632.
Mobasheri, M.R., Sadeghi Naeini, A., 2007. Using IRS Products to Recover 7ETM+ Defective Images. American Journal of Applied Science. 5(6): 618-625.
Mohammdy, M., Moradi, H.R., Zeinivand, H., Temme, A.J.A.M., Pourghasemi, H.R. and Alizadeh, H., 2014. Validating gap-filling of Landsat ETM+ satellite images in the Golestan Province, Iran. Arabian Journal of Geosciences, 7(9): 3633-3638.
Salomonson, V.V., Guenther, B. and Masuoka, E., 2001. A summary of the status of the EOS Terra Mission Moderate Resolution Imaging Spectroradiometer (MODIS) and attendant data product development after one year of on-orbit performance. In Geoscience and Remote Sensing Symposium, 2001. IGARSS'01. IEEE 2001 International, 3: 1197-1199. IEEE.
Savitzky, A. and Golay, M.J., 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, 36(8): 1627-1639.
Wan, Z., Zhang, Y., Zhang, Q. and Li, Z.L., 2002. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote sensing of Environment, 83(1): 163-180.
Wan, Z., Zhang, Y., Zhang, Q., Li, Z., 2004. Quality assessment and validation of the MODIS global land surface temperature. International Journal of Remote Sensing, 25(1): 261–274.
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.
Zhou, J., Jia, L. and Menenti, M., 2015. Reconstruction of global MODIS NDVI time series: Performance of harmonic analysis of time series (HANTS). Remote Sensing of Environment, 163: 217-228.
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.
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