Operational Research

, Volume 17, Issue 2, pp 371–393 | Cite as

A progressive approach for processing satellite data by operational research

  • Semih Kuter
  • Gerhard-Wilhelm Weber
  • Zuhal Akyürek
Original Paper


Satellite data, together with spatial technologies, have a vital importance in earth sciences to continuously monitor natural and physical processes. However, images taken by earth-observing satellites are often associated with uncertainties due to atmospheric effects (i.e., absorption and scattering by atmospheric gases and aerosols). In this paper, a more adaptable approach for the removal of atmospheric effects from satellite data is introduced within an operational research perspective by utilizing nonparametric regression splines. Regional atmospheric correction models via multivariate adaptive regression splines (MARS) are applied on a set of satellite images for Alps and Turkey to calculate surface reflectance values. A classical radiative transfer based atmospheric correction method is likewise employed on the same data set. The results are compared in terms of relative differences with respect to surface reflectance data. MARS provides significant improvement in the order of 40 and 37 % for Alps and Turkey, respectively.


MARS Operational research Remote sensing Atmospheric correction Nonparametric regression splines 



The authors would like to thank the anonymous reviewers for their valuable comments and remarks which improved the quality of the paper. The earth package used for MARS model building is available at The RT code SMAC is also open source and can be found at The MODIS image data used in this study can be downloaded free of charge from


  1. Adler-Golden SM, Matthew MW, Bernstein LS, Levine RY, Berk A, Richtsmeier SC, Acharya PK, Anderson GP, Felde G, Gardner J, Hoke M, Jeong LS, Pukall B, Mello J, Ratkowski A, Burke H-H (1999) Atmospheric correction for shortwave spectral imagery based on MODTRAN4. Imaging Spectrom V. doi: 10.1117/12.366315 Google Scholar
  2. Albert P, Smith KM, Bennartz R, Newnham DA, Fischer J (2004) Satellite- and ground-based observations of atmospheric water vapor absorption in the 940 nm region. J Quant Spectrosc Radiat Transf 84(2):181–193CrossRefGoogle Scholar
  3. Allan MG, Hamilton DP, Hicks BJ, Brabyn L (2011) Landsat remote sensing of chlorophyll a concentrations in central North Island lakes of New Zealand. Int J Remote Sens 32(7):2037–2055CrossRefGoogle Scholar
  4. Arabatzis GD, Kokkinakis AK (2005) Typology of the lagoons of Northern Greece according to their environmental characteristics and fisheries production. Oper Res Int J 5(1):21–34CrossRefGoogle Scholar
  5. Babajimopoulos C, Panoras A (2005) Estimation of the water balance of cultivated soils by mathematical models. Oper Res Int J 5(1):127–140CrossRefGoogle Scholar
  6. Beal D, Baret F, Weiss M, Gu X, Verbrugghe M (2003) A method for MERIS atmospheric correction based on the spectral and spatial observation. In: Proceedings of paper presented at the geoscience and remote sensing symposium, 2003. IGARSS 2003Google Scholar
  7. Ben-Tal A, Nemirovski A (2002) Robust optimization—methodology and applications. Math Progr 92(3):453–480CrossRefGoogle Scholar
  8. Berk A, Bernstein LS, Robertson DC (1989) MODTRAN: a moderate resolution model for LOWTRAN7. Final report, GL-TR-89-0122, AFGL, Hanscom AFB, MA, p 42Google Scholar
  9. Brauers W (2008) Multi-objective decision making by reference point theory for a wellbeing economy. Oper Res Int J 8(1):89–104CrossRefGoogle Scholar
  10. Conel JE, Green RO, Vane G, Bruegge CJ, Alley RE (1987) AIS-2 radiometry and a comparison of methods for the recovery of ground reflectance. In: Vane G (ed) Proceedings of the Third Airborne Imaging Spectrometer Data Analysis Workshop, JPL Publication 87–30, Jet Propulsion Laboratory, Pasadena, CA, pp 18–47Google Scholar
  11. Eldridge RG (1967) Water vapor absorption of visible and near infrared radiation. Appl Opt 6(4):709–713CrossRefGoogle Scholar
  12. Elith J, Leathwick J (2007) Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Divers Distrib 13(3):265–275CrossRefGoogle Scholar
  13. Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1–67CrossRefGoogle Scholar
  14. Galanopoulos K, Karagiannis G, Koutroumanidis T (2004) Malmquist productivity index estimates for European agriculture in the 1990s. Oper Res Int J 4(1):73–91CrossRefGoogle Scholar
  15. Hagolle O, Dedieu G, Mougenot B, Debaecker V, Duchemin B, Meygret A (2008) Correction of aerosol effects on multi-temporal images acquired with constant viewing angles: application to Formosat-2 images. Remote Sens Environ 112:1689–1701CrossRefGoogle Scholar
  16. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, NewYorkCrossRefGoogle Scholar
  17. Hubanks PA, King MD, Platnick S, Pincus R (2008) MODIS atmosphere L3 gridded product algorithm theoretical basis document (Collection 005 Version 1.1). Retrieved 29 Oct 2012.
  18. Jankowski P (1995) Integrating geographical information systems and multiple criteria decision-making methods. Int J Geogr Inf Syst 9:251–273CrossRefGoogle Scholar
  19. Kaloudis ST, Lorentzos NA, Sideridis AB, Yialouris CP (2005) A decision support system for forest fire management. Oper Res Int J 5(1):141–152CrossRefGoogle Scholar
  20. Kooperberg C, Bose S, Stone CJ (1997) Polychotomous regression. J Am Stat Assoc 92(437):117–127CrossRefGoogle Scholar
  21. Kotchenova SY, Vermote EF (2007) Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part II: homogeneous lambertian and anisotropic surfaces. Appl Opt 46:4455–4464CrossRefGoogle Scholar
  22. Kotchenova SY, Vermote EF, Matarrese R, Klemm FJ (2006) Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: path radiance. Appl Opt 45:6762–6774CrossRefGoogle Scholar
  23. Lu D, Mausel P, Brondízio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25(12):2365–2401CrossRefGoogle Scholar
  24. Maisongrande P, Duchemin B, Dedieu G (2004) VEGETATION/SPOT: an operational mission for the Earth monitoring; presentation of new standard products. Int J Remote Sens 25:9–14CrossRefGoogle Scholar
  25. Milborrow S (2012) Earth: multivariate adaptive regression spline models—derived from mda:mars by Trevor Hastie and Rob Tibshirani. R package version 3.2-2.
  26. Özmen A, Weber G-W, Batmaz İ, Kropat E (2011) RCMARS: robustification of CMARS with different scenarios under polyhedral uncertainty set. Commun Nonlinear Sci Numer Simul 16:4780–4787CrossRefGoogle Scholar
  27. Özmen A, Batmaz İ, Weber G-W (2014) Precipitation modeling by polyhedral RCMARS and comparison with MARS and CMARS. Environ Model Assess 19(5):425–435CrossRefGoogle Scholar
  28. Proud SR, Fensholt R, Rasmussen MO, Sandholt I (2010a) A comparison of the effectiveness of 6S and SMAC in correcting for atmospheric interference in Meteosat second generation images. J Geophys Res Atmos 115(D17209):17201–17214Google Scholar
  29. Proud SR, Rasmussen MO, Fensholt R, Sandholt I, Shisanya C, Mutero W, Mbow C, Anyamba A (2010b) Improving the SMAC atmospheric correction code by analysis of Meteosat second generation NDVI and surface reflectance data. Remote Sens Environ 114:1687–1698CrossRefGoogle Scholar
  30. Qu JJ, Gao W, Kafatos M, Murphy RE, Salomonson VV (2006) Earth science satellite remote sensing. Volume 1: science and instruments. Springer, BeijingCrossRefGoogle Scholar
  31. R_Software (2012) R Development Core Team, R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
  32. Rahman H, Dedeiu G (1994) SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum. Int J Remote Sens 15:123–143CrossRefGoogle Scholar
  33. Richter R, Schlaepfer D (2002) Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: atmospheric/topographic correction. Int J Remote Sens 23:2631–2649CrossRefGoogle Scholar
  34. Roberts D, Yamaguchi Y, Lyon R (1986) Comparison of various techniques for calibration of AIS data. NASA STI/Recon Tech Rep N 87:12970Google Scholar
  35. Singh A (1989) Review article digital change detection techniques using remotely-sensed data. Int J Remote Sens 10(6):989–1003CrossRefGoogle Scholar
  36. Skuras D, Wade A, Psaltopoulos D, Whitehead P, Kontolainou A, Erlandsson M (2014) An interdisciplinary modelling approach assessing the cost-effectiveness of agri-environmental measures on reducing nutrient concentration to WFD thresholds under climate change: the case of the Louros catchment. Oper Res Int J 14(2):205–224CrossRefGoogle Scholar
  37. Tanre D, Deroo C, Duhaut P, Herman M, Morcrette JJ, Perbos J, Deschamps PY (1990) Description of a computer code to simulate the satellite signal in the solar spectrum—the 5S code. Int J Remote Sens 11:659–668CrossRefGoogle Scholar
  38. Tso B, Mather PM (2009) Classification methods for remotely sensed data, 2nd edn. CRC Press, Boca RatonCrossRefGoogle Scholar
  39. Vasilyev A, Melnikova I (2011) Multiplicity of solutions of the inverse problem for determining optical atmospheric parameters from remote observations. Int J Remote Sens 32(3):875–889CrossRefGoogle Scholar
  40. Vazakidis A, Karagiannis I (2011) Activity-based management and traditional costing in tourist enterprises (a hotel implementation model). Oper Res Int J 11(2):123–147CrossRefGoogle Scholar
  41. Vermote E, Tanre D, Deuze J, Herman M, Morcette J-J (1997) Second simulation of the Satellite signal in the solar spectrum, 6S: an overview. IEEE Trans Geosci Remote Sens 35:675–686CrossRefGoogle Scholar
  42. Vermote EF, Kotchenova SY, Ray JP (2011) MODIS surface reflectance user’s guide (Ver. 1.3). Retrieved 10 Nov 2012.
  43. Weber G-W, Batmaz İ, Köksal G, Taylan P, Yerlikaya- Özkurt F (2011) CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization. Inverse Probl Sci Eng 20:371–400CrossRefGoogle Scholar
  44. Yang M-H, Yeh R-H (2016) Economic performances optimization of an organic Rankine cycle system with lower global warming potential working fluids in geothermal application. Renew Energy 85:1201–1213CrossRefGoogle Scholar
  45. Yerlikaya-Özkurt F, Askan A, Weber G-W (2014) An alternative approach to the ground motion prediction problem by a non-parametric adaptive regression method. Eng Optim 46(12):1651–1668CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Semih Kuter
    • 1
    • 2
  • Gerhard-Wilhelm Weber
    • 2
    • 4
  • Zuhal Akyürek
    • 3
    • 4
  1. 1.Department of Forest Engineering, Faculty of ForestryÇankırı Karatekin UniversityÇankırıTurkey
  2. 2.Institute of Applied MathematicsMiddle East Technical UniversityAnkaraTurkey
  3. 3.Department of Civil Engineering, Faculty of EngineeringMiddle East Technical UniversityAnkaraTurkey
  4. 4.Department of Geodetic and Geographic Information Technologies, Graduate School of Natural and Applied SciencesMiddle East Technical UniversityAnkaraTurkey

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