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Articles

Inversion of top of atmospheric reflectance values by conic multivariate adaptive regression splines

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Pages 651-669 | Received 03 Feb 2013, Accepted 09 Jun 2014, Published online: 24 Jul 2014

References

  • Jankowski P. Integrating geographical information systems and multiple criteria decision-making methods. Int. J. Geog. Inf. Syst. 1995;9:251–273.10.1080/02693799508902036
  • Nogué S, Rull V, Vegas-Vilarrúbia T. Modeling biodiversity loss by global warming on Pantepui, northern South America: projected upward migration and potential habitat loss. Clim. Change. 2009;94:77–85.10.1007/s10584-009-9554-x
  • Trotter CM. Remotely-sensed data as an information source for geographical information systems in natural resource management a review. Int. J. Geog. Inf. Syst. 1991;5:225–239.10.1080/02693799108927845
  • Hsu M, Chen AS, Chen L, Lee C, Lin F, Huang C. A GIS-based decision support system for typhoon emergency response in Taiwan. Geotech. Geol. Eng. 2011;29:7–12.10.1007/s10706-010-9362-0
  • Wilhelmi OV, Brunskill JC. Geographic information systems in weather, climate, and impacts. Bull. Am. Meteorol. Soc. 2003;84:1409–1414.10.1175/BAMS-84-10-1409
  • Voivontas D, Assimacopoulos D, Mourelatos A, Corominas J. Evaluation of renewable energy potential using a GIS decision support system. Renewable Energy. 1998;13:333–344.10.1016/S0960-1481(98)00006-8
  • Zeng TQ, Zhou Q. Optimal spatial decision making using GIS: a prototype of a real estate geographical information system (REGIS). Int. J. Geog. Inf. Sci. 2001;15:307–321.10.1080/136588101300304034
  • Masser I. Managing our urban future: the role of remote sensing and geographic information systems. Habitat Int. 2001;25:503–512.10.1016/S0197-3975(01)00021-2
  • Beeri O, Peled A. Geographical model for precise agriculture monitoring with real-time remote sensing. ISPRS J. Photogramm. Remote Sens. 2009;64:47–54.10.1016/j.isprsjprs.2008.07.007
  • Jeong JS, García-Moruno L, Hernández-Blanco J. A site planning approach for rural buildings into a landscape using a spatial multi-criteria decision analysis methodology. Land Use Policy. 2013;32:108–118.10.1016/j.landusepol.2012.09.018
  • Tulloch DL, Myers JR, Hasse JE, Parks PJ, Lathrop RG. Integrating GIS into farmland preservation policy and decision making. Landscape Urban Plan. 2003;63:33–48.10.1016/S0169-2046(02)00181-0
  • Coburn, TC, Yarus, JM. Geographic information systems in petroleum exploration and development (AAPG computer applications in geology, no. 4). Tulsa (OK): American Association of Petroleum Geologists; 2000.
  • Knox‐Robinson CM, Wyborn LAI. Towards a holistic exploration strategy: using geographic information systems as a tool to enhance exploration. Aust. J. Earth Sci. 1997;44:453–463.10.1080/08120099708728326
  • Tso B, Mather PM. Classification methods for remotely sensed data. 2nd ed. Boca Raton (FL): CRC Press; 2009.
  • Proud SR, Rasmussen MO, Fensholt R, Sandholt I, Shisanya C, Mutero W, Mbow C, Anyamba A. Improving the SMAC atmospheric correction code by analysis of Meteosat Second Generation NDVI and surface reflectance data. Remote Sens. Environ. 2010;114:1687–1698.10.1016/j.rse.2010.02.020
  • Anderson GP, Pukall B, Allred CL, Jeong LS, Hoke M, Chetwynd JH, Adler-Golden SM, Berk A, Bernstein LS, Richtsmeier SC, Acharya PK, Matthew MW. FLAASH and MODTRAN4: state-of-the-art atmospheric correction for hyperspectral data. In: 1999 IEEE Aerospace Conference Proceedings. Vol. 4. Piscataway (NJ); 1999. p. 177–181.
  • Bernstein LS, Adler-Golden SM, Sundberg RL, Levine RY, Perkins TC, Ratkowski AJ, Felde G, Hoke ML. A new method for atmospheric correction and aerosol optical property retrieval for VIS-SWIR multi- and hyperspectral imaging sensors: QUAC (QUick atmospheric correction). In: Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. Proceedings. 2005 IEEE International. Vol. 5. Seoul; 2005 July 25–29. p. 3549–3552.
  • Tanre D, Deroo C, Duhaut P, Herman M, Morcrette JJ, Perbos J, Deschamps PY. Technical note description of a computer code to simulate the satellite signal in the solar spectrum: the 5S code. Int. J. Remote Sens. 1990;11:659–668.10.1080/01431169008955048
  • Richards JA, Jia X. Remote sensing digital image analysis: an introduction. 4th ed. Berlin: Springer; 2006.
  • Gao B-C, Montes MJ, Davis CO, Goetz AFH. Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean. Remote Sens. Environ. 2009;113:S17–S24.10.1016/j.rse.2007.12.015
  • Vermote EF, El Saleous NZ, Justice CO. Atmospheric correction of MODIS data in the visible to middle infrared: first results. Remote Sens. Environ. 2002;83:97–111.10.1016/S0034-4257(02)00089-5
  • Rahman H, Dedeiu G. SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum. Int. J. Remote Sens. 1994;15:123–143.10.1080/01431169408954055
  • Weber G-W, Batmaz İ, Köksal G, Taylan P, Yerlikaya-Özkurt F. CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization. Inverse Prob. Sci. Eng. 2011;20:371–400.
  • Friedman JH. Multivariate adaptive regression splines. Ann. Stat. 1991;19:1–67.10.1214/aos/1176347963
  • Özmen A, Kropat E, Weber G-W. Spline regression models for complex multi-modal regulatory networks. Optim. Method Softw. 2014;29:515–534. doi:10.1080/10556788.2013.821611.
  • Deichmann J, Eshghi A, Haughton D, Sayek S, Teebagy N. Application of multiple adaptive regression splines (MARS) in direct response modeling. J. Interact. Marketing. 2002;16:15–27.10.1002/dir.10040
  • Krzyscin JW, Eerme K, Janouch M. Long-term variations of the UV-B radiation over Central Europe as derived from the reconstructed UV time series. Ann. Geophys. 2004;22:1473–1485.10.5194/angeo-22-1473-2004
  • Leathwick JR, Elith J, Hastie T. Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecol. Modell. 2006;199:188–196.10.1016/j.ecolmodel.2006.05.022
  • Vermote EF, Kotchenova SY, Ray JP. MODIS surface reflectance user’s guide (version. 1.3). 2011; [cited 2012 Nov 10]. Available from: http://dratmos.geog.umd.edu/products/MOD09_UserGuide_v1_3.pdf.
  • Proud SR, Fensholt R, Rasmussen MO, Sandholt IA. Comparison of the effectiveness of 6S and SMAC in correcting for atmospheric interference in Meteosat Second Generation images. J. Geophys. Res-Atmos, 2010. 115:( D17209)1–14. doi: 10.1029/2009JD013693.
  • Vermote E, Tanre D, Deuze J, Herman M, Morcette J-J. Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Trans. Geosci. Remote Sens. 1997;35:675–686.10.1109/36.581987
  • MODIS level 1B product user’s guide. NASA/Goddard Space Flight Center. 2009; [cited 2013 Oct 29]. Available from: http://ccplot.org/pub/resources/Aqua/MODIS%20Level%201B%20Product%20User%20Guide.pdf.
  • Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York (NY): Springer; 2009.
  • Taylan P, Weber G-W, Yerlikaya Özkurt FY. A new approach to multivariate adaptive regression splines by using Tikhonov regularization and continuous optimization. Top. 2010;18:377–395.10.1007/s11750-010-0155-7
  • Hubanks PA, King MD, Platnick S, Pincus R. MODIS atmosphere L3 gridded product algorithm theoretical basis document (Collection 005 Version 1.1). 2008; [cited 2012 Oct 29]. Available from: http://modis-atmos.gsfc.nasa.gov/_docs/L3_ATBD_2008_12_04.pdf.
  • Cameron AC, Windmeijer FAG. An R-squared measure of goodness of fit for some common nonlinear regression models. J. Econom. 1997;77:329–342.10.1016/S0304-4076(96)01818-0
  • MARS®. Salford Systems. Available from: http://www.salfordsystems.com.
  • MOSEKTM. Available from: http://www.mosek.com.
  • Özmen A, Weber G-W, Batmaz İ, Kropat E. RCMARS: robustification of CMARS with different scenarios under polyhedral uncertainty set. Commun. Nonlinear Sci. Numer. Simul. 2011;16:4780–4787.
  • Özmen A, Weber G-W, Çavuşoğlu Z, Defterli Ö. The new robust conic GPLM method with an application to finance: prediction of credit default. J. Global Optim. 2013;56:233–249.
  • Hansen PC. Rank-deficient and discrete Ill-posed problems: numerical aspects of linear inversion. Philadelphia, PA: SIAM; 1998.10.1137/1.9780898719697

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