Using Visible Spectral Information to Predict Long-Wave Infrared Spectral Emissivity: A Case Study over the Sokolov Area of the Czech Republic with an Airborne Hyperspectral Scanner Sensor
Remote-sensing platforms are often comprised of a cluster of different spectral range detectors or sensors to benefit from the spectral identification capabilities of each range. Missing data from these platforms, caused by problematic weather conditions, such as clouds, sensor failure, low temporal coverage or a narrow field of view (FOV), is one of the problems preventing proper monitoring of the Earth. One of the possible solutions is predicting a detector or sensor’s missing data using another detector/sensor. In this paper, we propose a new method of predicting spectral emissivity in the long-wave infrared (LWIR) spectral region using the visible (VIS) spectral region. The proposed method is suitable for two main scenarios of missing data: sensor malfunctions and narrow FOV. We demonstrate the usefulness and limitations of this prediction scheme using the airborne hyperspectral scanner (AHS) sensor, which consists of both VIS and LWIR spectral regions, in a case study over the Sokolov area, Czech Republic.
References
[1]
Cihlar, J.; Ly, H.; Li, Z.; Chen, J.; Pokrant, H.; Huang, F. Multitemporal, multichannel AVHRR data sets for land biosphere studies—Artifacts and corrections. Remote Sens. Environ 1997, 60, 35–57.
[2]
Roy, D.; Lewis, P.; Justice, C. Burned area mapping using multi-temporal moderate spatial resolution data-a bi-directional reflectance model-based expectation approach. Remote Sens. Environ 2002, 83, 263–286.
[3]
Jonsson, P.; Eklundh, L. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote Sens 2002, 40, 1824–1832.
[4]
Lunetta, R.S.; Knight, J.F.; Ediriwickrema, J.; Lyon, J.G.; Worthy, L.D. Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sens. Environ 2006, 105, 142–154.
[5]
Gao, F.; Masek, J.; Schwaller, M.; Hall, F. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens 2006, 44, 2207–2218.
[6]
Baladrón, C.; Aguiar, J.M.; Calavia, L.; Carro, B.; Sánchez-Esguevillas, A.; Hernández, L. Performance study of the application of artificial neural networks to the completion and prediction of data retrieved by underwater sensors. Sensors 2012, 12, 1468–1481.
[7]
Karnieli, A.; Ben-Dor, E.; Bayarjargal, Y.; Lugasi, R. Radiometric saturation of Landsat-7 ETM+ data over the Negev Desert (Israel): Problems and solutions. Int. Appl. Earth Obs. Geoinf 2004, 5, 219–237.
[8]
Shen, H.; Zhang, L. A MAP-based algorithm for destriping and inpainting of remotely sensed images. IEEE Trans. Geosci. Remote Sens 2009, 47, 1492–1502.
[9]
Little, R.J.; Rubin, D.B. Statistical Analysis with Missing Data; Wiley: New York, NY, USA, 1987; Volume 539.
[10]
Bertalmio, M.; Sapiro, G.; Caselles, V.; Ballester, C. Image Inpainting. Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH’00), New Orleans, LA, USA, 23–28 July 2000; pp. 417–424.
[11]
García-Laencina, P.J.; Sancho-Gómez, J.L.; Figueiras-Vidal, A.R. Pattern classification with missing data: A review. Neural Comput. Appl 2010, 19, 263–282.
[12]
Roy, D.P.; Ju, J.; Lewis, P.; Schaaf, C.; Gao, F.; Hansen, M.; Lindquist, E. Multi-temporal MODIS–Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sens. Environ 2008, 112, 3112–3130.
[13]
Gao, F.; Masek, J.G.; Huang, C.; Wolfe, R.E. Building a consistent medium resolution satellite data set using moderate resolution imaging spectroradiometer products as reference. J. Appl. Remote Sens 2010, 4, 043526:1–043526:22.
[14]
Walker, J.; de Beurs, K.; Wynne, R.; Gao, F. Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology. Remote Sens. Environ 2012, 117, 381–393.
[15]
Shen, H.; Wu, P.; Liu, Y.; Ai, T.; Wang, Y.; Liu, X. A spatial and temporal reflectance fusion model considering sensor observation differences. Int. J. Remote Sens 2013, 34, 4367–4383.
[16]
Ramsey, M.S.; Christensen, P.R.; Lancaster, N.; Howard, D.A. Identification of sand sources and transport pathways at the Kelso Dunes, California, using thermal infrared remote sensing. Geol. Soc. Am. Bull 1999, 111, 646–662.
[17]
Vaughan, R.; Calvin, W.M.; Taranik, J.V. SEBASS hyperspectral thermal infrared data: Surface emissivity measurement and mineral mapping. Remote Sens. Environ 2003, 85, 48–63.
[18]
Eisele, A.; Lau, I.; Hewson, R.; Carter, D.; Wheaton, B.; Ong, C.; Cudahy, T.J.; Chabrillat, S.; Kaufmann, H. Applicability of the thermal infrared spectral region for the prediction of soil properties across semi-arid agricultural landscapes. Remote Sens 2012, 4, 3265–3286.
[19]
Liang, S. Quantitative Remote Sensing of Land Surfaces; John Wiley & Sons: Hoboken, NJ, USA, 2005.
[20]
Zhou, L.; Dickinson, R.E.; Ogawa, K.; Tian, Y.; Jin, M.; Schmugge, T.; Tsvetsinskaya, E. Relations between albedos and emissivities from MODIS and ASTER data over North African Desert. Geophys. Res. Lett. 2003, doi:10.1029/2003GL018069..
[21]
Stathopoulou, M.; Cartalis, C.; Petrakis, M. Integrating corine land cover data and landsat TM for surface emissivity definition: Application to the urban area of Athens, Greece. Int. J. Remote Sens 2007, 28, 3291–3304.
[22]
Roerink, G.; Su, Z.; Menenti, M. S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance. Phys. Chem. Earth B 2000, 25, 147–157.
[23]
Courault, D.; Seguin, B.; Olioso, A. Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches. Irrig. Drain. Syst 2005, 19, 223–249.
[24]
Specim AISA Airborne Hyperspectral Imaging Systems—Spectral Cameras. Available online: http://www.specim.fi/index.php/products/airborne (1 March 2013).
[25]
Cover, T.; Hart, P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 1967, 13, 21–27.
[26]
García-Laencina, P.J.; Sancho-Gómez, J.-L.; Figueiras-Vidal, A.R. K nearest neighbors with mutual information for simultaneous classification and missing data imputation. Neurocomputing 2009, 72, 1483–1493.
[27]
Franco-Lopez, H.; Ek, A.R.; Bauer, M.E. Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. Remote Sens. Environ 2001, 77, 251–274.
[28]
Ohmann, J.L.; Gregory, M.J. Predictive mapping of forest composition and structure with direct gradient analysis and nearest-neighbor imputation in coastal Oregon, U.S.A. Can. J. For. Res 2002, 32, 725–741.
[29]
Haapanen, R.; Ek, A.R.; Bauer, M.E.; Finley, A.O. Delineation of forest/nonforest land use classes using nearest neighbor methods. Remote Sens. Environ 2004, 89, 265–271.
[30]
Hudak, A.T.; Crookston, N.L.; Evans, J.S.; Hall, D.E.; Falkowski, M.J. Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sens. Environ 2008, 112, 2232–2245.
[31]
Muinonen, E.; Parikka, H.; Pokharel, Y.; Shrestha, S.; Eerik？inen, K. Utilizing a multi-source forest inventory technique, MODIS data and Landsat TM images in the production of forest cover and volume maps for the Terai physiographic zone in Nepal. Remote Sens 2012, 4, 3920–3947.
[32]
Holopainen, M.; Haapanen, R.; Karjalainen, M.; Vastaranta, M.; Hyypp？, J.; Yu, X.; Tuominen, S.; Hyypp？, H. Comparing accuracy of airborne laser scanning and TerraSAR-X radar images in the estimation of plot-level forest variables. Remote Sens 2010, 2, 432–445.
[33]
Kankare, V.; Vastaranta, M.; Holopainen, M.; R？ty, M.; Yu, X.; Hyypp？, J.; Hyypp？, H.; Alho, P.; Viitala, R. Retrieval of forest aboveground biomass and stem volume with airborne scanning LiDAR. Remote Sens 2013, 5, 2257–2274.
[34]
Lindberg, E.; Holmgren, J.; Olofsson, K.; Wallerman, J.; Olsson, H. Estimation of tree lists from airborne laser scanning using tree model clustering and k-MSN imputation. Remote Sens 2013, 5, 1932–1955.
[35]
Friedman, J.H.; Bentley, J.L.; Finkel, R.A. An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw 1977, 3, 209–226.
[36]
Jones, P.W.; Osipov, A.; Rokhlin, V. A randomized approximate nearest neighbors algorithm. Appl. Computat. Harmon. A 2013, 34, 415–444.
[37]
Rojik, P. New stratigraphic subdivision of the Tertiary in the Sokolov Basin in Northwestern Bohemia. J. GEOsci 2004, 49, 173–185.
[38]
Murad, E.; Rojik, P. Iron mineralogy of mine-drainage precipitates as environmental indicators: Review of current concepts and a case study from the Sokolov Basin, Czech Republic. Clay Miner 2005, 40, 427–440.
[39]
Kopackova, V.; Chevrel, S.; Bourguignon, A.; Rojík, P. Application of high altitude and ground-based spectroradiometry to mapping hazardous low-pH material derived from the Sokolov open-pit mine. J. Map 2012, 8, 220–230.
[40]
Casal, G.; Sánchez-Carnero, N.; Domínguez-Gómez, J.A.; Kutser, T.; Freire, J. Assessment of AHS (Airborne Hyperspectral Scanner) sensor to map macroalgal communities on the Ría de vigo and Ría de Aldán coast (NW Spain). Mar. Biol 2012, 159, 1997–2013.
[41]
Green, R.O. Atmospheric Correction Now (ACORN); ImSpec LLC: Palmdale, CA, USA, 2001.
[42]
Gillespie, A.; Rokugawa, S.; Matsunaga, T.; Cothern, J.; Hook, S.; Kahle, A. A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE Trans. Geosci. Remote Sens 1998, 36, 1113–1126.
[43]
Schlapfer, D.; Schaepman, M.; Itten, K. PARGE: Parametric geocoding based on GCP-calibrated auxiliary data. Proc. SPIE 1998, doi:10.1117/12.328114..
[44]
Hapke, B. Theory of Reflectance and Emittance Spectroscopy; Cambridge University Press: Cambridge, UK, 2012.
[45]
Tokola, T.; Pitkanen, J.; Partinen, S.; Muinonen, E. Point accuracy of a non-parametric method in estimation of forest characteristics with different satellite materials. Int. J. Remote Sens 1996, 17, 2333–2351.
[46]
Nilsson, M. Estimation of Forest Variables Using Satellite Image Data and Airborne LidarPh.D. Thesis, The Department of Forest Resource Management and Geomatics, Swedish University of Agricultural Sciences, Uppsala, Sweden. 1997. Acta Universitatis Agriculturae Sueciae, Silvestria No. 17.
[47]
McGill, R.; Tukey, J.W.; Larsen, W.A. Variations of box plots. Am. Stat 1978, 32, 12–16.
[48]
Metz, C.E. Basic principles of ROC analysis. Semin. Nucl. Med 1978, 8, 283–298.
[49]
De Maesschalck, R.; Jouan-Rimbaud, D.; Massart, D. The Mahalanobis distance. Chemometr. Intell. Lab 2000, 50, 1–18.
[50]
Blitzer, J.; Weinberger, K.Q.; Saul, L.K. Distance metric learning for large margin nearest neighbor classification. Adv. Neural Inf. Process Syst 2005, 1473–1480.
[51]
Inamdar, A.K.; French, A.; Hook, S.; Vaughan, G.; Luckett, W. Land surface temperature retrieval at high spatial and temporal resolutions over the southwestern United States. J. Geophys. Res.:Atmos 2008, 113, 7–16.
[52]
Wu, P.; Shen, H.; Ai, T.; Liu, Y. Land-surface temperature retrieval at high spatial and temporal resolutions based on multi-sensor fusion. Int. J. Digital Earth 2013, 6, 1–21.
