Modified and Adaptation of SEBAL Methodology for Estimating LSE from LDCM Data: Fars Province, Iran

نویسندگان

1 Assistant Professor, Department of Geomatics, University of Tabriz

2 Professor, Department of Surveying and Geomatics, University of Tehran, Tehran, Iran

3 Assistant Professor, Department of Geomatics, Iran University of Science and Technology

چکیده

Land Surface Emissivity (LSE) is an important intrinsic property of materials which is variable through physical parameters and it is dependent to the Spectral Response Function (SRF) and the effective wavelength of channel. Surface Energy Balance Algorithm for Land (SEBAL) is one of the most widely applied models which is comprised of twenty-five sub-models that calculate different surface variables such as LSE and LST. This algorithm used within 3-14 μm and 8-14 μm spectral domain. Obviously, using of the broadband emissivity in one channel instead of the narrowband emissivity lead to large errors on the surface parameters. This study investigates the effects of SRF and effective wavelength on SEBAL-based LSE estimation method, MLSESEBAL, in the narrow domain of TIRS bands of LDCM. For comparison and validation of MLSESEBAL, it compared to three common LSE estimation methods and LSE product of ASTER as a reference, respectively. The results showed that if there is little difference in the effective wavelength between broadband and narrowband, the LSE estimation is almost identical in the non-vegetated area and there is no significant difference, while it is non-negligible in the vegetated area. In contrast, if there is a relatively large difference between the effective wavelength and SRF between them, in this case, areas with vegetation and no vegetation have the same performance and the greatest difference in LSE estimation. Moreover, the validation results of MLSESEBAL method showed that the RMSE of LSE are 1.59% and 1.21% in thermal band 10 of the first and second examined scenes, respectively. As well as, for the band 11, the error values are 1.56% and 0.98% in the two examined scenes, respectively. 

کلیدواژه‌ها


[1]     Mitraka, Z., et al., Improving the estimation of urban surface emissivity based on sub-pixel classification of high resolution satellite imagery. Remote Sensing of Environment, 2012. 117: p. 125-134.
[2]     Li, Z.-L., et al., Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 2013. 131: p. 14-37.
[3]     Li, Z.-L., et al., Evaluation of six methods for extracting relative emissivity spectra from thermal infrared images. Remote Sensing of Environment, 1999. 69(3): p. 197-214.
[4]     Becker, F. and Z.L. Li, Surface temperature and emissivity at various scales: Definition, measurement and related problems. Remote Sensing Reviews, 1995. 12(3-4): p. 225-253.
[5]     Tang, B.-H., et al., An improved NDVI-based threshold method for estimating land surface emissivity using MODIS satellite data. International Journal of Remote Sensing, 2015(ahead-of-print): p. 1-15.
[6]     Rong, Y., et al. Emissivity measurement for low emissivity objects by two blackbody tube methods. in IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2012. Munich, Germany, July 22-27.
[7]     Jiang, J.-x., Q.-h. Liu, and H. Li. A modified NDVI threshold method for estimating LSE from FY3A/VIRR data. in 2nd International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE). 2012. Nanjing, Jiangsu, China, 01-03 Jun: IEEE.
[8]     Boonmee, M., Land Surface Temperature and Emissivity Retrieval from Thermal Infrared Hyperspectral Imagery. 2007, Rochester Institute of Technology, PHD Thesis.
[9]     Sobrino, J.A., et al., Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 2008. 46(2): p. 316-327.
[10]  Van de Griend, A. and M. Owe, On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. International Journal of remote sensing, 1993. 14(6): p. 1119-1131.
[11]  Bastiaanssen, W., et al., A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of hydrology, 1998. 212: p. 198-212.
[12]  [12 Snyder, W.C., et al., Classification-based emissivity for land surface temperature measurement from space. International Journal of Remote Sensing, 1998. 19(14): p. 2753-2774.
[13]  Sun, D. and R.T. Pinker, Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES‐8). Journal of Geophysical Research: Atmospheres (1984–2012), 2003. 108(D11).
[14]  Wan, Z. and Z.-L. Li, A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data. Geoscience and Remote Sensing, IEEE Transactions on, 1997. 35(4): p. 980-996.
[15]  Realmuto, V. Separating the effects of temperature and emissivity: Emissivity spectrum normalization. in Proc. 2nd TIMS Workshop. 1990.
[16]  Coll, C., et al., Adjusted Normalized Emissivity Method for surface temperature and emissivity retrieval from optical and thermal infrared remote sensing data. Journal of Geophysical Research: Atmospheres (1984–2012), 2003. 108(D23).
[17]  Valor, E., et al. The Adjusted Normalized Emissivity Method (ANEM) for land surface temperature and emissivity recovery. in Geoscience and Remote Sensing Symposium, 2003. IGARSS'03. Proceedings. 2003 IEEE International. 2003. IEEE.
[18]  Richter, R. and D. Schläpfer, Atmospheric/topographic correction for satellite imagery, in DLR report DLR-IB. 2014: DLR-German Aerospace Center, Germany.
[19]  Kahle, A.B., D.P. Madura, and J.M. Soha, Middle infrared multispectral aircraft scanner data: Analysis for geological applications. Applied Optics, 1980. 19(14): p. 2279-2290.
[20]  Gillespie, A.R., et al., Temperature/emissivity separation algorithm theoretical basis document, version 2.4. ATBD contract NAS5-31372, NASA, 1999.
[21]  Barducci, A. and I. Pippi, Temperature and emissivity retrieval from remotely sensed images using the “grey body emissivity” method. Geoscience and Remote Sensing, IEEE Transactions on, 1996. 34(3): p. 681-695.
[22]  Wang, N., et al., Retrieval of atmospheric and land surface parameters from satellite-based thermal infrared hyperspectral data using a neural network technique. International Journal of Remote Sensing, 2013. 34(9-10): p. 3485-3502.
[23]  Ma, X.L., et al., Simultaneous retrieval of atmospheric profiles, land-surface temperature, and surface emissivity from Moderate-Resolution Imaging Spectroradiometer thermal infrared data: Extension of a two-step physical algorithm. Applied optics, 2002. 41(5): p. 909-924.
[24]  Taylor, S.E., Measured emissivity of soils in the southeast United States. Remote Sensing of Environment, 1979. 8(4): p. 359-364.
[25]  Salisbury, J.W. and D.M. D'Aria, Emissivity of terrestrial materials in the 8–14 μm atmospheric window. Remote Sensing of Environment, 1992. 42(2): p. 83-106.
[26]  Wang, K., et al., Estimation of surface long wave radiation and broadband emissivity using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature/emissivity products. Journal of Geophysical Research: Atmospheres (1984–2012), 2005. 110(D11).
[27]  Joseph, G., Fundamentals of remote sensing. 2005: Universities Press.
[28]  Minacapilli, M., et al., Estimation of actual evapotranspiration of Mediterranean perennial crops by means of remote-sensing based surface energy balance models. Hydrology and Earth System Sciences, 2009. 13(7): p. 1061-1074.
[29]  Allen, R., et al., SEBAL (Surface Energy Balance Algorithms for Land). Advance Training and Users Manual–Idaho Implementation, version, 2002. 1: p. 97.
[30]  [30] Feizizadeh, B., et al., Monitoring land surface temperature relationship to land use/land cover from satellite imagery in Maraqeh County, Iran. Journal of Environmental Planning and Management, 2013. 56(9): p. 1290-1315.
[31]  Reuter, D., et al. The thermal infrared sensor on the landsat data continuity mission. in Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International. 2010. IEEE.
[32]  Ogawa, K., et al., Estimation of broadband land surface emissivity from multi-spectral thermal infrared remote sensing. Agronomie, 2002. 22(6): p. 695-696.
[33]  Hulley, G.C., S.J. Hook, and A.M. Baldridge, Validation of the North American ASTER Land Surface Emissivity Database (NAALSED) version 2.0 using pseudo-invariant sand dune sites. Remote Sensing of Environment, 2009. 113(10): p. 2224-2233.
[34]  Tang, B.-H., et al., Estimation of broadband surface emissivity from narrowband emissivities. Optics express, 2011. 19(1): p. 185-192.
[35]  Wan, Z. and J. Dozier, A generalized split-window algorithm for retrieving land-surface temperature from space. Geoscience and Remote Sensing, IEEE Transactions on, 1996. 34(4): p. 892-905.
[36]  Becker, F. and Z.-L. Li, Temperature-independent spectral indices in thermal infrared bands. Remote Sensing of Environment, 1990. 32(1): p. 17-33.
[37]  Prata, A., Land surface temperature measurement from space: AATSR algorithm theoretical basis document. Contract Report to ESA, CSIRO Atmospheric Research, Aspendale, Victoria, Australia, 2002. 2002: p. 1-34.
[38]  Hu, H., F. Chen, and Q. Wang. Estimating the effective wavelength of the thermal band for accurate brightness temperature retrieval: methods and comparison. in Spatial Data Mining and Geographical Knowledge Services (ICSDM), 2011 IEEE International Conference on. 2011. IEEE.
[39]  Jiménez‐Muñoz, J.C. and J.A. Sobrino, A generalized single‐channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research: Atmospheres (1984–2012), 2003. 108(D22).
[40]  Irons, J.R., J.L. Dwyer, and J.A. Barsi, The next Landsat satellite: The Landsat data continuity mission. Remote Sensing of Environment, 2012. 122: p. 11-21.
[41]  Coll, C., et al., Validation of Landsat-7/ETM+ thermal-band calibration and atmospheric correction with ground-based measurements. Geoscience and Remote Sensing, IEEE Transactions on, 2010. 48(1): p. 547-555.
[42]  Salisbury, J.W., A. Wald, and D.M. D'Aria, Thermal‐infrared remote sensing and Kirchhoff's law: 1. Laboratory measurements. Journal of Geophysical Research: Solid Earth (1978–2012), 1994. 99(B6): p. 11897-11911.
[43]  Korb, A.R., et al., Portable Fourier transform infrared spectroradiometer for field measurements of radiance and emissivity. Applied Optics, 1996. 35(10): p. 1679-1692.
[44]  Quan, W., et al., A modified Becker’s split-window approach for retrieving land surface temperature from AVHRR and VIRR. Acta Meteorologica Sinica, 2012. 26: p. 229-240.
[45]  Baldridge, A., et al., The ASTER spectral library version 2.0. Remote Sensing of Environment, 2009. 113(4): p. 711-715.
[46]  Caselles, E., et al., Automatic classification-based generation of thermal infrared land surface emissivity maps using AATSR data over Europe. Remote Sensing of Environment, 2012. 124: p. 321-333.
[47]  Sobrino, J.A., J.C. Jiménez-Muñoz, and L. Paolini, Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of environment, 2004. 90(4): p. 434-440.
[48]  Bhowmick, D., N.A. Hamm, and E.J. Milton, Use of an Airborne Imaging Spectrometer as a Transfer Standard for Atmospheric Correction of SPOT-HRG Data. Spatial data quality: from process to decisions, 2009.
[49]  Coll, C., et al., Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data. Remote Sensing of Environment, 2005. 97(3): p. 288-300.
[50]  Zakšek, K., K. Oštir, and Ž. Kokalj, Sky-view factor as a relief visualization technique. Remote Sensing, 2011. 3(2): p. 398-415.
[51]  Baret, F., G. Guyot, and D. Major. TSAVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation. in Geoscience and Remote Sensing Symposium, 1989. IGARSS'89. 12th Canadian Symposium on Remote Sensing., 1989 International. 1989. IEEE.
[52]  Panda, S.S., D.P. Ames, and S. Panigrahi, Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sensing, 2010. 2(3): p. 673-696.
[53]  Qi, J., et al., A modified soil adjusted vegetation index. Remote sensing of environment, 1994. 48(2): p. 119-126.
[54]  Feizizadeh, B. and T. Blaschke. Thermal remote sensing for land surface temperature monitoring: Maraqeh County, Iran. in Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International. 2012. IEEE.
[55]  Choudhury, B.J., et al., Relations between evaporation coefficients and vegetation indices studied by model simulations. Remote sensing of environment, 1994. 50(1): p. 1-17.
[56]  Wang, K. and S. Liang, Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites. Remote Sensing of Environment, 2009. 113(7): p. 1556-1565.
[57]  Valor, E. and V. Caselles, Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Remote sensing of Environment, 1996. 57(3): p. 167-184.
[58]  Jiménez-Muñoz, J.C., et al., Improved land surface emissivities over agricultural areas using ASTER NDVI. Remote Sensing of Environment, 2006. 103(4): p. 474-487.
[59]  Momeni, M. and M. Saradjian, Evaluating NDVI-based emissivities of MODIS bands 31 and 32 using emissivities derived by Day/Night LST algorithm. Remote Sensing of Environment, 2007. 106(2): p. 190-198.
[60]  Oltra-Carrió, R., et al., Land surface emissivity retrieval from airborne sensor over urban areas. Remote Sensing of Environment, 2012. 123: p. 298-305.
[61]  Walawender, J.P., M.J. Hajto, and P. Iwaniuk. A new ArcGIS toolset for automated mapping of land surface temperature with the use of LANDSAT satellite data. in Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International. 2012. IEEE.
[62]  Cristóbal, J., et al., Improvements in land surface temperature retrieval from the Landsat series thermal band using water vapor and air temperature. Journal of Geophysical Research: Atmospheres (1984–2012), 2009. 114(D8).
[63]  Jiménez-Muñoz, J., et al. Fractional vegetation cover estimation from PROBA/CHRIS data: Methods, analysis of angular effects and application to the land surface emissivity retrieval. in Proc. 3rd Workshop CHRIS/Proba. 2005.
[64]  Li, Z.-L., et al., Land surface emissivity retrieval from satellite data. International Journal of Remote Sensing, 2013. 34(9-10): p. 3084-3127.
[65]  Peres, L.F. and C.C. DaCamara, Emissivity maps to retrieve land-surface temperature from MSG/SEVIRI. Geoscience and Remote Sensing, IEEE Transactions on, 2005. 43(8): p. 1834-1844.
[66]  Gao, Y. and W. Zhang, LULC classification and topographic correction of Landsat-7 ETM+ imagery in the Yangjia River Watershed: the influence of DEM resolution. Sensors, 2009. 9(3): p. 1980-1995.
[67]  Blaschke, T., Object based image analysis for remote sensing. ISPRS journal of photogrammetry and remote sensing, 2010. 65(1): p. 2-16.
[68]  Martínez, L., et al. Improvement of the thermal emissivity calculated with the vegetation cover method by using optical atmospherically corrected images. in Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International. 2007. IEEE.
[69]  Tang, H. and Z.-L. Li, Future Development and Perspectives, in Quantitative Remote Sensing in Thermal Infrared. 2014, Springer. p. 257-279.
[70]  Yu, X., X. Guo, and Z. Wu, Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Remote Sensing, 2014. 6(10): p. 9829-9852.
[71]  Weng, Q. and P. Fu, Modeling annual parameters of clear-sky land surface temperature variations and evaluating the impact of cloud cover using time series of Landsat TIR data. Remote Sensing of Environment, 2014. 140: p. 267-278.
[72]  Weng, Q., P. Fu, and F. Gao, Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sensing of Environment, 2014. 145: p. 55-67.
[73]  Roy, D.P., et al., Landsat-8: Science and product vision for terrestrial global change research. RS of Environment, 2014. 145: p. 154-172.
[74]  Cook, M., et al., Development of an operational calibration methodology for the Landsat thermal data archive and initial testing of the atmospheric compensation component of a Land Surface Temperature (LST) Product from the archive. Remote Sensing, 2014. 6(11): p. 11244-11266.
[75]  Markham, B.L., et al., Landsat sensor performance: history and current status. IEEE Transactions on Geoscience and Remote Sensing,, 2004. 42(12): p. 2691-2694.
[76]  Huang, C., et al., An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment, 2010. 114(1): p. 183-198.
[77]  Jiménez-Muñoz, J.C., et al., Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data. IEEE Transactions on Geoscience and Remote Sensing, , 2009. 47(1): p. 339-349.
[78]  Qin, Z.-h., A. Karnieli, and P. Berliner, A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 2001. 22(18): p. 3719-3746.
[79]  Jimenez-Munoz, J.C., et al., Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. Geoscience and Remote Sensing Letters, IEEE, 2014. 11(10): p. 1840-1843.
[80]  Du, C., et al., A practical split-window algorithm for estimating land surface temperature from Landsat 8 data. Remote Sensing, 2015. 7(1): p. 647-665.
[81]  Barsi, J.A., et al. Validation of a web-based atmospheric correction tool for single thermal band instruments. in Optics & Photonics 2005. 2005. International Society for Optics and Photonics.
[82]  Barsi, J., J.L. Barker, and J.R. Schott. An atmospheric correction parameter calculator for a single thermal band earth-sensing instrument. in IEEE International Geoscience and Remote Sensing Symposium, 2003. IGARSS'03. Proceedings. 2003. IEEE.
[83]  Coll, C., et al., Validation of Landsat-7/ETM+ thermal-band calibration and atmospheric correction with ground-based measurements. IEEE Transactions on Geoscience and Remote Sensing, , 2010. 48(1): p. 547-555.
[84]  Tang, B.-H., et al., Estimation and validation of land surface temperatures from Chinese second-generation polar-orbit FY-3A VIRR data. Remote Sensing, 2015. 7(3): p. 3250-3273.
[85]  Jiménez-Muñoz, J.C., et al., Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data. Geoscience and Remote Sensing Letters, IEEE, 2014. 99: p. 1-4.
[86]  Barsi, J.A., et al., Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration. Remote Sensing, 2014. 6(11): p. 11607-11626.