Estimate and calibration Land Surface Temperature Using Landsat8 Satellite Images And Split Window Algorithm Based On Mathematical Models-Case Study:Kurdestan Province

Document Type : Original Article

Authors

1 Salman Ahmadi,Assistant Professor Of Civil Engineering Department,Engineering Faculty,University Of Kurdistan

2 Reza Soudmand Afshar,MS Remote Sensing Student,Engineering,Engineering Faculty,Univrsity Of Kurdistan

Abstract

The temperature of the Earth's surface is a very important parameter in environmental studies, climate change, soil moisture content, Evapotranspiration and urban thermal islands at different scales. Currently, there is no perfect method for accurately measuring the temperature of the surface of the earth, but since high spectral resolution sensors prevent the vapor spectral absorption in the infrared bands, this Increases computational accuracy in determining vegetation index. The purpose of this paper is to calculate the surface temperature using satellite images of OLI and TIRS sensors of Landsat 8. In this research, the separate window algorithm has been used to calculate ground temperature. The algorithm uses spectral radiance and emissivity to calculate the surface temperature. To estimate the spectral radiance in Landsat 8, the bands of 10 and 11 have been used. Emissivity is also obtained by using the NDVI threshold technique by using the OLI bands 2, 3, 4 and 5. Also, In this paper the temperature is calculated by The algorithm has been calibrated and corrected by a two-dimensional projective mathematical model, which tried to bring the calculated temperature closer to the actual ground temperature. In the present paper, the RMSE value is equal to 0.3678°C and the correlation between Meteorological data and temperature estimated by the model is equal to 0.9791. Also, the performance of the model that used to estimate the Earth's surface temperature is equal to 0.9751.

Keywords

References

ابراهیمی، ح.، گندمکار، ا.، المدرسی، س.ع.، رامشت، م.ح.، 1395، برآورد دمای سطح زمین و تأثیر پوشش گیاهی بر دمای سطح با استفاده از تصاویر مودیس (مطالعة موردی: حوزة تویسرکان)، جغرافیا (برنامه‌ریزی منطقه‌ای)، دورة 6 (پیاپی 24)، شمارة 4، صص. 32-23.
امینی بازیانی، س.، زارع ابیانه، ح.، اکبری، م.، 1393، برآورد دما و شاخص پوشش گیاهی با استفاده از داده‌های سنجش از دور (مطالعة موردی: استان همدان)، پژوهش‌های جغرافیای طبیعی، دورة 46، شمارة 3، صص. 348-333.
پهلوان‌زاده، ن.، جانعلی‌پور، م.، عباس‌زادة طهرانی، ن.، فرهنج، ف.، 1398، بهبود صحت استخراج دمای سطح زمین از باندهای حرارتی ماهوارة لندست با استفاده از رگرسیون خطی و مشاهدات زمینی، جغرافیا و برنامه‌ریزی محیطی، سال 30، شمارة 3، صص. 78-59.
حنفی، ع.، حاتمی، ا.، 1392، تهیة نقشة اقلیمی استان کردستان با استفاده از سیستم اطلاعات جغرافیایی، فصلنامة علمی‌ـ پژوهشی اطلاعات جغرافیایی سپهر، دورة 22، شمارة 87، صص. ۲8-۲4.
رمضانی خوجین، ع.، خیرخواه زرکش، م.م.، دانشکار آراسته، پ.، مریدی، ع.، علیمحمدی نافچی، ر.، 1394، محاسبه و واسنجی دمای سطح زمین با استفاده از داده‌های حرارتی ماهوارة Landsat 8، سنجش از دور و GIS ایران،سال 7، شمارة 3، صص. 64-49.‎
Alemu, M.M., 2019, Analysis of Spatio-Temporal Land Surface Temperature and Normalized Difference Vegetation Index Changes in the Andassa Watershed, Blue Nile Basin, Ethiopia, Journal of Resources and Ecology, 10(1), PP. 77-85.
Artis, D.A. & Carnahan, W.H., 1982, Survey of Emissivity Variability in Thermography of Urban Areas, Remote Sensing of Environment, 12(4), PP. 313-329.
Barsi, J.A., Barker, J.L. & Schott, J.R., 2003, An Atmospheric Correction Parameter Calculator for a Single Thermal Band Earth-Sensing Instrument, In IGARSS 2003, 2003 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (IEEE Cat. No. 03CH37477) (Vol. 5, pp. 3014-3016). IEEE.
 
 
Barsi, J.A., Schott, J.R., Hook, S.J., Raqueno, N.G., Markham, B.L. & Radocinski, R.G., 2014, Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration, Remote Sensing, 6(11), PP. 11607-11626.
Benali, A., Carvalho, A.C., Nunes, J.P., Carvalhais, N. & Santos, A., 2012, Estimating Air Surface Temperature in Portugal Using MODIS LST Data, Remote Sensing of Environment, 124, PP. 108-121.
Bussieres, N., Louie, P.Y.T. & Hogg, W., 1990, Progress Report on the Implementation of an Algorithm to Estimate Regional Evaportanspiration Using Satellite Data, In Proceeding of the Workshop on Applications of Remote Sensing in Hydrology.
Carlson, T.N. & Ripley, D.A., 1997, On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index, Remote Sensing of Environment, 62(3), PP. 241-252.
Chai, T. & Draxler, R.R., 2014, Root Mean Square Error (RMSE) or Mean Absolute Error (MAE)?–Arguments against Avoiding RMSE in the Literature, Geoscientific Model Development, 7(3), PP. 1247-1250.
Chander, G., Markham, B.L. & Helder, D.L., 2009, Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors, Remote Sensing of Environment, 113(5), PP. 893-903.
Chedin, A., Scott, N.A., Wahiche, C. & Moulinier, P., 1985, The Improved Initialization Inversion Method: A High Resolution Physical Method for Temperature Retrievals from Satellites of the TIROS-N Series, Journal of Climate and Applied Meteorology, 24(2), PP. 128-143.
Corbari, C., Mancini, M., Li, J. & Su, Z., 2015, Can Satellite Land Surface Temperature Data Be Used Similarly to River Discharge Measurements for Distributed Hydrological Model Calibration?, Hydrological Sciences Journal, 60(2), PP. 202-217.
Du, C., Ren, H., Qin, Q., Meng, J. & Zhao, S., 2015, A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data, Remote Sensing, 7(1), PP. 647-665.
Gao, C., Li, Z.L., Qiu, S., Tang, B., Wu, H. & Jiang, X., 2013, An Improved Algorithm for Retrieving Land Surface Emissivity and Temperature from MSG-2/SEVIRI Data, IEEE Transactions on Geoscience and Remote Sensing, 52(6), PP. 3175-3191.
 
Guha, S., Govil, H., Dey, A. & Gill, N., 2018, Analytical Study of Land Surface Temperature with NDVI and NDBI Using Landsat 8 OLI and TIRS Data in Florence and Naples City, Italy, European Journal of Remote Sensing, 51(1), PP. 667-678.
Guo, G., Wu, Z., Xiao, R., Chen, Y., Liu, X. & Zhang, X., 2015, Impacts of Urban Biophysical Composition on Land Surface Temperature in Urban Heat Island Clusters, Landscape and Urban Planning, 135, PP. 1-10.
Gutman, G. & Ignatov, A., 1998, The Derivation of the Green Vegetation Fraction from NOAA/AVHRR Data for Use in Numerical Weather Prediction Models, International Journal of remote sensing, 19(8), PP.1533-1543.
Hook, S.J., Gabell, A.R., Green, A.A. and Kealy, P.S., 1992. A Comparison of Techniques for Extracting Emissivity Information from Thermal Infrared Data for Geologic Studies, Remote Sensing of Environment, 42(2), PP. 123-135.
Hough, I., Just, A.C., Zhou, B., Dorman, M., Lepeule, J. & Kloog, I., 2020, A Multi-Resolution Air Temperature Model for France from MODIS and Landsat Thermal Data, Environmental Research, 183, P. 109244.
Ingram, P.M. & Muse, A.H., 2001, Sensitivity of Iterative Spectrally Smooth Temperature/ Emissivity Separation to Algorithmic Assumptions and Measurement Noise, IEEE Transactions on Geoscience and Remote Sensing, 39(10), PP. 2158-2167.
Jiménez-Muñoz, J.C., Cristóbal, J., Sobrino, J.A., Sòria, G., Ninyerola, M. & Pons, X., 2008, Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval from Landsat Thermal-Infrared Data, Ieee Transactions on Geoscience and Remote Sensing, 47(1), PP. 339-349.
Jiménez-Muñoz, J.C., Sobrino, J.A., Skoković, D., Mattar, C. & Cristóbal, J., 2014, Land Surface Temperature Retrieval Methods from Landsat-8 Thermal Infrared Sensor Data, IEEE Geoscience and Remote Sensing Letters, 11(10), PP. 1840-1843.
Karnieli, A., Agam, N., Pinker, R.T., Anderson, M., Imhoff, M.L., Gutman, G.G., Panov, N. & Goldberg, A., 2010, Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations, Journal of Climate, 23(3), PP. 618-633.
Kealy, P.S. & Hook, S.J., 1993, Separating Temperature and Emissivity in Thermal Infrared Multispectral Scanner Data: Implications for Recovering Land Surface Temperatures, IEEE Transactions on Geoscience and Remote Sensing, 31(6), PP. 1155-1164.
Landsat Project Science Office (2002) Landsat 7 Science Data User’s Hand-book, URL: http://www.gsfc.nasa.gov, GoddardSpace Flight Center, NASA, Washington, DC, last date accessed: 10 September 2003.
Li, Z.L., Tang, B.H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I.F. & Sobrino, J.A., 2013, Satellite-Derived Land Surface Temperature: Current Status and Perspectives, Remote Sensing of Environment, 131, PP. 14-37.
Li, Z.L., Wu, H., Wang, N., Qiu, S., Sobrino, J.A., Wan, Z., Tang, B.H. & Yan, G., 2013, Land Surface Emissivity Retrieval from Satellite Data, International Journal of Remote Sensing, 34(9-10), PP. 3084-3127.
Markham, B.L. & Barker, J.L., 1985, Spectral Characterization of the Landsat Thematic Mapper Sensors, International Journal of Remote Sensing, 6(5), PP. 697-716.
Miller, W. & Millis, E., 1989, Estimating Evaporation from Utah's Great Salt Lake Using Thermal Infrared Satellite Imagery, Water Resources Association, 25(3), PP. 541-550.
Price, J.C., 1980, The Potential of Remotely Sensed Thermal Infrared Data to Infer Surface Soil Moisture and Evaporation, Water Resources Research, 16(4), PP. 787-795.
Price, J.C., 1983, Estimating Surface Temperatures from Satellite Thermal Infrared Data—A Simple Formulation for the Atmospheric Effect, Remote Sensing of Environment, 13(4), PP. 353-361.
Rampal, K.K., 1976, Least Squares Collocation in Photogrammetry, Photogrammetric Engineering and Remote Sensing, 42(5), PP. 659-669.
Rongali, G., Keshari, A.K., Gosain, A.K. & Khosa, R., 2018a, A Mono-Window Algorithm for Land Surface Temperature Estimation from Landsat 8 Thermal Infrared Sensor Data: A Case Study of the Beas River Basin, India, Pertanika J Sci Technol, 26, PP. 829-840.
Rongali, G., Keshari, A.K., Gosain, A.K. & Khosa, R., 2018b, Split-Window Algorithm for Retrieval of Land Surface Temperature Using Landsat 8 Thermal Infrared Data, Journal of Geovisualization and Spatial Analysis, 2(2), P. 14.
Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W., 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication, 351(1974), p.309.
Schmugge, T.J. & André, J.C. (eds.), 2012, Land Surface Evaporation: Measurement and Parameterization, Springer Science & Business Media.
Serafini, Y.V., 1987, Estimation of the Evapotranspiration Using Surface and Satellite Data, International Journal of Remote Sensing, 8(10), PP. 1547-1562.
Skoković, D., Sobrino, J.A., Jimenez-Munoz, J.C., Soria, G., Juşien, Y., Mattar, C. & Cristóbal, J., 2014, Calibration and Validation of Land Surface Temperature for Landsat8-TIRS Sensor, LPVE (Land Product Validation and Evolution).
Sobrino, J.A., Li, Z.L., Stoll, M.P. & Becker, F., 1996, Multi-Channel and Multi-Angle Algorithms for Estimating Sea and Land Surface Temperature with ATSR Data, International Journal of Remote Sensing, 17(11), PP. 2089-2114.
Sobrino, J.A., Sòria, G. & Prata, A.J., 2004, Surface Temperature Retrieval from Along Track Scanning Radiometer 2 Data: Algorithms and Validation, Journal of Geophysical Research: Atmospheres, 109(D11).
Song, W., Mu, X., Ruan, G., Gao, Z., Li, L. & Yan, G., 2017, Estimating Fractional Vegetation Cover and the Vegetation Index of Bare Soil and Highly Dense Vegetation with a Physically Based Method, International Journal of Applied Earth Observation and Geoinformation, 58, PP. 168-176.
Williamson, S.N., Hik, D.S., Gamon, J.A., Kavanaugh, J.L. & Flowers, G.E., 2014, Estimating Temperature Fields from MODIS Land Surface Temperature and Air Temperature Observations in a Sub-Arctic Alpine Environment, Remote Sensing, 6(2), PP. 946-963.
Willmott, C.J. & Matsuura, K., 2005, Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in Assessing Average Model Performance, Climate Research, 30(1), PP. 79-82.
Xu, J.Z., 2004, The Rational Function Model (RFM) in Photogrammetric Mapping: Method and Accuracy, Department of Earth and Space Science & Engineering, York University.
Yang, Y.Z., Cai, W.H. & Yang, J., 2017, Evaluation of MODIS land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China, Remote Sensing, 9(5), P. 410.
Yang, J.S., Wang, Y.Q. & August, P.V., 2004, Estimation of Land Surface Temperature Using Spatial Interpolation and Satellite-Derived Surface Emissivity, Journal of Environmental Informatics, 4(1), PP. 37-44.
Yoo, C., Im, J., Park, S. & Quackenbush, L.J., 2018, Estimation of Daily Maximum and Minimum Air Temperatures in Urban Landscapes Using MODIS Time Series Satellite Data, ISPRS Journal of Photogrammetry and Remote Sensing, 137, PP. 149-162.
Yue, W., Xu, J., Tan, W. & Xu, L., 2007, The Relationship between Land Surface Temperature and NDVI with Remote Sensing: Application to Shanghai Landsat 7 ETM+ Data, International Journal of Remote Sensing, 28(15), PP. 3205-3226.
Zeng, L., Wardlow, B.D., Tadesse, T., Shan, J., Hayes, M.J., Li, D. & Xiang, D., 2015, Estimation of Daily Air Temperature Based on MODIS Land Surface Temperature Products over the Corn Belt in the US, Remote Sensing, 7(1), PP. 951-970.
Iranian Journal of Remote Sensing & GIS
Volume 12, Issue 3 - Serial Number 47
February 2021
Pages 103-122

Files

Share

How to cite

Statistics