{"id":6180,"date":"2026-05-10T06:40:34","date_gmt":"2026-05-09T22:40:34","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=6180"},"modified":"2026-07-11T17:27:49","modified_gmt":"2026-07-11T09:27:49","slug":"land-surface-temperature-estimation-of-mandya-district-using-landsat-8-data","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=land-surface-temperature-estimation-of-mandya-district-using-landsat-8-data","title":{"rendered":"Land Surface Temperature Estimation of Mandya district using LANDSAT-8 data"},"content":{"rendered":"\n
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<\/i> 2020<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 23, Issue 4<\/a><\/p>\n<\/div>\n\n\n\n

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Aryalekshmi B N1<\/sup><\/i><\/a>, Mohammed Ahamed J2<\/sup>, Dr. R C Biradar1<\/sup>, and Dr. Chandrasekar K3<\/sup><\/p>\n\n\n\n

1<\/sup>School of ECE, REVA University, Bangalore, India <\/p>\n\n\n\n

2<\/sup>RRSC\u2013 South, NRSC, ISRO, Bengaluru, India <\/p>\n\n\n\n

3<\/sup>RRSC-Hyderabad, NRSC, ISRO, India<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0December 10, 2019
Accepted:\u00a0May 29, 2020
Publication Date:\u00a0May 10, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Location map of the Study Area<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202012_23(4).0002<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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This paper focuses on the estimation of LST over Mandya district (120\u00b031\u2019N, 760\u00b053\u2019E), which is a part of the Cauvery river basin in Karnataka, India, using LANDSAT-8 satellite data for 12 meteorological stations. Landsat 8 data is used for experimentation during 21st Jan 2018 and 6th Feb 2018 for LST analysis of identified area. More than half of the land area in the Mandya district is under agricultural use. Fluctuations in LST affects the quality of agricultural production. As a result, proper estimation of LST is very much required for the management of crop growth, development, and yield component. Spectral radiance from band 10 and emissivity of thermal infrared bands was used as input for LST estimation. Surface emissivity was obtained with the help of NDVI (Normalized Difference Vegetation Index), Leaf Area Index (LAI), and Albedo in the Mandya region. ERDAS IMAGINE and ArcGIS software were used for modeling. The obtained data compared with weather station data collected by KSNDMC (Karnataka State Natural Disaster Monitoring Centre). For this analysis, we considered two sets of data taken on different dates during the said period. The Root Mean Square Error (RMSE) and correlation coefficient are used as statistical criteria for evaluating the model efficiency. From the comparison, it is found that RMSE and R for the first date are 0.94\u00b0C, 0.62, and for the second date, it is 1.26\u00b0C, 0.88, respectively. It is observed that LST and weather data do follow the same pattern for the selected 12 meteorological stations.<\/p>\n\n\n\n

Keywords:\u00a0Emissivity; Land surface temperature (LST); NDVI; LAI; LANDSAT-8; Mandya<\/em><\/p>\n\n\n\n

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  1. [1] M Bisquert, J. M. S\u00e1nchez, R. L\u00f3pez-Urrea, and V. Caselles. Estimating high resolution evapotranspiration from disaggregated thermal images. Remote Sensing of Environment, 187:423\u2013433, 2016.<\/span><\/li>\n
  2. [2] Qinqin Sun, Jianjun Tan, and Yonghang Xu. An ERDAS image processing method for retrieving LST and describing urban h<\/span>eat evolution: A case study in the Pearl River Delta Region in South China. Environmental Earth Sciences, 59(5):1047\u20131055, jan 2010.<\/span><\/li>\n
  3. [3] Marina Stathopoulou and Constantinos Cartalis. Daytime urban heat islands from Landsat<\/span> ETM+and Corine land cover data: An application to major cities in Greece. Solar Energy, 81(3):358\u2013368, 2007.<\/span><\/li>\n
  4. [4] Stefania Bonafoni and Chaiyapon Keeratikasikorn. Land surface temperature <\/span>and urban density: Multiyear modeling and<\/span> relationship analysis using modis and landsat data. Remote Sensing, 10(9), 2018.<\/span><\/li>\n
  5. [5] Lidia Vlassova, Fernando P\u00e9rez-Cabello, Marcos Rodrigues Mimbrero, Raquel Montorio Llover\u00eda, and Alberto Garc\u00eda-<\/span>Mart\u00edn. Analysis of the relationship between land surface temperature and wildfire severity in a series of landsat images. Remote Sensing, 6(7):6136\u2013<\/span>6162, 2014.<\/span><\/li>\n
  6. [6] Salman \u00d6zupekce. Use of Land Surface Temperature (Lst) Data in the Determination <\/span>of High Areas of Forest Fire Risk in Erdemli District. Journal of International Social Research<\/span>, 11(57):2<\/span>27\u2013232, 2018.<\/span><\/li>\n
  7. [<\/span>7] Ruyan He, Yufang Jin, Maziar M Kandelous, Daniele Zaccaria, Blake L Sanden, Richard L Snyder, Jinbao Jiang, and Jan W Ho<\/span>pmans. Evapotranspiration estimate over an almond orchard using Landsat satellite observations. Remote Sensing, 9(5), 201<\/span>7.<\/span><\/li>\n
  8. [<\/span>8] B. N. Aryalekshmi, Rajashekhar C. Biradar, and J. Mohammed Ahamed. Thermal imaging techniques in agricultural applications. International Journal of Innovative Technology and Exploring Engineering, 8(12):2162\u20132168, oct 2019.<\/span><\/li>\n
  9. [9] Ann Kathrin Holtgrave, Michael <\/span>F\u00f6rster, Felix Greifeneder, Claudia Notarnicola, and Birgit Kleinschmi<\/span>t. Estimation o<\/span>f Soil Moisture in Vegetation-Covered Floodplains with Sentinel-1 SAR Data Using Support Vector Regression. PFG- Journal of Photogrammetry, Rem<\/span>ote Sensing and Geoinformation Science, 86(2):85\u2013101, apr 2018.<\/li>\n
  10. [10] Syed Muhammad Zubair Younis and Javed Iqbal. Estimation of soil moisture using multispectral and FTIR techniques. Egyptian Journal of Remote Sensing and Space Science, 18(2):151\u2013161, 2015.<\/span><\/li>\n
  11. [11] S K Dash and J. C.R. Hunt. Variability of climate change in India, 2007.<\/span><\/li>\n
  12. [12] <\/span>Lingli Wang and John J. Qu. Satellite remote sensing applications for surface soil moistur<\/span>e monitoring: A review, 20<\/span>09.<\/span><\/li>\n
  13. [13] Ricardo Trezza, Richard G Allen, and Masahiro Tasumi. Estimation of actual evapotranspiration along the Middle Rio <\/span>Grande of New Mexico using MODIS and landsat imagery with the METRIC model. Remote Sensing, 5(10):5397\u20135423, 2013<\/li>\n
  14. [14] Geng Ming Jiang and Ronggao Liu. Retrieval of sea and land surface temperature from SVISSR\/FY2C\/D\/E measurements. IEEE Transactions on Geoscience and Remote Sensing, 52(10):6132\u20136140, 2014.<\/li>\n
  15. [15] C. Ottl\u00e9 and D Vidal-Madjar. Estimation of land surface temperature with NOAA9 data. Remote Sensing of Environment, 40(1):27\u201341, 1992.<\/span><\/li>\n
  16. [16] A Rajeshwari. ESTIMATION OF LAND SURFACE TEMPERATURE OF DINDIGUL <\/span>DISTRICT USING LANDSAT 8 DATA. International Jour<\/span>nal of Research in Engineering and Technology, 03(05):122\u2013126, 2014.<\/span><\/li>\n
  17. [17] Ugur Avdan and Gordana Jovanovska. Algorithm for automated mapping of land surface temperature using LAND<\/span>SAT8 satellite data. Journal of Sensors, 2016, 2016.<\/li>\n
  18. [18] Emily G. Kullberg, Kendall C. DeJonge, and Jos\u00e9 L. Ch\u00e1vez. Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients. Agricultural Water Management, 179:64\u201373, 2017.<\/span><\/li>\n
  19. [19] William Kustas and Martha Anderson. <\/span>Advances in thermal infrared remote sensing for land surface modeling. Agricul<\/span>tural and Forest Meteorology, 149(12):2071\u2013<\/span>2081, 2009.<\/span><\/li>\n
  20. [20] USGS. Landsat Missions, 2019.<\/span><\/li>\n
  21. [21] Larry M. McMillin. Estimation of sea surface temperatures from <\/span>two infrared window measurements with different abs<\/span>orption. Journal of Geophysical Research, 80(36):5113\u20135117, dec 1975.<\/span><\/li>\n
  22. [22] Offer Rozenstein, Zhihao Qin, Yevgeny Derimian, and Arnon Karnieli. Derivation of <\/span>land surface temperature for landsat-8 TIR<\/span>S using a split window algorithm. Sensors (Switzerland), 14(4):5768\u20135780, 2014.<\/span><\/li>\n
  23. [23] Zhao Liang Li, Bo Hui Tang, Hua Wu, Huazhong Ren, Guangjian Yan, Zhengming Wan, <\/span>Isabel F. Trigo, and Jos\u00e9 A. Sobrino. Sat<\/span>ellite-derived land surface temperature: Current status and perspectives, 2013.<\/span><\/li>\n
  24. [<\/span>24] ARCGIS. Chapter 5 Geospatial Modeling 5.1 Rationale 5.2 SEBAL modeling for evapotranspiration 5.3 Penman-Monteith<\/span> method for evapotranspiration <\/span>5.4 Runoff modeling 5.5 Recharge modeling 5.6 Weighed Overlay Analysis using GIS 5.1 Rationale. Techni<\/span>cal report.<\/span><\/li>\n
  25. [<\/span>25] H. R. Shwetha and D. Nagesh Kumar. Performance evaluation of satellite-based approaches for the estimation of dail<\/span>y air temperature and reference evapotranspiration. Hydrological Sciences Journal, 63(9):1347\u20131367, jul 20<\/span>18.<\/li>\n
  26. [26] Athos Agapiou, Nikos Papadopoulos, and Apostolos Sarris. Monitoring olive mills waste disposal areas in Crete using very high resolution satellite data. Egyptian Journal of Remote Sensing and Space Science, 1<\/span>9(2):285\u2013<\/span>295, 2016.<\/li>\n
  27. [27] WGMBastiaanssen. Remote Sensingin Water Resources Management : The State of the Art. 1998.<\/li>\n
  28. [28] Jos\u00e9 A. Sobrino, Juan C. Jim\u00e9nez-Mu\u00f1oz, Guillem S\u00f2ria, Mireia Romaguera, Luis Guanter, Jos\u00e9 Moreno, Antonio Plaza, and Pablo Mart\u00ednez. Land surface emissivity retrieval from <\/span>different VNIR and TIR sensors. In IEEE Transactions on Geoscienc<\/span>e and Remote Sensing, volume 46, pages 316\u2013<\/span>327, 2008.<\/span><\/li>\n
  29. [2<\/span>9] Kalifa Go\u00efta and Alain Royer. Surface temperature and emissivity separability over land<\/span> surface from combined tir and swir avhrr data. IEEE<\/span> Transactions on Geoscience and Remote Sensing, 35(3):718\u2013733, 1997.<\/span><\/li>\n
  30. [30] Andrew M. Coutts, Richard J. Harris, Thu Phan, Stephen J. Livesley, Nicholas S.G. Williams, and Nigel J. Tapper. Thermal infrared remote sensing of urban heat: Hotspots, <\/span>vegetation, and an assessment of techniques for<\/span> use in urban planning. Remote Sensing of Environment, 186:637\u2013651, 2016.<\/span><\/li>\n
  31. [<\/span>31] De Cai Wang, Gan Lin Zhang, Ming Song Zhao, Xian Zhang Pan, Yu Guo Zhao, De Cheng Li, and Bob Macmillan. Retrieval <\/span>and <\/span>mapping of soil texture based on land surface diurnal temperature range data from MODIS. PLoS ONE,10(6), 2015<\/span>.<\/span><\/li>\n
  32. [32] Masahiro Tasumi, Richard G Allen, Ricardo Trezza, and James L Wright. Satellite-based energy balance to assess withi<\/span>n-population variance of crop coefficient curves. Journal of Irrigation and Drainage Engineering, 131(1):94\u2013109, jan 2005.<\/li>\n
  33. [33] Rizqi I. Sholihah, Bambang H. Trisasongko, Diar Shiddiq, La Ode S.Iman, Selamet Kusdaryanto, Manijo, and Dyah R. Panuju. Identification of Agricultural Drought Extent Based on Vegetation Health Indices of Landsat Data: Case of Subang and Karawang, Indonesia. Procedia Environmental Sciences, 33:14\u201320, 2016.<\/span><\/li>\n
  34. [<\/span>34] Anju Bala, Prakashkiran S Pawar, Anil Kumar Misra, and Kishan Singh Rawat. Estimation and validation of actual ev<\/span>apo<\/span>transpiration for wheat crop using SEBAL model over Hisar district, Haryana, India. Current Scien<\/span>ce, 113(1):134\u2013141, 2017.<\/span><\/li>\n<\/ol>\n<\/div>\n\n\n\n

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