Determination of surface temperature in water bodies with the use of multiband landsat satellite images: Case study of Seyfe Lake

Year 2023, Volume: 41 Issue: 6, 1144 - 1156, 29.12.2023

Cansu Yurteri , Türker Kurttaş

Abstract

Lake Seyfe is a shallow lake 30 km east of the city center of Kırşehir with an average surface area of 34 km2 and located 1110 m above sea level. Due to various interventions (via drainage and/or irrigation channels, groundwater withdrawal with wells, agricultural activities, domes-tic wastes, etc.) in Seyfe Lake and its surroundings from 1960s to the present, natural functions of the lake have been deteriorated. As a result of the aforementioned actions and changes such as water elevation, shrinking lake surface area, decrease in volume and changes in surface temperature were observed in the lake. In this paper, the seasonal changes in the surface tem-perature distribution of Seyfe Lake were analyzed with the Geographical Information Systems (GIS) using multi-band Landsat satellite images from various years. In September 1990, the lake surface temperature varied between 10.94-24.14 °C while it varied between 12.36-26.67 °C in May 1990. In May 2020, the temperatures varied between 16.31-34.06 °C, and in Septem-ber 2020, it varied between 25.67-29.67 °C. When the lake surface temperature distribution was analyzed; it was determined that lower temperatures were observed in the northern parts, which are usually the deepest parts of the lake, while the temperature increased towards the central and coastal parts of the lake. However, it was seen that lake surface temperature has been affected by both climatic conditions and anthropogenic activities from the past to the present.

Keywords

Lake Surface Temperature, Landsat, MNDWI, Seyfe Lake, Thermal Infrared Band

References

• REFERENCES
• [1] Lamaro A, Mariñelarena A, Torrusio S, Sala S. Water surface temperature estimation from Landsat 7 ETM+ thermal infrared data using the generalized single-channel method: Case study of Embalse del Río Tercero (Córdoba, Argentina). Advances in Space Research 2012;51. [CrossRef]
• [2] Sharaf N, Fadel A, Bresciani M, Giardino C, Lemaire B, Slim K, Faour G, Vinçon-Leite B. Lake surface temperature retrieval from Landsat-8 and retrospective analysis in Karaoun Reservoir, Lebanon. J Appl Remote Sens 2019;13. [CrossRef]
• [3] Yang K, Yu Z, Luo Y, Zhou X, Shang C. Spatial-Temporal Variation of Lake Surface Water Temperature and Its Driving Factors in Yunnan-Guizhou Plateau. Water Resour Res 2019;55:46884703. [CrossRef]
• [4] Malik MS, Shukla JP. Retrieving of land surface temperature using thermal Remote Sensing and GIS techniques in Kandaihimmat watershed, Hoshangabad, Madhya Pradesh. J Geol Soc India 2018;92:298304. [CrossRef]
• [5] Taloor AK, Manhas DS, Girish CK. Retrieval of land surface temperature, normalized difference moisture index, normalized difference water index of the Ravi basin using Landsat data. Appl Comput Geosci 2021;9:100051. [CrossRef]
• [6] Şekertekin A, Bonafoni S. Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sensors 2020;12:294. [CrossRef]
• [7] Avdan U, Jovanovska G. Algorithm for automated mapping of land surface temperature using Landsat 8 satellite data. J Sensors 2016;18. [CrossRef]
• [8] Şener E. Determination of seasonal changes in Burdur lake surface water temperature using Landsat 8 satellite images. J Eng Sci Design 2016;4:6773.
• [9] Anderson MC, Allen RG, Morse A, Kustas WP. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sensing Environ 2012;122:5065. [CrossRef]
• [10] Sobrino JA, Li ZL, Stoll MP, Becker F. Multichannel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data. Int J Remote Sensing 1996;17:20892114. [CrossRef]
• [11] Wang F, Qin Z, Song C, Tu L, Karnieli A, Zhao S. An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote Sensing 2015;7:42684289. [CrossRef]
• [12] Yu X, Guo X, Wu Z. 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;6:98299852. [CrossRef]
• [13] Jimenez-Munoz JC, Sobrino JA. A generalized single-channel method for retrieving land surface temperature from remote sensing data. J Geophys Res 2003;108:46884694. [CrossRef]
• [14] Gillespie AR, Rokugawa S, Matsunaga T, Cothern JS, Hook SJ, Kahle AB. A temperature and emissivity separation algorithm for advanced space borne thermal emission and reflection radiometer (Aster) images. IEEE Trans Geosci Remote Sensing 1998;36:11131126. [CrossRef]
• [15] Qin Z, Karnieli A, Berliner P. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. Int J Remote Sensing 2010;22:37193746. [CrossRef]
• [16] Artis DA, Carnahan WH. Survey of emissivity variability in thermography of urban areas. Remote Sensing Environ 1982;12:313329. [CrossRef]
• [17] Reis S, Yilmaz H. Temporal monitoring of water level changes in Seyfe Lake using remote sensing. Hydrol Proces 2008;22:44484454. [CrossRef]
• [18] Yaman M, Yigit Avdan Z. Monitoring water body change with using satellite images (Seyfe Lake) VII. Remote Sensing ve GIS Symposium (UZAL-CBS), September 2018; Eskişehir, Turkey.
• [19] Palmer SC, Kutser T, Hunter PD. Remote sensing of inland waters: Challenges, progress and future directions Remote Sensing Environ 2015;157:18. [CrossRef]
• [20] Xu H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sensing 2006;27:30253033.
• [21] https://earthexplorer.usgs.gov. 11 October 2021.
• [22] Chander G, Markham BL, Helder DL. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+ and EO1 ALI sensors. Remote Sensing Environ 2009;113:893–903. [CrossRef]
• [23] USGS, Landsat 8 data users handbook, Ed. Zanter, K., EROS Sioux Falls, South Dakota, USA, 2019. p. 114.
• [24] Olioso A, Sarrio MM, Courault D, Marloie O, Guillevic P. Impact of surface emissivity and atmospheric conditions on surface temperatures estimated from top of canopy brightness temperatures derived from Landsat 7 data. IEEE International Geoscience and Remote Sensing Symposium 2013; Melbourne, Australia. [CrossRef]
• [25] Kriegler FJ, Malila WA, Nalepka RF, Richardson W. Preprocessing transformations and their effects on multispectral recognition. Remote Sensing Environ 1969;6:97.
• [26] Carlson TN, Ripley DA. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment 62,1997;62:241252. [CrossRef]
• [27] Norman JM, Becker F. Terminology in thermal infrared remote sensing of natural surfaces. Agric Forest Meteorol 1995;77:153166. [CrossRef]