Assessing the Influence of Coastal Salt Pans on Land Surface Temperature: A Google Earth Engine-Based Study
DOI:
https://doi.org/10.52151/jae2026631.1981Keywords:
evaporative landscape, Landsat 8, remote sensing, spatial analysis, thermal infrared sensorAbstract
Land surface temperature (LST) provides critical insight into the thermal behaviour of different land cover types, especially in sensitive coastal landscapes. In India, salt fields in the Thoothukudi district of Tamil Nadu State, covering about 101 km2, contribute a major share in the total salt output of the country. This study assessed the influence of coastal salt pans on LST dynamics in Thoothukudi district using multi-temporal Landsat 8 data of the 2019-2023 period within Google Earth Engine (GEE). Unlike the earlier studies dealing with LST dynamics in urban or agricultural landscapes, this study quantifies the distinct thermal behaviour of active salt-production land and the surrounding vegetation. Differences in the LST dynamics were comparatively evaluated in different land cover types, i.e., sand, vegetation zones near and away from salt pans, built-up land and salt pans. This study uniquely contributes by introducing a comparative LST evaluation in two vegetation covers, one in close proximity to salt pans and another away from them. The results revealed the effect of a localized microclimate, which is not documented in the earlier studies. The Normalized difference vegetation index (NDVI), surface emissivity, and thermal band (Band 10) of Landsat 8 were used to determine LST. The results showed that the mean LST values of 27.11°C in sand, 26.95°C in built-up areas, 25.78°C in salt pans and 24.54°C in distant vegetation cover in the order of sand > built-up areas > salt pans > vegetation cover (near salt pans) > vegetation cover (away from salt pans). Further, the vegetation covers close to salt pans and built-up areas had comparatively higher LST values due to the influence of surrounding salt pans and built-up areas. Also, the LST of sand was almost comparable to that of salt pans. Findings of this study are useful to policymakers in planning appropriate strategies for land management and to overcome the impact of LST in future.
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Abidin, M. R., Nur, R., Mayzarah, E. M., & Umar, R. (2021). Estimating and monitoring the land surface temperature (LST) using Landsat OLI 8 TIRS. International Journal of Environment Engineering and Education, 3(1), 17–24. https://doi.org/10.55151/ijeedu.v3i1.43 DOI: https://doi.org/10.55151/ijeedu.v3i1.43
Abulibdeh, A. (2021). Analysis of urban heat island characteristics and mitigation strategies for eight arid and semi-arid gulf region cities. Environmental Earth Sciences, 80(7), 259. https://doi.org/10.1007/s12665-021-09540-7 DOI: https://doi.org/10.1007/s12665-021-09540-7
Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., …, & Brisco, B. (2020). Google Earth Engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350. https://doi.org/10.1109/jstars.2020.3021052 DOI: https://doi.org/10.1109/JSTARS.2020.3021052
Awuh, M. E., Japhets, P. O., Officha, M. C., Okolie, A. O., & Enete, I. C. (2019). A correlation analysis of the relationship between land use and land cover/land surface temperature in Abuja municipal, FCT, Nigeria. Journal of Geographic Information System, 11(01), 44–55. https://doi.org/10.4236/jgis.2019.111004 DOI: https://doi.org/10.4236/jgis.2019.111004
Bindajam, A. A., Mallick, J., AlQadhi, S., Singh, C. K., & Hang, H. T. (2020). Impacts of vegetation and topography on land surface temperature variability over the semi-arid mountain cities of Saudi Arabia. Atmosphere, 11(7), 762. https://doi.org/10.3390/atmos11070762 DOI: https://doi.org/10.3390/atmos11070762
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), 893–903. https://doi.org/10.1016/j.rse.2009.01.007 DOI: https://doi.org/10.1016/j.rse.2009.01.007
Ding, N., Zhang, Y., Wang, Y., Chen, L., Qin, K., & Yang, X. (2023). Effect of landscape pattern of urban surface evapotranspiration on land surface temperature. Urban Climate, 49, 101540. https://doi.org/10.1016/j.uclim.2023.101540 DOI: https://doi.org/10.1016/j.uclim.2023.101540
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), 647–665. https://doi.org/10.3390/rs70100647 DOI: https://doi.org/10.3390/rs70100647
Ermida, S. L., Soares, P., Mantas, V., Göttsche, F., & Trigo, I. F. (2020). Google Earth Engine open-source code for land surface temperature estimation from the Landsat series. Remote Sensing, 12(9), 1471. https://doi.org/10.3390/rs12091471 DOI: https://doi.org/10.3390/rs12091471
Feng, L., Hussain, S., Pricope, N. G., Arshad, S., Tariq, A., Feng, L., …., & El-Askary, H. (2024). Seasonal dynamics in land surface temperature in response to land use land cover changes using Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 17983-17997. https://doi.org/10.1109/jstars.2024.3466191 DOI: https://doi.org/10.1109/JSTARS.2024.3466191
Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Dilley, R. D., Beckmann, T., … , & Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379–390. https://doi.org/10.1016/j.rse.2017.03.026 DOI: https://doi.org/10.1016/j.rse.2017.03.026
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031 DOI: https://doi.org/10.1016/j.rse.2017.06.031
Gupta, A., Sadab, A., & De, B. (2024). Assessment of critical thermal characteristics and land surface dynamics of an Indian metropolitan city. Journal of Water and Climate Change, 15(7), 3409–3430. https://doi.org/10.2166/wcc.2024.370 DOI: https://doi.org/10.2166/wcc.2024.370
Hong, T., Huang, X., Lv, Q., Zhao, S., Wang, Z., & Yang, Y. (2025). Thermal mitigation in coastal cities: Marine and urban morphology effects on land surface temperature in Xiamen. Buildings, 15(7), 1170. https://doi.org/10.3390/buildings15071170 DOI: https://doi.org/10.3390/buildings15071170
Jiménez-Muñoz, J. C., Sobrino, J. A., Skokovic, 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), 1840-1843. DOI: https://doi.org/10.1109/LGRS.2014.2312032
Jodhani, K. H., Patel, H., Soni, U., Patel, R., Valodara, B., Gupta, N., Patel, A., & Omar, P. J. (2024). Assessment of forest fire severity and land surface temperature using Google Earth Engine: A case study of Gujarat State, India. Fire Ecology, 20(1), 23. https://doi.org/10.1186/s42408-024-00254-2 DOI: https://doi.org/10.1186/s42408-024-00254-2
Karimi, N., Ng, K. T. W., Richter, A., Williams, J., & Ibrahim, H. (2021). Thermal heterogeneity in the proximity of municipal solid waste landfills on forest and agricultural lands. Journal of Environmental Management, 287, 112320. https://doi.org/10.1016/j.jenvman.2021.112320 DOI: https://doi.org/10.1016/j.jenvman.2021.112320
Kowe, P., Dube, T., Mushore, T. D., Ncube, A., Nyenda, T., Mutowo, G., … , & Kizilirmak, G. (2022). Impacts of the spatial configuration of built-up areas and urban vegetation on land surface temperature using spectral and local spatial autocorrelation indices. Remote Sensing Letters, 13(12), 1222–1235. https://doi.org/10.1080/2150704x.2022.2142073 DOI: https://doi.org/10.1080/2150704X.2022.2142073
Lai, D., Liu, W., Gan, T., Liu, K., & Chen, Q. (2019). A review of mitigating strategies to improve the thermal environment and thermal comfort in urban outdoor spaces. The Science of the Total Environment, 661, 337–353. https://doi.org/10.1016/j.scitotenv.2019.01.062 DOI: https://doi.org/10.1016/j.scitotenv.2019.01.062
Laskar, S., Mishra, P., & Behera, B. (2024). Impact of land use dynamics on land surface temperature: a multi-decadal analysis in Indian Sundarbans region. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-024-05710-2 DOI: https://doi.org/10.1007/s10668-024-05710-2
Manimozhi, N., Rani V., Sudhan C., Manimekalai D., Shalini R., & Abarna, K. M. (2022). Spatiotemporal occurrence, distribution, and characterization of microplastics in salt pans of the coastal region of the Gulf of Mannar, southeast coast of India. Regional Studies in Marine Science, 53, 102350. https://doi.org/10.1016/j.rsma.2022.102350 DOI: https://doi.org/10.1016/j.rsma.2022.102350
Massaro, E., Schifanella, R., Piccardo, M., Caporaso, L., Taubenböck, H., Cescatti, A., & Duveiller, G. (2023). Spatially-optimized urban greening for reduction of population exposure to land surface temperature extremes. Nature Communications, 14(1), 2903. https://doi.org/10.1038/s41467-023-38596-1 DOI: https://doi.org/10.1038/s41467-023-38596-1
Mustaquim, N. S. M. (2024). Utilizing remote sensing data and ArcGIS for advanced computational analysis in land surface temperature modeling and land use property characterization. World Journal of Advanced Research and Reviews, 21(1), 1496–1507. https://doi.org/10.30574/wjarr.2024.21.1.0231 DOI: https://doi.org/10.30574/wjarr.2024.21.1.0231
Naima, A. (2024). The contribution of remote sensing in highlighting the resilience of rangelands through indicators of land surface temperature and the normalized difference vegetation index in the Tihama alluvial plain. Geographia Technica, 19(2) 139–151. https://doi.org/10.21163/gt_2024.192.11 DOI: https://doi.org/10.21163/GT_2024.192.11
Ogashawara, I., & Bastos, V. (2012). A quantitative approach for analyzing the relationship between urban heat islands and land cover. Remote Sensing, 4(11), 3596–3618. https://doi.org/10.3390/rs4113596 DOI: https://doi.org/10.3390/rs4113596
Orhan, O., Dadaser-Celi̇K, F., & Ekerci̇N, S. (2019). Investigating land surface temperature changes using Landsat-5 data and real-time infrared thermometer measurements at Konya Closed Basin in Turkey. International Journal of Engineering and Geosciences, 4(1), 16–27. https://doi.org/10.26833/ijeg.417151 DOI: https://doi.org/10.26833/ijeg.417151
Orhan, O., Ekercin, S., & Dadaser-Celik, F. (2014). Use of Landsat land surface temperature and vegetation indices for monitoring drought in the Salt Lake Basin area, Turkey. The Scientific World Journal, 2014, 42939. https://doi.org/10.1155/2014/142939 DOI: https://doi.org/10.1155/2014/142939
Pandian, S. H., & Janaki, D. (2021). An economic analysis of health problems of salt workers in Thoothukudi District. Elementary Education Online, 20(5), 6373–6378. https://doi.org/10.17051/ilkonline.2021.05.719
Panigrahi, M., & Sharma, A. (2025). Urban growth dynamics and its influence on land surface temperature in Bhubaneswar metropolitan city: a 1990–2021 analysis. Discover Applied Sciences, 7(2), 118. https://doi.org/10.1007/s42452-025-06535-y DOI: https://doi.org/10.1007/s42452-025-06535-y
Parida, B. R., Oinam, B., Patel, N. R., Sharma, N., Kandwal, R., & Hazarika, M. K. (2008). Land surface temperature variation in relation to vegetation type using MODIS satellite data in Gujarat state of India. International Journal of Remote Sensing, 29(14), 4219–4235. https://doi.org/10.1080/01431160701871096 DOI: https://doi.org/10.1080/01431160701871096
Potapov, P., Hansen, M. C., Pickens, A., Hernandez-Serna, A., Tyukavina, A., Turubanova, S., …, & Kommareddy, A. (2022). The global 2000-2020 land cover and land use change dataset derived from the Landsat archive: First results. Frontiers in Remote Sensing, 3, 856903. https://doi.org/10.3389/frsen.2022.856903 DOI: https://doi.org/10.3389/frsen.2022.856903
Ramachandran, J., Lalitha, R., & Sivasubramanian, K. (2019). Remote sensing based land surface temperature analysis in diverse environment of Lalgudi block. International Journal of Environment and Climate Change, 9(3), 142–149. https://doi.org/10.9734/ijecc/2019/v9i330103 DOI: https://doi.org/10.9734/ijecc/2019/v9i330103
Reuter, D., Richardson, C., Pellerano, F., Irons, J., Allen, R., Anderson, M., …, & Thome, K. (2015). The thermal infrared sensor (TIRS) on Landsat 8: Design overview and pre-launch characterization. Remote Sensing, 7(1), 1135–1153. https://doi.org/10.3390/rs70101135 DOI: https://doi.org/10.3390/rs70101135
Rongali, G., Keshari, A. K., Gosain, A. K., & Khosa, R. (2018). Split-Window algorithm for retrieval of land surface temperature using LaNDsAt 8 thermal infrared data. Journal of Geovisualization and Spatial Analysis, 2, 14. https://doi.org/10.1007/s41651-018-0021-y DOI: https://doi.org/10.1007/s41651-018-0021-y
Roy, B., & Bari, E. (2022). Examining the relationship between land surface temperature and landscape features using spectral indices with Google Earth Engine. Heliyon, 8(9), e10668. https://doi.org/10.1016/j.heliyon.2022.e10668 DOI: https://doi.org/10.1016/j.heliyon.2022.e10668
Sam, S. C., & Balasubramanian, G. (2023). Spatiotemporal detection of land use/land cover changes and land surface temperature using Landsat and MODIS data across the coastal Kanyakumari district, India. Geodesy and Geodynamics, 14(2), 172–181. https://doi.org/10.1016/j.geog.2022.09.002 DOI: https://doi.org/10.1016/j.geog.2022.09.002
Shen, W., Li, M., Huang, C., He, T., Tao, X., & Wei, A. (2019). Local land surface temperature change induced by afforestation based on satellite observations in Guangdong plantation forests in China. Agricultural and Forest Meteorology, 276–277, 107641. https://doi.org/10.1016/j.agrformet.2019.107641 DOI: https://doi.org/10.1016/j.agrformet.2019.107641
Smith, I. A., Fabian, M. P., & Hutyra, L. R. (2022). Urban green space and albedo impacts on surface temperature across seven United States cities. The Science of the Total Environment, 857, 159663. https://doi.org/10.1016/j.scitotenv.2022.159663 DOI: https://doi.org/10.1016/j.scitotenv.2022.159663
Sobrino, J. A., Jiménez-Muñoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4), 434–440. https://doi.org/10.1016/j.rse.2004.02.003 DOI: https://doi.org/10.1016/j.rse.2004.02.003
Srivastava, A., & Sharma, H. (2024). AI-Driven environmental monitoring using Google Earth Engine. In: B. Pradhan, S. Mukhopadhyay (Eds), IoT Sensors, ML, AI and XAI: Empowering A smarter world. Smart Sensors, Measurement and Instrumentation (Vol. 50, pp. 375–385).), Springer, Cham. https://doi.org/10.1007/978-3-031-68602-3_19 DOI: https://doi.org/10.1007/978-3-031-68602-3_19
Suman, S., Maurya, S., Pandey, V., Srivastava, P. K., & Gupta, D. K. (2025). Cloud computing platforms–based remote sensing big data applications. In: V. Sood, D. K. Gupta, S. Singh, B. Pradhan (Eds), Google Earth Engine and artificial intelligence for earth observation- Algorithms and Sustainable Applications (pp. 77–88), Elsevier. https://doi.org/10.1016/b978-0-443-27372-8.00003-9 DOI: https://doi.org/10.1016/B978-0-443-27372-8.00003-9
Tian, F., Hou, M., Qiu, Y., Zhang, T., & Yuan, Y. (2019). Salinity stress effects on transpiration and plant growth under different salinity soil levels based on thermal infrared remote (TIR) technique. Geoderma, 357, 113961. https://doi.org/10.1016/j.geoderma.2019.113961 DOI: https://doi.org/10.1016/j.geoderma.2019.113961
Toscano, S., Ferrante, A., Romano, D., & Tribulato, A. (2021). Interactive effects of drought and saline aerosol stress on morphological and physiological characteristics of two ornamental shrub species. Horticulturae, 7(12), 517. https://doi.org/10.3390/horticulturae7120517 DOI: https://doi.org/10.3390/horticulturae7120517
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0 DOI: https://doi.org/10.1016/0034-4257(79)90013-0
Varamesh, S., Anbaran, S. M., Shirmohammadi, B., Al-Ansari, N., Shabani, S., & Jaafari, A. (2022). How do different land uses/covers contribute to land surface temperature and albedo? Sustainability, 14(24), 16963. https://doi.org/10.3390/su142416963 DOI: https://doi.org/10.3390/su142416963
Vijayakumar, S., Saravanakumar, R., Arulanandam, M., & Ilakkiya, S. (2024). Google Earth Engine: empowering developing countries with large-scale geospatial data analysis—a comprehensive review. Arabian Journal of Geosciences, 17(4), 139. https://doi.org/10.1007/s12517-024-11948-x DOI: https://doi.org/10.1007/s12517-024-11948-x
Weng, Q. (2009). Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4), 335–344. https://doi.org/10.1016/j.isprsjprs.2009.03.007 DOI: https://doi.org/10.1016/j.isprsjprs.2009.03.007
Wu, Z., & Zhang, Y. (2018). Spatial variation of urban thermal environment and its relation to green space patterns: Implication to sustainable landscape planning. Sustainability, 10(7), 2249. https://doi.org/10.3390/su10072249 DOI: https://doi.org/10.3390/su10072249
Yankyera, S., & Alam, B. M. (2025). Five decades of transformation due to Human-Environment stressors: land cover, vegetation, and land surface temperature change analysis in the largest wetland ecosystem in Bangladesh. Earth Systems and Environment, 9, 589–604. https://doi.org/10.1007/s41748-025-00652-9 DOI: https://doi.org/10.1007/s41748-025-00652-9
Zhou, J., Theroux, S. M., De Mesquita, C. P. B., Hartman, W. H., Tian, Y., & Tringe, S. G. (2021). Microbial drivers of methane emissions from unrestored industrial salt ponds. The ISME Journal, 16(1), 284–295. https://doi.org/10.1038/s41396-021-01067-w DOI: https://doi.org/10.1038/s41396-021-01067-w
