COMPARISON OF SPLIT WINDOWS ALGORITHM AND PLANCK METHODS FOR SURFACE TEMPERATURE ESTIMATION BASED ON REMOTE SENSING DATA  IN SEMARANG

Siti Zahrotunisa; Projo Danoedoro; Sanjiwana Arjasakusuma

doi:10.24114/jg.v14i1.24603

COMPARISON OF SPLIT WINDOWS ALGORITHM AND PLANCK METHODS FOR SURFACE TEMPERATURE ESTIMATION BASED ON REMOTE SENSING DATA IN SEMARANG

Siti Zahrotunisa, Projo Danoedoro, Sanjiwana Arjasakusuma

Abstract

Surface temperature is one of the parameters in land–surface physical processes and is applied to global warming, climate change, and cycle hydrology. Two thermal bands in Landsat 8 imagery can be used as input for surface temperature extraction using the Split Windows Algorithm (SWA) and Planck method. This study aims to compare surface temperature estimates using the SWA and Planck methods and determine the surface temperature distribution based on the condition of land cover and its changes. The remote sensing data used are Landsat-8 OLI/TIRS Aqua MODIS images on August 27, 2013, and October 1, 2020. The results showed that Landsat 8 could obtain land cover information with an accuracy of 90% in 2013 and 91% in 2020. Planck surface temperature in 2013 was 1-3°C higher than SWA, while in 2020, Planck was 0.001-0.05°C lower than SWA but had similar distribution and pattern. The vegetation in the study area's central and south sides has a lower surface temperature than the built-up area on the north side. Land cover changes from non-built up to build-up area cause surface temperatures to increase. Each land cover has a different emissivity value and affects the surface temperature value, i.e., the lower the emissivity, the higher the surface temperature. The emissivity with surface temperature has a pearson correlation value ≥-0.8**.

Keywords: Surface Temperature, Split Windows Algorithm, Planck

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References

Abas, N., Kalair, A. R., Khan, N., & Kalair, A. R. (2017). Impact of Urbanization Growth on Land Surface Temperature using remote. Renewable and Sustainable Energy Reviews, 80(June), 990–1016. http://dx.doi.org/10.1016/j.rser.2017.04.022

Azevedo, J. A., Chapman, L., & Muller, C. L. (2016). Quantifying the daytime and night-time urban heat Island in Birmingham, UK: A comparison of satellite derived land surface temperature and high resolution air temperature observations. Remote Sensing, 8(2). https://doi.org/10.3390/rs8020153

Bokaie, M., Zarkesh, M. K., Arasteh, P. D., & Hosseini, A. (2016). Assessment of Urban Heat Island based on the relationship between land surface temperature and Land Use/ Land Cover in Tehran. Sustainable Cities and Society, 23, 94–104. https://doi.org/10.1016/j.scs.2016.03.009

Chen, Y., Duan, S. B., Ren, H., Labed, J., & Li, Z. L. (2017). Algorithm development for land surface temperature retrieval: Application to Chinese Gaofen-5 data. Remote Sensing, 9(2). https://doi.org/10.3390/rs9020161

Ferreira, E., Mannaerts, C. M., Dantas, A. A., & Maathuis, B. H. (2016). Surface energy balance system (SEBS) and satellite data for monitoring water consumption of irrigated sugarcane. Engenharia Agricola, 36(6), 1176–1185. https://doi.org/10.1590/1809-4430-Eng.Agric.v36n6p1176-1185/2016

García-Santos, V., Cuxart, J., Martínez-Villagrasa, D., Jiménez, M. A., & Simó, G. (2018). Comparison of three methods for estimating land surface temperature from Landsat 8-TIRS Sensor data. Remote Sensing, 10(9), 1–13. https://doi.org/10.3390/rs10091450

Indarto, I., Mandala, M., Febrian Arifin, F., & Lukman Hakim, F. (2020). Aplikasi Citra Sentinel-2 Untuk Pemetaan Tutupan Dan Peruntukan Lahan Pada Tingkat Desa. Jurnal Geografi, 12(02), 189. https://doi.org/10.24114/jg.v12i02.16970

Jatmiko, R. (2016). Penggunaan Citra Saluran Inframerah Termal untuk Studi Perubahan Liputan Lahan dan Suhu sebagai Indikator Perubahan Iklim Perkotaan di Yogyakarta. Universitas Gadjah Mada.

Jensen, J. R. (2014). Remote Sensing of the Environtment An Earth Resource Perspective Second Edition. Pearson Educaton Limited.

Li, X., Zhou, W., & Ouyang, Z. (2013). Relationship between land surface temperature and spatial pattern of greenspace: What are the effects of spatial resolution? Landscape and Urban Planning, 114(June), 1–8. https://doi.org/10.1016/j.landurbplan.2013.02.005

Li, Z., Liu, X., Ma, T., Kejia, D., & Zhou, Q. (2013). Retrieval of the surface evapotranspiration patterns in the alpine grassland – wetland ecosystem applying SEBAL model in the source region of the Yellow River , China. Ecological Modelling, 270, 64–75. https://doi.org/10.1016/j.ecolmodel.2013.09.004

Meng, X., Cheng, J., Zhao, S., Liu, S., & Yao, Y. (2019). Estimating land surface temperature from Landsat-8 data using the NOAA JPSS enterprise algorithm. Remote Sensing, 11(2), 1–18. https://doi.org/10.3390/rs11020155

Ndossi, M. I., & Avdan, U. (2016). Inversion of land surface temperature (lst) using terra aster data: A comparison of three algorithms. Remote Sensing, 8(12), 1–19. https://doi.org/10.3390/rs8120993

Nurman, A. (2010). Pemanfaatan Data Modis Untuk Mendeteksi Daerah Tangkapan Ikan Pantai Timur Dan Barat Sumatera Utara. Jurnal Geografi, 2(2), 17–30.

Nurwanda, A., & Honjo, T. (2018). Analysis of land use change and expansion of surface urban heat island in Bogor city by remote sensing. ISPRS International Journal of Geo-Information, 7(5). https://doi.org/10.3390/ijgi7050165

Putri, L. M., & Wicaksono, P. (2021). Mapping Of Land Use Changes In The Core Zone Of Parangtritis Sand Dunes Using Obia Method 2015-2020. Jurnal Geografi, 13(1), 109–120. https://doi.org/10.24114/jg.v%vi%i.21465

Sasky, P., Sobirin, & Wibowo, A. (2017). Pengaruh Perubahan Penggunaan Tanah Terhadap Suhu Permukaan Daratan Metropolitan Bandung Raya Tahun 2000 – 2016. Industrial Research Workshop and National Seminar, 8(July), 354–361.

Sejati, A. E., Hasan, M., Nursalam, L. O., & Harianto, E. (2020). Kesesuaian Pemetaan Penggunaan Lahan Permukiman Dengan Kondisi Sebenarnya Di Kecamatan Katobu Dan Kecamatan Duruka Kabupaten Muna. Jurnal Tunas Geografi, 09(01), 55–68.

Sekertekin, A., & Bonafoni, S. (2020). 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 Sensing, 12(2). https://doi.org/10.3390/rs12020294

Sugiyono. (2007). Stasitik Untuk Penelitian. Alfabeta.

Tan, K., Liao, Z., Du, P., & Wu, L. (2017). Land surface temperature retrieval from Landsat 8 data and validation with geosensor network. Frontiers of Earth Science, 11(1), 20–34. https://doi.org/10.1007/s11707-016-0570-7

Wang, L., Lu, Y., & Yao, Y. (2019). Comparison of three algorithms for the retrieval of land surface temperature from landsat 8 images. Sensors (Switzerland), 19(22). https://doi.org/10.3390/s19225049

Weng, Q., Firozjaei, M. K., Kiavarz, M., Alavipanah, S. K., & Hamzeh, S. (2019). Normalizing land surface temperature for environmental parameters in mountainous and urban areas of a cold semi-arid climate. Science of the Total Environment, 650, 515–529. https://doi.org/10.1016/j.scitotenv.2018.09.027

Wiweka, W. (2014). Pola Suhu Permukaan Dan Udara Menggunakan Citra Satelit Landsat Multitemporal. Jurnal Ecolab, 8(1), 11–22. https://doi.org/10.20886/jklh.2014.8.1.11-22

DOI: https://doi.org/10.24114/jg.v14i1.24603