UPSI Digital Repository (UDRep)
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UPSI Digital Repository (UDRep)
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| Abstract : Universiti Pendidikan Sultan Idris |
| Suhu permukaan darat dikenalpasti salah satu parameter penting yang sentiasa diperhati dan direkod oleh Earth System Data Record oleh pihak National Aeronautics and Space Administration (NASA), World Meteorology Organization dan jabatan penyelidik antarabangsa yang lain. Perkara ini kerana suhu permukaan darat merupakan kunci penting yang menpengaruhi iklim, hidrologi, ekologi dan biokimia. Teknologi Penderiaan Jauh menawarkan pelbagai jenis satelit kepada penyelidik untuk mengkaji cuaca dan iklim. Walaubagaimanapun satelit MODIS dan Landsat merupakan satelit yang penting dalam mengkaji suhu permukaan darat. Objektif kajian ini untuk menilai keberkesanan kedua-dua satelit dalam mengukur suhu permukaan. Bagi mencapai objektif kajian ini memerlukan kedua-dua data melalui pra proses seperti pembetulan radiometrik, atmosfera dan geometrik. Langkah seterusnya melakukan penukaran nilai digital nombor menggunakan formula yang kerap digunapakai penyelidik yang lepas dalam mendapatkan nilai suhu. Data suhu daripada meteorologi daripada Jabatan Meteorologi Malaysia (JMM) digunakkan dalam menentu ukur keberkesanan kedua-dua data tersebut dengan menggunakan kaedah korelasi antara nilai suhu daripada satelit MODIS dan Landsat dengan suhu daripada JMM. Dapatan kajian menunjukkan bahawa nilai korelasi antara suhu daripada satelit Landsat lebih tinggi berbanding dengan satelit MODIS. Dapatan kajian ini penting sebagai panduan penyelidik, pelajar dan pihak berkepentingan akan datang dalam membuat pemilihan data untuk kajian masing-masing. |
| References |
Karnieli, A., Agam, B., Pinker, R. T., Anderson, M., Gutman, G. G., Panov, N., & Goldner, A. (2009). Use of NDVI and land surface temperature for drought assessment: Merits and limitations. Journal of Climate, 23 (3), 618 – 633. Batatia, H., & Bessaih, N. (1997). Satellite land surface temperature for Sarawak area. Dalam Proceedings of the 1997 Asian Conference on Remote sensing. Global Environment Session. de Beurs, K. M., & Henebry, G. M. (2004). Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan. Remote Sensing of Environment, 89 (4), 497-509. Chatterjee, R. S., Singh, N., Thapa, S., Sharma, D., & Kumar, D. (2017). Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs. International Journal of Applied Earth Observation and Geoinformation, 58, 264 – 277. Coll, C., Caselles, V., Valor, E., & Niclòs, R. (2012). Comparison between different sources of atmospheric profiles for land surface temperature retrieval from single channel thermal infrared data. Remote Sensing of Environment, 117, 199 – 210. Dash, P., Göttsche, F. M., Olesen, F. S., & Fischer, H. (2002). Land surface temperature and emissivity estimation from passive sensor data: Theory and practice-current trends. International Journal of Remote Sensing, 23 (13), 2563 – 2594. Garfinkel, C. I., Gordon, A., Oman, L. D., Li, F., Davis, S., & Pawson, S. (2018). Nonlinear response of tropical lower-stratospheric temperature and water vapor to ENSO. Atmospheric Chemistry and Physics, 18 (7), 4597 – 4615. Hereher, M. E. (2016). Time series trends of land surface temperatures in Egypt: a signal for global warming. Environmental Earth Sciences, 75 (17), 1 – 11. Jabatan Perangkaan Malaysia (2011). Banci Penduduk dan Perumahan Malaysia 2010. Wilayah Persekutuan: Putrajaya. Jiménez-Muñoz, J. C., & Sobrino, J. A. (2009). A single-channel algorithm for land-surface temperature retrieval from ASTER data. IEEE Geoscience and Remote Sensing Letters, 7 (1), 176 – 179. 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), 1840 – 1843. Kerr, Y. H, Lagouarde, J. P., & Inmbernon, J. (1992). Accurate land surface temperature retrieval from AVHRR data with use of an improved Split Window algorithm. Remote Sensing Environment, 41, 197 – 209. Kumar, K. S., Bhaskar, P. U., & Padmakumari, K. (2012). Estimation of land surface temperature to study urband heat island effect using landsat ETM + image. International journal of Engineering Science and technology, 4 (2), 771 – 778. Li, Z. L., Tang, B. H., Wu, H., Ren, H., Yan, G., Wan, Z., & Sobrino, J. A. (2013). Satellite-derived land surface temperature: Current status and perspectives. Remote sensing of environment, 131, 14 – 37. Meyer, H., Katurji, M., Appelhans, T., Müller, M. U., Nauss, T., Roudier, P., & Zawar-Reza, P. (2016). Mapping daily air temperature for Antarctica based on MODIS LST. Remote Sensing, 8 (9), 732. Muhammad, I. S., Baharun, A., Ibrahim, H. S., & ZB, W. A. W. (2016). Investigation of Ground Temperature for Heat Sink Application in Kuching, Sarawak, Malaysia. Journal of Civil Engineering, Science and Technology, 7 (1), 20 – 29. NourEldeen, N., Mao, K., Yuan, Z., Shen, X., Xu, T., & Qin, Z. (2020). Analysis of the spatiotemporal change in land surface temperature for a long-term sequence in Africa (2003–2017). Remote Sensing, 12 (3), 488. Niclòs, R., Galve, J. M., Valiente, J. A., Estrela, M. J., & Coll, C. (2011). Accuracy assessment of land surface temperature retrievals from MSG2-SEVIRI data. Remote Sensing of Environment, 115 (8), 2126 – 2140. Ozelkan, E., Bagis, S., Ozelkan, E. C., Ustundag, B. B., Yucel, M., & Ormeci, C. (2015). Spatial interpolation of climatic variables using land surface temperature and modified inverse distance weighting. International Journal of Remote Sensing, 36 (4), 1000 – 1025. Reuter, D. C., Richardson, C. M., Pellerano, F. A., Irons, J. R., Allen, R. G., Anderson, M., ... & Thome, K. J. (2015). The Thermal Infrared Sensor (TIRS) on Landsat 8: Design overview and pre-launch characterization. Remote Sensing, 7 (1), 1135 – 1153. Ricky, A. K., & Oliver, O. E. (2021). The Impact of El Niño–Southern Oscillation (ENSO) on Temperature: A Case Study in Kuching, Sarawak. Malaysian Journal of Social Sciences and Humanities (MJSSH), 6 (1), 289 – 297. Ricky, A. K., & Oliver, O. E. (2020). Analyses Water Bodies Effect in Mitigation of Urban Heat Effect: Case Study Small Size Cities Kuching, Sarawak. In IOP Conference Series: Earth and Environmental Science, 540 (1), 012010. IOP Publishing. 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 (2), 1 – 19. Sun, D., & Pinker, R. T. (2003). Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES‐8). Journal of Geophysical Research: Atmospheres, 108 (D11). Tan, J., NourEldeen, N., Mao, K., Shi, J., Li, Z., Xu, T., & Yuan, Z. (2019). Deep learning convolutional neural network for the retrieval of land surface temperature from AMSR2 data in China. Sensors, 19 (13), 2987. U.S. Environmental Protection Agency, 2020. Dilanggan pada 13 April 2021 di https://www.epa.gov/heatislands Varentsov, M. I., Grishchenko, M. Y., & Wouters, H. (2019). Simultaneous assessment of the summer urban heat island in Moscow megacity based on in situ observations, thermal satellite images and mesoscale modeling. Geography, Environment, Sustainability, 12 (4), 74 – 95. Vlassova, L., Perez-Cabello, F., Nieto, H., Martín, P., Riaño, D., & De La Riva, J. (2014). Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling. Remote Sensing, 6 (5), 4345 – 4368. Wan, Z., & Dozier, J. (1996). A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on geoscience and remote sensing, 34 (4), 892 – 905. Yu, X., Guo, X., & Wu, Z. (2014). Land surface temperature retrieval from Landsat 8 TIRS—Comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote sensing, 6 (10), 9829 – 9852. Zhou, J., Li, J., Zhang, L., Hu, D., & Zhan, W. (2012). Intercomparison of methods for estimating land surface temperature from a Landsat-5 TM image in an arid region with low water vapour in the atmosphere. International Journal of Remote Sensing, 33 (8), 2582 – 2602. Zhao, W., He, J., Wu, Y., Xiong, D., Wen, F., & Li, A. (2019). An analysis of land surface temperature trends in the central Himalayan region based on MODIS products. Remote Sensing, 11 (8), 900. |
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