Komparasi Dataset Suhu Udara Berbasis Penginderaan Jauh Dalam Mengestimasi Suhu Udara Bulanan di Provinsi Papua Barat

Arif Faisol, Bertha Ollin Paga, Baso Daeng


Abstrak. Pada umumnya data suhu udara diperoleh dari hasil pengamatan pada stasiun iklim Badan Meteorologi, Klimatologi, dan Geofisika (BMKG). Metode ini dapat digunakan untuk merepresentasikan suhu udara suatu wilayah yang berada pada radius ≤ 10 km dari lokasi stasiun iklim, sehingga dibutuhkan sebuah solusi alternatif untuk mendapatkan data suhu udara yang dapat merepresentasikan wilayah yang lebih luas, salah satunya memanfaatkan dataset suhu udara berbasis penginderaan jauh. Penelitian ini bertujuan membandingkan performa sejumlah dataset suhu udara berbasis penginderaan jauh, yaitu Climatic Research Unit gridded Time Series (CRU-TS), Climatologies at High Resolution for the Earth Land Surface Areas (CHELSA), dan TerraClimate dalam mengestimasi curah hujan bulanan di Provinsi Papua Barat. Penelitian ini terdiri atas 6 (enam) tahapan utama, yaitu; (1) inventarisasi data yang bertujuan mengumpulkan dataset suhu udara dan data suhu udara hasil perekaman pada Automatic Weather Station (AWS) tahun 1996 sampai tahun 2019, (2) ekstraksi data, (3) screening data untuk mengganti nilai ekstrim dengan nilai rata-rata, (4) evaluasi data untuk membandingkan dataset dengan data AWS, (5) komparasi data untuk membandingkan performa dataset, dan (6) rekomendasi yang bertujuan untuk menentukan dataset yang paling sesuai untuk digunakan di Provinsi Papua Barat. Hasil penelitian menunjukkan bahwa CHELSA, TerraClimate, dan CRU-TS sangat akurat dalam mengestimasi suhu udara bulanan di Provinsi Papua Barat yang ditunjukkan dengan nilai RBIAS < 0,1. Disamping itu CHELSA, TerraClimate, dan CRU-TS memiliki tingkat keeratan hubungan yang sedang terhadap data AWS dengan nilai r = 0,36 – 0,68. Sehingga TerraClimate, CHELSA, dan CRU-TS dapat digunakan sebagai solusi alternatif untuk mendapatkan informasi suhu udara bulanan di Provinsi Papua Barat.

Comparison of Remote Sensing-Based Air Temperature Dataset in Estimating Monthly Air Temperature in West Papua

Abstract. Air temperature is one of the important components in agriculture. Generally, air temperature data is obtained from climate stations of the Meteorological, Climatological, and Geophysical Agency (BMKG). These methods represented an area within a radius of 10 km from the climate station, therefore an alternative solution is needed for a larger area. The utilization of the air temperature dataset is one alternative solution. This research aims to compare Climatic Research Unit gridded Time Series (CRU-TS), Climatologies at High Resolution for the Earth Land Surface Areas (CHELSA), and TerraClimate as an air temperature dataset in estimating the monthly temperature in West Papua. The main stages in this research are data inventory, data extraction, data screening, data evaluation, data comparison, and data recommendation. The data used in this research are CRU-TS, TerraClimate, CHELSA, and local AWS data recording from 1996 to 2019. The research showed that CRU-TS, TerraClimate, and CHELSA are very accurate in estimating the monthly temperature in West Papua as indicated by RBIAS < 0.1. Furthermore, CRU-TS, TerraClimate, and CHELSA have a moderate correlation with AWS data in estimating monthly air temperature with r= 0.36 - 0.68. Therefore, CRU-TS, TerraClimate, and CHELSA can be used as an alternative solution to obtain monthly air temperature information in West Papua.    


Climatic Research; Earth Land Surface Areas;TerraClimate; Automatic Weather Station

Full Text:



Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a High-resolution Global Dataset of Monthly Climate and Climatic Water Balance from 1958-2015. Scientific Data, 5(1), 1–12. https://doi.org/10.1038/sdata.2017.191

Harris, I., Osborn, T. J., Jones, P., & Lister, D. (2020). Version 4 of the CRU TS Monthly High-Resolution Gridded Multivariate Climate Dataset. Scientific Data, 7(1), 1–18. https://doi.org/10.1038/s41597-020-0453-3

Hassan, I., Kalin, R. M., White, C. J., & Aladejana, J. A. (2020). Evaluation of Daily Gridded Meteorological Datasets over the Niger Delta Region of Nigeria and Implication to Water Resources Management. Atmospheric and Climate Sciences, 10(01), 21–39. https://doi.org/10.4236/acs.2020.101002

Hatfield, J. L., & Prueger, J. H. (2015). Temperature Extremes: Effect on Plant Growth and Development. Weather and Climate Extremes, 10, 4–10. https://doi.org/10.1016/j.wace.2015.08.001

Jackson, S. L. (2009). Research Methods and Statistics : A Critical Thinking Approach (3rd ed.). Wadsworth. www.ichapters.com

Kanda, N., Negi, H. S., Rishi, M. S., & Kumar, A. (2020). Performance of Various gridded Temperature and Precipitation Datasets over Northwest Himalayan Region. Environmental Research Communications, 2, 20. https://doi.org/10.1088/2515-7620/ab9991

Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the earth’s land surface areas. Scientific Data, 4, 1–20. https://doi.org/10.1038/sdata.2017.122

Khaydarov, M., & Gerlitz, L. (2019). Climate Variability and Change over Uzbekistan – an Analysis Based on High Resolution CHELSA Data. Central Asian Journal of Water Research, 5(2), 1–19. https://doi.org/10.29258/cajwr/2019-r1.v5-2/1-19.eng

Lawrimore, J. H., Menne, M. J., Gleason, B. E., Williams, C. N., Wuertz, D. B., Vose, R. S., & Rennie, J. (2011). An Overview of the Global Historical Climatology Network Monthly Mean Temperature Data set, version 3. Journal of Geophysical Research Atmospheres, 116(19), 1–18. https://doi.org/10.1029/2011JD016187

Liu, Q. H., Wu, X., Li, T., Ma, J. Q., & Zhou, X. B. (2013). Effects of Elevated Air Temperature on Physiological Characteristics of Flag Leaves and Grain Yield in Rice. Chilean Journal of Agricultural Research, 73(2), 85–90. https://doi.org/10.4067/S0718-58392013000200001

Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., & Houston, T. G. (2012). An Overview of the Global Historical Climatology Network-Daily Database. Journal of Atmospheric and Oceanic Technology, 29(7), 897–910. https://doi.org/10.1175/JTECH-D-11-00103.1

Mondal, S., Ghosal, S., & Barua, R. (2016). Impact of Elevated Soil and Air Temperature on Plants Growth, Yield and Physiological Interaction: a Critical Review. Scientia Agriculturae, 13(1), 293–305. https://doi.org/10.15192/PSCP.SA.2016

Moore, C. E., Meacham-Hensold, K., Lemonnier, P., Slattery, R. A., Benjamin, C., Bernacchi, C. J., Lawson, T., & Cavanagh, A. P. (2021). The Effect of Increasing Temperature on Crop Photosynthesis: From Enzymes to Ecosystems. Journal of Experimental Botany, 72(8), 2822–2844. https://doi.org/10.1093/jxb/erab090

Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulation. American Society of Agricultural and Biological Engineers, 50(3), 885–900

Muthoni, F. (2020). Spatial-temporal trends of rainfall, maximum and minimum temperatures over West Africa. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 2960–2973. https://doi.org/10.1109/JSTARS.2020.2997075

National Center for Atmospheric Research Staff. (2020). The Climate Data Guide: Precipitation Data Sets: Overview & Comparison table. Agustus. https://climatedataguide.ucar.edu/climate-data/precipitation-data-sets-overview-comparison-table

Ott, R. L., & Longnecker, M. (2016). An Introduction to Statistical Methods & Data Analysis (7th ed.). Cengage Learning.

Peterson, T. C., & Vose, R. S. (1997). An overview of the global historical climatology network-daily database. Bulletin of The American Meteorological Society, 78(12), 2837–2850. https://doi.org/10.1175/JTECH-D-11-00103.1

Porter, J. R., & Semenov, M. A. (2005). Crop responses to climatic variation. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1463), 2021–2035. https://doi.org/10.1098/rstb.2005.1752

Randolph, K. A., & Myers, L. L. (2013). Basic Statistics in Multivariate Analysis. In Basic Statistics in Multivariate Analysis (1st ed.). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199764044.001.0001

Tamil Nadu Agricultural University. (2016). Agrometeorology: Temperature and Plant Growth. TNAU Agritech Portal. http://www.agritech.tnau.ac.in/agriculture/agri_agrometeorology_temp.html

World Meteorological Organization. (2010). Commission for Instruments and Methods of Observation (WMO-No. 1064). In Fifteenth session - Abridged final report with resolutions and recommendations (Issue 1064). http://www.wmo.int/pages/prog/www/CIMO/CIMO15-WMO1064/1064_en.pdf

DOI: https://doi.org/10.17969/rtp.v15i1.25319


  • There are currently no refbacks.

Creative Commons LicenseISSN: 2085-2614E-ISSN: 2528-2652
Copyright© 2009-2021 | ISSN: 2085-2614 | EISSN: 2528-2654
Rona Teknik Pertanian is licensed under a Creative Commons Attribution 4.0 International License.


Published by: 
Program Studi Teknik PertanianFakultas Pertanian, Universitas Syiah Kuala 
associated with Indonesia Society of Agricultural Engineering (ISAE) Aceh.
Jl. Tgk. Hasan Krueng Kalee No. 3, Kopelma Darussalam,
Banda Aceh, 23111, Indonesia.
Email: jronatp@unsyiah.ac.id

Online Submissions & Guidelines Editorial Policies | Contact Statistics Indexing | Citations