AI-Based Prediction of Water Vapor Changes from Kalimantan Forest Fires Using Terra MODIS Imagery

Authors

  • Oktalia Triananda Lovita Universitas Bina Bangsa Getsempena, Indonesia
  • Zharifah Muthi’ah Universitas Bina Bangsa Getsempena, Indonesia
  • Mohd. Iqbal Muttaqin Universitas Bina Bangsa Getsempena, Indonesia
  • Mulyati Universitas Bina Bangsa Getsempena, Indonesia
  • Khairunnisa Universitas Bina Bangsa Getsempena, Indonesia

DOI:

https://doi.org/10.47709/brilliance.v6i4.9749

Keywords:

Water Vapor, GPS CORS, weather, TERRA MODIS

Abstract

The weather changes from forest fires in Kalimantan resulted in the earth's changing very ekstrim. Water Vapor or Vapor is a manifestation of water in the form of gas. It is still a job has been difficult for researchers atmosphere. Global Positioning System (GPS) in the troposphere and are equipped with surface data, this study used 8 (eight) GPS stations Kalhutra GPS Array (SuGAR) and 2 (two) points which is the world IGS CORS station. This method is called inversion GPS, while the TERRA MODIS satellite imagery used methods transmittance. From the experiments conducted in this research process, obtained. From the results of plotting graphs of temporal variations, it is known that the average moisture content in the dry months ranged from 56.59 mm to 38.95 mm, while the wettest months in this case because the graph ranges from 58.71 mm to 47.72 mm. The obtained results from average Water Vapor during the fire that was in 2010 ranged from 70.26% in 2011 ranged between 84.49% and in 2026 ranged from 90.15%, but the correlation (r) between the Water Vapor from TERRA MODIS satellite imagery and GPS is quite high. Based on forest fires, MODIS estimate the humidity around 54-77%. But Water Vapor obtained TERRA MODIS satellite imagery ranges between ± 3-8 cm / day is ± 89.49%. Then it will affect human health, because it influences the weather cycle in Indonesia.

References

Anderson, H. (2022, September 20). Global Positioning System status and modernization [Conference presentation]. Civil GPS Service Interface Committee. https://www.GPS.gov/cgsic/meetings/2022/anderson.pdf

Baker, B. (2022, November 16). Military communications & positioning, navigation, and timing overview & GPS enterprise update [Conference presentation]. PNT Advisory Board. https://www.GPS.gov/governance/advisory/meetings/2022-11/baker.pdf

Feng, L., Zhang, T., Koh, T. Y., & Hill, E. M. (2021). Selected years of monsoon variations and extratropical dry-air intrusions compared with the Sumatran GPS Array observations in Indonesia. Journal of the Meteorological Society of Japan, 99(2), 505–536. https://www.jstage.jst.go.jp/article/jmsj/99/2/99_2021-026/_article

GPS.gov. (n.d.-a). What is GPS? https://gps.gov/systems/gps

Hsu, J., Chen, C.-A., Lan, C.-W., Chiang, C.-L., Li, C.-H., & Lo, M.-H. (2025). Impact of land use changes and global warming on extreme precipitation patterns in the Maritime Continent. npj Climate and Atmospheric Science. https://doi.org/10.1038/s41612-024-00883-z

Intergovernmental Panel on Climate Change. (2023). Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC. https://doi.org/10.59327/IPCC/AR6-9789291691647

Li, Y. (2021). Analysis of GAMIT/GLOBK in high-precision GNSS data processing for crustal deformation. Earthquake Research Advances, 1(3), 100028. https://doi.org/10.1016/j.eqrea.2021.100028

Lovita, O. (2017). Analisa Perubahan Cuaca Di Pulau Sumatera Akibat Kebakaran Hutan Dengan Data Water Vapor Dari Citra Satelit Terra Modis. Jurnal Inotera. https://inotera.poltas.ac.id/

Ma, X., Yao, Y., Zhang, B., Qin, Y., Zhang, Q., & Zhu, H. (2022). An improved MODIS NIR PWV retrieval algorithm based on an artificial neural network considering the land-cover types. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–12. https://doi.org/10.1109/TGRS.2022.3170078

Mustafovski, R., & Petrovski, A. (2025). State of the art on global navigation satellite systems: A comparative study of GPS, GLONASS, and Galileo. Contemporary Macedonian Defence, 25(49), 119–132. https://eprints.ugd.edu.mk/37256/

NASA. (n.d.). Terra & Aqua Moderate Resolution Imaging Spectroradiometer (MODIS). LAADS DAAC. https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/modis/

Su, H., Yang, T., Wang, K., Sun, B., & Yang, X. (2021). Evaluation of precipitable water vapor retrieval from homogeneously reprocessed long-term GNSS tropospheric zenith wet delay, and multi-technique comparison. Remote Sensing, 13(21), 4490. https://doi.org/10.3390/rs13214490

Trakolkul, C., Charoenphon, C., & Satirapod, C. (2024). Investigating the relationship between precipitable water vapor and rainfall data during flood events: A GNSS-based study in Thailand. International Journal of Geoinformatics. https://ijg.journals.publicknowledgeproject.org/index.php/journal/article/view/3677

Vaquero-Martínez, J., & Antón, M. (2021). Review on the role of GNSS meteorology in monitoring water vapor for atmospheric physics. Remote Sensing, 13(12), 2287. https://doi.org/10.3390/rs13122287

Wang, Y., Yang, F., Li, P., Gong, X., Liu, M., Xu, T., Lin, X., & Wang, Y. (2024). An optimal calibration method for MODIS precipitable water vapor using GNSS observations. Atmospheric Research, 309, 107591. https://doi.org/10.1016/j.atmosres.2024.107591

World Meteorological Organization. (2025). WMO Greenhouse Gas Bulletin, No. 21. https://wmo.int/media/41825

Zhai, W., Zhu, J., Lin, M., Ma, C., Chen, C., Huang, X., Zhang, Y., Zhou, W., Wang, H., & Yan, L. (2022). GNSS data processing and validation of the altimeter zenith wet delay around the Wanshan calibration site. Remote Sensing, 14(24), 6235. https://doi.org/10.3390/rs14246235

Zhao, Y., Zhao, H., Li, J., & Xiao, G. (2022). Comprehensive validation and calibration of MODIS PWV over mainland China. Atmosphere, 13(11), 1763. https://doi.org/10.3390/atmos13111763

Zhu, D., Zhang, K., Yang, L., Wu, S., & Li, L. (2021). Evaluation and calibration of MODIS near-infrared precipitable water vapor over China using GNSS observations and ERA-5 reanalysis dataset. Remote Sensing, 13(14), 2761. https://doi.org/10.3390/rs13142761

Downloads

Published

2026-11-01

How to Cite

Lovita, O. T., Muthi’ah, Z., Muttaqin, M. I., Mulyati, M., & Khairunnisa, K. (2026). AI-Based Prediction of Water Vapor Changes from Kalimantan Forest Fires Using Terra MODIS Imagery. Brilliance: Research of Artificial Intelligence, 6(4), 780–791. https://doi.org/10.47709/brilliance.v6i4.9749

Citation Tracker

Citation data is temporarily unavailable.

Source: OpenAlex