Dzikrullah Akbar, Siti Najma Nindya Utami, Rista Hernandi Virgianto


One of the disasters can cause losses in various sectors and have an impact on people's lives is drought. Lombok Island is an area with a high risk of drought. The Standardized Precipitation Index (SPI) describes meteorological drought using rainfall as the main parameter. The Normalized Differences Vegetation Index (NDVI) describes agricultural drought based on remote sensing. This research aims to determine the relationship between SPI using the reanalysis rainfall data Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) with observed rainfall (CH Obs) and NDVI at 22 rain observation stations on Lombok Island during the 2001 – 2018. The use method is to calculate the Pearson correlation and the significance of SPI with CH Obs and NDVI. The correlation between SPI with CH Obs and NDVI is positive and significant, respectively 0.31 and 0.21 with p-value <0.05. This illustrates that drought monitoring using reanalysis and remote sensing data can be done because it describes the actual drought in the study area. In addition, it can be concluded that the meteorological drought that occurred could have an impact on agricultural drought in the Lombok during 2001 - 2018.


drought, SPI, NDVI, Pearson Correlation

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Badan Informasi Geospaisal, Peta Dinding Administrasi Provinsi Nusa Tenggara Barat,, Diakses pada10 Oktober 2020.

Badan Nasional Penanggulangan Bencana. (2015). Kajian Risiko Bencana Nusa Tenggara Barat 2016 - 2020. Badan Nasional Penanggulangan Bencana, Jakarta. 51

Badan Nasional Penanggulangan Bencana, Sejarah Bencana Kekeringan Pulau Lombok,, Diakses pada 7 Juni 2020.

Dutta, D., Kundu, A., & Patel, N. R. (2013). Predicting agricultural drought in eastern Rajasthan of India using NDVI and standardized precipitation index. Geocarto International, 28(3), 192-209.

Funk, C. C., Peterson, P. J., Landsfeld, M. F., Pedreros, D. H., Verdin, J. P., Rowland, J. D., ... & Verdin, A. P. (2014). A quasi-global precipitation time series for drought monitoring. US Geological Survey data series, 832(4), 1-12. Diakses pada 01 Mei 2020

Giovanni The Bridge Between Data and Science, Data, National Aeronautics and Space Administration,, Diakses pada 23 September 2020.

Jain, S. K., Keshri, R., Goswami, A., & Sarkar, A. (2010). Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India. Natural hazards, 54(3), 643-656.

Jiang, S., Ren, L., Zhou, M., Yong, B., Zhang, Y., & Ma, M. (2017). Drought monitoring and reliability evaluation of the latest TMPA precipitation data in the Weihe River Basin, Northwest China. Journal of Arid Land, 9(2), 256-269.

Ji, L., & Peters, A. J. (2003). Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sensing of Environment, 87(1), 85-98.

Lillesand, T. M., Kiefer, R. W., & Chipman, J. (1987). Remote sensing and image processing.

McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology (Vol. 17, No. 22, pp. 179-183).

Sudjana, N. (1996). Statistik Dasar, Tarsito, Bandung

Tao, H., Fischer, T., Zeng, Y., & Fraedrich, K. (2016). Evaluation of TRMM 3B43 precipitation data for drought monitoring in Jiangsu Province, China. Water, 8(6), 221.

Wilhite, D. A., & Glantz, M. H. (1985). Understanding: the drought phenomenon: the role of definitions. Water international, 10(3), 111-120.

Yan, G., Liu, Y., & Chen, X. (2018). Evaluating satellite-based precipitation products in monitoring drought events in southwest China. International Journal of Remote Sensing, 39(10), 3186-3214.

Yasin, I., Ma'shum, M., Abawi, Y. dan Hadiahwaty, L., 2004, Penggunaan Indeks Osilasi Selatan untuk Memprakirakan Sifat Hujan Musiman Guna Menentukan Strategi Tanaman di Lahan Tadah Hujan Di Pulau Lombok. Jurnal Agromet Indonesia, Vol. 18.

Zhao, Q., Chen, Q., Jiao, M., Wu, P., Gao, X., Ma, M., & Hong, Y. (2018). The temporal-spatial characteristics of drought in the Loess Plateau using the remote-sensed TRMM precipitation data from 1998 to 2014. Remote Sensing, 10(6), 838.

Zuo, D., Cai, S., Xu, Z., Peng, D., Kan, G., Sun, W., ... & Yang, H. (2019). Assessment of meteorological and agricultural droughts using in-situ observations and remote sensing data. Agricultural Water Management, 222, 125-138.



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