Drought Level Analysis of Paddy Fields Using the NDDI Method Based on Sentinel-2A Imagery in South Polombangkeng District, Indonesia
Article Sidebar
Drought is a significant climate-related hazard that severely impacts agricultural productivity, particularly in rainfed paddy fields. This study aimed to analyze the spatial distribution and severity of drought in paddy fields using the Normalized Difference Drought Index (NDDI) derived from Sentinel-2A satellite imagery. The research was conducted in South Polombangkeng District, Takalar Regency, South Sulawesi, Indonesia, during the dry season in October 2023. The NDDI was calculated by integrating the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). The results indicated that 85.78% of the paddy fields experienced severe drought, while mild and moderate drought covered 9.30% and 4.92%, respectively. NDVI analysis revealed that 87.81% of the area had very low to low vegetation density, and NDWI confirmed extreme moisture deficiency, with 99.88% of the area under very severe drought conditions. The accuracy of the NDDI drought map, validated using the Area Under the Curve (AUC), was 0.62, indicating acceptable model performance. These findings provide critical spatial information for drought mitigation and water management in vulnerable agricultural regions. The study demonstrates the utility of Sentinel-2A and NDDI for localized drought assessment and supports evidence-based decision-making for sustainable farming practices in drought-prone areas.
Badan Meteorologi, Klimatologi, dan Geofisika. (2023, November 2). Dampak lanjutan kemarau kering, BMKG sebut sektor ini akan sangat terpukul. BMKG. https://www.bmkg.go.id/siaran-pers/dampak-lanjutan-kemarau-kering-bmkg-sebut-sektor-ini-akan-sangat-terpukul
Boer, R., & Subbiah, A. R. (2005). Agricultural drought in Indonesia. In M. K. Sivakumar, R. M. Gommes, & W. Baier (Eds.), Monitoring and Predicting Agricultural Drought: A Global Study (pp. 330–344). Oxford University Press. https://doi.org/10.1093/oso/9780195162349.003.0037
Cahyono, B. E., Rahagian, R., & Nugroho, A. T. (2023). Analisis produktivitas padi berdasarkan indeks kekeringan (NDWI dan NDDI) lahan sawah menggunakan data citra Sentinel 2A di Kecamatan Ambulu. Indonesian Journal of Applied Physics, 13(1), 88–98.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Food and Agriculture Organization of the United Nations. (2017). The State of Food and Agriculture 2017: Leveraging food systems for inclusive rural transformation. FAO. https://openknowledge.fao.org/handle/20.500.14283/i7658en
Gao, B.-C. (1996). NDWI—A Normalized Difference Water Index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
Gu, Y., Brown, J. F., Verdin, J. P., & Wardlow, B. (2007). A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysical Research Letters, 34(6), L06407. https://doi.org/10.1029/2006GL029127
Hastina, H., Hutabarat, O. S., & Useng, D. (2023). Investigating the correlation between rice production and RGB vegetation index from drone imagery and NIR based index from Sentinel images. SALAGA Journal of Appropriate Technology for Agriculture Production and Processing, 1(1), 14–21. https://doi.org/10.70124/salaga.v1i1.1100
Liku, Y. O., Useng, D., & Faridah, S. N. (2024). Estimating corn productivity using Sentinel 2 imagery and spectrometer. SALAGA Journal of Appropriate Technology for Agriculture Production and Processing, 2(2), 91–100. https://doi.org/10.70124/salaga.v2i2.1779
Luqman, A. D., Wiyono, R. U. A., & Hidayah, E. (2021). Akurasi pemetaan kekeringan lahan pertanian menggunakan metode Normalized Difference Drought Index (NDDI) di Kecamatan Wuluhan dan Rambipuji Jember. Jurnal Geografi, 14(2), 111–120.
Marlina, D. (2022). Klasifikasi tutupan lahan pada citra Sentinel 2 Kabupaten Kuningan dengan NDVI dan algoritme Random Forest. STRING, 7(1), 41–49.
Mladenova, I. E., Bolten, J., Crow, W., Sazib, N., & Reynolds, C. (2020). Agricultural drought monitoring via the assimilation of SMAP soil moisture retrievals into a global soil water balance model. Frontiers in Big Data, 3, 10. https://doi.org/10.3389/fdata.2020.00010
Phan, T. N., & Kappas, M. (2018). Application of MODIS land surface temperature data: a systematic literature review and analysis. Journal of Applied Remote Sensing, 12(4), 041501. https://doi.org/10.1117/1.JRS.12.041501
Pramesto, V., Sukmono, A., & Suprayogi, A. (2019). Analisis perbandingan metode NDDI dan TVx dalam menentukan kekeringan lahan sawah (Studi kasus: Kabupaten Kendal). Jurnal Geodesi Undip, 8(1), 318–327.
Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W., & Harlan, J. C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation (Final report, NASA/GSFC). Remote Sensing Center, Texas A&M University. https://skclivinglandscapes.org/remote_sensing/resources/Section6Resources/Rouse_et_al.1974NDVI.pdf
Salas Martínez, F., Valdés Rodríguez, O. A., Palacios Wassenaar, O. M., Márquez Grajales, A., & Rodríguez Hernández, L. D. (2023). Methodological estimation to quantify drought intensity based on the NDDI index with Landsat 8 multispectral images in the central zone of the Gulf of Mexico. Frontiers in Earth Science, 11, 1027483. https://doi.org/10.3389/feart.2023.1027483
Velpuri, N. M., Senay, G. B., & Morisette, J. T. (2016). Evaluating new SMAP soil moisture for drought monitoring in the rangelands of the US High Plains. Rangelands, 38(4), 183–190. https://doi.org/10.1016/j.rala.2016.06.002
Wilhite, D. A. (2000). Drought as a natural hazard: Concepts and definitions. In D. A. Wilhite (Ed.), Drought: A global assessment (Vol. 1, pp. 3–18). Routledge.
Nur Rahmi
Faculty of Agricultural Technology, Hasanuddin University, Makassar, Indonesia
Indonesia
Husnul Mubarak husnul.mubarak@unhas.ac.id
Faculty of Agricultural Technology, Hasanuddin University, Makassar, Indonesia
Indonesia
Sitti Nur Faridah
Faculty of Agricultural Technology, Hasanuddin University, Makassar, Indonesia
Indonesia
Muhammad Tahir Sapsal
Faculty of Agricultural Technology, Hasanuddin University, Makassar, Indonesia
Indonesia
Copyright (c) 2025 Nur Rahmi, Husnul Mubarak, Sitti Nur Faridah, Muhammad Tahir Sapsal

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.