Amsari Mudzakir Setiawan, Yonny Koesmaryono, Akhmad Faqih, Dodo Gunawan


Drought is becoming one of the most important issues for government and policy makers. National food security highly concerned, especially when drought occurred in food production center areas. Climate variability, especially in South Sulawesi as one of the primary national rice production centers is influenced by global climate phenomena such as El Niño Southern Oscillation or ENSO. This phenomenon can lead to drought occurrences. Monitoring of drought potential occurrences in near real-time manner becomes a primary key element to anticipate the drought impact. This study was conducted to determine potential occurrences and the evolution of drought that occurred as a result of the 2015 El Niño event using the Vegetation Health Index (VHI) from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellite products. Composites analysis was performed using weekly Smoothed and Normalized Difference Vegetation Index (or smoothed NDVI) (SMN), Smoothed Brightness Temperature Index (SMT), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and  Vegetation Health Index (VHI).  This data were obtained from The Center for Satellite Applications and Research (STAR) - Global Vegetation Health Products (NOAA) website during 35-year period (1981-2015). Lowest potential drought occurrences (highest VHI and VCI value) caused by 2015 El Niño is showed by composite analysis result. Strong El Niño induced drought over the study area indicated by decreasing VHI value started at week 21st. Spatial characteristic differences in drought occurrences observed, especially on the west coast and east coast of South Sulawesi during strong El Niño. Weekly evolution of potential drought due to the El Niño impact in 2015 indicated by lower VHI values (VHI < 40) concentrated on the east coast of South Sulawesi, and then spread to another region along with the El Nino stage.   


drought; near real-time monitoring; NOAA-AVHRR; VHI; VCI; TCI; El Niño

Full Text:



Anderson MC, Hain C., Otkin J., Zhan X., Mo K., Svoboda M., Wardlow B., Pimstein A., (2013), An Intercomparison of Drought Indicators Based on Thermal Remote Sensing and NLDAS-2 Simulations with U.S. Drought Monitor Classifications. J Hydrometeorol 14:1035–1056. doi: 10. 1175/JHM-D-12-0140.1.

Bhuiyan C., Singh RP, Kogan FN, (2006), Monitoring drought dynamics in the Avavalli region (India) using different indices based on ground and remote sensing data. Int J Appl Earth Obs Geoinformatics 8:289–302.

BNPB NA for DM, (2010), Peta kejadian bencana kekeringan di Indonesia tahun 1979 - 2009. In: 22 Sept. 2010. http:// geospasial.bnpb.go.id/2010/09/23/peta-kejadian-bencana-kekeringan-di-indonesia- tahun-1979-2009/Accessed 19 Jun 2016.

Chang CP, Wang Z, Ju J, Li T,, (2004), On the relationship between western maritime continent monsoon rainfall and ENSO during northern winter. J Clim 17:665–672. doi: 10.1175/1520-0442(2004)017 <0665:OTRBWM>2.0.CO;2.

Chen S., Wu R., Chen W., (2016), Genesis of westerly wind bursts over the equatorial western Pacific during the onset of the strong 2015 – 2016 El Niño. doi: 10.1002/asl.669.

D’Arrigo R., Wilson R., (2008), El Nino and Indian Ocean influences on Indonesian drought: implications for forecasting rainfall and crop productivity. Int J Climatol 28:611–616.

Erasmi S., Propastin P., Kappas M., Panferov O., (2009), Spatial Patterns of NDVI Variation over Indonesia and Their Relationship to ENSO Warm Events during the Period 1982–2006. J Clim 22:6612–6623. doi: 10. 1175/2009 JCLI2460.1.

Harger JRE, (1995), Air-temperature variations and ENSO effects in Indonesia, the Philippines and El Salvador: ENSO patterns and changes from 1866-1993. Atmos Environ 29:1919–1942. doi: 10. 1016/1352-2310(95)00017-S.

Hidayat R., Ando K., Masumoto Y., Luo JJ, (2016), Interannual Variability of Rainfall over Indonesia: Impacts of ENSO and IOD and Their Predictability. IOP Conf Ser Earth Environ Sci 31:12043. doi: 10.1088/ 1755-1315/31/1/012043.

Jalili M., Gharibshah J., Ghavami SM, Beheshtifar M., Farshi R., (2014), Nationwide prediction of drought conditions in Iran based on remote sensing data. IEEE Trans Comput 63:90–101. doi: 10.1109/TC.2013.118.

Jan Null C., (2016), El Niño and La Niña Years and Intensities. http://ggweather.com/ enso/oni.htm. Accessed 1 May 2016.

Jiang L., Tarpley JD, Mitchell KE, et al., (2008), Adjusting for long-term anomalous trends in NOAA’S global vegetation index data sets. IEEE Trans Geosci Remote Sens 46:409–421. doi: 10.1109/TGRS.2007. 902844.

Kerdprasop K., Kerdprasop N., (2016), Rainfall Estimation Models Induced from Ground Station and Satellite Data. In: Proceedings of the International MultiConference of Engineers and Computer Scientists. IMECS 2016, Hongkong,

Kogan F., Adamenko T., Guo W., (2013), Global and regional drought dynamics in the climate warming era. Remote Sens Lett 4:364–372. doi: 10.1080/2150704X. 2012. 736033.

Kogan F., Guo W., Strashnaia A., Kleshenko A., Chub O., Virchenko O., (2015), Modelling and prediction of crop losses from NOAA polar-orbiting operational satellites. Geomatics, Nat Hazards Risk 7:886–900. doi: 10.1080/19475705.2015.1009178.

Kogan F., Salazar L., Roytman L., (2012), Forecasting crop production using satellite-based vegetation health indices in Kansas, USA. Int J Remote Sens 33:2798–2814. doi: 10.1080/01431161.2011.621464.

Kogan FN, (1995), Application of vegetation index and brightness temperature for drought detection. Adv Sp Res 15:91–100. doi: 10.1016/0273-1177(95)00079-T.

Kogan FN, (1997), Global Drought Watch from Space. Bull Am Meteorol Soc 78:621–636. doi: 10.1175/1520-0477(1997) 078<0621: GDWFS>2.0.CO;2.

Nizamuddin M., Akhand K., Roytman L., Kogan F., Goldberg M., (2015), Using NOAA/ AVHRR based remote sensing data and PCR method for estimation of Aus rice yield in Bangladesh. 9488:1–10. doi: 10.1117/12.2086186.

Qian J-H, Robertson AW, Moron V., (2010), Interactions among ENSO, the Monsoon, and Diurnal Cycle in Rainfall Variability over Java, Indonesia. J Atmos Sci 67:3509–3524. doi: 10.1175/2010JA S3348.1.

Ropelewski CF, Halpert MS, (1987), Global and Regional Scale Precipitation Patterns Associated with the El Niño/Southern Oscillation. Mon Weather Rev 115:1606–1626. doi: 10.1175/1520-0493(1987) 115 <1606:GARSPP>2.0.CO;2.

Setiawan AM, (2014), Drought Characteristics in Indonesia Related to Warm ENSO Episodes. APEC Climate Center, Busan.

Setiawan AM, (2011), Determination of Reference ENSO Index for Indonesian Region Based on Correlation Analysis of Spatial and Temporal Pattern with Standardized Precipitation Index (SPI). Institut Teknologi Bandung (ITB).

Setiawan AM, (2007), Mapping of South Sulawesi Rainfall Distribution using Arcview GIS [in Indonesian]. Makassar State University (UNM).

Sholihah RI, Trisasongko BH, Shiddiq D., (2016), Identification of Agricultural Drought Extent Based on Vegetation Health Indices of Landsat Data: Case of Subang and Karawang, Indonesia. Procedia Environ Sci 33:14–20. doi: 10.1016/j.proenv. 2016. 03. 051.

Sivakumar MVK, Stefanski R., Bazza M., et al., (2014), High Level Meeting on National Drought Policy : Summary and Major Outcomes. Weather Clim Extrem 3:126–132. doi: 10.1016/j.wace.2014.03.007.

Surmaini E., Hadi TW, Subagyono K., Puspito NT, (2015), Early detection of drought impact on rice paddies in Indonesia by means of Niño 3.4 index. Theor Appl Climatol 121:669–684. doi: 10.1007/ s00704-014-1258-0.

Wei Guo, (2013), AVHRR Vegetation Health Product (AVHRR-VHP) User Guide.

Zargar A., Sadiq R., Naser B., Khan FI, (2011). A review of drought indices. Environ Rev 19:333–349. doi: 10.1139/a11-013.


  • There are currently no refbacks.