Prediction of temporal and spatial variations of the NDDI drought index in the Khorramabad watershed using remote sensing

Document Type : Research Paper

Authors

1 Researcher, Soil Conservation and Watershed Management Research Department, Lorestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Khorramabad, Iran

2 Department of Range and Watershed Management Engineering, Faculty of Natural Resources, Lorestan University, Khorramabad, Iran

3 Assistant Professor, Soil Conservation and Watershed Management Research Department, Lorestan Agricultural and Natural Resources Research and Education Center, AREEO, Khorramabad, Iran

Abstract

Remote sensing-based indices are effective tools for monitoring drought and wet conditions. In this study, the NDDI, derived from NDVI and NDWI data obtained from Sentinel-2 satellite imagery, was employed to assess drought conditions. Time series of these indices were generated using coding in the GEE platform, and the NDDI was subsequently calculated in Excel. Additionally, future predictions of the NDDI were conducted using time series modeling techniques. The results indicate that the NDDI is a reliable indicator for representing droughts caused by water scarcity and reduced vegetation cover. Analysis of NDDI values from 2016 to 2023 in the Khorramabad watershed revealed a range between -3.20 and -11.21, suggesting that the region generally experienced very low drought intensity during this period. Furthermore, drought prediction results based on NDDI, using time series modeling, identified the MA2 model as the most accurate, with a high coefficient of determination (R² = 0.92) and an Akaike Information Criterion (AIC) value of less than 50. The findings indicate that the decline in NDDI during the spring (-5.3) and winter (-5.4) of 2024 reflects improvements in relative vegetation cover, precipitation levels, and water reserves. However, an increase in this index during the summer and autumn (approximately -3) of 2024 suggests worsening drought conditions and reduced rainfall. This trend is projected to persist across different seasons in 2025 and 2026. In conclusion, the NDDI is recommended as a valuable tool for analyzing vegetation cover status and water fluctuations, enabling optimal watershed management strategies.

Keywords

Main Subjects


Alshahrani, M., Muhammad, L., Noor-ul-Amin, M., Yasmeen, U., Nabi, M. 2024. A support vector machine based drought index for regional drought analysis. Scientific Reports. 14, 1- 9849.
Amiri, A., Gheysouri, M., Saberi, A. 2023. Runoff Modeling Using HBV Model and Random Forest Algorithm (Study Area: Chamanjir Watershed, Lorestan Province). Iran's water .
Feyzollahpour, M. 2023. Detection of changes in the water area of the Miqan Wetland using spectral indices NDWI, MNDWI, AWEI, and supervised SVM models during the period 1994 to 2023. Geographical Studies of Arid Regions. 14(54), 104-119.
Soleimani-Motlagh, M., Shakerami, M. 2021. Analysis of the drought curve of the Karesti-Matari Khorramabad karst spring based on drought coefficient during hydroclimatic fluctuations. Geographical Studies of Arid Regions. 12(44), 72-83.
Affandy, N. A., Iranata, D., Anwar, N., Maulana, M. A., Prastyo, D. D., Wardoyo, W., Sukojo B. M. 2024. Assessment of Agricultural Drought Using the Normalized Difference Drought Index (NDDI) to Prediction Drought at Corong River Basin. International Journal of Integrated Engineering. 16 (1), 378-393.
Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control. 19, 716–723.
Artikanur, S.,  Widiatmaka Setiawan, D., Marimin, Y. 2022. Normalized Difference Drought Index (NDDI) computation for mapping drought severity in Bojonegoro Regency, East Java, Indonesia. IOP Conference Series: Earth and Environmental Science. 1109 (012027).
Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.D. 2016. Time series analysis: forecasting and control, 5th Edition. Wiley Press. New Jersey, 720.
Campos, J.C., Sillero, N., Brito, J.C. 2012. Normalized difference water indexes have dissimilar performances in detecting seasonal and permanent water in the Sahara–Sahel transition zone. Journal of Hydrology. 464, 438-446.
Dalezios, N. R., Dercas, N., Spyropoulos, N. V., Psomiadis, E. 2019. Remotely sensed methodologies for crop water availability and requirements in precision farming of vulnerable agriculture. Water Resources Management. 33(4), 1499–1519.
Du, T.L.T., Bui, D. Du., Nguyen, M.D., Lee, H. 2018. Satellite-based, multi-indices for evaluation of agricultural droughts in a highly dynamic tropical catchment, Central Vietnam. Water. 10 (5), 659.
Gerardo, R., de Lima, I.P. 2022. Monitoring Duckweeds (Lemna minor) in Small Rivers Using Sentinel-2 Satellite Imagery: Application of Vegetation and Water Indices to the Lis River (Portugal). Water. 14, 2284.
Giovanni, N. 2018. Identifikasi kekeringan padi sawah dengan indeks NDDI dan VHI dari Citra Landsat 8 di Kabupaten Subang. Thesis. Institut Pertanian Bogor.
Guha, S., Govil, H., Diwan, P. 2019. Analytical study of seasonal variability in land surface temperature with normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index. Journal of Applied Remote Sensing. 13(2), 1.
Hao, C., Zhang, J., Yao, F. 2015. Combination of multi-sensor remote sensing data for drought monitoring over Southwest China. International Journal of Applied Earth Observation and Geoinformation. 35, 270–283.
Huang, B., Yuan, Z., Zheng, M., Liao, Y., Nguyen, K.L., Nguyen, T.H., Sombatpanit, S., Li, D. 2022. Soil and water conservation techniques in tropical and subtropical Asia: A Review. Sustainability. 14(9), 5035.
Kamble, M. V., Ghosh, K., Rajeevan, M., Samui, R. P. 2010. Drought monitoring over India through normalized difference vegetation index (NDVI). MAUSAM. 61(4), 537–546.
Lesk, C., Rowhani, P., Ramankutty, N. 2016. Influence of extreme weather disasters on global crop production. Nature. 529, 84–87.
Li, J., Li, Y., Yin, L., Zhao, Q. 2024. A novel composite drought index combining precipitation, temperature and evapotranspiration used for drought monitoring in the Huang-Huai-Hai Plain. Agricultural Water Management. 291, 108626.
Liu, D., Mishra, A. K., Yu, Z., Yang, C., Konapala, G., Vu, T. 2017. Performance of SMAP, AMSR-E and LAI for weekly agricultural drought forecasting over continental United States. Journal of Hydrology. 553, 88–104.
McFeeters, S.K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing. 17, 1425–1432.
Mirzavand, M., Ghazavi, R. 2015. A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods. Water Resources Management. 29 (4), 1315–1328.
Mishra, D., Goswami, S., Matin, S., Sarup, J. 2022. Analyzing the extent of drought in the Rajasthan state of India using vegetation condition index and standardized precipitation index. Modeling Earth Systems and Environment. 8, 601–610.
Nepal, S., Tripathi, S., Adhikari, H. 2021. Geospatial approach to the risk assessment of climate-induced disasters (drought and erosion) and impacts on out-migration in Nepal. International Journal of Disaster Risk Reduction. 59, 102241.
Patil, P.P., Jagtap, M. P., Khatri, N. M., Hakka, A.V., Aditya, P.T. 2024. Exploration and advancement of NDDI leveraging NDVI and NDWI in Indian semi-arid regions: A remote sensing-based study. Chemical and Environmental Engineering. 9 (100573), ISSN 2666-0164.
Rismayatika, F., Saraswati, R., Shidiq, I. P. A., Taqyyudin. 2020. Identification of Dry Areas on Agricultural Land using Normalized Difference Drought Index in Magetan Regency. IOP Conference Series: Earth and Environmental Science. 540, 012029.
Rouse, J., Haas, R., Schell, J., Deering. D. 1973. Monitoring Vegetation Systems in the Great Plains with ERTS. Third ERTS Symposium. NASA, 309-317.
Sahoo, A. K., Sheffield, J., Pan, M., Wood, E. F. 2015. Evaluation of the tropical rainfall measuring mission multi-satellite precipitation analysis (TMPA) for assessment of large-scale meteorological drought. Remote Sensing of Environment. 159, 181–193.
Salas-Martinez, F., Valdes-Rodriguez, O.A., Palacios-Wassenaar, O.M., Marquez-Grajales, A., Rodriguez-Hernandez, 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.
Soleimani Motlagh, M.,  Ghasemieh, H., Talebi, A.,  Abdollahi, Kh. 2017. Identification and Analysis of Drought Propagation of Groundwater During Past and Future Periods. Water Resources Management. 31, 109–125.
Tucker, C., Choudhury, B. 1987. Satellite remote sensing of drought conditions. Remote Sensing of Environment. 23 (2), 243-251.
Trinh, H., Danh, T. 2019. Application of remote sensing technique for drought assessment based on normalized difference drought index, a case study of Bac Binh district, Binh Thuan province (Vietnam), Russian Journal of Earth Sciences. 19, ES2003.
West, H., Quinn, N., Horswell, M. 2019. Remote sensing for drought monitoring and impact assessment: progress, past challenges and future opportunities. Remote Sensing of Environment. 232, 111291.
Xie, F., Fan, H. 2021. Deriving drought indices from MODIS vegetation indices (NDVI/EVI) and land surface temperature (LST): Is data reconstruction necessary?. International Journal of Applied Earth Observation and Geoinformation. 101, 102352. 
Xue, J., Su, B. 2017. Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017, 1–17.
Zhang, H., Ma, J., Chen, C., Tian, X. 2020. NDVI-Net: A fusion network for generating high-resolution normalized difference vegetation index in remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing. 168, 182–196. 
Zhang, J., Zhang, R., Qu, Y., Bento, V.A., Zhou, T., Lin, Y., Wu, X., Qi, J., Shui, W., Wang, Q. 2022. Improving the drought monitoring capability of VHI at the global scale via ensemble indices for various vegetation types from 2001 to 2018. Weather and Climate Extremes. 35, 100412.